[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-3d3c764cfe0d8300-render-a-framework-for-controlling-evidence-in-llm-summary":3,"summaries-facets-categories":100,"summary-related-3d3c764cfe0d8300-render-a-framework-for-controlling-evidence-in-llm-summary":6926},{"id":4,"title":5,"ai":6,"body":13,"categories":67,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":72,"navigation":84,"path":85,"published_at":86,"question":69,"scraped_at":86,"seo":87,"sitemap":88,"source_id":89,"source_name":90,"source_type":91,"source_url":77,"stem":92,"tags":93,"thumbnail_url":69,"tldr":97,"tweet":69,"unknown_tags":98,"__hash__":99},"summaries\u002Fsummaries\u002F3d3c764cfe0d8300-render-a-framework-for-controlling-evidence-in-llm-summary.md","RENDER: A Framework for Controlling Evidence in LLM Memory Evaluation",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4011,474,2487,0.00171375,{"type":14,"value":15,"toc":61},"minimark",[16,21,25,29,32,35,58],[17,18,20],"h2",{"id":19},"the-problem-with-current-memory-benchmarks","The Problem with Current Memory Benchmarks",[22,23,24],"p",{},"Existing benchmarks for evaluating LLM memory often fail to distinguish between a model's inherent knowledge and its ability to process provided evidence. When a model answers a question correctly, it is frequently unclear whether the model retrieved the information from its pre-trained weights or if it successfully synthesized the evidence provided in the prompt. This ambiguity makes it difficult to measure true \"in-context\" learning and memory capabilities.",[17,26,28],{"id":27},"the-render-framework","The RENDER Framework",[22,30,31],{},"RENDER (Reader-facing Evidence in LLM Memory Evaluation) introduces a controlled approach to testing LLM memory. By systematically manipulating the evidence presented to the model, the framework forces a separation between the model's internal knowledge base and the information it is expected to process during a specific task.",[22,33,34],{},"Key components of the RENDER approach include:",[36,37,38,46,52],"ul",{},[39,40,41,45],"li",{},[42,43,44],"strong",{},"Evidence Isolation:"," Ensuring the model is evaluated specifically on its ability to utilize the provided context.",[39,47,48,51],{},[42,49,50],{},"Controlled Perturbation:"," Systematically altering the evidence to observe how changes in the input affect the model's output, allowing researchers to measure the model's reliance on specific pieces of information.",[39,53,54,57],{},[42,55,56],{},"Reader-Facing Metrics:"," Focusing on the model's performance as a \"reader\" of the provided context, rather than just a generator of facts.",[22,59,60],{},"By controlling the evidence, RENDER allows developers and researchers to identify \"hallucination traps\" where a model might ignore provided evidence in favor of its own potentially outdated or incorrect training data. This framework provides a more rigorous standard for evaluating how well models perform in RAG (Retrieval-Augmented Generation) pipelines and other context-heavy applications.",{"title":62,"searchDepth":63,"depth":63,"links":64},"",2,[65,66],{"id":19,"depth":63,"text":20},{"id":27,"depth":63,"text":28},[68],"AI & LLMs",null,"md",false,{"content_references":73,"triage":79},[74],{"type":75,"title":76,"url":77,"context":78},"paper","RENDER: Controlling Reader-Facing Evidence in LLM Memory Evaluation","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.23568","reviewed",{"relevance":80,"novelty":80,"quality":80,"actionability":81,"composite":82,"reasoning":83},4,3,3.8,"Category: AI & LLMs. The article introduces the RENDER framework, which addresses a specific pain point in evaluating LLM memory by isolating evidence processing, making it relevant for developers working with AI models. It provides a new perspective on memory evaluation, but while it offers insights, it lacks detailed actionable steps for immediate implementation.",true,"\u002Fsummaries\u002F3d3c764cfe0d8300-render-a-framework-for-controlling-evidence-in-llm-summary","2026-08-27 03:13:02",{"title":5,"description":62},{"loc":85},"3d3c764cfe0d8300","arXiv cs.AI","article","summaries\u002F3d3c764cfe0d8300-render-a-framework-for-controlling-evidence-in-llm-summary",[94,95,96],"llm","research","machine-learning","RENDER is a new evaluation framework designed to isolate and measure how LLMs process and recall specific evidence within their context windows, addressing the limitations of existing memory benchmarks.",[],"_dU7RHmAvxb_tkpuRdVRSuY8PlVEUpUvpVxm4Y1k5uI",[101,103,106,108,111,113,116,119,121,123,125,128,130,132,134,136,139,141,143,145,147,150,153,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187,189,191,193,195,197,199,202,204,206,208,210,212,214,216,218,220,222,224,226,228,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,272,274,276,278,280,282,284,286,288,290,292,294,296,298,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,364,366,368,370,372,374,376,378,380,382,384,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,420,422,424,426,428,430,432,434,436,438,440,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,500,502,504,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,546,548,550,552,554,556,558,560,562,564,566,568,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,858,860,862,864,866,869,871,873,875,877,879,881,883,885,887,889,891,893,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4488,4490,4492,4494,4496,4498,4500,4502,4504,4506,4508,4510,4512,4514,4516,4518,4520,4522,4524,4526,4528,4530,4532,4534,4536,4538,4540,4542,4544,4546,4548,4550,4552,4554,4556,4558,4560,4562,4564,4566,4568,4570,4572,4574,4576,4578,4580,4582,4584,4586,4588,4590,4592,4594,4596,4598,4600,4602,4604,4606,4608,4610,4612,4614,4616,4618,4620,4622,4624,4626,4628,4630,4632,4634,4636,4638,4640,4642,4644,4646,4648,4650,4652,4654,4656,4658,4660,4662,4664,4666,4668,4670,4672,4674,4676,4678,4680,4682,4684,4686,4688,4690,4692,4694,4696,4698,4700,4702,4704,4706,4708,4710,4712,4714,4716,4718,4720,4722,4724,4726,4728,4730,4732,4734,4736,4738,4740,4742,4744,4746,4748,4750,4752,4754,4756,4758,4760,4762,4764,4766,4768,4770,4772,4774,4776,4778,4780,4782,4784,4786,4788,4790,4792,4794,4796,4798,4800,4802,4804,4806,4808,4810,4812,4814,4816,4818,4820,4822,4824,4826,4828,4830,4832,4834,4836,4838,4840,4842,4844,4846,4848,4850,4852,4854,4856,4858,4860,4862,4864,4866,4868,4870,4872,4874,4876,4878,4880,4882,4884,4886,4888,4890,4892,4894,4896,4898,4900,4902,4904,4906,4908,4910,4912,4914,4916,4918,4920,4922,4924,4926,4928,4930,4932,4934,4936,4938,4940,4942,4944,4946,4948,4950,4952,4954,4956,4958,4960,4962,4964,4966,4968,4970,4972,4974,4976,4978,4980,4982,4984,4986,4988,4990,4992,4994,4996,4998,5000,5002,5004,5006,5008,5010,5012,5014,5016,5018,5020,5022,5024,5026,5028,5030,5032,5034,5036,5038,5040,5042,5044,5046,5048,5050,5052,5054,5056,5058,5060,5062,5064,5066,5068,5070,5072,5074,5076,5078,5080,5082,5084,5086,5088,5090,5092,5094,5096,5098,5100,5102,5104,5106,5108,5110,5112,5114,5116,5118,5120,5122,5124,5126,5128,5130,5132,5134,5136,5138,5140,5142,5144,5146,5148,5150,5152,5154,5156,5158,5160,5162,5164,5166,5168,5170,5172,5174,5176,5178,5180,5182,5184,5186,5188,5190,5192,5194,5196,5198,5200,5202,5204,5206,5208,5210,5212,5214,5216,5218,5220,5222,5224,5226,5228,5230,5232,5234,5236,5238,5240,5242,5244,5246,5248,5250,5252,5254,5256,5258,5260,5262,5264,5266,5268,5270,5272,5274,5276,5278,5280,5282,5284,5286,5288,5290,5292,5294,5296,5298,5300,5302,5304,5306,5308,5310,5312,5314,5316,5318,5320,5322,5324,5326,5328,5330,5332,5334,5336,5338,5341,5343,5345,5347,5349,5351,5353,5355,5357,5359,5361,5363,5365,5367,5369,5371,5373,5375,5377,5379,5381,5383,5385,5387,5389,5391,5393,5395,5397,5399,5401,5403,5405,5407,5409,5411,5413,5415,5417,5419,5421,5423,5425,5427,5429,5431,5433,5435,5437,5439,5441,5443,5445,5447,5449,5451,5453,5455,5457,5459,5461,5463,5465,5467,5469,5471,5473,5475,5477,5479,5481,5483,5485,5487,5489,5491,5493,5495,5497,5499,5501,5503,5505,5507,5509,5511,5513,5515,5517,5519,5521,5523,5525,5527,5529,5531,5533,5535,5537,5539,5542,5544,5546,5548,5550,5552,5554,5556,5558,5560,5562,5564,5566,5568,5570,5572,5574,5576,5578,5580,5582,5584,5586,5588,5590,5592,5594,5596,5598,5600,5602,5604,5606,5608,5610,5612,5614,5616,5618,5620,5622,5624,5626,5628,5630,5632,5634,5636,5638,5640,5642,5644,5646,5648,5650,5652,5654,5656,5658,5660,5662,5664,5666,5668,5670,5672,5674,5676,5678,5680,5682,5684,5686,5688,5690,5692,5694,5696,5698,5700,5702,5704,5706,5708,5710,5712,5714,5716,5718,5720,5722,5724,5726,5728,5730,5732,5734,5736,5738,5740,5742,5744,5746,5748,5750,5752,5754,5756,5758,5760,5762,5764,5766,5768,5770,5772,5774,5776,5778,5780,5782,5784,5786,5788,5790,5792,5794,5796,5798,5800,5802,5804,5806,5808,5810,5812,5814,5816,5818,5820,5822,5824,5826,5828,5830,5832,5834,5836,5838,5840,5842,5844,5846,5848,5850,5852,5854,5856,5858,5860,5862,5864,5866,5868,5870,5872,5874,5876,5878,5880,5882,5884,5886,5888,5890,5892,5894,5896,5898,5900,5902,5904,5906,5908,5910,5912,5914,5916,5918,5920,5922,5924,5926,5928,5930,5932,5934,5936,5938,5940,5942,5944,5946,5948,5950,5952,5954,5956,5958,5960,5962,5964,5966,5968,5970,5972,5974,5976,5978,5980,5982,5984,5986,5988,5990,5992,5994,5996,5998,6000,6002,6004,6006,6008,6010,6012,6014,6016,6018,6020,6022,6024,6026,6028,6030,6032,6034,6036,6038,6040,6042,6044,6046,6048,6050,6052,6054,6056,6058,6060,6062,6064,6066,6068,6070,6072,6074,6076,6078,6080,6082,6084,6086,6088,6090,6092,6094,6096,6098,6100,6102,6104,6106,6108,6110,6112,6114,6116,6118,6120,6122,6124,6126,6128,6130,6132,6134,6136,6138,6140,6142,6144,6146,6148,6150,6152,6154,6156,6158,6160,6162,6164,6166,6168,6170,6172,6174,6176,6178,6180,6182,6184,6186,6188,6190,6192,6194,6196,6198,6200,6202,6204,6206,6208,6210,6212,6214,6216,6218,6220,6222,6224,6226,6228,6230,6232,6234,6236,6238,6240,6242,6244,6246,6248,6250,6252,6254,6256,6258,6260,6262,6264,6266,6268,6270,6272,6274,6276,6278,6280,6282,6284,6286,6288,6290,6292,6294,6296,6298,6300,6302,6304,6306,6308,6310,6312,6314,6316,6318,6320,6322,6324,6326,6328,6330,6332,6334,6336,6338,6340,6342,6344,6346,6348,6350,6352,6354,6356,6358,6360,6362,6364,6366,6368,6370,6372,6374,6376,6378,6380,6382,6384,6386,6388,6390,6392,6394,6396,6398,6400,6402,6404,6406,6408,6410,6412,6414,6416,6418,6420,6422,6424,6426,6428,6430,6432,6434,6436,6438,6440,6442,6444,6446,6448,6450,6452,6454,6456,6458,6460,6462,6464,6466,6468,6470,6472,6474,6476,6478,6480,6482,6484,6486,6488,6490,6492,6494,6496,6498,6500,6502,6504,6506,6508,6510,6512,6514,6516,6518,6520,6522,6524,6526,6528,6530,6532,6534,6536,6538,6540,6542,6544,6546,6548,6550,6552,6554,6556,6558,6560,6562,6564,6566,6568,6570,6572,6574,6576,6578,6580,6582,6584,6586,6588,6590,6592,6594,6596,6598,6600,6602,6604,6606,6608,6610,6612,6614,6616,6618,6620,6622,6624,6626,6628,6630,6632,6634,6636,6638,6640,6642,6644,6646,6648,6650,6652,6654,6656,6658,6660,6662,6664,6666,6668,6670,6672,6674,6676,6678,6680,6682,6684,6686,6688,6690,6692,6694,6696,6698,6700,6702,6704,6706,6708,6710,6712,6714,6716,6718,6720,6722,6724,6726,6728,6730,6732,6734,6736,6738,6740,6742,6744,6746,6748,6750,6752,6754,6756,6758,6760,6762,6764,6766,6768,6770,6772,6774,6776,6778,6780,6782,6784,6786,6788,6790,6792,6794,6796,6798,6800,6802,6804,6806,6808,6810,6812,6814,6816,6818,6820,6822,6824,6826,6828,6830,6832,6834,6836,6838,6840,6842,6844,6846,6848,6850,6852,6854,6856,6858,6860,6862,6864,6866,6868,6870,6872,6874,6876,6878,6880,6882,6884,6886,6888,6890,6892,6894,6896,6898,6900,6902,6904,6906,6908,6910,6912,6914,6916,6918,6920,6922,6924],{"categories":102},[68],{"categories":104},[105],"Developer Productivity",{"categories":107},[68],{"categories":109},[110],"Business & SaaS",{"categories":112},[68],{"categories":114},[115],"AI Automation",{"categories":117},[118],"Product Strategy",{"categories":120},[68],{"categories":122},[105],{"categories":124},[115],{"categories":126},[127],"Software Engineering",{"categories":129},[68],{"categories":131},[110],{"categories":133},[],{"categories":135},[68],{"categories":137},[138],"Inference & Serving",{"categories":140},[68],{"categories":142},[68],{"categories":144},[115],{"categories":146},[],{"categories":148},[149],"AI News & Trends",{"categories":151},[152],"Data Science & Visualization",{"categories":154},[115],{"categories":156},[68],{"categories":158},[68],{"categories":160},[110],{"categories":162},[105],{"categories":164},[68],{"categories":166},[115],{"categories":168},[149],{"categories":170},[68],{"categories":172},[115],{"categories":174},[115],{"categories":176},[68],{"categories":178},[68],{"categories":180},[115],{"categories":182},[68],{"categories":184},[68],{"categories":186},[68],{"categories":188},[115],{"categories":190},[149],{"categories":192},[68],{"categories":194},[68],{"categories":196},[68],{"categories":198},[],{"categories":200},[201],"Design & Frontend",{"categories":203},[152],{"categories":205},[149],{"categories":207},[68],{"categories":209},[68],{"categories":211},[68],{"categories":213},[],{"categories":215},[68],{"categories":217},[68],{"categories":219},[115],{"categories":221},[127],{"categories":223},[68],{"categories":225},[115],{"categories":227},[68],{"categories":229},[230],"Marketing & Growth",{"categories":232},[201],{"categories":234},[68],{"categories":236},[115],{"categories":238},[68],{"categories":240},[68],{"categories":242},[127],{"categories":244},[68],{"categories":246},[],{"categories":248},[],{"categories":250},[201],{"categories":252},[68],{"categories":254},[115],{"categories":256},[105],{"categories":258},[127],{"categories":260},[115],{"categories":262},[201],{"categories":264},[118],{"categories":266},[68],{"categories":268},[127],{"categories":270},[271],"DevOps & Cloud",{"categories":273},[115],{"categories":275},[118],{"categories":277},[149],{"categories":279},[68],{"categories":281},[],{"categories":283},[68],{"categories":285},[68],{"categories":287},[],{"categories":289},[115],{"categories":291},[127],{"categories":293},[],{"categories":295},[127],{"categories":297},[68],{"categories":299},[300],"Governance & Standards",{"categories":302},[110],{"categories":304},[],{"categories":306},[],{"categories":308},[68],{"categories":310},[68],{"categories":312},[115],{"categories":314},[68],{"categories":316},[68],{"categories":318},[115],{"categories":320},[68],{"categories":322},[68],{"categories":324},[68],{"categories":326},[],{"categories":328},[127],{"categories":330},[],{"categories":332},[],{"categories":334},[68],{"categories":336},[127],{"categories":338},[],{"categories":340},[127],{"categories":342},[68],{"categories":344},[68],{"categories":346},[230],{"categories":348},[68],{"categories":350},[68],{"categories":352},[201],{"categories":354},[201],{"categories":356},[68],{"categories":358},[127],{"categories":360},[115],{"categories":362},[363],"GovTech & Public-Sector Adoption",{"categories":365},[127],{"categories":367},[68],{"categories":369},[68],{"categories":371},[68],{"categories":373},[115],{"categories":375},[115],{"categories":377},[152],{"categories":379},[68],{"categories":381},[149],{"categories":383},[115],{"categories":385},[386],"Legal AI Tools",{"categories":388},[68],{"categories":390},[115],{"categories":392},[68],{"categories":394},[230],{"categories":396},[115],{"categories":398},[118],{"categories":400},[68],{"categories":402},[127],{"categories":404},[363],{"categories":406},[],{"categories":408},[115],{"categories":410},[],{"categories":412},[110],{"categories":414},[115],{"categories":416},[115],{"categories":418},[419],"RAG & Retrieval",{"categories":421},[110],{"categories":423},[68],{"categories":425},[127],{"categories":427},[127],{"categories":429},[271],{"categories":431},[201],{"categories":433},[115],{"categories":435},[68],{"categories":437},[68],{"categories":439},[],{"categories":441},[442],"Agents & Orchestration",{"categories":444},[127],{"categories":446},[68],{"categories":448},[],{"categories":450},[115],{"categories":452},[110],{"categories":454},[],{"categories":456},[68],{"categories":458},[],{"categories":460},[68],{"categories":462},[105],{"categories":464},[127],{"categories":466},[110],{"categories":468},[68],{"categories":470},[115],{"categories":472},[68],{"categories":474},[149],{"categories":476},[68],{"categories":478},[],{"categories":480},[68],{"categories":482},[],{"categories":484},[68],{"categories":486},[127],{"categories":488},[68],{"categories":490},[152],{"categories":492},[],{"categories":494},[68],{"categories":496},[201],{"categories":498},[499],"Models & Frontier Labs",{"categories":501},[],{"categories":503},[201],{"categories":505},[506],"Regulation & Governance of AI",{"categories":508},[118],{"categories":510},[115],{"categories":512},[],{"categories":514},[68],{"categories":516},[68],{"categories":518},[115],{"categories":520},[115],{"categories":522},[149],{"categories":524},[68],{"categories":526},[110],{"categories":528},[68],{"categories":530},[115],{"categories":532},[],{"categories":534},[127],{"categories":536},[115],{"categories":538},[68],{"categories":540},[118],{"categories":542},[68],{"categories":544},[545],"AI Policy & Regulation",{"categories":547},[],{"categories":549},[68],{"categories":551},[115],{"categories":553},[118],{"categories":555},[115],{"categories":557},[68],{"categories":559},[68],{"categories":561},[68],{"categories":563},[115],{"categories":565},[],{"categories":567},[152],{"categories":569},[570],"Evals & Reliability",{"categories":572},[68],{"categories":574},[68],{"categories":576},[],{"categories":578},[105],{"categories":580},[363],{"categories":582},[545],{"categories":584},[68],{"categories":586},[110],{"categories":588},[68],{"categories":590},[115],{"categories":592},[68],{"categories":594},[115],{"categories":596},[442],{"categories":598},[68],{"categories":600},[127],{"categories":602},[68],{"categories":604},[],{"categories":606},[201],{"categories":608},[],{"categories":610},[68],{"categories":612},[363],{"categories":614},[68],{"categories":616},[68],{"categories":618},[68],{"categories":620},[],{"categories":622},[68],{"categories":624},[201],{"categories":626},[127],{"categories":628},[],{"categories":630},[68],{"categories":632},[],{"categories":634},[115],{"categories":636},[68],{"categories":638},[201],{"categories":640},[],{"categories":642},[68],{"categories":644},[68],{"categories":646},[152],{"categories":648},[115],{"categories":650},[68],{"categories":652},[110],{"categories":654},[115],{"categories":656},[68],{"categories":658},[68],{"categories":660},[127],{"categories":662},[201],{"categories":664},[68],{"categories":666},[115],{"categories":668},[],{"categories":670},[127],{"categories":672},[115],{"categories":674},[152],{"categories":676},[],{"categories":678},[68],{"categories":680},[149],{"categories":682},[68],{"categories":684},[],{"categories":686},[68],{"categories":688},[68],{"categories":690},[68],{"categories":692},[110,230],{"categories":694},[],{"categories":696},[127],{"categories":698},[68],{"categories":700},[68],{"categories":702},[115],{"categories":704},[68],{"categories":706},[],{"categories":708},[],{"categories":710},[68],{"categories":712},[201],{"categories":714},[68],{"categories":716},[],{"categories":718},[68],{"categories":720},[271],{"categories":722},[],{"categories":724},[115],{"categories":726},[149],{"categories":728},[68],{"categories":730},[68],{"categories":732},[201],{"categories":734},[],{"categories":736},[149],{"categories":738},[68],{"categories":740},[138],{"categories":742},[68],{"categories":744},[68],{"categories":746},[115],{"categories":748},[149],{"categories":750},[499],{"categories":752},[68],{"categories":754},[230],{"categories":756},[],{"categories":758},[115],{"categories":760},[110],{"categories":762},[127],{"categories":764},[68],{"categories":766},[115],{"categories":768},[],{"categories":770},[68,271],{"categories":772},[68],{"categories":774},[68],{"categories":776},[68],{"categories":778},[115],{"categories":780},[68,127],{"categories":782},[152],{"categories":784},[68],{"categories":786},[68],{"categories":788},[68],{"categories":790},[127],{"categories":792},[68],{"categories":794},[115],{"categories":796},[115],{"categories":798},[545],{"categories":800},[230],{"categories":802},[68],{"categories":804},[115],{"categories":806},[68],{"categories":808},[68],{"categories":810},[115],{"categories":812},[],{"categories":814},[115],{"categories":816},[68],{"categories":818},[68],{"categories":820},[115],{"categories":822},[68],{"categories":824},[68,110],{"categories":826},[68],{"categories":828},[110],{"categories":830},[],{"categories":832},[201],{"categories":834},[201],{"categories":836},[68],{"categories":838},[],{"categories":840},[],{"categories":842},[68],{"categories":844},[149],{"categories":846},[],{"categories":848},[105],{"categories":850},[68],{"categories":852},[127],{"categories":854},[68],{"categories":856},[857],"Generative UI & Design-to-Code",{"categories":859},[68],{"categories":861},[68],{"categories":863},[201],{"categories":865},[68],{"categories":867},[868],"Algorithmic Accountability",{"categories":870},[115],{"categories":872},[127],{"categories":874},[149],{"categories":876},[201],{"categories":878},[68],{"categories":880},[],{"categories":882},[118],{"categories":884},[68],{"categories":886},[68],{"categories":888},[68],{"categories":890},[68],{"categories":892},[115],{"categories":894},[895],"MLOps & Infrastructure",{"categories":897},[68],{"categories":899},[68],{"categories":901},[68],{"categories":903},[68],{"categories":905},[68],{"categories":907},[127],{"categories":909},[149],{"categories":911},[68],{"categories":913},[118],{"categories":915},[105],{"categories":917},[68],{"categories":919},[115],{"categories":921},[271],{"categories":923},[68],{"categories":925},[110],{"categories":927},[68],{"categories":929},[201],{"categories":931},[68],{"categories":933},[68],{"categories":935},[115],{"categories":937},[],{"categories":939},[],{"categories":941},[68],{"categories":943},[138],{"categories":945},[201],{"categories":947},[149],{"categories":949},[152],{"categories":951},[],{"categories":953},[68],{"categories":955},[68],{"categories":957},[110],{"categories":959},[115],{"categories":961},[68],{"categories":963},[68],{"categories":965},[68],{"categories":967},[68],{"categories":969},[149],{"categories":971},[138],{"categories":973},[68],{"categories":975},[201],{"categories":977},[68],{"categories":979},[],{"categories":981},[115],{"categories":983},[127],{"categories":985},[],{"categories":987},[68],{"categories":989},[68],{"categories":991},[115],{"categories":993},[127],{"categories":995},[68],{"categories":997},[152],{"categories":999},[201],{"categories":1001},[],{"categories":1003},[68],{"categories":1005},[],{"categories":1007},[68],{"categories":1009},[],{"categories":1011},[68],{"categories":1013},[68],{"categories":1015},[118],{"categories":1017},[110],{"categories":1019},[115],{"categories":1021},[115],{"categories":1023},[],{"categories":1025},[68],{"categories":1027},[105],{"categories":1029},[68],{"categories":1031},[68],{"categories":1033},[110],{"categories":1035},[149],{"categories":1037},[105],{"categories":1039},[],{"categories":1041},[68],{"categories":1043},[],{"categories":1045},[68],{"categories":1047},[],{"categories":1049},[149],{"categories":1051},[149],{"categories":1053},[],{"categories":1055},[442],{"categories":1057},[68],{"categories":1059},[201],{"categories":1061},[127],{"categories":1063},[],{"categories":1065},[386],{"categories":1067},[115],{"categories":1069},[110],{"categories":1071},[],{"categories":1073},[],{"categories":1075},[105],{"categories":1077},[152],{"categories":1079},[],{"categories":1081},[230],{"categories":1083},[115],{"categories":1085},[110],{"categories":1087},[115],{"categories":1089},[110],{"categories":1091},[68],{"categories":1093},[127],{"categories":1095},[],{"categories":1097},[138],{"categories":1099},[118],{"categories":1101},[68],{"categories":1103},[201],{"categories":1105},[127],{"categories":1107},[110],{"categories":1109},[68],{"categories":1111},[68],{"categories":1113},[115],{"categories":1115},[110],{"categories":1117},[68],{"categories":1119},[68],{"categories":1121},[68],{"categories":1123},[68],{"categories":1125},[68],{"categories":1127},[],{"categories":1129},[],{"categories":1131},[127],{"categories":1133},[152],{"categories":1135},[118],{"categories":1137},[68],{"categories":1139},[115],{"categories":1141},[127],{"categories":1143},[127],{"categories":1145},[68],{"categories":1147},[],{"categories":1149},[149],{"categories":1151},[118],{"categories":1153},[118],{"categories":1155},[127],{"categories":1157},[68],{"categories":1159},[570],{"categories":1161},[271],{"categories":1163},[],{"categories":1165},[115],{"categories":1167},[68],{"categories":1169},[],{"categories":1171},[105],{"categories":1173},[],{"categories":1175},[68],{"categories":1177},[68],{"categories":1179},[68],{"categories":1181},[201],{"categories":1183},[230],{"categories":1185},[68],{"categories":1187},[127],{"categories":1189},[68],{"categories":1191},[115],{"categories":1193},[],{"categories":1195},[127],{"categories":1197},[68],{"categories":1199},[105],{"categories":1201},[],{"categories":1203},[110],{"categories":1205},[68],{"categories":1207},[68],{"categories":1209},[149],{"categories":1211},[68,271],{"categories":1213},[68],{"categories":1215},[1216],"Design Systems for AI",{"categories":1218},[68],{"categories":1220},[68],{"categories":1222},[149],{"categories":1224},[68],{"categories":1226},[68],{"categories":1228},[68],{"categories":1230},[110],{"categories":1232},[68],{"categories":1234},[68],{"categories":1236},[68],{"categories":1238},[],{"categories":1240},[68],{"categories":1242},[68],{"categories":1244},[110],{"categories":1246},[68],{"categories":1248},[],{"categories":1250},[115],{"categories":1252},[127],{"categories":1254},[149],{"categories":1256},[127],{"categories":1258},[68],{"categories":1260},[201],{"categories":1262},[149],{"categories":1264},[152],{"categories":1266},[68],{"categories":1268},[68],{"categories":1270},[115],{"categories":1272},[105],{"categories":1274},[545],{"categories":1276},[68],{"categories":1278},[115],{"categories":1280},[68],{"categories":1282},[127],{"categories":1284},[127],{"categories":1286},[],{"categories":1288},[],{"categories":1290},[68],{"categories":1292},[115],{"categories":1294},[118],{"categories":1296},[],{"categories":1298},[110],{"categories":1300},[68],{"categories":1302},[],{"categories":1304},[201],{"categories":1306},[127],{"categories":1308},[115],{"categories":1310},[127],{"categories":1312},[201],{"categories":1314},[68],{"categories":1316},[68],{"categories":1318},[201],{"categories":1320},[],{"categories":1322},[],{"categories":1324},[149],{"categories":1326},[115],{"categories":1328},[115],{"categories":1330},[68],{"categories":1332},[68],{"categories":1334},[68],{"categories":1336},[68],{"categories":1338},[110],{"categories":1340},[68],{"categories":1342},[68],{"categories":1344},[],{"categories":1346},[127],{"categories":1348},[127],{"categories":1350},[68],{"categories":1352},[127],{"categories":1354},[110],{"categories":1356},[],{"categories":1358},[68],{"categories":1360},[68],{"categories":1362},[68],{"categories":1364},[68],{"categories":1366},[68],{"categories":1368},[115],{"categories":1370},[105],{"categories":1372},[110],{"categories":1374},[68],{"categories":1376},[115],{"categories":1378},[149],{"categories":1380},[115],{"categories":1382},[138],{"categories":1384},[230],{"categories":1386},[68],{"categories":1388},[115],{"categories":1390},[68],{"categories":1392},[68],{"categories":1394},[68],{"categories":1396},[],{"categories":1398},[201],{"categories":1400},[],{"categories":1402},[68],{"categories":1404},[68],{"categories":1406},[],{"categories":1408},[68],{"categories":1410},[127],{"categories":1412},[110],{"categories":1414},[1415],"Visual & Generative Media",{"categories":1417},[115],{"categories":1419},[],{"categories":1421},[68],{"categories":1423},[68],{"categories":1425},[127],{"categories":1427},[271],{"categories":1429},[68],{"categories":1431},[152],{"categories":1433},[545],{"categories":1435},[127],{"categories":1437},[230],{"categories":1439},[68],{"categories":1441},[201],{"categories":1443},[68],{"categories":1445},[68],{"categories":1447},[127],{"categories":1449},[115],{"categories":1451},[68],{"categories":1453},[],{"categories":1455},[],{"categories":1457},[115],{"categories":1459},[127],{"categories":1461},[105],{"categories":1463},[115],{"categories":1465},[499],{"categories":1467},[68],{"categories":1469},[118],{"categories":1471},[68],{"categories":1473},[110],{"categories":1475},[],{"categories":1477},[68],{"categories":1479},[118],{"categories":1481},[68],{"categories":1483},[68],{"categories":1485},[68],{"categories":1487},[118],{"categories":1489},[68],{"categories":1491},[68],{"categories":1493},[230],{"categories":1495},[68],{"categories":1497},[442],{"categories":1499},[68],{"categories":1501},[115],{"categories":1503},[68],{"categories":1505},[68],{"categories":1507},[68],{"categories":1509},[68],{"categories":1511},[201],{"categories":1513},[115],{"categories":1515},[],{"categories":1517},[115],{"categories":1519},[],{"categories":1521},[271],{"categories":1523},[127],{"categories":1525},[],{"categories":1527},[499],{"categories":1529},[68],{"categories":1531},[115],{"categories":1533},[115],{"categories":1535},[68],{"categories":1537},[201,68],{"categories":1539},[105],{"categories":1541},[68],{"categories":1543},[201],{"categories":1545},[],{"categories":1547},[68],{"categories":1549},[105],{"categories":1551},[68],{"categories":1553},[1554],"Medical Imaging & Radiology",{"categories":1556},[68],{"categories":1558},[68],{"categories":1560},[68],{"categories":1562},[201],{"categories":1564},[115],{"categories":1566},[127],{"categories":1568},[],{"categories":1570},[68],{"categories":1572},[68],{"categories":1574},[68],{"categories":1576},[],{"categories":1578},[],{"categories":1580},[68],{"categories":1582},[68],{"categories":1584},[442],{"categories":1586},[68],{"categories":1588},[105],{"categories":1590},[68],{"categories":1592},[68],{"categories":1594},[],{"categories":1596},[115],{"categories":1598},[68],{"categories":1600},[118],{"categories":1602},[127],{"categories":1604},[68],{"categories":1606},[115],{"categories":1608},[442],{"categories":1610},[68],{"categories":1612},[115],{"categories":1614},[68],{"categories":1616},[68],{"categories":1618},[68],{"categories":1620},[201],{"categories":1622},[115],{"categories":1624},[271],{"categories":1626},[201],{"categories":1628},[110],{"categories":1630},[115],{"categories":1632},[149],{"categories":1634},[68],{"categories":1636},[68],{"categories":1638},[118],{"categories":1640},[68],{"categories":1642},[68],{"categories":1644},[68],{"categories":1646},[68],{"categories":1648},[115],{"categories":1650},[68],{"categories":1652},[127],{"categories":1654},[127],{"categories":1656},[68],{"categories":1658},[118],{"categories":1660},[],{"categories":1662},[149],{"categories":1664},[],{"categories":1666},[118],{"categories":1668},[115],{"categories":1670},[68],{"categories":1672},[115],{"categories":1674},[1216],{"categories":1676},[1216],{"categories":1678},[201],{"categories":1680},[68],{"categories":1682},[68],{"categories":1684},[68],{"categories":1686},[115],{"categories":1688},[127],{"categories":1690},[201],{"categories":1692},[115],{"categories":1694},[149],{"categories":1696},[],{"categories":1698},[68],{"categories":1700},[],{"categories":1702},[68],{"categories":1704},[68],{"categories":1706},[68],{"categories":1708},[68],{"categories":1710},[115],{"categories":1712},[1713],"Contract Review & E-Discovery",{"categories":1715},[68],{"categories":1717},[201],{"categories":1719},[68],{"categories":1721},[105],{"categories":1723},[68],{"categories":1725},[149],{"categories":1727},[68],{"categories":1729},[68],{"categories":1731},[230],{"categories":1733},[127],{"categories":1735},[68],{"categories":1737},[68],{"categories":1739},[115],{"categories":1741},[115],{"categories":1743},[868],{"categories":1745},[68],{"categories":1747},[68],{"categories":1749},[115],{"categories":1751},[115],{"categories":1753},[68],{"categories":1755},[68],{"categories":1757},[68],{"categories":1759},[115],{"categories":1761},[68],{"categories":1763},[68],{"categories":1765},[442],{"categories":1767},[419],{"categories":1769},[68],{"categories":1771},[115],{"categories":1773},[68],{"categories":1775},[1776],"Law-Firm Practice & Adoption",{"categories":1778},[68],{"categories":1780},[115],{"categories":1782},[201],{"categories":1784},[68],{"categories":1786},[68],{"categories":1788},[68],{"categories":1790},[],{"categories":1792},[],{"categories":1794},[127],{"categories":1796},[68],{"categories":1798},[],{"categories":1800},[115],{"categories":1802},[105],{"categories":1804},[271],{"categories":1806},[68],{"categories":1808},[],{"categories":1810},[105],{"categories":1812},[110],{"categories":1814},[68],{"categories":1816},[230],{"categories":1818},[],{"categories":1820},[110],{"categories":1822},[115],{"categories":1824},[110],{"categories":1826},[],{"categories":1828},[68],{"categories":1830},[118],{"categories":1832},[68],{"categories":1834},[127],{"categories":1836},[],{"categories":1838},[],{"categories":1840},[],{"categories":1842},[],{"categories":1844},[68],{"categories":1846},[118],{"categories":1848},[115],{"categories":1850},[271],{"categories":1852},[68],{"categories":1854},[105],{"categories":1856},[127],{"categories":1858},[68],{"categories":1860},[68],{"categories":1862},[127],{"categories":1864},[118],{"categories":1866},[68],{"categories":1868},[68],{"categories":1870},[68],{"categories":1872},[895],{"categories":1874},[68],{"categories":1876},[127],{"categories":1878},[68],{"categories":1880},[230],{"categories":1882},[127],{"categories":1884},[110],{"categories":1886},[68],{"categories":1888},[68],{"categories":1890},[68],{"categories":1892},[201],{"categories":1894},[68],{"categories":1896},[68],{"categories":1898},[68],{"categories":1900},[68],{"categories":1902},[110],{"categories":1904},[115],{"categories":1906},[68,105],{"categories":1908},[442],{"categories":1910},[68],{"categories":1912},[68],{"categories":1914},[127],{"categories":1916},[127],{"categories":1918},[201],{"categories":1920},[115],{"categories":1922},[115],{"categories":1924},[127],{"categories":1926},[68],{"categories":1928},[68],{"categories":1930},[],{"categories":1932},[],{"categories":1934},[68],{"categories":1936},[68],{"categories":1938},[115],{"categories":1940},[],{"categories":1942},[68],{"categories":1944},[68],{"categories":1946},[127],{"categories":1948},[152],{"categories":1950},[149],{"categories":1952},[201],{"categories":1954},[68],{"categories":1956},[115],{"categories":1958},[68],{"categories":1960},[127],{"categories":1962},[],{"categories":1964},[115],{"categories":1966},[68],{"categories":1968},[68],{"categories":1970},[68],{"categories":1972},[68],{"categories":1974},[],{"categories":1976},[115],{"categories":1978},[68],{"categories":1980},[68],{"categories":1982},[68],{"categories":1984},[],{"categories":1986},[115],{"categories":1988},[68],{"categories":1990},[68],{"categories":1992},[110],{"categories":1994},[68],{"categories":1996},[68],{"categories":1998},[],{"categories":2000},[105],{"categories":2002},[68],{"categories":2004},[68],{"categories":2006},[68],{"categories":2008},[201],{"categories":2010},[68],{"categories":2012},[127],{"categories":2014},[68],{"categories":2016},[105],{"categories":2018},[68],{"categories":2020},[127],{"categories":2022},[230],{"categories":2024},[115],{"categories":2026},[115],{"categories":2028},[68],{"categories":2030},[68],{"categories":2032},[68,201],{"categories":2034},[68],{"categories":2036},[115],{"categories":2038},[149],{"categories":2040},[68],{"categories":2042},[149],{"categories":2044},[115],{"categories":2046},[201],{"categories":2048},[68],{"categories":2050},[],{"categories":2052},[127],{"categories":2054},[271],{"categories":2056},[201],{"categories":2058},[127],{"categories":2060},[68],{"categories":2062},[118],{"categories":2064},[68],{"categories":2066},[68],{"categories":2068},[115],{"categories":2070},[],{"categories":2072},[],{"categories":2074},[68],{"categories":2076},[],{"categories":2078},[],{"categories":2080},[118],{"categories":2082},[127],{"categories":2084},[68],{"categories":2086},[115],{"categories":2088},[115],{"categories":2090},[110],{"categories":2092},[115],{"categories":2094},[271],{"categories":2096},[68],{"categories":2098},[68],{"categories":2100},[68],{"categories":2102},[138],{"categories":2104},[68],{"categories":2106},[68],{"categories":2108},[68],{"categories":2110},[127],{"categories":2112},[115],{"categories":2114},[68],{"categories":2116},[68],{"categories":2118},[386],{"categories":2120},[868],{"categories":2122},[],{"categories":2124},[201],{"categories":2126},[1776],{"categories":2128},[127],{"categories":2130},[],{"categories":2132},[],{"categories":2134},[68],{"categories":2136},[115],{"categories":2138},[],{"categories":2140},[],{"categories":2142},[68],{"categories":2144},[230],{"categories":2146},[68],{"categories":2148},[230],{"categories":2150},[115],{"categories":2152},[68],{"categories":2154},[68],{"categories":2156},[127],{"categories":2158},[118],{"categories":2160},[],{"categories":2162},[68],{"categories":2164},[68],{"categories":2166},[127],{"categories":2168},[1713],{"categories":2170},[201],{"categories":2172},[201],{"categories":2174},[68],{"categories":2176},[115],{"categories":2178},[105],{"categories":2180},[68],{"categories":2182},[68],{"categories":2184},[68],{"categories":2186},[68],{"categories":2188},[201],{"categories":2190},[201],{"categories":2192},[115],{"categories":2194},[115],{"categories":2196},[115],{"categories":2198},[68],{"categories":2200},[68],{"categories":2202},[],{"categories":2204},[68],{"categories":2206},[],{"categories":2208},[2209],"Interaction & Product Design",{"categories":2211},[68],{"categories":2213},[115],{"categories":2215},[127],{"categories":2217},[300],{"categories":2219},[149],{"categories":2221},[127],{"categories":2223},[68],{"categories":2225},[68],{"categories":2227},[68],{"categories":2229},[127],{"categories":2231},[68],{"categories":2233},[105],{"categories":2235},[115],{"categories":2237},[68],{"categories":2239},[],{"categories":2241},[115],{"categories":2243},[115],{"categories":2245},[115],{"categories":2247},[],{"categories":2249},[127],{"categories":2251},[68],{"categories":2253},[105],{"categories":2255},[2209],{"categories":2257},[68],{"categories":2259},[105],{"categories":2261},[105],{"categories":2263},[],{"categories":2265},[115],{"categories":2267},[127],{"categories":2269},[],{"categories":2271},[115],{"categories":2273},[149],{"categories":2275},[68],{"categories":2277},[115],{"categories":2279},[68],{"categories":2281},[115],{"categories":2283},[68],{"categories":2285},[68],{"categories":2287},[149],{"categories":2289},[152],{"categories":2291},[68],{"categories":2293},[118],{"categories":2295},[127],{"categories":2297},[2298],"Coding Agents & Dev Productivity",{"categories":2300},[149],{"categories":2302},[201],{"categories":2304},[68],{"categories":2306},[68],{"categories":2308},[],{"categories":2310},[68],{"categories":2312},[868],{"categories":2314},[],{"categories":2316},[68],{"categories":2318},[68],{"categories":2320},[271],{"categories":2322},[68],{"categories":2324},[149],{"categories":2326},[],{"categories":2328},[],{"categories":2330},[68],{"categories":2332},[],{"categories":2334},[115],{"categories":2336},[68],{"categories":2338},[],{"categories":2340},[127],{"categories":2342},[127],{"categories":2344},[68],{"categories":2346},[152],{"categories":2348},[],{"categories":2350},[68],{"categories":2352},[68],{"categories":2354},[68],{"categories":2356},[152],{"categories":2358},[127],{"categories":2360},[115],{"categories":2362},[],{"categories":2364},[],{"categories":2366},[68],{"categories":2368},[68],{"categories":2370},[115],{"categories":2372},[115],{"categories":2374},[363],{"categories":2376},[127],{"categories":2378},[127],{"categories":2380},[115],{"categories":2382},[149],{"categories":2384},[149],{"categories":2386},[115],{"categories":2388},[115],{"categories":2390},[68],{"categories":2392},[105],{"categories":2394},[2209],{"categories":2396},[118],{"categories":2398},[68,271],{"categories":2400},[152],{"categories":2402},[],{"categories":2404},[201],{"categories":2406},[115],{"categories":2408},[127],{"categories":2410},[105],{"categories":2412},[68],{"categories":2414},[115],{"categories":2416},[2417],"The Designer's Role & Craft",{"categories":2419},[201],{"categories":2421},[],{"categories":2423},[115],{"categories":2425},[68],{"categories":2427},[115],{"categories":2429},[115],{"categories":2431},[68],{"categories":2433},[230],{"categories":2435},[68],{"categories":2437},[127],{"categories":2439},[68],{"categories":2441},[201],{"categories":2443},[68],{"categories":2445},[],{"categories":2447},[115],{"categories":2449},[201],{"categories":2451},[118],{"categories":2453},[68],{"categories":2455},[68],{"categories":2457},[68],{"categories":2459},[2460],"AI UX Patterns",{"categories":2462},[115],{"categories":2464},[115],{"categories":2466},[115],{"categories":2468},[115],{"categories":2470},[230],{"categories":2472},[152],{"categories":2474},[68],{"categories":2476},[115],{"categories":2478},[68],{"categories":2480},[1216],{"categories":2482},[],{"categories":2484},[230],{"categories":2486},[115],{"categories":2488},[149],{"categories":2490},[127],{"categories":2492},[68],{"categories":2494},[115],{"categories":2496},[],{"categories":2498},[],{"categories":2500},[68],{"categories":2502},[68],{"categories":2504},[115],{"categories":2506},[68],{"categories":2508},[115],{"categories":2510},[363],{"categories":2512},[201],{"categories":2514},[68],{"categories":2516},[149],{"categories":2518},[127],{"categories":2520},[68],{"categories":2522},[115],{"categories":2524},[115],{"categories":2526},[],{"categories":2528},[68],{"categories":2530},[],{"categories":2532},[],{"categories":2534},[68],{"categories":2536},[68],{"categories":2538},[68],{"categories":2540},[115],{"categories":2542},[127],{"categories":2544},[],{"categories":2546},[],{"categories":2548},[152],{"categories":2550},[138],{"categories":2552},[68],{"categories":2554},[68],{"categories":2556},[68],{"categories":2558},[152],{"categories":2560},[68],{"categories":2562},[68],{"categories":2564},[149],{"categories":2566},[68],{"categories":2568},[68],{"categories":2570},[68],{"categories":2572},[115],{"categories":2574},[68],{"categories":2576},[115],{"categories":2578},[68],{"categories":2580},[68],{"categories":2582},[68],{"categories":2584},[115],{"categories":2586},[],{"categories":2588},[68],{"categories":2590},[],{"categories":2592},[68],{"categories":2594},[68],{"categories":2596},[271],{"categories":2598},[68],{"categories":2600},[],{"categories":2602},[],{"categories":2604},[201],{"categories":2606},[895],{"categories":2608},[115],{"categories":2610},[105],{"categories":2612},[2417],{"categories":2614},[],{"categories":2616},[],{"categories":2618},[68],{"categories":2620},[],{"categories":2622},[],{"categories":2624},[127],{"categories":2626},[149],{"categories":2628},[230],{"categories":2630},[115],{"categories":2632},[110],{"categories":2634},[68],{"categories":2636},[68],{"categories":2638},[110],{"categories":2640},[],{"categories":2642},[201],{"categories":2644},[118],{"categories":2646},[68],{"categories":2648},[68],{"categories":2650},[115],{"categories":2652},[110],{"categories":2654},[68],{"categories":2656},[68],{"categories":2658},[105],{"categories":2660},[68],{"categories":2662},[68],{"categories":2664},[],{"categories":2666},[105],{"categories":2668},[68],{"categories":2670},[230],{"categories":2672},[115],{"categories":2674},[149],{"categories":2676},[68],{"categories":2678},[127],{"categories":2680},[68],{"categories":2682},[68],{"categories":2684},[110],{"categories":2686},[68],{"categories":2688},[68],{"categories":2690},[68],{"categories":2692},[115],{"categories":2694},[68],{"categories":2696},[],{"categories":2698},[68],{"categories":2700},[127],{"categories":2702},[105],{"categories":2704},[68],{"categories":2706},[68],{"categories":2708},[68],{"categories":2710},[],{"categories":2712},[68],{"categories":2714},[442],{"categories":2716},[115],{"categories":2718},[110],{"categories":2720},[149],{"categories":2722},[68],{"categories":2724},[68],{"categories":2726},[],{"categories":2728},[110],{"categories":2730},[110],{"categories":2732},[68],{"categories":2734},[68],{"categories":2736},[118],{"categories":2738},[68],{"categories":2740},[68],{"categories":2742},[68],{"categories":2744},[68],{"categories":2746},[127],{"categories":2748},[127],{"categories":2750},[68],{"categories":2752},[],{"categories":2754},[127],{"categories":2756},[68],{"categories":2758},[127],{"categories":2760},[115],{"categories":2762},[545],{"categories":2764},[],{"categories":2766},[],{"categories":2768},[68],{"categories":2770},[149],{"categories":2772},[],{"categories":2774},[271],{"categories":2776},[68],{"categories":2778},[68],{"categories":2780},[68],{"categories":2782},[201],{"categories":2784},[857],{"categories":2786},[],{"categories":2788},[68],{"categories":2790},[68],{"categories":2792},[68],{"categories":2794},[127],{"categories":2796},[68],{"categories":2798},[68],{"categories":2800},[68,271],{"categories":2802},[68],{"categories":2804},[68],{"categories":2806},[201],{"categories":2808},[115],{"categories":2810},[],{"categories":2812},[115],{"categories":2814},[115],{"categories":2816},[68],{"categories":2818},[68],{"categories":2820},[68],{"categories":2822},[68],{"categories":2824},[152],{"categories":2826},[68],{"categories":2828},[2460],{"categories":2830},[105],{"categories":2832},[152],{"categories":2834},[105],{"categories":2836},[127],{"categories":2838},[201],{"categories":2840},[115],{"categories":2842},[68],{"categories":2844},[],{"categories":2846},[110],{"categories":2848},[68],{"categories":2850},[68],{"categories":2852},[149],{"categories":2854},[68],{"categories":2856},[68],{"categories":2858},[68],{"categories":2860},[115],{"categories":2862},[68],{"categories":2864},[68],{"categories":2866},[68],{"categories":2868},[110],{"categories":2870},[],{"categories":2872},[271],{"categories":2874},[68],{"categories":2876},[363],{"categories":2878},[201],{"categories":2880},[201],{"categories":2882},[127],{"categories":2884},[115],{"categories":2886},[68],{"categories":2888},[110],{"categories":2890},[149],{"categories":2892},[68],{"categories":2894},[68],{"categories":2896},[201],{"categories":2898},[115],{"categories":2900},[115],{"categories":2902},[68],{"categories":2904},[68],{"categories":2906},[499],{"categories":2908},[115],{"categories":2910},[],{"categories":2912},[68],{"categories":2914},[68],{"categories":2916},[68],{"categories":2918},[],{"categories":2920},[],{"categories":2922},[68],{"categories":2924},[68],{"categories":2926},[115],{"categories":2928},[68],{"categories":2930},[68],{"categories":2932},[68],{"categories":2934},[127],{"categories":2936},[68],{"categories":2938},[68],{"categories":2940},[115],{"categories":2942},[68],{"categories":2944},[68],{"categories":2946},[68],{"categories":2948},[68],{"categories":2950},[68],{"categories":2952},[],{"categories":2954},[127],{"categories":2956},[152],{"categories":2958},[68],{"categories":2960},[115],{"categories":2962},[115],{"categories":2964},[68],{"categories":2966},[68],{"categories":2968},[],{"categories":2970},[],{"categories":2972},[68],{"categories":2974},[68],{"categories":2976},[68],{"categories":2978},[149],{"categories":2980},[152],{"categories":2982},[],{"categories":2984},[68],{"categories":2986},[201],{"categories":2988},[68],{"categories":2990},[271],{"categories":2992},[1776],{"categories":2994},[149],{"categories":2996},[127],{"categories":2998},[68],{"categories":3000},[127],{"categories":3002},[127],{"categories":3004},[68],{"categories":3006},[68],{"categories":3008},[127],{"categories":3010},[149],{"categories":3012},[149],{"categories":3014},[271],{"categories":3016},[115],{"categories":3018},[],{"categories":3020},[149],{"categories":3022},[68],{"categories":3024},[115],{"categories":3026},[105],{"categories":3028},[127],{"categories":3030},[68],{"categories":3032},[149],{"categories":3034},[],{"categories":3036},[68],{"categories":3038},[127],{"categories":3040},[127],{"categories":3042},[152],{"categories":3044},[68],{"categories":3046},[149],{"categories":3048},[68],{"categories":3050},[127],{"categories":3052},[115],{"categories":3054},[149],{"categories":3056},[115],{"categories":3058},[271],{"categories":3060},[115],{"categories":3062},[68],{"categories":3064},[68],{"categories":3066},[68],{"categories":3068},[68],{"categories":3070},[127],{"categories":3072},[68],{"categories":3074},[],{"categories":3076},[115],{"categories":3078},[110],{"categories":3080},[127],{"categories":3082},[],{"categories":3084},[],{"categories":3086},[68],{"categories":3088},[115],{"categories":3090},[68],{"categories":3092},[68],{"categories":3094},[3095],"Frameworks & Tooling",{"categories":3097},[68],{"categories":3099},[68],{"categories":3101},[127],{"categories":3103},[68],{"categories":3105},[68],{"categories":3107},[],{"categories":3109},[152],{"categories":3111},[152],{"categories":3113},[105],{"categories":3115},[68],{"categories":3117},[115],{"categories":3119},[68],{"categories":3121},[201],{"categories":3123},[],{"categories":3125},[1776],{"categories":3127},[68],{"categories":3129},[127],{"categories":3131},[68],{"categories":3133},[271],{"categories":3135},[271],{"categories":3137},[],{"categories":3139},[115],{"categories":3141},[68],{"categories":3143},[68],{"categories":3145},[149],{"categories":3147},[115],{"categories":3149},[149],{"categories":3151},[68],{"categories":3153},[115],{"categories":3155},[],{"categories":3157},[201],{"categories":3159},[68],{"categories":3161},[68],{"categories":3163},[],{"categories":3165},[68],{"categories":3167},[115],{"categories":3169},[68],{"categories":3171},[68],{"categories":3173},[68],{"categories":3175},[],{"categories":3177},[127],{"categories":3179},[68],{"categories":3181},[127],{"categories":3183},[271],{"categories":3185},[68],{"categories":3187},[68],{"categories":3189},[127],{"categories":3191},[110],{"categories":3193},[68],{"categories":3195},[1776],{"categories":3197},[],{"categories":3199},[115],{"categories":3201},[105],{"categories":3203},[68],{"categories":3205},[105],{"categories":3207},[68],{"categories":3209},[],{"categories":3211},[115],{"categories":3213},[68],{"categories":3215},[68],{"categories":3217},[3218],"AI Design Tooling",{"categories":3220},[201],{"categories":3222},[68],{"categories":3224},[68],{"categories":3226},[127],{"categories":3228},[201],{"categories":3230},[68],{"categories":3232},[68],{"categories":3234},[127],{"categories":3236},[149],{"categories":3238},[118],{"categories":3240},[127],{"categories":3242},[68],{"categories":3244},[68],{"categories":3246},[68],{"categories":3248},[115],{"categories":3250},[68],{"categories":3252},[],{"categories":3254},[115],{"categories":3256},[68],{"categories":3258},[68],{"categories":3260},[115],{"categories":3262},[68],{"categories":3264},[68],{"categories":3266},[68],{"categories":3268},[115],{"categories":3270},[],{"categories":3272},[115],{"categories":3274},[3095],{"categories":3276},[68],{"categories":3278},[68],{"categories":3280},[115],{"categories":3282},[115],{"categories":3284},[127],{"categories":3286},[127],{"categories":3288},[68],{"categories":3290},[],{"categories":3292},[127],{"categories":3294},[68],{"categories":3296},[68],{"categories":3298},[115],{"categories":3300},[110],{"categories":3302},[68],{"categories":3304},[],{"categories":3306},[68],{"categories":3308},[68],{"categories":3310},[2209],{"categories":3312},[],{"categories":3314},[68],{"categories":3316},[68],{"categories":3318},[68],{"categories":3320},[68],{"categories":3322},[201],{"categories":3324},[68],{"categories":3326},[],{"categories":3328},[68],{"categories":3330},[68],{"categories":3332},[68],{"categories":3334},[68],{"categories":3336},[230],{"categories":3338},[149],{"categories":3340},[68],{"categories":3342},[68],{"categories":3344},[1776],{"categories":3346},[105],{"categories":3348},[68],{"categories":3350},[68],{"categories":3352},[152],{"categories":3354},[68],{"categories":3356},[68],{"categories":3358},[149],{"categories":3360},[115],{"categories":3362},[],{"categories":3364},[68],{"categories":3366},[68],{"categories":3368},[201],{"categories":3370},[68],{"categories":3372},[230],{"categories":3374},[115],{"categories":3376},[68],{"categories":3378},[115],{"categories":3380},[],{"categories":3382},[],{"categories":3384},[],{"categories":3386},[105],{"categories":3388},[149],{"categories":3390},[115],{"categories":3392},[68],{"categories":3394},[68],{"categories":3396},[68],{"categories":3398},[68],{"categories":3400},[386],{"categories":3402},[201],{"categories":3404},[115],{"categories":3406},[68],{"categories":3408},[],{"categories":3410},[115],{"categories":3412},[115],{"categories":3414},[],{"categories":3416},[68],{"categories":3418},[115],{"categories":3420},[68],{"categories":3422},[],{"categories":3424},[68],{"categories":3426},[68],{"categories":3428},[68],{"categories":3430},[149],{"categories":3432},[201],{"categories":3434},[115],{"categories":3436},[201],{"categories":3438},[115],{"categories":3440},[68],{"categories":3442},[110],{"categories":3444},[],{"categories":3446},[],{"categories":3448},[68],{"categories":3450},[68],{"categories":3452},[68],{"categories":3454},[105],{"categories":3456},[115],{"categories":3458},[149],{"categories":3460},[],{"categories":3462},[201],{"categories":3464},[],{"categories":3466},[127],{"categories":3468},[68],{"categories":3470},[127],{"categories":3472},[201],{"categories":3474},[127],{"categories":3476},[68],{"categories":3478},[],{"categories":3480},[68],{"categories":3482},[68],{"categories":3484},[],{"categories":3486},[68],{"categories":3488},[230],{"categories":3490},[68],{"categories":3492},[271],{"categories":3494},[127],{"categories":3496},[68],{"categories":3498},[],{"categories":3500},[115],{"categories":3502},[68],{"categories":3504},[105],{"categories":3506},[499],{"categories":3508},[68],{"categories":3510},[68],{"categories":3512},[115],{"categories":3514},[68],{"categories":3516},[115],{"categories":3518},[68],{"categories":3520},[68],{"categories":3522},[68],{"categories":3524},[68],{"categories":3526},[],{"categories":3528},[68],{"categories":3530},[105],{"categories":3532},[68],{"categories":3534},[110],{"categories":3536},[127],{"categories":3538},[201],{"categories":3540},[],{"categories":3542},[68],{"categories":3544},[],{"categories":3546},[68],{"categories":3548},[],{"categories":3550},[115],{"categories":3552},[68],{"categories":3554},[127],{"categories":3556},[201],{"categories":3558},[149],{"categories":3560},[68],{"categories":3562},[149],{"categories":3564},[115],{"categories":3566},[201],{"categories":3568},[68],{"categories":3570},[],{"categories":3572},[68],{"categories":3574},[138],{"categories":3576},[115],{"categories":3578},[68],{"categories":3580},[201],{"categories":3582},[149],{"categories":3584},[110],{"categories":3586},[127],{"categories":3588},[68],{"categories":3590},[68],{"categories":3592},[68],{"categories":3594},[68],{"categories":3596},[149],{"categories":3598},[230],{"categories":3600},[],{"categories":3602},[],{"categories":3604},[152],{"categories":3606},[442],{"categories":3608},[68],{"categories":3610},[115],{"categories":3612},[68,127],{"categories":3614},[149],{"categories":3616},[68],{"categories":3618},[68],{"categories":3620},[68],{"categories":3622},[68],{"categories":3624},[68],{"categories":3626},[68],{"categories":3628},[115],{"categories":3630},[68],{"categories":3632},[115],{"categories":3634},[68],{"categories":3636},[68],{"categories":3638},[68],{"categories":3640},[],{"categories":3642},[68],{"categories":3644},[1216],{"categories":3646},[127],{"categories":3648},[201],{"categories":3650},[68],{"categories":3652},[68],{"categories":3654},[68],{"categories":3656},[152],{"categories":3658},[115],{"categories":3660},[230],{"categories":3662},[271],{"categories":3664},[],{"categories":3666},[127],{"categories":3668},[68],{"categories":3670},[110],{"categories":3672},[115],{"categories":3674},[105],{"categories":3676},[115],{"categories":3678},[68],{"categories":3680},[115],{"categories":3682},[115],{"categories":3684},[118],{"categories":3686},[127],{"categories":3688},[68],{"categories":3690},[68],{"categories":3692},[],{"categories":3694},[],{"categories":3696},[],{"categories":3698},[271],{"categories":3700},[68],{"categories":3702},[149],{"categories":3704},[68],{"categories":3706},[68],{"categories":3708},[68],{"categories":3710},[68],{"categories":3712},[],{"categories":3714},[68],{"categories":3716},[152],{"categories":3718},[110],{"categories":3720},[115],{"categories":3722},[68],{"categories":3724},[],{"categories":3726},[68],{"categories":3728},[115],{"categories":3730},[68],{"categories":3732},[271],{"categories":3734},[],{"categories":3736},[201],{"categories":3738},[201],{"categories":3740},[68],{"categories":3742},[115],{"categories":3744},[],{"categories":3746},[127],{"categories":3748},[68],{"categories":3750},[201],{"categories":3752},[68],{"categories":3754},[110],{"categories":3756},[115],{"categories":3758},[68],{"categories":3760},[],{"categories":3762},[149],{"categories":3764},[68],{"categories":3766},[68],{"categories":3768},[68],{"categories":3770},[201],{"categories":3772},[115],{"categories":3774},[149],{"categories":3776},[],{"categories":3778},[115],{"categories":3780},[110],{"categories":3782},[115],{"categories":3784},[201],{"categories":3786},[68],{"categories":3788},[68],{"categories":3790},[68],{"categories":3792},[442],{"categories":3794},[68],{"categories":3796},[115],{"categories":3798},[],{"categories":3800},[68],{"categories":3802},[68],{"categories":3804},[271],{"categories":3806},[149],{"categories":3808},[152],{"categories":3810},[545],{"categories":3812},[152],{"categories":3814},[152],{"categories":3816},[68],{"categories":3818},[],{"categories":3820},[],{"categories":3822},[],{"categories":3824},[115],{"categories":3826},[115],{"categories":3828},[115],{"categories":3830},[127],{"categories":3832},[68],{"categories":3834},[419],{"categories":3836},[127],{"categories":3838},[68],{"categories":3840},[68],{"categories":3842},[68],{"categories":3844},[68],{"categories":3846},[68],{"categories":3848},[115],{"categories":3850},[68],{"categories":3852},[],{"categories":3854},[],{"categories":3856},[68],{"categories":3858},[],{"categories":3860},[68],{"categories":3862},[115],{"categories":3864},[201],{"categories":3866},[68],{"categories":3868},[68],{"categories":3870},[],{"categories":3872},[115],{"categories":3874},[68],{"categories":3876},[68],{"categories":3878},[118],{"categories":3880},[68],{"categories":3882},[201],{"categories":3884},[68],{"categories":3886},[115],{"categories":3888},[110],{"categories":3890},[68],{"categories":3892},[230],{"categories":3894},[115],{"categories":3896},[68],{"categories":3898},[68],{"categories":3900},[857],{"categories":3902},[68],{"categories":3904},[115],{"categories":3906},[68],{"categories":3908},[127],{"categories":3910},[68],{"categories":3912},[499],{"categories":3914},[201],{"categories":3916},[],{"categories":3918},[68],{"categories":3920},[68],{"categories":3922},[149],{"categories":3924},[442],{"categories":3926},[115],{"categories":3928},[68],{"categories":3930},[],{"categories":3932},[149],{"categories":3934},[363],{"categories":3936},[115],{"categories":3938},[115],{"categories":3940},[115],{"categories":3942},[68],{"categories":3944},[68],{"categories":3946},[115],{"categories":3948},[],{"categories":3950},[110],{"categories":3952},[68],{"categories":3954},[110],{"categories":3956},[115],{"categories":3958},[],{"categories":3960},[127],{"categories":3962},[68],{"categories":3964},[68],{"categories":3966},[105],{"categories":3968},[149],{"categories":3970},[271],{"categories":3972},[138],{"categories":3974},[115],{"categories":3976},[115],{"categories":3978},[68],{"categories":3980},[68],{"categories":3982},[115],{"categories":3984},[68],{"categories":3986},[105],{"categories":3988},[],{"categories":3990},[115],{"categories":3992},[68],{"categories":3994},[68],{"categories":3996},[68],{"categories":3998},[115],{"categories":4000},[68],{"categories":4002},[],{"categories":4004},[68],{"categories":4006},[],{"categories":4008},[201],{"categories":4010},[115],{"categories":4012},[68,110],{"categories":4014},[115],{"categories":4016},[68],{"categories":4018},[],{"categories":4020},[105],{"categories":4022},[152],{"categories":4024},[110],{"categories":4026},[68],{"categories":4028},[127],{"categories":4030},[68],{"categories":4032},[68],{"categories":4034},[115],{"categories":4036},[68],{"categories":4038},[68],{"categories":4040},[68],{"categories":4042},[149],{"categories":4044},[1216],{"categories":4046},[115],{"categories":4048},[68],{"categories":4050},[],{"categories":4052},[],{"categories":4054},[68],{"categories":4056},[115],{"categories":4058},[68],{"categories":4060},[68],{"categories":4062},[271],{"categories":4064},[],{"categories":4066},[68],{"categories":4068},[115],{"categories":4070},[138],{"categories":4072},[115],{"categories":4074},[442],{"categories":4076},[],{"categories":4078},[386],{"categories":4080},[115],{"categories":4082},[68],{"categories":4084},[68],{"categories":4086},[230],{"categories":4088},[115],{"categories":4090},[68],{"categories":4092},[152],{"categories":4094},[118],{"categories":4096},[115],{"categories":4098},[68],{"categories":4100},[442],{"categories":4102},[68],{"categories":4104},[271],{"categories":4106},[110],{"categories":4108},[],{"categories":4110},[68],{"categories":4112},[68],{"categories":4114},[230],{"categories":4116},[201],{"categories":4118},[68],{"categories":4120},[68],{"categories":4122},[68],{"categories":4124},[],{"categories":4126},[230],{"categories":4128},[149],{"categories":4130},[68],{"categories":4132},[68],{"categories":4134},[68],{"categories":4136},[545],{"categories":4138},[105],{"categories":4140},[68],{"categories":4142},[118],{"categories":4144},[68],{"categories":4146},[],{"categories":4148},[],{"categories":4150},[201],{"categories":4152},[68],{"categories":4154},[152],{"categories":4156},[230],{"categories":4158},[115],{"categories":4160},[68],{"categories":4162},[68],{"categories":4164},[230],{"categories":4166},[149],{"categories":4168},[68],{"categories":4170},[],{"categories":4172},[68],{"categories":4174},[68],{"categories":4176},[],{"categories":4178},[68],{"categories":4180},[68],{"categories":4182},[570],{"categories":4184},[68],{"categories":4186},[68],{"categories":4188},[115],{"categories":4190},[127],{"categories":4192},[442],{"categories":4194},[68],{"categories":4196},[68],{"categories":4198},[68],{"categories":4200},[],{"categories":4202},[68,127],{"categories":4204},[149],{"categories":4206},[115],{"categories":4208},[127],{"categories":4210},[115],{"categories":4212},[895],{"categories":4214},[127],{"categories":4216},[127],{"categories":4218},[115],{"categories":4220},[68],{"categories":4222},[105],{"categories":4224},[],{"categories":4226},[],{"categories":4228},[115],{"categories":4230},[68],{"categories":4232},[127],{"categories":4234},[68],{"categories":4236},[105],{"categories":4238},[127],{"categories":4240},[127],{"categories":4242},[68],{"categories":4244},[230],{"categories":4246},[68],{"categories":4248},[127],{"categories":4250},[68],{"categories":4252},[],{"categories":4254},[68],{"categories":4256},[68],{"categories":4258},[201,68],{"categories":4260},[271],{"categories":4262},[105],{"categories":4264},[68],{"categories":4266},[],{"categories":4268},[68],{"categories":4270},[68],{"categories":4272},[110],{"categories":4274},[68],{"categories":4276},[110],{"categories":4278},[68],{"categories":4280},[68],{"categories":4282},[363],{"categories":4284},[68],{"categories":4286},[110],{"categories":4288},[127],{"categories":4290},[152],{"categories":4292},[115],{"categories":4294},[68],{"categories":4296},[127],{"categories":4298},[68],{"categories":4300},[68],{"categories":4302},[149],{"categories":4304},[230],{"categories":4306},[201],{"categories":4308},[68],{"categories":4310},[68],{"categories":4312},[68],{"categories":4314},[68],{"categories":4316},[105],{"categories":4318},[68],{"categories":4320},[115],{"categories":4322},[115],{"categories":4324},[127],{"categories":4326},[149],{"categories":4328},[127],{"categories":4330},[127],{"categories":4332},[68],{"categories":4334},[68],{"categories":4336},[],{"categories":4338},[],{"categories":4340},[152],{"categories":4342},[68],{"categories":4344},[127],{"categories":4346},[68],{"categories":4348},[201],{"categories":4350},[442],{"categories":4352},[386],{"categories":4354},[363],{"categories":4356},[68],{"categories":4358},[68],{"categories":4360},[68],{"categories":4362},[152],{"categories":4364},[68],{"categories":4366},[68],{"categories":4368},[68],{"categories":4370},[68],{"categories":4372},[68],{"categories":4374},[68],{"categories":4376},[68],{"categories":4378},[115],{"categories":4380},[105],{"categories":4382},[115],{"categories":4384},[68,110],{"categories":4386},[],{"categories":4388},[201],{"categories":4390},[],{"categories":4392},[118],{"categories":4394},[68],{"categories":4396},[149],{"categories":4398},[105],{"categories":4400},[68],{"categories":4402},[105],{"categories":4404},[115],{"categories":4406},[152],{"categories":4408},[115],{"categories":4410},[118],{"categories":4412},[115],{"categories":4414},[68],{"categories":4416},[68],{"categories":4418},[110],{"categories":4420},[115],{"categories":4422},[127],{"categories":4424},[230],{"categories":4426},[68],{"categories":4428},[68],{"categories":4430},[],{"categories":4432},[149],{"categories":4434},[68],{"categories":4436},[68],{"categories":4438},[68],{"categories":4440},[68],{"categories":4442},[68],{"categories":4444},[68],{"categories":4446},[127],{"categories":4448},[149],{"categories":4450},[127],{"categories":4452},[127],{"categories":4454},[68],{"categories":4456},[68],{"categories":4458},[68],{"categories":4460},[68],{"categories":4462},[386],{"categories":4464},[68],{"categories":4466},[115],{"categories":4468},[149],{"categories":4470},[68],{"categories":4472},[68],{"categories":4474},[68],{"categories":4476},[115],{"categories":4478},[68],{"categories":4480},[68],{"categories":4482},[68],{"categories":4484},[3095],{"categories":4486},[4487],"Clinical AI",{"categories":4489},[201],{"categories":4491},[68],{"categories":4493},[68],{"categories":4495},[68],{"categories":4497},[68],{"categories":4499},[271],{"categories":4501},[2460],{"categories":4503},[68],{"categories":4505},[118],{"categories":4507},[68],{"categories":4509},[115],{"categories":4511},[68],{"categories":4513},[68],{"categories":4515},[149],{"categories":4517},[68],{"categories":4519},[115],{"categories":4521},[127],{"categories":4523},[230],{"categories":4525},[68],{"categories":4527},[68],{"categories":4529},[110],{"categories":4531},[68],{"categories":4533},[68],{"categories":4535},[499],{"categories":4537},[68],{"categories":4539},[],{"categories":4541},[115],{"categories":4543},[68],{"categories":4545},[127],{"categories":4547},[105],{"categories":4549},[68],{"categories":4551},[],{"categories":4553},[],{"categories":4555},[68],{"categories":4557},[],{"categories":4559},[110],{"categories":4561},[68],{"categories":4563},[68],{"categories":4565},[115],{"categories":4567},[68],{"categories":4569},[149],{"categories":4571},[149],{"categories":4573},[149],{"categories":4575},[149],{"categories":4577},[],{"categories":4579},[105],{"categories":4581},[115],{"categories":4583},[149],{"categories":4585},[68],{"categories":4587},[570],{"categories":4589},[118],{"categories":4591},[115],{"categories":4593},[68],{"categories":4595},[105],{"categories":4597},[68],{"categories":4599},[115],{"categories":4601},[68],{"categories":4603},[68],{"categories":4605},[68],{"categories":4607},[68,115],{"categories":4609},[115],{"categories":4611},[271],{"categories":4613},[149],{"categories":4615},[115],{"categories":4617},[149],{"categories":4619},[115],{"categories":4621},[68],{"categories":4623},[],{"categories":4625},[149],{"categories":4627},[230],{"categories":4629},[105],{"categories":4631},[68],{"categories":4633},[68],{"categories":4635},[],{"categories":4637},[127],{"categories":4639},[],{"categories":4641},[105],{"categories":4643},[115],{"categories":4645},[149],{"categories":4647},[68],{"categories":4649},[149],{"categories":4651},[105],{"categories":4653},[149],{"categories":4655},[149],{"categories":4657},[],{"categories":4659},[110],{"categories":4661},[115],{"categories":4663},[149],{"categories":4665},[149],{"categories":4667},[149],{"categories":4669},[149],{"categories":4671},[149],{"categories":4673},[149],{"categories":4675},[149],{"categories":4677},[149],{"categories":4679},[149],{"categories":4681},[149],{"categories":4683},[152],{"categories":4685},[105],{"categories":4687},[68],{"categories":4689},[68],{"categories":4691},[115],{"categories":4693},[115],{"categories":4695},[],{"categories":4697},[68],{"categories":4699},[68,105],{"categories":4701},[],{"categories":4703},[115],{"categories":4705},[68],{"categories":4707},[149],{"categories":4709},[115],{"categories":4711},[895],{"categories":4713},[68],{"categories":4715},[68],{"categories":4717},[68],{"categories":4719},[68],{"categories":4721},[68],{"categories":4723},[363],{"categories":4725},[68],{"categories":4727},[68],{"categories":4729},[115],{"categories":4731},[68],{"categories":4733},[68],{"categories":4735},[110],{"categories":4737},[118],{"categories":4739},[115],{"categories":4741},[115],{"categories":4743},[],{"categories":4745},[115],{"categories":4747},[201],{"categories":4749},[149],{"categories":4751},[68],{"categories":4753},[],{"categories":4755},[118],{"categories":4757},[],{"categories":4759},[127],{"categories":4761},[68],{"categories":4763},[115],{"categories":4765},[201],{"categories":4767},[68],{"categories":4769},[],{"categories":4771},[68],{"categories":4773},[],{"categories":4775},[230],{"categories":4777},[68],{"categories":4779},[115],{"categories":4781},[],{"categories":4783},[],{"categories":4785},[149],{"categories":4787},[105],{"categories":4789},[68],{"categories":4791},[68],{"categories":4793},[110],{"categories":4795},[68],{"categories":4797},[68],{"categories":4799},[115],{"categories":4801},[68],{"categories":4803},[110],{"categories":4805},[110],{"categories":4807},[201],{"categories":4809},[],{"categories":4811},[68],{"categories":4813},[149],{"categories":4815},[],{"categories":4817},[68],{"categories":4819},[68],{"categories":4821},[201],{"categories":4823},[68],{"categories":4825},[68],{"categories":4827},[230],{"categories":4829},[68],{"categories":4831},[271],{"categories":4833},[],{"categories":4835},[115],{"categories":4837},[68],{"categories":4839},[230],{"categories":4841},[127],{"categories":4843},[],{"categories":4845},[68],{"categories":4847},[],{"categories":4849},[115],{"categories":4851},[201],{"categories":4853},[127],{"categories":4855},[],{"categories":4857},[3095],{"categories":4859},[110],{"categories":4861},[105],{"categories":4863},[68],{"categories":4865},[152],{"categories":4867},[115],{"categories":4869},[201],{"categories":4871},[68],{"categories":4873},[127],{"categories":4875},[],{"categories":4877},[],{"categories":4879},[68],{"categories":4881},[105],{"categories":4883},[68],{"categories":4885},[230],{"categories":4887},[],{"categories":4889},[115],{"categories":4891},[115],{"categories":4893},[68],{"categories":4895},[115],{"categories":4897},[68],{"categories":4899},[149],{"categories":4901},[127],{"categories":4903},[68],{"categories":4905},[115],{"categories":4907},[118],{"categories":4909},[68],{"categories":4911},[68],{"categories":4913},[68],{"categories":4915},[115],{"categories":4917},[68],{"categories":4919},[118],{"categories":4921},[230],{"categories":4923},[149],{"categories":4925},[],{"categories":4927},[230],{"categories":4929},[68],{"categories":4931},[],{"categories":4933},[127],{"categories":4935},[115],{"categories":4937},[],{"categories":4939},[68],{"categories":4941},[68],{"categories":4943},[68],{"categories":4945},[68],{"categories":4947},[68],{"categories":4949},[115],{"categories":4951},[110],{"categories":4953},[105],{"categories":4955},[115],{"categories":4957},[68],{"categories":4959},[201],{"categories":4961},[127],{"categories":4963},[127],{"categories":4965},[68],{"categories":4967},[152],{"categories":4969},[115],{"categories":4971},[68],{"categories":4973},[68],{"categories":4975},[115],{"categories":4977},[68],{"categories":4979},[68],{"categories":4981},[115],{"categories":4983},[110],{"categories":4985},[68],{"categories":4987},[201],{"categories":4989},[127],{"categories":4991},[115],{"categories":4993},[68],{"categories":4995},[118],{"categories":4997},[68],{"categories":4999},[115],{"categories":5001},[68],{"categories":5003},[68],{"categories":5005},[149],{"categories":5007},[68],{"categories":5009},[],{"categories":5011},[105],{"categories":5013},[68],{"categories":5015},[68],{"categories":5017},[68],{"categories":5019},[127],{"categories":5021},[127],{"categories":5023},[68],{"categories":5025},[127],{"categories":5027},[68],{"categories":5029},[115],{"categories":5031},[68],{"categories":5033},[68],{"categories":5035},[68],{"categories":5037},[68],{"categories":5039},[68],{"categories":5041},[],{"categories":5043},[68],{"categories":5045},[201],{"categories":5047},[115],{"categories":5049},[110],{"categories":5051},[149],{"categories":5053},[68],{"categories":5055},[115],{"categories":5057},[68],{"categories":5059},[115],{"categories":5061},[68],{"categories":5063},[68],{"categories":5065},[201],{"categories":5067},[115],{"categories":5069},[68],{"categories":5071},[230],{"categories":5073},[68],{"categories":5075},[152],{"categories":5077},[68],{"categories":5079},[68],{"categories":5081},[149],{"categories":5083},[68],{"categories":5085},[68],{"categories":5087},[68],{"categories":5089},[68],{"categories":5091},[115],{"categories":5093},[271],{"categories":5095},[68],{"categories":5097},[127],{"categories":5099},[115],{"categories":5101},[152],{"categories":5103},[],{"categories":5105},[115],{"categories":5107},[127],{"categories":5109},[68],{"categories":5111},[68],{"categories":5113},[2298],{"categories":5115},[201],{"categories":5117},[300],{"categories":5119},[68],{"categories":5121},[68],{"categories":5123},[68],{"categories":5125},[68],{"categories":5127},[105],{"categories":5129},[68],{"categories":5131},[68],{"categories":5133},[127],{"categories":5135},[110],{"categories":5137},[68],{"categories":5139},[127],{"categories":5141},[68],{"categories":5143},[],{"categories":5145},[115],{"categories":5147},[115],{"categories":5149},[68],{"categories":5151},[68],{"categories":5153},[68],{"categories":5155},[152],{"categories":5157},[],{"categories":5159},[149],{"categories":5161},[],{"categories":5163},[149],{"categories":5165},[68],{"categories":5167},[68],{"categories":5169},[115],{"categories":5171},[68],{"categories":5173},[115],{"categories":5175},[115],{"categories":5177},[],{"categories":5179},[68],{"categories":5181},[149],{"categories":5183},[68],{"categories":5185},[],{"categories":5187},[68],{"categories":5189},[68],{"categories":5191},[],{"categories":5193},[68],{"categories":5195},[68],{"categories":5197},[201],{"categories":5199},[127],{"categories":5201},[115],{"categories":5203},[68],{"categories":5205},[68],{"categories":5207},[68],{"categories":5209},[68],{"categories":5211},[230],{"categories":5213},[68],{"categories":5215},[68],{"categories":5217},[68],{"categories":5219},[105],{"categories":5221},[68],{"categories":5223},[68],{"categories":5225},[],{"categories":5227},[68],{"categories":5229},[68],{"categories":5231},[68],{"categories":5233},[],{"categories":5235},[105],{"categories":5237},[68],{"categories":5239},[68],{"categories":5241},[149],{"categories":5243},[127],{"categories":5245},[118],{"categories":5247},[115],{"categories":5249},[442],{"categories":5251},[68],{"categories":5253},[68],{"categories":5255},[68],{"categories":5257},[127],{"categories":5259},[149],{"categories":5261},[201],{"categories":5263},[68],{"categories":5265},[68],{"categories":5267},[68],{"categories":5269},[68],{"categories":5271},[149],{"categories":5273},[68],{"categories":5275},[201],{"categories":5277},[68],{"categories":5279},[68],{"categories":5281},[149],{"categories":5283},[201],{"categories":5285},[68],{"categories":5287},[149],{"categories":5289},[68],{"categories":5291},[115],{"categories":5293},[115],{"categories":5295},[115],{"categories":5297},[127],{"categories":5299},[149],{"categories":5301},[115],{"categories":5303},[115],{"categories":5305},[68],{"categories":5307},[127],{"categories":5309},[201],{"categories":5311},[68],{"categories":5313},[68],{"categories":5315},[115],{"categories":5317},[68],{"categories":5319},[],{"categories":5321},[115],{"categories":5323},[],{"categories":5325},[68],{"categories":5327},[68],{"categories":5329},[],{"categories":5331},[],{"categories":5333},[115],{"categories":5335},[110],{"categories":5337},[115],{"categories":5339},[5340],"Liability & Ethics",{"categories":5342},[68],{"categories":5344},[68],{"categories":5346},[68],{"categories":5348},[115],{"categories":5350},[105],{"categories":5352},[115],{"categories":5354},[110],{"categories":5356},[230],{"categories":5358},[115],{"categories":5360},[68],{"categories":5362},[68],{"categories":5364},[],{"categories":5366},[545],{"categories":5368},[115],{"categories":5370},[],{"categories":5372},[68],{"categories":5374},[105],{"categories":5376},[115],{"categories":5378},[],{"categories":5380},[115],{"categories":5382},[68],{"categories":5384},[68],{"categories":5386},[127],{"categories":5388},[68],{"categories":5390},[149],{"categories":5392},[68],{"categories":5394},[68],{"categories":5396},[115],{"categories":5398},[68],{"categories":5400},[68],{"categories":5402},[68],{"categories":5404},[149],{"categories":5406},[115],{"categories":5408},[127],{"categories":5410},[201],{"categories":5412},[105],{"categories":5414},[68],{"categories":5416},[68],{"categories":5418},[68],{"categories":5420},[],{"categories":5422},[115],{"categories":5424},[115],{"categories":5426},[115],{"categories":5428},[442],{"categories":5430},[201],{"categories":5432},[115],{"categories":5434},[271],{"categories":5436},[127],{"categories":5438},[149],{"categories":5440},[68],{"categories":5442},[201],{"categories":5444},[68],{"categories":5446},[105],{"categories":5448},[],{"categories":5450},[115],{"categories":5452},[68],{"categories":5454},[68],{"categories":5456},[68],{"categories":5458},[68],{"categories":5460},[115],{"categories":5462},[68],{"categories":5464},[68],{"categories":5466},[201],{"categories":5468},[],{"categories":5470},[115],{"categories":5472},[118],{"categories":5474},[149],{"categories":5476},[115],{"categories":5478},[110],{"categories":5480},[],{"categories":5482},[68],{"categories":5484},[68],{"categories":5486},[118],{"categories":5488},[68],{"categories":5490},[115],{"categories":5492},[149],{"categories":5494},[105],{"categories":5496},[271],{"categories":5498},[68],{"categories":5500},[68],{"categories":5502},[68],{"categories":5504},[149],{"categories":5506},[110],{"categories":5508},[68],{"categories":5510},[201],{"categories":5512},[149],{"categories":5514},[271],{"categories":5516},[68],{"categories":5518},[115],{"categories":5520},[],{"categories":5522},[499],{"categories":5524},[],{"categories":5526},[68],{"categories":5528},[271],{"categories":5530},[68],{"categories":5532},[152],{"categories":5534},[68],{"categories":5536},[115],{"categories":5538},[115],{"categories":5540},[5541],"Design News & Tools",{"categories":5543},[68],{"categories":5545},[149],{"categories":5547},[68],{"categories":5549},[68],{"categories":5551},[105],{"categories":5553},[115],{"categories":5555},[68],{"categories":5557},[201],{"categories":5559},[115],{"categories":5561},[115],{"categories":5563},[201],{"categories":5565},[68],{"categories":5567},[442],{"categories":5569},[115],{"categories":5571},[68],{"categories":5573},[68],{"categories":5575},[442],{"categories":5577},[68],{"categories":5579},[230],{"categories":5581},[68],{"categories":5583},[115],{"categories":5585},[],{"categories":5587},[68],{"categories":5589},[68],{"categories":5591},[68],{"categories":5593},[149],{"categories":5595},[105],{"categories":5597},[],{"categories":5599},[68],{"categories":5601},[68],{"categories":5603},[68],{"categories":5605},[127],{"categories":5607},[570],{"categories":5609},[127],{"categories":5611},[201],{"categories":5613},[68],{"categories":5615},[68,115],{"categories":5617},[230,110],{"categories":5619},[127],{"categories":5621},[68],{"categories":5623},[68],{"categories":5625},[68],{"categories":5627},[68],{"categories":5629},[],{"categories":5631},[115],{"categories":5633},[68],{"categories":5635},[],{"categories":5637},[68],{"categories":5639},[127],{"categories":5641},[68],{"categories":5643},[127],{"categories":5645},[],{"categories":5647},[115],{"categories":5649},[68],{"categories":5651},[110],{"categories":5653},[68],{"categories":5655},[149],{"categories":5657},[68],{"categories":5659},[],{"categories":5661},[115],{"categories":5663},[68],{"categories":5665},[],{"categories":5667},[201],{"categories":5669},[68],{"categories":5671},[68],{"categories":5673},[115],{"categories":5675},[68],{"categories":5677},[68],{"categories":5679},[105],{"categories":5681},[115],{"categories":5683},[68],{"categories":5685},[],{"categories":5687},[68],{"categories":5689},[271],{"categories":5691},[230],{"categories":5693},[110],{"categories":5695},[110],{"categories":5697},[68],{"categories":5699},[105],{"categories":5701},[105],{"categories":5703},[68],{"categories":5705},[115],{"categories":5707},[68],{"categories":5709},[68],{"categories":5711},[68],{"categories":5713},[68],{"categories":5715},[127],{"categories":5717},[68],{"categories":5719},[105],{"categories":5721},[68],{"categories":5723},[68],{"categories":5725},[115],{"categories":5727},[68],{"categories":5729},[230],{"categories":5731},[68],{"categories":5733},[149],{"categories":5735},[68],{"categories":5737},[68],{"categories":5739},[115],{"categories":5741},[68],{"categories":5743},[68],{"categories":5745},[115],{"categories":5747},[],{"categories":5749},[127],{"categories":5751},[],{"categories":5753},[127],{"categories":5755},[115],{"categories":5757},[105],{"categories":5759},[68],{"categories":5761},[],{"categories":5763},[152],{"categories":5765},[271],{"categories":5767},[68],{"categories":5769},[127],{"categories":5771},[68],{"categories":5773},[],{"categories":5775},[149],{"categories":5777},[115],{"categories":5779},[127],{"categories":5781},[201],{"categories":5783},[110],{"categories":5785},[68],{"categories":5787},[68],{"categories":5789},[115],{"categories":5791},[127],{"categories":5793},[115],{"categories":5795},[149],{"categories":5797},[68],{"categories":5799},[118],{"categories":5801},[105],{"categories":5803},[118],{"categories":5805},[149],{"categories":5807},[68],{"categories":5809},[127],{"categories":5811},[68],{"categories":5813},[201],{"categories":5815},[110],{"categories":5817},[68],{"categories":5819},[68],{"categories":5821},[68],{"categories":5823},[68],{"categories":5825},[68],{"categories":5827},[68],{"categories":5829},[115],{"categories":5831},[68],{"categories":5833},[115],{"categories":5835},[68],{"categories":5837},[68],{"categories":5839},[105],{"categories":5841},[68],{"categories":5843},[115],{"categories":5845},[115],{"categories":5847},[201],{"categories":5849},[115],{"categories":5851},[115],{"categories":5853},[68],{"categories":5855},[105],{"categories":5857},[115],{"categories":5859},[201],{"categories":5861},[],{"categories":5863},[68],{"categories":5865},[152],{"categories":5867},[442],{"categories":5869},[68],{"categories":5871},[68],{"categories":5873},[68],{"categories":5875},[127],{"categories":5877},[68],{"categories":5879},[],{"categories":5881},[68],{"categories":5883},[115],{"categories":5885},[68],{"categories":5887},[230],{"categories":5889},[68],{"categories":5891},[127],{"categories":5893},[68],{"categories":5895},[149],{"categories":5897},[115],{"categories":5899},[68],{"categories":5901},[230],{"categories":5903},[115],{"categories":5905},[110],{"categories":5907},[110],{"categories":5909},[68],{"categories":5911},[68],{"categories":5913},[68],{"categories":5915},[68],{"categories":5917},[68],{"categories":5919},[68],{"categories":5921},[105],{"categories":5923},[],{"categories":5925},[68],{"categories":5927},[68],{"categories":5929},[115],{"categories":5931},[115],{"categories":5933},[68],{"categories":5935},[68],{"categories":5937},[68],{"categories":5939},[68],{"categories":5941},[68],{"categories":5943},[127],{"categories":5945},[],{"categories":5947},[105],{"categories":5949},[68],{"categories":5951},[68],{"categories":5953},[115],{"categories":5955},[115],{"categories":5957},[],{"categories":5959},[127],{"categories":5961},[127],{"categories":5963},[68],{"categories":5965},[230],{"categories":5967},[110],{"categories":5969},[201],{"categories":5971},[],{"categories":5973},[68],{"categories":5975},[115],{"categories":5977},[105],{"categories":5979},[68],{"categories":5981},[68],{"categories":5983},[127],{"categories":5985},[105],{"categories":5987},[68],{"categories":5989},[68],{"categories":5991},[149],{"categories":5993},[152],{"categories":5995},[68],{"categories":5997},[149],{"categories":5999},[115],{"categories":6001},[68],{"categories":6003},[],{"categories":6005},[149],{"categories":6007},[115],{"categories":6009},[201],{"categories":6011},[152],{"categories":6013},[68],{"categories":6015},[68],{"categories":6017},[],{"categories":6019},[115],{"categories":6021},[115],{"categories":6023},[115],{"categories":6025},[3095],{"categories":6027},[149],{"categories":6029},[68],{"categories":6031},[127],{"categories":6033},[68],{"categories":6035},[68],{"categories":6037},[68],{"categories":6039},[68],{"categories":6041},[68],{"categories":6043},[110],{"categories":6045},[68],{"categories":6047},[105],{"categories":6049},[1776],{"categories":6051},[271],{"categories":6053},[105],{"categories":6055},[],{"categories":6057},[68],{"categories":6059},[],{"categories":6061},[149],{"categories":6063},[115],{"categories":6065},[201],{"categories":6067},[68],{"categories":6069},[68],{"categories":6071},[68],{"categories":6073},[149],{"categories":6075},[],{"categories":6077},[115],{"categories":6079},[68],{"categories":6081},[115],{"categories":6083},[115],{"categories":6085},[],{"categories":6087},[68],{"categories":6089},[],{"categories":6091},[149],{"categories":6093},[105],{"categories":6095},[201],{"categories":6097},[68],{"categories":6099},[115],{"categories":6101},[149],{"categories":6103},[68],{"categories":6105},[149],{"categories":6107},[],{"categories":6109},[149],{"categories":6111},[68],{"categories":6113},[105],{"categories":6115},[442],{"categories":6117},[115],{"categories":6119},[68],{"categories":6121},[],{"categories":6123},[127],{"categories":6125},[115],{"categories":6127},[118],{"categories":6129},[115],{"categories":6131},[105],{"categories":6133},[68],{"categories":6135},[],{"categories":6137},[],{"categories":6139},[],{"categories":6141},[201],{"categories":6143},[68],{"categories":6145},[115],{"categories":6147},[68],{"categories":6149},[68],{"categories":6151},[],{"categories":6153},[],{"categories":6155},[],{"categories":6157},[68],{"categories":6159},[115],{"categories":6161},[201],{"categories":6163},[68],{"categories":6165},[],{"categories":6167},[115],{"categories":6169},[68],{"categories":6171},[68],{"categories":6173},[105],{"categories":6175},[],{"categories":6177},[],{"categories":6179},[68],{"categories":6181},[68],{"categories":6183},[115],{"categories":6185},[201],{"categories":6187},[68],{"categories":6189},[149],{"categories":6191},[],{"categories":6193},[68],{"categories":6195},[68],{"categories":6197},[230],{"categories":6199},[149],{"categories":6201},[230],{"categories":6203},[152],{"categories":6205},[68],{"categories":6207},[68],{"categories":6209},[],{"categories":6211},[],{"categories":6213},[115],{"categories":6215},[],{"categories":6217},[68],{"categories":6219},[442],{"categories":6221},[68],{"categories":6223},[68],{"categories":6225},[68],{"categories":6227},[68],{"categories":6229},[],{"categories":6231},[115],{"categories":6233},[68],{"categories":6235},[68],{"categories":6237},[],{"categories":6239},[115],{"categories":6241},[68],{"categories":6243},[149],{"categories":6245},[68],{"categories":6247},[230],{"categories":6249},[110],{"categories":6251},[68],{"categories":6253},[68],{"categories":6255},[115],{"categories":6257},[152],{"categories":6259},[115],{"categories":6261},[115],{"categories":6263},[],{"categories":6265},[68],{"categories":6267},[115],{"categories":6269},[],{"categories":6271},[68],{"categories":6273},[],{"categories":6275},[149],{"categories":6277},[110],{"categories":6279},[],{"categories":6281},[68],{"categories":6283},[68],{"categories":6285},[],{"categories":6287},[115],{"categories":6289},[201],{"categories":6291},[105],{"categories":6293},[68],{"categories":6295},[],{"categories":6297},[110],{"categories":6299},[230],{"categories":6301},[68],{"categories":6303},[127],{"categories":6305},[105],{"categories":6307},[152],{"categories":6309},[110],{"categories":6311},[127],{"categories":6313},[115],{"categories":6315},[127],{"categories":6317},[],{"categories":6319},[68],{"categories":6321},[118],{"categories":6323},[68],{"categories":6325},[],{"categories":6327},[115],{"categories":6329},[105],{"categories":6331},[201],{"categories":6333},[68],{"categories":6335},[105],{"categories":6337},[115],{"categories":6339},[271],{"categories":6341},[68],{"categories":6343},[68],{"categories":6345},[68],{"categories":6347},[68],{"categories":6349},[105],{"categories":6351},[68],{"categories":6353},[152],{"categories":6355},[115],{"categories":6357},[],{"categories":6359},[68],{"categories":6361},[68],{"categories":6363},[68],{"categories":6365},[127],{"categories":6367},[115],{"categories":6369},[149],{"categories":6371},[127],{"categories":6373},[68],{"categories":6375},[118],{"categories":6377},[],{"categories":6379},[201],{"categories":6381},[127],{"categories":6383},[149],{"categories":6385},[105],{"categories":6387},[115],{"categories":6389},[68],{"categories":6391},[68],{"categories":6393},[115],{"categories":6395},[118],{"categories":6397},[68],{"categories":6399},[115],{"categories":6401},[68],{"categories":6403},[110],{"categories":6405},[115],{"categories":6407},[115,271],{"categories":6409},[68],{"categories":6411},[68],{"categories":6413},[115],{"categories":6415},[127],{"categories":6417},[68],{"categories":6419},[68],{"categories":6421},[152],{"categories":6423},[115],{"categories":6425},[230],{"categories":6427},[115],{"categories":6429},[110],{"categories":6431},[],{"categories":6433},[115],{"categories":6435},[68],{"categories":6437},[110],{"categories":6439},[],{"categories":6441},[],{"categories":6443},[127],{"categories":6445},[68],{"categories":6447},[68],{"categories":6449},[115],{"categories":6451},[152],{"categories":6453},[230],{"categories":6455},[68],{"categories":6457},[68],{"categories":6459},[68],{"categories":6461},[115],{"categories":6463},[],{"categories":6465},[115],{"categories":6467},[149],{"categories":6469},[68],{"categories":6471},[115],{"categories":6473},[115],{"categories":6475},[68],{"categories":6477},[],{"categories":6479},[149],{"categories":6481},[127],{"categories":6483},[3095],{"categories":6485},[105],{"categories":6487},[127],{"categories":6489},[68],{"categories":6491},[115],{"categories":6493},[68],{"categories":6495},[68],{"categories":6497},[230],{"categories":6499},[127],{"categories":6501},[152],{"categories":6503},[],{"categories":6505},[149],{"categories":6507},[68],{"categories":6509},[68],{"categories":6511},[],{"categories":6513},[115],{"categories":6515},[68],{"categories":6517},[68],{"categories":6519},[68],{"categories":6521},[68],{"categories":6523},[115],{"categories":6525},[68],{"categories":6527},[68],{"categories":6529},[68],{"categories":6531},[118],{"categories":6533},[68],{"categories":6535},[115],{"categories":6537},[68],{"categories":6539},[68],{"categories":6541},[68],{"categories":6543},[68],{"categories":6545},[68],{"categories":6547},[68],{"categories":6549},[68],{"categories":6551},[110],{"categories":6553},[],{"categories":6555},[118],{"categories":6557},[149],{"categories":6559},[115],{"categories":6561},[68],{"categories":6563},[127],{"categories":6565},[],{"categories":6567},[127],{"categories":6569},[127],{"categories":6571},[115],{"categories":6573},[127],{"categories":6575},[68],{"categories":6577},[68],{"categories":6579},[68],{"categories":6581},[115],{"categories":6583},[127],{"categories":6585},[68],{"categories":6587},[68],{"categories":6589},[68],{"categories":6591},[115],{"categories":6593},[149],{"categories":6595},[68],{"categories":6597},[68],{"categories":6599},[68],{"categories":6601},[110],{"categories":6603},[68],{"categories":6605},[115],{"categories":6607},[201],{"categories":6609},[],{"categories":6611},[68],{"categories":6613},[152],{"categories":6615},[115],{"categories":6617},[68],{"categories":6619},[68],{"categories":6621},[],{"categories":6623},[68],{"categories":6625},[68],{"categories":6627},[149],{"categories":6629},[68],{"categories":6631},[68],{"categories":6633},[115],{"categories":6635},[230],{"categories":6637},[],{"categories":6639},[],{"categories":6641},[127],{"categories":6643},[68],{"categories":6645},[68],{"categories":6647},[149],{"categories":6649},[68],{"categories":6651},[127],{"categories":6653},[149],{"categories":6655},[68],{"categories":6657},[68],{"categories":6659},[230],{"categories":6661},[152],{"categories":6663},[68],{"categories":6665},[68],{"categories":6667},[105],{"categories":6669},[115],{"categories":6671},[68],{"categories":6673},[68],{"categories":6675},[115],{"categories":6677},[110],{"categories":6679},[115],{"categories":6681},[127],{"categories":6683},[68],{"categories":6685},[110],{"categories":6687},[],{"categories":6689},[68],{"categories":6691},[152],{"categories":6693},[68],{"categories":6695},[68],{"categories":6697},[],{"categories":6699},[149],{"categories":6701},[68],{"categories":6703},[115],{"categories":6705},[152],{"categories":6707},[68],{"categories":6709},[127],{"categories":6711},[127],{"categories":6713},[127],{"categories":6715},[68],{"categories":6717},[115],{"categories":6719},[115],{"categories":6721},[68],{"categories":6723},[115],{"categories":6725},[68],{"categories":6727},[68],{"categories":6729},[201],{"categories":6731},[152],{"categories":6733},[152],{"categories":6735},[],{"categories":6737},[149],{"categories":6739},[68],{"categories":6741},[68],{"categories":6743},[127],{"categories":6745},[],{"categories":6747},[149],{"categories":6749},[149],{"categories":6751},[149],{"categories":6753},[],{"categories":6755},[115],{"categories":6757},[68],{"categories":6759},[],{"categories":6761},[105],{"categories":6763},[110],{"categories":6765},[],{"categories":6767},[68],{"categories":6769},[68],{"categories":6771},[],{"categories":6773},[127],{"categories":6775},[],{"categories":6777},[],{"categories":6779},[],{"categories":6781},[],{"categories":6783},[68],{"categories":6785},[149],{"categories":6787},[],{"categories":6789},[],{"categories":6791},[68],{"categories":6793},[68],{"categories":6795},[68],{"categories":6797},[152],{"categories":6799},[68],{"categories":6801},[152],{"categories":6803},[],{"categories":6805},[152],{"categories":6807},[152],{"categories":6809},[271],{"categories":6811},[115],{"categories":6813},[127],{"categories":6815},[],{"categories":6817},[],{"categories":6819},[152],{"categories":6821},[127],{"categories":6823},[127],{"categories":6825},[127],{"categories":6827},[],{"categories":6829},[105],{"categories":6831},[127],{"categories":6833},[127],{"categories":6835},[105],{"categories":6837},[127],{"categories":6839},[110],{"categories":6841},[127],{"categories":6843},[127],{"categories":6845},[127],{"categories":6847},[152],{"categories":6849},[149],{"categories":6851},[149],{"categories":6853},[68],{"categories":6855},[127],{"categories":6857},[152],{"categories":6859},[271],{"categories":6861},[152],{"categories":6863},[152],{"categories":6865},[152],{"categories":6867},[],{"categories":6869},[110],{"categories":6871},[],{"categories":6873},[271],{"categories":6875},[127],{"categories":6877},[127],{"categories":6879},[127],{"categories":6881},[115],{"categories":6883},[149,110],{"categories":6885},[152],{"categories":6887},[],{"categories":6889},[],{"categories":6891},[152],{"categories":6893},[],{"categories":6895},[152],{"categories":6897},[149],{"categories":6899},[115],{"categories":6901},[],{"categories":6903},[127],{"categories":6905},[68],{"categories":6907},[201],{"categories":6909},[],{"categories":6911},[68],{"categories":6913},[],{"categories":6915},[149],{"categories":6917},[105],{"categories":6919},[152],{"categories":6921},[],{"categories":6923},[127],{"categories":6925},[149],[6927,6984,7037,7092],{"id":6928,"title":6929,"ai":6930,"body":6935,"categories":6963,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":6964,"navigation":84,"path":6974,"published_at":6975,"question":69,"scraped_at":6975,"seo":6976,"sitemap":6977,"source_id":6978,"source_name":90,"source_type":91,"source_url":6968,"stem":6979,"tags":6980,"thumbnail_url":69,"tldr":6981,"tweet":69,"unknown_tags":6982,"__hash__":6983},"summaries\u002Fsummaries\u002Ffede902dc6ec2be1-optimizing-masked-diffusion-llms-for-real-world-ha-summary.md","Optimizing Masked Diffusion LLMs for Real-World Hardware",{"provider":7,"model":8,"input_tokens":6931,"output_tokens":6932,"processing_time_ms":6933,"cost_usd":6934},3991,458,2982,0.00168475,{"type":14,"value":6936,"toc":6959},[6937,6941,6944,6948],[17,6938,6940],{"id":6939},"understanding-masked-diffusion-llm-bottlenecks","Understanding Masked Diffusion LLM Bottlenecks",[22,6942,6943],{},"Masked Diffusion LLMs represent a departure from standard autoregressive models, introducing distinct computational patterns that challenge traditional inference engines. Unlike standard LLMs that generate tokens sequentially, these models utilize iterative masking and refinement processes. The research characterizes these models on real hardware, revealing that the primary performance bottleneck is not merely memory bandwidth—as is common in standard LLMs—but the high frequency of small, iterative compute kernels required during the diffusion steps. This creates a mismatch with standard GPU scheduling, which is optimized for large, dense matrix operations.",[17,6945,6947],{"id":6946},"hardware-aware-design-principles-for-inference","Hardware-Aware Design Principles for Inference",[22,6949,6950,6951,6954,6955,6958],{},"The authors propose several design principles to mitigate these inefficiencies. First, they advocate for ",[42,6952,6953],{},"operator fusion"," specifically tailored to the masking cycles, reducing the overhead of constant kernel launches. Second, they highlight the importance of ",[42,6956,6957],{},"dynamic memory management"," to handle the fluctuating memory requirements of the diffusion process, which differs significantly from the static KV-cache patterns used in autoregressive models. Finally, the paper suggests that hardware-aware scheduling—prioritizing the latency of the iterative refinement loop over raw throughput—is essential for achieving production-grade performance. By aligning the model's iterative structure with the underlying hardware's execution model, developers can significantly reduce latency and improve resource utilization compared to naive deployment strategies.",{"title":62,"searchDepth":63,"depth":63,"links":6960},[6961,6962],{"id":6939,"depth":63,"text":6940},{"id":6946,"depth":63,"text":6947},[68],{"content_references":6965,"triage":6970},[6966],{"type":75,"title":6967,"url":6968,"context":6969},"Serving Masked Diffusion LLMs: Characterization and Design Principles from Real Hardware","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.23807","cited",{"relevance":6971,"novelty":80,"quality":80,"actionability":81,"composite":6972,"reasoning":6973},5,4.15,"Category: AI & LLMs. The article provides in-depth insights into optimizing Masked Diffusion LLMs for real-world hardware, addressing a specific pain point of performance bottlenecks in AI models. It proposes actionable design principles like operator fusion and dynamic memory management, which can be applied by developers working on AI-powered products.","\u002Fsummaries\u002Ffede902dc6ec2be1-optimizing-masked-diffusion-llms-for-real-world-ha-summary","2026-08-27 03:13:03",{"title":6929,"description":62},{"loc":6974},"fede902dc6ec2be1","summaries\u002Ffede902dc6ec2be1-optimizing-masked-diffusion-llms-for-real-world-ha-summary",[94,96,95],"This paper provides a characterization of Masked Diffusion LLMs, identifying unique computational bottlenecks and proposing hardware-aware design principles to improve inference efficiency.",[],"6zGIU837zpqlcQvwRPwimb--82MrKrU6Eajvj4iitsA",{"id":6985,"title":6986,"ai":6987,"body":6990,"categories":7018,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":7019,"navigation":84,"path":7027,"published_at":7028,"question":69,"scraped_at":7028,"seo":7029,"sitemap":7030,"source_id":7031,"source_name":90,"source_type":91,"source_url":7023,"stem":7032,"tags":7033,"thumbnail_url":69,"tldr":7034,"tweet":69,"unknown_tags":7035,"__hash__":7036},"summaries\u002Fsummaries\u002F824d14d4bfa1e35c-architecture-aware-credit-transport-for-llm-reinfo-summary.md","Architecture-Aware Credit Transport for LLM Reinforcement Learning",{"provider":7,"model":8,"input_tokens":9,"output_tokens":6932,"processing_time_ms":6988,"cost_usd":6989},2310,0.00168975,{"type":14,"value":6991,"toc":7013},[6992,6996,6999,7003,7006,7010],[17,6993,6995],{"id":6994},"the-credit-assignment-problem-in-llm-training","The Credit Assignment Problem in LLM Training",[22,6997,6998],{},"Traditional reinforcement learning (RL) for Large Language Models often struggles with the 'credit assignment problem'—the difficulty of determining which specific tokens or internal computations contributed most to a final reward. When training models via RL, the feedback signal is typically sparse or delayed, making it hard for the model to learn which parts of its reasoning chain were effective. This paper argues that standard approaches treat the model as a black box, ignoring the structural reality of how information flows through the transformer architecture.",[17,7000,7002],{"id":7001},"architecture-aware-credit-transport","Architecture-Aware Credit Transport",[22,7004,7005],{},"The authors propose 'Architecture-Aware Credit Transport,' a framework that explicitly maps reward signals back to the specific computational paths taken during inference. By leveraging the internal structure of the transformer—specifically the attention mechanisms and layer-wise activations—the method ensures that 'credit' for a successful output is distributed proportionally to the nodes and layers that performed the heavy lifting. This approach moves beyond global reward signals, allowing for more granular updates to the model's weights.",[17,7007,7009],{"id":7008},"impact-on-training-efficiency","Impact on Training Efficiency",[22,7011,7012],{},"By aligning the credit assignment with the model's architecture, the researchers demonstrate a more stable and efficient training process. This method reduces the noise inherent in standard policy gradient methods, as the model receives more precise feedback on which internal representations led to high-quality outputs. The result is faster convergence and better performance on complex reasoning tasks where multi-step logic is required, as the model learns to prioritize the specific computational pathways that reliably produce correct answers.",{"title":62,"searchDepth":63,"depth":63,"links":7014},[7015,7016,7017],{"id":6994,"depth":63,"text":6995},{"id":7001,"depth":63,"text":7002},{"id":7008,"depth":63,"text":7009},[68],{"content_references":7020,"triage":7024},[7021],{"type":75,"title":7022,"url":7023,"context":6969},"Let Credit Follow Computation: Architecture-Aware Credit Transport for Large Language Model Reinforcement Learning","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.21501",{"relevance":81,"novelty":80,"quality":80,"actionability":63,"composite":7025,"reasoning":7026},3.25,"Category: AI & LLMs. The article discusses a novel approach to improving reinforcement learning for LLMs by addressing the credit assignment problem, which is relevant to AI engineering. However, it lacks practical applications or frameworks that the audience can directly implement in their work.","\u002Fsummaries\u002F824d14d4bfa1e35c-architecture-aware-credit-transport-for-llm-reinfo-summary","2026-08-26 03:10:18",{"title":6986,"description":62},{"loc":7027},"824d14d4bfa1e35c","summaries\u002F824d14d4bfa1e35c-architecture-aware-credit-transport-for-llm-reinfo-summary",[94,96,95],"The paper introduces a method to improve LLM reinforcement learning by aligning credit assignment with the underlying computational architecture, ensuring rewards are distributed based on actual processing paths.",[],"GItKQXEc2ihTFaRO89Jxh_IkNVQYDPhaKrNjw6vUphE",{"id":7038,"title":7039,"ai":7040,"body":7045,"categories":7073,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":7074,"navigation":84,"path":7082,"published_at":7083,"question":69,"scraped_at":7083,"seo":7084,"sitemap":7085,"source_id":7086,"source_name":90,"source_type":91,"source_url":7078,"stem":7087,"tags":7088,"thumbnail_url":69,"tldr":7089,"tweet":69,"unknown_tags":7090,"__hash__":7091},"summaries\u002Fsummaries\u002F6c09e8ea53dac05b-adapting-llms-for-hate-speech-detection-in-low-res-summary.md","Adapting LLMs for Hate Speech Detection in Low-Resource Languages",{"provider":7,"model":8,"input_tokens":7041,"output_tokens":7042,"processing_time_ms":7043,"cost_usd":7044},4042,542,2806,0.0018235,{"type":14,"value":7046,"toc":7068},[7047,7051,7054,7058,7061,7065],[17,7048,7050],{"id":7049},"optimizing-llm-adaptation-for-low-resource-contexts","Optimizing LLM Adaptation for Low-Resource Contexts",[22,7052,7053],{},"Adapting large language models (LLMs) to low-resource languages—specifically Roman Urdu—presents a significant challenge due to data scarcity and the linguistic nuances of informal, code-mixed text. The core research objective is to identify the most efficient fine-tuning strategies that enable accurate hate speech detection without requiring the computational intensity of full-parameter fine-tuning.",[17,7055,7057],{"id":7056},"comparative-efficacy-of-fine-tuning-strategies","Comparative Efficacy of Fine-Tuning Strategies",[22,7059,7060],{},"The study evaluates various parameter-efficient fine-tuning (PEFT) methods against traditional full-parameter approaches. By leveraging techniques like LoRA (Low-Rank Adaptation), the research demonstrates that it is possible to achieve competitive performance metrics in hate speech classification while updating only a small fraction of the model's total parameters. This approach is critical for practitioners working with limited hardware or datasets where overfitting is a high risk. The findings suggest that for low-resource languages, the choice of adapter rank and the selection of base model architecture are more impactful than simply increasing the volume of training data, which is often noisy or unavailable in these linguistic domains.",[17,7062,7064],{"id":7063},"addressing-linguistic-nuance-in-roman-urdu","Addressing Linguistic Nuance in Roman Urdu",[22,7066,7067],{},"Roman Urdu presents unique obstacles, including non-standardized orthography, code-switching between Urdu and English, and the absence of formal grammatical structures. The research highlights that effective detection models must be robust to these variations. By comparing different model architectures, the authors provide a framework for selecting base models that possess sufficient cross-lingual transfer capabilities to handle Romanized scripts. The study concludes that targeted fine-tuning on domain-specific, annotated datasets significantly outperforms zero-shot or few-shot prompting approaches, which often struggle with the cultural and linguistic context inherent in hate speech detection tasks.",{"title":62,"searchDepth":63,"depth":63,"links":7069},[7070,7071,7072],{"id":7049,"depth":63,"text":7050},{"id":7056,"depth":63,"text":7057},{"id":7063,"depth":63,"text":7064},[68],{"content_references":7075,"triage":7079},[7076],{"type":75,"title":7077,"url":7078,"context":6969},"Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.18142",{"relevance":81,"novelty":80,"quality":80,"actionability":81,"composite":7080,"reasoning":7081},3.45,"Category: AI & LLMs. The article discusses adapting LLMs for hate speech detection, which is relevant to AI engineering and addresses a specific challenge in low-resource languages. It presents new insights into fine-tuning strategies, but while it offers a framework, it lacks detailed actionable steps for practitioners.","\u002Fsummaries\u002F6c09e8ea53dac05b-adapting-llms-for-hate-speech-detection-in-low-res-summary","2026-08-21 03:13:13",{"title":7039,"description":62},{"loc":7082},"6c09e8ea53dac05b","summaries\u002F6c09e8ea53dac05b-adapting-llms-for-hate-speech-detection-in-low-res-summary",[94,96,95],"Efficiently adapting LLMs for Roman Urdu hate speech detection requires balancing parameter-efficient fine-tuning (PEFT) techniques with limited data availability to maintain performance without the overhead of full model retraining.",[],"0ozKyJsPzINrxOm4bENh9V9ey0L-NHOc1CePHCooP3Q",{"id":7093,"title":7094,"ai":7095,"body":7100,"categories":7128,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":7129,"navigation":84,"path":7137,"published_at":7138,"question":69,"scraped_at":7138,"seo":7139,"sitemap":7140,"source_id":7141,"source_name":90,"source_type":91,"source_url":7134,"stem":7142,"tags":7143,"thumbnail_url":69,"tldr":7144,"tweet":69,"unknown_tags":7145,"__hash__":7146},"summaries\u002Fsummaries\u002F66485da47e448689-the-reliability-gap-in-automated-safety-benchmarks-summary.md","The Reliability Gap in Automated Safety Benchmarks for Small Models",{"provider":7,"model":8,"input_tokens":7096,"output_tokens":7097,"processing_time_ms":7098,"cost_usd":7099},4027,515,2803,0.00177925,{"type":14,"value":7101,"toc":7123},[7102,7106,7109,7113,7116,7120],[17,7103,7105],{"id":7104},"the-fragility-of-automated-safety-evaluation","The Fragility of Automated Safety Evaluation",[22,7107,7108],{},"The research highlights a critical disconnect in the current AI safety landscape: automated benchmarks, which are increasingly used to validate small language models (SLMs), often fail to capture the nuances of model behavior in adversarial environments. The authors argue that relying solely on these automated metrics creates a false sense of security, as the benchmarks themselves are susceptible to overfitting and lack the adversarial depth needed to stress-test smaller, resource-constrained models.",[17,7110,7112],{"id":7111},"discrepancies-in-performance-metrics","Discrepancies in Performance Metrics",[22,7114,7115],{},"The study demonstrates that safety scores derived from automated benchmarks do not consistently correlate with human-evaluated safety or robustness against novel jailbreak attempts. For small language models, which are often deployed in edge or sensitive environments, this gap is particularly dangerous. The authors suggest that current evaluation frameworks prioritize static datasets that models can easily memorize during training, rather than testing for generalized safety behaviors. Consequently, a model might achieve a high score on a standard benchmark while remaining highly vulnerable to simple, non-standardized adversarial prompts.",[17,7117,7119],{"id":7118},"moving-toward-robust-evaluation","Moving Toward Robust Evaluation",[22,7121,7122],{},"To address these shortcomings, the paper advocates for a shift away from static, automated-only evaluation. The authors propose that developers must integrate dynamic, adversarial testing—where models are subjected to evolving, human-in-the-loop, or agent-based attack scenarios—to gain a true measure of safety. For builders, this means that passing a benchmark should be viewed as a baseline, not a validation of production-readiness. The research underscores the necessity of building custom, domain-specific safety evaluations that reflect the actual deployment context of the model rather than relying on generalized, potentially misleading benchmark scores.",{"title":62,"searchDepth":63,"depth":63,"links":7124},[7125,7126,7127],{"id":7104,"depth":63,"text":7105},{"id":7111,"depth":63,"text":7112},{"id":7118,"depth":63,"text":7119},[68],{"content_references":7130,"triage":7135},[7131],{"type":75,"title":7132,"author":7133,"url":7134,"context":6969},"Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language Models","Not specified","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.17183",{"relevance":80,"novelty":80,"quality":80,"actionability":81,"composite":82,"reasoning":7136},"Category: AI & LLMs. The article addresses a significant issue in the evaluation of small language models, which is relevant to AI product builders concerned about safety and robustness. It provides insights into the limitations of current benchmarks and suggests a more dynamic evaluation approach, which can inform developers on improving their safety assessments.","\u002Fsummaries\u002F66485da47e448689-the-reliability-gap-in-automated-safety-benchmarks-summary","2026-08-20 03:12:42",{"title":7094,"description":62},{"loc":7137},"66485da47e448689","summaries\u002F66485da47e448689-the-reliability-gap-in-automated-safety-benchmarks-summary",[94,96,95],"Automated safety benchmarks for small language models often lack the robustness required for production, revealing significant discrepancies between benchmark scores and real-world safety performance.",[],"jf2AyKSWYd9DikFyKUBngVvyaFWRsVlg61R5jSSgLa4"]