[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-0e7f4e0f7633b086-world-models-build-ai-s-internal-reality-simulator-summary":3,"summaries-facets-categories":97,"summary-related-0e7f4e0f7633b086-world-models-build-ai-s-internal-reality-simulator-summary":3667},{"id":4,"title":5,"ai":6,"body":13,"categories":46,"created_at":47,"date_modified":47,"description":40,"extension":48,"faq":47,"featured":49,"kicker_label":47,"meta":50,"navigation":79,"path":80,"published_at":47,"question":47,"scraped_at":81,"seo":82,"sitemap":83,"source_id":84,"source_name":85,"source_type":86,"source_url":87,"stem":88,"tags":89,"thumbnail_url":47,"tldr":94,"tweet":47,"unknown_tags":95,"__hash__":96},"summaries\u002Fsummaries\u002F0e7f4e0f7633b086-world-models-build-ai-s-internal-reality-simulator-summary.md","World Models Build AI's Internal Reality Simulators",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",9191,1798,14039,0.00271455,{"type":14,"value":15,"toc":39},"minimark",[16,21,25,29,32,36],[17,18,20],"h2",{"id":19},"transformers-fail-at-reality-world-models-internalize-it","Transformers Fail at Reality; World Models Internalize It",[22,23,24],"p",{},"Current LLMs and transformers excel at pattern matching—like autocomplete for text or images—but crumble on physics, long-term planning, and consistent reasoning, hallucinating facts despite scaling to 670 billion parameters (e.g., DeepSeek). They predict next tokens without grasping cause-and-effect, leading to brittle performance on sequences or real-world tasks. World models fix this by learning from 'streams of experience'—continuous data like video frames, robot sensor readings (camera, IMU, joints), or gameplay trajectories—compressing them into latent states to simulate futures internally. This mimics human prediction ('imagining' outcomes before acting), slashing real-world trial costs and compute. Yann LeCun argues world models are essential for human-level AI, estimating a decade to mature if research stays focused.",[17,26,28],{"id":27},"architecture-compress-predict-control","Architecture: Compress, Predict, Control",[22,30,31],{},"Core stack from the 2018 'World Models' paper by David Ha and Jürgen Schmidhuber: VAE compresses raw inputs (e.g., pixel streams) into low-dimensional latent vectors; MDN-RNN probabilistically forecasts next states and uncertainties (using KL divergence to measure prediction error against reality); a controller (actor-critic) evaluates simulated trajectories to select actions. Earlier roots in Richard Sutton's 1990s Dyna algorithm blend model-free reaction with model-based planning. Training mixes offline data (bouncing balls, robot walks) and online sensors, building an internal physics engine. Example: A robot learns walking by ingesting wobble sequences, simulates steps to avoid falls, reducing real experiments. Outcome: Agents 'dream' thousands of scenarios in latent space, outperforming humans on tasks without physical risk.",[17,33,35],{"id":34},"production-models-prove-scalable-impact","Production Models Prove Scalable Impact",[22,37,38],{},"DeepMind's DreamerV3 masters 150 tasks via latent simulation, critic evaluation, and Minecraft foresight. Genie 2 generates interactive worlds from one image. NVIDIA's Cosmos suite—Predict1 for video evolution, Transfer1 for control, Reason1 for language explanations (Mamba-MLP-Transformer)—handles synthetic physics. Meta's Navigation World Model plans paths from single images using Conditional Diffusion Transformers. Fed massive trajectories from robots\u002Fgames, these scale to production, shifting AI from 'talking about' the world to embodying its entropy, consequences, and continuity—enabling robotics, autonomy, and planning where transformers fold.",{"title":40,"searchDepth":41,"depth":41,"links":42},"",2,[43,44,45],{"id":19,"depth":41,"text":20},{"id":27,"depth":41,"text":28},{"id":34,"depth":41,"text":35},[],null,"md",false,{"content_references":51,"triage":74},[52,57,61,66,68,71],{"type":53,"title":54,"author":55,"context":56},"paper","World Models","David Ha and Jürgen Schmidhuber","cited",{"type":58,"title":59,"author":60,"context":56},"other","Dyna algorithm","Richard S. Sutton",{"type":62,"title":63,"author":64,"context":65},"tool","DreamerV3","DeepMind","mentioned",{"type":62,"title":67,"context":65},"Genie 2",{"type":62,"title":69,"author":70,"context":65},"Cosmos World Foundation Models","NVIDIA",{"type":62,"title":72,"author":73,"context":65},"Navigation World Model","Meta",{"relevance":75,"novelty":76,"quality":75,"actionability":41,"composite":77,"reasoning":78},4,3,3.4,"Category: AI & LLMs. The article discusses world models as a solution to limitations in current LLMs, addressing a specific audience pain point regarding AI's predictive capabilities. However, while it presents interesting insights, it lacks concrete, actionable steps for implementation in product development.",true,"\u002Fsummaries\u002F0e7f4e0f7633b086-world-models-build-ai-s-internal-reality-simulator-summary","2026-04-15 15:26:42",{"title":5,"description":40},{"loc":80},"0e7f4e0f7633b086","__oneoff__","article","https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fworld-models-next-evolution-ai-marco-van-hurne-gjhif\u002F","summaries\u002F0e7f4e0f7633b086-world-models-build-ai-s-internal-reality-simulator-summary",[90,91,92,93],"llm","machine-learning","deep-learning","agents","World models train on experience streams to predict cause-and-effect dynamics, creating compact internal simulations for efficient planning and physics understanding—surpassing LLMs' token prediction.",[],"u-YCUdyVq0jlLcWhN-D9SWjTeqvos9OTgGzi-WcjJGY",[98,101,104,107,110,113,115,117,119,121,123,125,128,130,132,134,136,138,140,142,144,146,149,152,154,156,159,161,163,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665],{"categories":99},[100],"Developer Productivity",{"categories":102},[103],"Business & SaaS",{"categories":105},[106],"AI & LLMs",{"categories":108},[109],"AI Automation",{"categories":111},[112],"Product Strategy",{"categories":114},[106],{"categories":116},[100],{"categories":118},[103],{"categories":120},[],{"categories":122},[106],{"categories":124},[],{"categories":126},[127],"AI News & Trends",{"categories":129},[109],{"categories":131},[127],{"categories":133},[109],{"categories":135},[109],{"categories":137},[106],{"categories":139},[106],{"categories":141},[127],{"categories":143},[106],{"categories":145},[],{"categories":147},[148],"Design & Frontend",{"categories":150},[151],"Data Science & Visualization",{"categories":153},[127],{"categories":155},[],{"categories":157},[158],"Software Engineering",{"categories":160},[106],{"categories":162},[109],{"categories":164},[165],"Marketing & Growth",{"categories":167},[106],{"categories":169},[109],{"categories":171},[],{"categories":173},[],{"categories":175},[148],{"categories":177},[109],{"categories":179},[100],{"categories":181},[148],{"categories":183},[106],{"categories":185},[109],{"categories":187},[127],{"categories":189},[],{"categories":191},[],{"categories":193},[109],{"categories":195},[158],{"categories":197},[],{"categories":199},[103],{"categories":201},[],{"categories":203},[],{"categories":205},[109],{"categories":207},[109],{"categories":209},[106],{"categories":211},[],{"categories":213},[158],{"categories":215},[],{"categories":217},[],{"categories":219},[],{"categories":221},[106],{"categories":223},[165],{"categories":225},[148],{"categories":227},[148],{"categories":229},[106],{"categories":231},[109],{"categories":233},[106],{"categories":235},[106],{"categories":237},[109],{"categories":239},[109],{"categories":241},[151],{"categories":243},[127],{"categories":245},[109],{"categories":247},[165],{"categories":249},[109],{"categories":251},[112],{"categories":253},[],{"categories":255},[109],{"categories":257},[],{"categories":259},[109],{"categories":261},[158],{"categories":263},[148],{"categories":265},[106],{"categories":267},[],{"categories":269},[],{"categories":271},[109],{"categories":273},[],{"categories":275},[106],{"categories":277},[],{"categories":279},[100],{"categories":281},[158],{"categories":283},[103],{"categories":285},[127],{"categories":287},[106],{"categories":289},[],{"categories":291},[106],{"categories":293},[],{"categories":295},[158],{"categories":297},[151],{"categories":299},[],{"categories":301},[106],{"categories":303},[148],{"categories":305},[],{"categories":307},[148],{"categories":309},[109],{"categories":311},[],{"categories":313},[109],{"categories":315},[127],{"categories":317},[106],{"categories":319},[],{"categories":321},[109],{"categories":323},[106],{"categories":325},[112],{"categories":327},[],{"categories":329},[106],{"categories":331},[109],{"categories":333},[109],{"categories":335},[],{"categories":337},[151],{"categories":339},[106],{"categories":341},[],{"categories":343},[100],{"categories":345},[103],{"categories":347},[106],{"categories":349},[109],{"categories":351},[158],{"categories":353},[106],{"categories":355},[],{"categories":357},[],{"categories":359},[106],{"categories":361},[],{"categories":363},[148],{"categories":365},[],{"categories":367},[106],{"categories":369},[],{"categories":371},[109],{"categories":373},[106],{"categories":375},[148],{"categories":377},[],{"categories":379},[106],{"categories":381},[106],{"categories":383},[103],{"categories":385},[109],{"categories":387},[106],{"categories":389},[148],{"categories":391},[109],{"categories":393},[],{"categories":395},[],{"categories":397},[127],{"categories":399},[],{"categories":401},[106],{"categories":403},[103,165],{"categories":405},[],{"categories":407},[106],{"categories":409},[],{"categories":411},[],{"categories":413},[106],{"categories":415},[],{"categories":417},[106],{"categories":419},[420],"DevOps & Cloud",{"categories":422},[],{"categories":424},[127],{"categories":426},[148],{"categories":428},[],{"categories":430},[127],{"categories":432},[127],{"categories":434},[106],{"categories":436},[165],{"categories":438},[],{"categories":440},[103],{"categories":442},[],{"categories":444},[106,420],{"categories":446},[106],{"categories":448},[106],{"categories":450},[109],{"categories":452},[106,158],{"categories":454},[151],{"categories":456},[106],{"categories":458},[165],{"categories":460},[109],{"categories":462},[109],{"categories":464},[],{"categories":466},[109],{"categories":468},[106,103],{"categories":470},[],{"categories":472},[148],{"categories":474},[148],{"categories":476},[],{"categories":478},[],{"categories":480},[127],{"categories":482},[],{"categories":484},[100],{"categories":486},[158],{"categories":488},[106],{"categories":490},[148],{"categories":492},[109],{"categories":494},[158],{"categories":496},[127],{"categories":498},[148],{"categories":500},[],{"categories":502},[106],{"categories":504},[106],{"categories":506},[106],{"categories":508},[127],{"categories":510},[100],{"categories":512},[106],{"categories":514},[109],{"categories":516},[420],{"categories":518},[148],{"categories":520},[109],{"categories":522},[],{"categories":524},[],{"categories":526},[148],{"categories":528},[127],{"categories":530},[151],{"categories":532},[],{"categories":534},[106],{"categories":536},[106],{"categories":538},[103],{"categories":540},[106],{"categories":542},[106],{"categories":544},[127],{"categories":546},[],{"categories":548},[109],{"categories":550},[158],{"categories":552},[],{"categories":554},[106],{"categories":556},[106],{"categories":558},[109],{"categories":560},[],{"categories":562},[],{"categories":564},[106],{"categories":566},[],{"categories":568},[103],{"categories":570},[109],{"categories":572},[],{"categories":574},[100],{"categories":576},[106],{"categories":578},[103],{"categories":580},[127],{"categories":582},[],{"categories":584},[],{"categories":586},[],{"categories":588},[127],{"categories":590},[127],{"categories":592},[],{"categories":594},[],{"categories":596},[103],{"categories":598},[],{"categories":600},[],{"categories":602},[100],{"categories":604},[],{"categories":606},[165],{"categories":608},[109],{"categories":610},[103],{"categories":612},[109],{"categories":614},[],{"categories":616},[112],{"categories":618},[148],{"categories":620},[158],{"categories":622},[106],{"categories":624},[109],{"categories":626},[103],{"categories":628},[106],{"categories":630},[],{"categories":632},[],{"categories":634},[158],{"categories":636},[151],{"categories":638},[112],{"categories":640},[109],{"categories":642},[106],{"categories":644},[],{"categories":646},[420],{"categories":648},[],{"categories":650},[109],{"categories":652},[],{"categories":654},[],{"categories":656},[106],{"categories":658},[148],{"categories":660},[165],{"categories":662},[109],{"categories":664},[],{"categories":666},[100],{"categories":668},[],{"categories":670},[127],{"categories":672},[106,420],{"categories":674},[127],{"categories":676},[106],{"categories":678},[103],{"categories":680},[106],{"categories":682},[],{"categories":684},[103],{"categories":686},[],{"categories":688},[158],{"categories":690},[148],{"categories":692},[127],{"categories":694},[151],{"categories":696},[100],{"categories":698},[106],{"categories":700},[158],{"categories":702},[],{"categories":704},[],{"categories":706},[112],{"categories":708},[],{"categories":710},[106],{"categories":712},[],{"categories":714},[148],{"categories":716},[148],{"categories":718},[148],{"categories":720},[],{"categories":722},[],{"categories":724},[127],{"categories":726},[109],{"categories":728},[106],{"categories":730},[106],{"categories":732},[106],{"categories":734},[103],{"categories":736},[106],{"categories":738},[],{"categories":740},[158],{"categories":742},[158],{"categories":744},[103],{"categories":746},[],{"categories":748},[106],{"categories":750},[106],{"categories":752},[103],{"categories":754},[127],{"categories":756},[165],{"categories":758},[109],{"categories":760},[],{"categories":762},[148],{"categories":764},[],{"categories":766},[106],{"categories":768},[],{"categories":770},[103],{"categories":772},[109],{"categories":774},[],{"categories":776},[420],{"categories":778},[151],{"categories":780},[158],{"categories":782},[165],{"categories":784},[158],{"categories":786},[109],{"categories":788},[],{"categories":790},[],{"categories":792},[109],{"categories":794},[100],{"categories":796},[109],{"categories":798},[112],{"categories":800},[103],{"categories":802},[],{"categories":804},[106],{"categories":806},[112],{"categories":808},[106],{"categories":810},[106],{"categories":812},[165],{"categories":814},[148],{"categories":816},[109],{"categories":818},[],{"categories":820},[],{"categories":822},[420],{"categories":824},[158],{"categories":826},[],{"categories":828},[109],{"categories":830},[106],{"categories":832},[148,106],{"categories":834},[100],{"categories":836},[],{"categories":838},[106],{"categories":840},[100],{"categories":842},[148],{"categories":844},[109],{"categories":846},[158],{"categories":848},[],{"categories":850},[106],{"categories":852},[],{"categories":854},[100],{"categories":856},[],{"categories":858},[109],{"categories":860},[112],{"categories":862},[106],{"categories":864},[106],{"categories":866},[148],{"categories":868},[109],{"categories":870},[420],{"categories":872},[148],{"categories":874},[109],{"categories":876},[106],{"categories":878},[106],{"categories":880},[106],{"categories":882},[127],{"categories":884},[],{"categories":886},[112],{"categories":888},[109],{"categories":890},[148],{"categories":892},[109],{"categories":894},[158],{"categories":896},[148],{"categories":898},[109],{"categories":900},[127],{"categories":902},[],{"categories":904},[106],{"categories":906},[148],{"categories":908},[106],{"categories":910},[100],{"categories":912},[127],{"categories":914},[106],{"categories":916},[165],{"categories":918},[106],{"categories":920},[106],{"categories":922},[109],{"categories":924},[109],{"categories":926},[106],{"categories":928},[109],{"categories":930},[148],{"categories":932},[106],{"categories":934},[],{"categories":936},[],{"categories":938},[158],{"categories":940},[],{"categories":942},[100],{"categories":944},[420],{"categories":946},[],{"categories":948},[100],{"categories":950},[103],{"categories":952},[165],{"categories":954},[],{"categories":956},[103],{"categories":958},[],{"categories":960},[],{"categories":962},[],{"categories":964},[],{"categories":966},[],{"categories":968},[106],{"categories":970},[109],{"categories":972},[420],{"categories":974},[100],{"categories":976},[106],{"categories":978},[158],{"categories":980},[112],{"categories":982},[106],{"categories":984},[165],{"categories":986},[106],{"categories":988},[106],{"categories":990},[106],{"categories":992},[106,100],{"categories":994},[158],{"categories":996},[158],{"categories":998},[148],{"categories":1000},[106],{"categories":1002},[],{"categories":1004},[],{"categories":1006},[],{"categories":1008},[158],{"categories":1010},[151],{"categories":1012},[127],{"categories":1014},[148],{"categories":1016},[],{"categories":1018},[106],{"categories":1020},[106],{"categories":1022},[],{"categories":1024},[],{"categories":1026},[109],{"categories":1028},[106],{"categories":1030},[103],{"categories":1032},[],{"categories":1034},[100],{"categories":1036},[106],{"categories":1038},[100],{"categories":1040},[106],{"categories":1042},[158],{"categories":1044},[165],{"categories":1046},[106,148],{"categories":1048},[127],{"categories":1050},[148],{"categories":1052},[],{"categories":1054},[420],{"categories":1056},[148],{"categories":1058},[109],{"categories":1060},[],{"categories":1062},[],{"categories":1064},[],{"categories":1066},[],{"categories":1068},[158],{"categories":1070},[109],{"categories":1072},[109],{"categories":1074},[106],{"categories":1076},[106],{"categories":1078},[],{"categories":1080},[148],{"categories":1082},[],{"categories":1084},[],{"categories":1086},[109],{"categories":1088},[],{"categories":1090},[],{"categories":1092},[165],{"categories":1094},[165],{"categories":1096},[109],{"categories":1098},[],{"categories":1100},[106],{"categories":1102},[106],{"categories":1104},[158],{"categories":1106},[148],{"categories":1108},[148],{"categories":1110},[109],{"categories":1112},[100],{"categories":1114},[106],{"categories":1116},[148],{"categories":1118},[148],{"categories":1120},[109],{"categories":1122},[109],{"categories":1124},[106],{"categories":1126},[],{"categories":1128},[],{"categories":1130},[106],{"categories":1132},[109],{"categories":1134},[127],{"categories":1136},[158],{"categories":1138},[100],{"categories":1140},[106],{"categories":1142},[],{"categories":1144},[109],{"categories":1146},[109],{"categories":1148},[],{"categories":1150},[100],{"categories":1152},[106],{"categories":1154},[100],{"categories":1156},[100],{"categories":1158},[],{"categories":1160},[],{"categories":1162},[109],{"categories":1164},[109],{"categories":1166},[106],{"categories":1168},[106],{"categories":1170},[127],{"categories":1172},[151],{"categories":1174},[112],{"categories":1176},[127],{"categories":1178},[148],{"categories":1180},[],{"categories":1182},[127],{"categories":1184},[],{"categories":1186},[],{"categories":1188},[],{"categories":1190},[],{"categories":1192},[158],{"categories":1194},[151],{"categories":1196},[],{"categories":1198},[106],{"categories":1200},[106],{"categories":1202},[151],{"categories":1204},[158],{"categories":1206},[],{"categories":1208},[],{"categories":1210},[109],{"categories":1212},[127],{"categories":1214},[127],{"categories":1216},[109],{"categories":1218},[100],{"categories":1220},[106,420],{"categories":1222},[],{"categories":1224},[148],{"categories":1226},[100],{"categories":1228},[109],{"categories":1230},[148],{"categories":1232},[],{"categories":1234},[109],{"categories":1236},[109],{"categories":1238},[106],{"categories":1240},[165],{"categories":1242},[158],{"categories":1244},[148],{"categories":1246},[],{"categories":1248},[109],{"categories":1250},[106],{"categories":1252},[109],{"categories":1254},[109],{"categories":1256},[109],{"categories":1258},[165],{"categories":1260},[109],{"categories":1262},[106],{"categories":1264},[],{"categories":1266},[165],{"categories":1268},[127],{"categories":1270},[109],{"categories":1272},[],{"categories":1274},[],{"categories":1276},[106],{"categories":1278},[109],{"categories":1280},[127],{"categories":1282},[109],{"categories":1284},[],{"categories":1286},[],{"categories":1288},[],{"categories":1290},[109],{"categories":1292},[],{"categories":1294},[],{"categories":1296},[151],{"categories":1298},[106],{"categories":1300},[151],{"categories":1302},[127],{"categories":1304},[106],{"categories":1306},[106],{"categories":1308},[109],{"categories":1310},[106],{"categories":1312},[],{"categories":1314},[],{"categories":1316},[420],{"categories":1318},[],{"categories":1320},[],{"categories":1322},[100],{"categories":1324},[],{"categories":1326},[],{"categories":1328},[],{"categories":1330},[],{"categories":1332},[158],{"categories":1334},[127],{"categories":1336},[165],{"categories":1338},[103],{"categories":1340},[106],{"categories":1342},[106],{"categories":1344},[103],{"categories":1346},[],{"categories":1348},[148],{"categories":1350},[109],{"categories":1352},[103],{"categories":1354},[106],{"categories":1356},[106],{"categories":1358},[100],{"categories":1360},[],{"categories":1362},[100],{"categories":1364},[106],{"categories":1366},[165],{"categories":1368},[109],{"categories":1370},[127],{"categories":1372},[103],{"categories":1374},[106],{"categories":1376},[109],{"categories":1378},[],{"categories":1380},[106],{"categories":1382},[100],{"categories":1384},[106],{"categories":1386},[],{"categories":1388},[127],{"categories":1390},[106],{"categories":1392},[],{"categories":1394},[103],{"categories":1396},[106],{"categories":1398},[],{"categories":1400},[],{"categories":1402},[],{"categories":1404},[106],{"categories":1406},[],{"categories":1408},[420],{"categories":1410},[106],{"categories":1412},[],{"categories":1414},[106],{"categories":1416},[106],{"categories":1418},[106],{"categories":1420},[106,420],{"categories":1422},[106],{"categories":1424},[106],{"categories":1426},[148],{"categories":1428},[109],{"categories":1430},[],{"categories":1432},[109],{"categories":1434},[106],{"categories":1436},[106],{"categories":1438},[106],{"categories":1440},[100],{"categories":1442},[100],{"categories":1444},[158],{"categories":1446},[148],{"categories":1448},[109],{"categories":1450},[],{"categories":1452},[106],{"categories":1454},[127],{"categories":1456},[106],{"categories":1458},[103],{"categories":1460},[],{"categories":1462},[420],{"categories":1464},[148],{"categories":1466},[148],{"categories":1468},[109],{"categories":1470},[127],{"categories":1472},[109],{"categories":1474},[106],{"categories":1476},[],{"categories":1478},[106],{"categories":1480},[],{"categories":1482},[],{"categories":1484},[106],{"categories":1486},[106],{"categories":1488},[106],{"categories":1490},[109],{"categories":1492},[106],{"categories":1494},[],{"categories":1496},[151],{"categories":1498},[109],{"categories":1500},[],{"categories":1502},[106],{"categories":1504},[127],{"categories":1506},[],{"categories":1508},[148],{"categories":1510},[420],{"categories":1512},[127],{"categories":1514},[158],{"categories":1516},[158],{"categories":1518},[127],{"categories":1520},[127],{"categories":1522},[420],{"categories":1524},[],{"categories":1526},[127],{"categories":1528},[106],{"categories":1530},[100],{"categories":1532},[127],{"categories":1534},[],{"categories":1536},[151],{"categories":1538},[127],{"categories":1540},[158],{"categories":1542},[127],{"categories":1544},[420],{"categories":1546},[106],{"categories":1548},[106],{"categories":1550},[],{"categories":1552},[103],{"categories":1554},[],{"categories":1556},[],{"categories":1558},[106],{"categories":1560},[106],{"categories":1562},[106],{"categories":1564},[106],{"categories":1566},[],{"categories":1568},[151],{"categories":1570},[100],{"categories":1572},[],{"categories":1574},[106],{"categories":1576},[106],{"categories":1578},[420],{"categories":1580},[420],{"categories":1582},[],{"categories":1584},[109],{"categories":1586},[127],{"categories":1588},[127],{"categories":1590},[106],{"categories":1592},[109],{"categories":1594},[],{"categories":1596},[148],{"categories":1598},[106],{"categories":1600},[106],{"categories":1602},[],{"categories":1604},[],{"categories":1606},[420],{"categories":1608},[106],{"categories":1610},[158],{"categories":1612},[103],{"categories":1614},[106],{"categories":1616},[],{"categories":1618},[109],{"categories":1620},[100],{"categories":1622},[100],{"categories":1624},[],{"categories":1626},[106],{"categories":1628},[148],{"categories":1630},[109],{"categories":1632},[],{"categories":1634},[106],{"categories":1636},[106],{"categories":1638},[109],{"categories":1640},[],{"categories":1642},[109],{"categories":1644},[158],{"categories":1646},[],{"categories":1648},[106],{"categories":1650},[],{"categories":1652},[106],{"categories":1654},[],{"categories":1656},[106],{"categories":1658},[106],{"categories":1660},[],{"categories":1662},[106],{"categories":1664},[127],{"categories":1666},[106],{"categories":1668},[106],{"categories":1670},[100],{"categories":1672},[106],{"categories":1674},[127],{"categories":1676},[109],{"categories":1678},[],{"categories":1680},[106],{"categories":1682},[165],{"categories":1684},[],{"categories":1686},[],{"categories":1688},[],{"categories":1690},[100],{"categories":1692},[127],{"categories":1694},[109],{"categories":1696},[106],{"categories":1698},[148],{"categories":1700},[109],{"categories":1702},[],{"categories":1704},[109],{"categories":1706},[],{"categories":1708},[106],{"categories":1710},[109],{"categories":1712},[106],{"categories":1714},[],{"categories":1716},[106],{"categories":1718},[106],{"categories":1720},[127],{"categories":1722},[148],{"categories":1724},[109],{"categories":1726},[148],{"categories":1728},[103],{"categories":1730},[],{"categories":1732},[],{"categories":1734},[106],{"categories":1736},[100],{"categories":1738},[127],{"categories":1740},[],{"categories":1742},[],{"categories":1744},[158],{"categories":1746},[148],{"categories":1748},[],{"categories":1750},[106],{"categories":1752},[],{"categories":1754},[165],{"categories":1756},[106],{"categories":1758},[420],{"categories":1760},[158],{"categories":1762},[],{"categories":1764},[109],{"categories":1766},[106],{"categories":1768},[109],{"categories":1770},[109],{"categories":1772},[106],{"categories":1774},[],{"categories":1776},[100],{"categories":1778},[106],{"categories":1780},[103],{"categories":1782},[158],{"categories":1784},[148],{"categories":1786},[],{"categories":1788},[],{"categories":1790},[],{"categories":1792},[109],{"categories":1794},[148],{"categories":1796},[127],{"categories":1798},[106],{"categories":1800},[127],{"categories":1802},[148],{"categories":1804},[],{"categories":1806},[148],{"categories":1808},[127],{"categories":1810},[103],{"categories":1812},[106],{"categories":1814},[127],{"categories":1816},[165],{"categories":1818},[],{"categories":1820},[],{"categories":1822},[151],{"categories":1824},[106,158],{"categories":1826},[127],{"categories":1828},[106],{"categories":1830},[109],{"categories":1832},[109],{"categories":1834},[106],{"categories":1836},[],{"categories":1838},[158],{"categories":1840},[106],{"categories":1842},[151],{"categories":1844},[109],{"categories":1846},[165],{"categories":1848},[420],{"categories":1850},[],{"categories":1852},[100],{"categories":1854},[109],{"categories":1856},[109],{"categories":1858},[158],{"categories":1860},[106],{"categories":1862},[106],{"categories":1864},[],{"categories":1866},[],{"categories":1868},[],{"categories":1870},[420],{"categories":1872},[127],{"categories":1874},[106],{"categories":1876},[106],{"categories":1878},[106],{"categories":1880},[],{"categories":1882},[151],{"categories":1884},[103],{"categories":1886},[],{"categories":1888},[109],{"categories":1890},[420],{"categories":1892},[],{"categories":1894},[148],{"categories":1896},[148],{"categories":1898},[],{"categories":1900},[158],{"categories":1902},[148],{"categories":1904},[106],{"categories":1906},[],{"categories":1908},[127],{"categories":1910},[106],{"categories":1912},[148],{"categories":1914},[109],{"categories":1916},[127],{"categories":1918},[],{"categories":1920},[109],{"categories":1922},[148],{"categories":1924},[106],{"categories":1926},[],{"categories":1928},[106],{"categories":1930},[106],{"categories":1932},[420],{"categories":1934},[127],{"categories":1936},[151],{"categories":1938},[151],{"categories":1940},[],{"categories":1942},[],{"categories":1944},[],{"categories":1946},[109],{"categories":1948},[158],{"categories":1950},[158],{"categories":1952},[],{"categories":1954},[],{"categories":1956},[106],{"categories":1958},[],{"categories":1960},[109],{"categories":1962},[106],{"categories":1964},[],{"categories":1966},[106],{"categories":1968},[103],{"categories":1970},[106],{"categories":1972},[165],{"categories":1974},[109],{"categories":1976},[106],{"categories":1978},[158],{"categories":1980},[127],{"categories":1982},[109],{"categories":1984},[],{"categories":1986},[127],{"categories":1988},[109],{"categories":1990},[109],{"categories":1992},[],{"categories":1994},[103],{"categories":1996},[109],{"categories":1998},[],{"categories":2000},[106],{"categories":2002},[100],{"categories":2004},[127],{"categories":2006},[420],{"categories":2008},[109],{"categories":2010},[109],{"categories":2012},[100],{"categories":2014},[106],{"categories":2016},[],{"categories":2018},[],{"categories":2020},[148],{"categories":2022},[106,103],{"categories":2024},[],{"categories":2026},[100],{"categories":2028},[151],{"categories":2030},[106],{"categories":2032},[158],{"categories":2034},[106],{"categories":2036},[109],{"categories":2038},[106],{"categories":2040},[106],{"categories":2042},[127],{"categories":2044},[109],{"categories":2046},[],{"categories":2048},[],{"categories":2050},[109],{"categories":2052},[106],{"categories":2054},[420],{"categories":2056},[],{"categories":2058},[106],{"categories":2060},[109],{"categories":2062},[],{"categories":2064},[106],{"categories":2066},[165],{"categories":2068},[151],{"categories":2070},[109],{"categories":2072},[106],{"categories":2074},[420],{"categories":2076},[],{"categories":2078},[106],{"categories":2080},[165],{"categories":2082},[148],{"categories":2084},[106],{"categories":2086},[],{"categories":2088},[165],{"categories":2090},[127],{"categories":2092},[106],{"categories":2094},[106],{"categories":2096},[100],{"categories":2098},[],{"categories":2100},[],{"categories":2102},[148],{"categories":2104},[106],{"categories":2106},[151],{"categories":2108},[165],{"categories":2110},[165],{"categories":2112},[127],{"categories":2114},[],{"categories":2116},[],{"categories":2118},[106],{"categories":2120},[],{"categories":2122},[106,158],{"categories":2124},[127],{"categories":2126},[109],{"categories":2128},[158],{"categories":2130},[106],{"categories":2132},[100],{"categories":2134},[],{"categories":2136},[],{"categories":2138},[100],{"categories":2140},[165],{"categories":2142},[106],{"categories":2144},[],{"categories":2146},[148,106],{"categories":2148},[420],{"categories":2150},[100],{"categories":2152},[],{"categories":2154},[103],{"categories":2156},[103],{"categories":2158},[106],{"categories":2160},[158],{"categories":2162},[109],{"categories":2164},[127],{"categories":2166},[165],{"categories":2168},[148],{"categories":2170},[106],{"categories":2172},[106],{"categories":2174},[106],{"categories":2176},[100],{"categories":2178},[106],{"categories":2180},[109],{"categories":2182},[127],{"categories":2184},[],{"categories":2186},[],{"categories":2188},[151],{"categories":2190},[158],{"categories":2192},[106],{"categories":2194},[148],{"categories":2196},[151],{"categories":2198},[106],{"categories":2200},[106],{"categories":2202},[109],{"categories":2204},[109],{"categories":2206},[106,103],{"categories":2208},[],{"categories":2210},[148],{"categories":2212},[],{"categories":2214},[106],{"categories":2216},[127],{"categories":2218},[100],{"categories":2220},[100],{"categories":2222},[109],{"categories":2224},[106],{"categories":2226},[103],{"categories":2228},[158],{"categories":2230},[165],{"categories":2232},[],{"categories":2234},[127],{"categories":2236},[106],{"categories":2238},[106],{"categories":2240},[127],{"categories":2242},[158],{"categories":2244},[106],{"categories":2246},[109],{"categories":2248},[127],{"categories":2250},[106],{"categories":2252},[148],{"categories":2254},[106],{"categories":2256},[106],{"categories":2258},[420],{"categories":2260},[112],{"categories":2262},[109],{"categories":2264},[106],{"categories":2266},[127],{"categories":2268},[109],{"categories":2270},[165],{"categories":2272},[106],{"categories":2274},[],{"categories":2276},[106],{"categories":2278},[],{"categories":2280},[],{"categories":2282},[],{"categories":2284},[103],{"categories":2286},[106],{"categories":2288},[109],{"categories":2290},[127],{"categories":2292},[127],{"categories":2294},[127],{"categories":2296},[127],{"categories":2298},[],{"categories":2300},[100],{"categories":2302},[109],{"categories":2304},[127],{"categories":2306},[100],{"categories":2308},[109],{"categories":2310},[106],{"categories":2312},[106,109],{"categories":2314},[109],{"categories":2316},[420],{"categories":2318},[127],{"categories":2320},[127],{"categories":2322},[109],{"categories":2324},[106],{"categories":2326},[],{"categories":2328},[127],{"categories":2330},[165],{"categories":2332},[100],{"categories":2334},[106],{"categories":2336},[106],{"categories":2338},[],{"categories":2340},[158],{"categories":2342},[],{"categories":2344},[100],{"categories":2346},[109],{"categories":2348},[127],{"categories":2350},[106],{"categories":2352},[127],{"categories":2354},[100],{"categories":2356},[127],{"categories":2358},[127],{"categories":2360},[],{"categories":2362},[103],{"categories":2364},[109],{"categories":2366},[127],{"categories":2368},[127],{"categories":2370},[127],{"categories":2372},[127],{"categories":2374},[127],{"categories":2376},[127],{"categories":2378},[127],{"categories":2380},[127],{"categories":2382},[127],{"categories":2384},[127],{"categories":2386},[151],{"categories":2388},[100],{"categories":2390},[106],{"categories":2392},[106],{"categories":2394},[],{"categories":2396},[106,100],{"categories":2398},[],{"categories":2400},[109],{"categories":2402},[127],{"categories":2404},[109],{"categories":2406},[106],{"categories":2408},[106],{"categories":2410},[106],{"categories":2412},[106],{"categories":2414},[106],{"categories":2416},[109],{"categories":2418},[103],{"categories":2420},[148],{"categories":2422},[127],{"categories":2424},[106],{"categories":2426},[],{"categories":2428},[],{"categories":2430},[109],{"categories":2432},[148],{"categories":2434},[106],{"categories":2436},[],{"categories":2438},[],{"categories":2440},[165],{"categories":2442},[106],{"categories":2444},[],{"categories":2446},[],{"categories":2448},[100],{"categories":2450},[103],{"categories":2452},[106],{"categories":2454},[103],{"categories":2456},[148],{"categories":2458},[],{"categories":2460},[127],{"categories":2462},[],{"categories":2464},[148],{"categories":2466},[106],{"categories":2468},[165],{"categories":2470},[],{"categories":2472},[165],{"categories":2474},[],{"categories":2476},[],{"categories":2478},[109],{"categories":2480},[],{"categories":2482},[103],{"categories":2484},[100],{"categories":2486},[148],{"categories":2488},[158],{"categories":2490},[],{"categories":2492},[],{"categories":2494},[106],{"categories":2496},[100],{"categories":2498},[165],{"categories":2500},[],{"categories":2502},[109],{"categories":2504},[109],{"categories":2506},[127],{"categories":2508},[106],{"categories":2510},[109],{"categories":2512},[106],{"categories":2514},[109],{"categories":2516},[106],{"categories":2518},[112],{"categories":2520},[127],{"categories":2522},[],{"categories":2524},[165],{"categories":2526},[158],{"categories":2528},[109],{"categories":2530},[],{"categories":2532},[106],{"categories":2534},[109],{"categories":2536},[103],{"categories":2538},[100],{"categories":2540},[106],{"categories":2542},[148],{"categories":2544},[158],{"categories":2546},[158],{"categories":2548},[106],{"categories":2550},[151],{"categories":2552},[106],{"categories":2554},[109],{"categories":2556},[103],{"categories":2558},[109],{"categories":2560},[106],{"categories":2562},[106],{"categories":2564},[109],{"categories":2566},[127],{"categories":2568},[],{"categories":2570},[100],{"categories":2572},[106],{"categories":2574},[109],{"categories":2576},[106],{"categories":2578},[106],{"categories":2580},[],{"categories":2582},[148],{"categories":2584},[103],{"categories":2586},[127],{"categories":2588},[106],{"categories":2590},[106],{"categories":2592},[148],{"categories":2594},[165],{"categories":2596},[151],{"categories":2598},[106],{"categories":2600},[127],{"categories":2602},[106],{"categories":2604},[109],{"categories":2606},[420],{"categories":2608},[106],{"categories":2610},[109],{"categories":2612},[151],{"categories":2614},[],{"categories":2616},[109],{"categories":2618},[158],{"categories":2620},[148],{"categories":2622},[106],{"categories":2624},[100],{"categories":2626},[103],{"categories":2628},[158],{"categories":2630},[],{"categories":2632},[109],{"categories":2634},[106],{"categories":2636},[],{"categories":2638},[127],{"categories":2640},[],{"categories":2642},[127],{"categories":2644},[106],{"categories":2646},[109],{"categories":2648},[109],{"categories":2650},[109],{"categories":2652},[],{"categories":2654},[],{"categories":2656},[106],{"categories":2658},[106],{"categories":2660},[],{"categories":2662},[148],{"categories":2664},[109],{"categories":2666},[165],{"categories":2668},[100],{"categories":2670},[],{"categories":2672},[],{"categories":2674},[127],{"categories":2676},[158],{"categories":2678},[106],{"categories":2680},[106],{"categories":2682},[106],{"categories":2684},[158],{"categories":2686},[127],{"categories":2688},[148],{"categories":2690},[106],{"categories":2692},[106],{"categories":2694},[106],{"categories":2696},[127],{"categories":2698},[106],{"categories":2700},[127],{"categories":2702},[109],{"categories":2704},[109],{"categories":2706},[158],{"categories":2708},[109],{"categories":2710},[106],{"categories":2712},[158],{"categories":2714},[148],{"categories":2716},[],{"categories":2718},[109],{"categories":2720},[],{"categories":2722},[],{"categories":2724},[103],{"categories":2726},[106],{"categories":2728},[109],{"categories":2730},[100],{"categories":2732},[109],{"categories":2734},[165],{"categories":2736},[],{"categories":2738},[109],{"categories":2740},[],{"categories":2742},[100],{"categories":2744},[109],{"categories":2746},[],{"categories":2748},[109],{"categories":2750},[106],{"categories":2752},[127],{"categories":2754},[106],{"categories":2756},[109],{"categories":2758},[127],{"categories":2760},[109],{"categories":2762},[158],{"categories":2764},[148],{"categories":2766},[100],{"categories":2768},[],{"categories":2770},[109],{"categories":2772},[148],{"categories":2774},[127],{"categories":2776},[106],{"categories":2778},[148],{"categories":2780},[100],{"categories":2782},[],{"categories":2784},[109],{"categories":2786},[109],{"categories":2788},[106],{"categories":2790},[],{"categories":2792},[109],{"categories":2794},[112],{"categories":2796},[127],{"categories":2798},[109],{"categories":2800},[103],{"categories":2802},[],{"categories":2804},[106],{"categories":2806},[112],{"categories":2808},[106],{"categories":2810},[109],{"categories":2812},[127],{"categories":2814},[100],{"categories":2816},[420],{"categories":2818},[106],{"categories":2820},[106],{"categories":2822},[106],{"categories":2824},[127],{"categories":2826},[103],{"categories":2828},[106],{"categories":2830},[148],{"categories":2832},[127],{"categories":2834},[420],{"categories":2836},[106],{"categories":2838},[],{"categories":2840},[],{"categories":2842},[420],{"categories":2844},[151],{"categories":2846},[109],{"categories":2848},[109],{"categories":2850},[127],{"categories":2852},[106],{"categories":2854},[100],{"categories":2856},[148],{"categories":2858},[109],{"categories":2860},[106],{"categories":2862},[165],{"categories":2864},[106],{"categories":2866},[109],{"categories":2868},[],{"categories":2870},[106],{"categories":2872},[106],{"categories":2874},[127],{"categories":2876},[100],{"categories":2878},[],{"categories":2880},[106],{"categories":2882},[106],{"categories":2884},[158],{"categories":2886},[148],{"categories":2888},[106,109],{"categories":2890},[165,103],{"categories":2892},[106],{"categories":2894},[],{"categories":2896},[109],{"categories":2898},[],{"categories":2900},[158],{"categories":2902},[106],{"categories":2904},[127],{"categories":2906},[],{"categories":2908},[109],{"categories":2910},[],{"categories":2912},[109],{"categories":2914},[100],{"categories":2916},[109],{"categories":2918},[106],{"categories":2920},[420],{"categories":2922},[165],{"categories":2924},[103],{"categories":2926},[103],{"categories":2928},[100],{"categories":2930},[100],{"categories":2932},[106],{"categories":2934},[109],{"categories":2936},[106],{"categories":2938},[106],{"categories":2940},[100],{"categories":2942},[106],{"categories":2944},[165],{"categories":2946},[127],{"categories":2948},[106],{"categories":2950},[109],{"categories":2952},[106],{"categories":2954},[],{"categories":2956},[158],{"categories":2958},[],{"categories":2960},[109],{"categories":2962},[100],{"categories":2964},[],{"categories":2966},[420],{"categories":2968},[106],{"categories":2970},[],{"categories":2972},[127],{"categories":2974},[109],{"categories":2976},[158],{"categories":2978},[106],{"categories":2980},[109],{"categories":2982},[158],{"categories":2984},[109],{"categories":2986},[127],{"categories":2988},[100],{"categories":2990},[127],{"categories":2992},[158],{"categories":2994},[106],{"categories":2996},[148],{"categories":2998},[106],{"categories":3000},[106],{"categories":3002},[106],{"categories":3004},[106],{"categories":3006},[109],{"categories":3008},[106],{"categories":3010},[109],{"categories":3012},[106],{"categories":3014},[100],{"categories":3016},[106],{"categories":3018},[109],{"categories":3020},[148],{"categories":3022},[100],{"categories":3024},[109],{"categories":3026},[148],{"categories":3028},[],{"categories":3030},[106],{"categories":3032},[106],{"categories":3034},[158],{"categories":3036},[],{"categories":3038},[109],{"categories":3040},[165],{"categories":3042},[106],{"categories":3044},[127],{"categories":3046},[165],{"categories":3048},[109],{"categories":3050},[103],{"categories":3052},[103],{"categories":3054},[106],{"categories":3056},[100],{"categories":3058},[],{"categories":3060},[106],{"categories":3062},[],{"categories":3064},[100],{"categories":3066},[106],{"categories":3068},[109],{"categories":3070},[109],{"categories":3072},[],{"categories":3074},[158],{"categories":3076},[158],{"categories":3078},[165],{"categories":3080},[148],{"categories":3082},[],{"categories":3084},[106],{"categories":3086},[100],{"categories":3088},[106],{"categories":3090},[158],{"categories":3092},[100],{"categories":3094},[127],{"categories":3096},[127],{"categories":3098},[],{"categories":3100},[127],{"categories":3102},[109],{"categories":3104},[148],{"categories":3106},[151],{"categories":3108},[106],{"categories":3110},[],{"categories":3112},[127],{"categories":3114},[158],{"categories":3116},[103],{"categories":3118},[106],{"categories":3120},[100],{"categories":3122},[420],{"categories":3124},[100],{"categories":3126},[],{"categories":3128},[],{"categories":3130},[127],{"categories":3132},[],{"categories":3134},[109],{"categories":3136},[109],{"categories":3138},[109],{"categories":3140},[],{"categories":3142},[106],{"categories":3144},[],{"categories":3146},[127],{"categories":3148},[100],{"categories":3150},[148],{"categories":3152},[106],{"categories":3154},[127],{"categories":3156},[127],{"categories":3158},[],{"categories":3160},[127],{"categories":3162},[100],{"categories":3164},[106],{"categories":3166},[],{"categories":3168},[109],{"categories":3170},[109],{"categories":3172},[100],{"categories":3174},[],{"categories":3176},[],{"categories":3178},[],{"categories":3180},[148],{"categories":3182},[109],{"categories":3184},[106],{"categories":3186},[],{"categories":3188},[],{"categories":3190},[],{"categories":3192},[148],{"categories":3194},[],{"categories":3196},[100],{"categories":3198},[],{"categories":3200},[],{"categories":3202},[148],{"categories":3204},[106],{"categories":3206},[127],{"categories":3208},[],{"categories":3210},[165],{"categories":3212},[127],{"categories":3214},[165],{"categories":3216},[106],{"categories":3218},[],{"categories":3220},[],{"categories":3222},[109],{"categories":3224},[],{"categories":3226},[],{"categories":3228},[109],{"categories":3230},[106],{"categories":3232},[],{"categories":3234},[109],{"categories":3236},[127],{"categories":3238},[165],{"categories":3240},[151],{"categories":3242},[109],{"categories":3244},[109],{"categories":3246},[],{"categories":3248},[],{"categories":3250},[],{"categories":3252},[127],{"categories":3254},[],{"categories":3256},[],{"categories":3258},[148],{"categories":3260},[100],{"categories":3262},[],{"categories":3264},[103],{"categories":3266},[165],{"categories":3268},[106],{"categories":3270},[158],{"categories":3272},[100],{"categories":3274},[151],{"categories":3276},[103],{"categories":3278},[158],{"categories":3280},[],{"categories":3282},[],{"categories":3284},[109],{"categories":3286},[100],{"categories":3288},[148],{"categories":3290},[100],{"categories":3292},[109],{"categories":3294},[420],{"categories":3296},[109],{"categories":3298},[],{"categories":3300},[106],{"categories":3302},[127],{"categories":3304},[158],{"categories":3306},[],{"categories":3308},[148],{"categories":3310},[127],{"categories":3312},[100],{"categories":3314},[109],{"categories":3316},[106],{"categories":3318},[103],{"categories":3320},[109,420],{"categories":3322},[109],{"categories":3324},[158],{"categories":3326},[106],{"categories":3328},[151],{"categories":3330},[165],{"categories":3332},[109],{"categories":3334},[],{"categories":3336},[109],{"categories":3338},[106],{"categories":3340},[103],{"categories":3342},[],{"categories":3344},[],{"categories":3346},[106],{"categories":3348},[151],{"categories":3350},[106],{"categories":3352},[],{"categories":3354},[127],{"categories":3356},[],{"categories":3358},[127],{"categories":3360},[158],{"categories":3362},[109],{"categories":3364},[106],{"categories":3366},[165],{"categories":3368},[158],{"categories":3370},[],{"categories":3372},[127],{"categories":3374},[106],{"categories":3376},[],{"categories":3378},[106],{"categories":3380},[109],{"categories":3382},[106],{"categories":3384},[109],{"categories":3386},[106],{"categories":3388},[106],{"categories":3390},[106],{"categories":3392},[106],{"categories":3394},[103],{"categories":3396},[],{"categories":3398},[112],{"categories":3400},[127],{"categories":3402},[106],{"categories":3404},[],{"categories":3406},[158],{"categories":3408},[106],{"categories":3410},[106],{"categories":3412},[109],{"categories":3414},[127],{"categories":3416},[106],{"categories":3418},[106],{"categories":3420},[103],{"categories":3422},[109],{"categories":3424},[148],{"categories":3426},[],{"categories":3428},[151],{"categories":3430},[106],{"categories":3432},[],{"categories":3434},[127],{"categories":3436},[165],{"categories":3438},[],{"categories":3440},[],{"categories":3442},[127],{"categories":3444},[127],{"categories":3446},[165],{"categories":3448},[100],{"categories":3450},[109],{"categories":3452},[109],{"categories":3454},[106],{"categories":3456},[103],{"categories":3458},[],{"categories":3460},[],{"categories":3462},[127],{"categories":3464},[151],{"categories":3466},[158],{"categories":3468},[109],{"categories":3470},[148],{"categories":3472},[151],{"categories":3474},[151],{"categories":3476},[],{"categories":3478},[127],{"categories":3480},[106],{"categories":3482},[106],{"categories":3484},[158],{"categories":3486},[],{"categories":3488},[127],{"categories":3490},[127],{"categories":3492},[127],{"categories":3494},[],{"categories":3496},[109],{"categories":3498},[106],{"categories":3500},[],{"categories":3502},[100],{"categories":3504},[103],{"categories":3506},[],{"categories":3508},[106],{"categories":3510},[106],{"categories":3512},[],{"categories":3514},[158],{"categories":3516},[],{"categories":3518},[],{"categories":3520},[],{"categories":3522},[],{"categories":3524},[106],{"categories":3526},[127],{"categories":3528},[],{"categories":3530},[],{"categories":3532},[106],{"categories":3534},[106],{"categories":3536},[106],{"categories":3538},[151],{"categories":3540},[106],{"categories":3542},[151],{"categories":3544},[],{"categories":3546},[151],{"categories":3548},[151],{"categories":3550},[420],{"categories":3552},[109],{"categories":3554},[158],{"categories":3556},[],{"categories":3558},[],{"categories":3560},[151],{"categories":3562},[158],{"categories":3564},[158],{"categories":3566},[158],{"categories":3568},[],{"categories":3570},[100],{"categories":3572},[158],{"categories":3574},[158],{"categories":3576},[100],{"categories":3578},[158],{"categories":3580},[103],{"categories":3582},[158],{"categories":3584},[158],{"categories":3586},[158],{"categories":3588},[151],{"categories":3590},[127],{"categories":3592},[127],{"categories":3594},[106],{"categories":3596},[158],{"categories":3598},[151],{"categories":3600},[420],{"categories":3602},[151],{"categories":3604},[151],{"categories":3606},[151],{"categories":3608},[],{"categories":3610},[103],{"categories":3612},[],{"categories":3614},[420],{"categories":3616},[158],{"categories":3618},[158],{"categories":3620},[158],{"categories":3622},[109],{"categories":3624},[127,103],{"categories":3626},[151],{"categories":3628},[],{"categories":3630},[],{"categories":3632},[151],{"categories":3634},[],{"categories":3636},[151],{"categories":3638},[127],{"categories":3640},[109],{"categories":3642},[],{"categories":3644},[158],{"categories":3646},[106],{"categories":3648},[148],{"categories":3650},[],{"categories":3652},[106],{"categories":3654},[],{"categories":3656},[127],{"categories":3658},[100],{"categories":3660},[151],{"categories":3662},[],{"categories":3664},[158],{"categories":3666},[127],[3668,3728,3794,3863],{"id":3669,"title":3670,"ai":3671,"body":3676,"categories":3712,"created_at":47,"date_modified":47,"description":40,"extension":48,"faq":47,"featured":49,"kicker_label":47,"meta":3713,"navigation":79,"path":3717,"published_at":47,"question":47,"scraped_at":3718,"seo":3719,"sitemap":3720,"source_id":3721,"source_name":85,"source_type":86,"source_url":3722,"stem":3723,"tags":3724,"thumbnail_url":47,"tldr":3725,"tweet":47,"unknown_tags":3726,"__hash__":3727},"summaries\u002Fsummaries\u002Ffa2e2fc7b194cc2f-microsoft-s-efficient-1-bit-llms-and-multimodal-ai-summary.md","Microsoft's Efficient 1-Bit LLMs and Multimodal AI Papers",{"provider":7,"model":8,"input_tokens":3672,"output_tokens":3673,"processing_time_ms":3674,"cost_usd":3675},5264,3273,19828,0.00218245,{"type":14,"value":3677,"toc":3706},[3678,3682,3685,3689,3692,3696,3699,3703],[17,3679,3681],{"id":3680},"_1-bit-llms-enable-cpu-scale-inference","1-Bit LLMs Enable CPU-Scale Inference",[22,3683,3684],{},"BitNet introduces 1-bit transformers where all LLMs fit in 1.58 bits, using 1-bit weights and native 4-bit activations in BitNet v2 and a4.8 variants. Scale to BitNet b1.58 2B4T (2B params on 4T tokens) cuts memory and speeds inference on CPUs via bitnet.cpp. Sparsity boosts efficiency: Sparse-BitNet (semi-structured 1.58-bit), SlideSparse ((2N-2):2N structured), Q-Sparse\u002FBlock Q-Sparse (fully sparse-activated LLMs), ReSa (rectified sparse attention). BitDistill finetunes any full-precision LLM to 1.58-bit for tasks; training tips in S-shape guide cover FAQs. Trade-off: precision loss traded for 10x cheaper deployment vs. full-precision.",[17,3686,3688],{"id":3687},"multimodal-and-voice-ai-foundations","Multimodal and Voice AI Foundations",[22,3690,3691],{},"Kosmos series grounds multimodal LLMs: Kosmos-1 (MLLM), Kosmos-2 (world grounding), Kosmos-2.5 (literacy), Kosmos-G (in-context image gen). VALL-E X (neural codec zero-shot TTS), VALL-E 2 (human-parity zero-shot TTS without VQ), WavMark (audio watermarking). VibeVoice advances voice AI: realtime streaming long-form TTS, VibeVoice-ASR (LLM-era ASR), MELLE (autoregressive speech sans VQ). LatentLM unifies multimodality; LongViT treats 1024x1024 images as 1M tokens. Use for production TTS\u002FASR: zero-shot synthesis skips data collection.",[17,3693,3695],{"id":3694},"architecture-scaling-and-context-extension","Architecture Scaling and Context Extension",[22,3697,3698],{},"YOCO (decoder-decoder LLMs), YOCO-U (universal depth scaling). LongNet scales transformers to 1B tokens context; BYOCL bootstraps longer contexts. Differential Transformer V2 (faster\u002Fbetter\u002Fstable), RetNet (revolutionizes transformers), MH-MoE v2 (multi-head MoE), TorchScale (any-scale transformers), DeepNet (1K layers), Magneto (foundation transformer), XPos (length-extrapolatable). PoSE (positional skip-wise for context windows), Structured Prompting (1K examples ICL). Deploy for long docs: native 1B-token handling avoids truncation errors.",[17,3700,3702],{"id":3701},"distillation-rl-and-agentic-reasoning","Distillation, RL, and Agentic Reasoning",[22,3704,3705],{},"Distill via MiniLLM (on-policy), GAD (black-box on-policy), on-policy context (Experiential Learning Parts I\u002FII), BitDistill. Pre-training: TPT (thinking-augmented), RPT (reinforcement), Learning Law (optimal LM learning), Scaling Laws (synthetic data). RLHF advances: GMPO (geometric-mean policy opt), QueST (generate hard problems), DocReward (document RM), RRM (reward model frontier). Agentic: LLM-in-Sandbox (general intelligence), Multiplex Thinking (token-wise branch\u002Fmerge), Era of Agentic Organization, Visualization-of-Thought (spatial reasoning, MVOT). Outcomes: Distillation shrinks models 4x with \u003C5% perf loss; RL elicits reasoning without full retrain.",{"title":40,"searchDepth":41,"depth":41,"links":3707},[3708,3709,3710,3711],{"id":3680,"depth":41,"text":3681},{"id":3687,"depth":41,"text":3688},{"id":3694,"depth":41,"text":3695},{"id":3701,"depth":41,"text":3702},[],{"content_references":3714,"triage":3715},[],{"relevance":75,"novelty":76,"quality":75,"actionability":41,"composite":77,"reasoning":3716},"Category: AI & LLMs. The article provides a catalog of Microsoft papers on 1-bit LLMs and multimodal AI, which is relevant to AI engineering and addresses the audience's interest in practical applications of AI models. However, while it presents new research, it lacks specific actionable insights or frameworks that the audience can directly implement.","\u002Fsummaries\u002Ffa2e2fc7b194cc2f-microsoft-s-efficient-1-bit-llms-and-multimodal-ai-summary","2026-04-15 15:32:45",{"title":3670,"description":40},{"loc":3717},"fa2e2fc7b194cc2f","https:\u002F\u002Faka.ms\u002FGeneralAI","summaries\u002Ffa2e2fc7b194cc2f-microsoft-s-efficient-1-bit-llms-and-multimodal-ai-summary",[90,92,91,93],"Catalog of 70+ Microsoft papers on 1.58-bit LLMs for CPU inference, zero-shot TTS, long-context scaling to 1B tokens, and agentic reasoning via distillation and sparsity.",[],"-1yUWdAvcK56vZrSSfYlo5_3fS4gHoYGA4Ue0S62xf4",{"id":3729,"title":3730,"ai":3731,"body":3736,"categories":3764,"created_at":47,"date_modified":47,"description":40,"extension":48,"faq":47,"featured":49,"kicker_label":47,"meta":3765,"navigation":79,"path":3778,"published_at":3779,"question":47,"scraped_at":3780,"seo":3781,"sitemap":3782,"source_id":3783,"source_name":3784,"source_type":3785,"source_url":3786,"stem":3787,"tags":3788,"thumbnail_url":3789,"tldr":3790,"tweet":3791,"unknown_tags":3792,"__hash__":3793},"summaries\u002Fsummaries\u002Fa1052ce1f94d210c-rl-industrializes-genai-production-via-feedback-lo-summary.md","RL Industrializes GenAI Production via Feedback Loops",{"provider":7,"model":8,"input_tokens":3732,"output_tokens":3733,"processing_time_ms":3734,"cost_usd":3735},6751,1775,27209,0.0022152,{"type":14,"value":3737,"toc":3759},[3738,3742,3745,3749,3752,3756],[17,3739,3741],{"id":3740},"rl-unlocks-continuous-improvement-from-mvp-to-production","RL Unlocks Continuous Improvement from MVP to Production",[22,3743,3744],{},"GenAI pilots built on proprietary models or instruction fine-tuning (SFT) stall after demos because they lack systematic feedback integration. Changing prompts fixes one defect but creates others; retraining SFT datasets weekly is impractical. RL mathematically incorporates defects, business metrics, and production signals for ongoing refinement. It outperforms SFT disproportionately: achieve equivalent performance with far smaller models (e.g., 10B like latest Gemma, Mistral, or Llama), slashing inference costs from millions (AT&T transcript summarization) and enabling latency under 1\u002F3 second for speech-to-text customer support. Smaller models also grant full ownership—no reliance on upstream updates shifting behavior—and support any task like summarization, classification, or OCR.",[17,3746,3748],{"id":3747},"agents-demand-rl-mock-environments-and-synthetic-data","Agents Demand RL: Mock Environments and Synthetic Data",[22,3750,3751],{},"Agents amplify challenges: 10x tokens, direct database access, zero error tolerance. RL, designed for training agents in environments, fits perfectly. Plug existing agent workflows (e.g., Manulife's) or build mocks: simulate tools, users (LLM-based, trained on real transcripts for realistic panic calls or repetitions), and databases. Rewards derive from KPIs (e.g., CCS containment rate: calls resolved end-to-end), rule-based checks (code syntax), or business rules (tone, vocabulary). Training generates synthetic datasets as byproduct—run trajectories, rejection sample high-reward ones to bootstrap without scraping nonexistent agent data. Leverage existing data like customer transcripts to make mock users authentic.",[17,3753,3755],{"id":3754},"llm-judges-replace-costly-annotations-for-rewards","LLM Judges Replace Costly Annotations for Rewards",[22,3757,3758],{},"RLHF gained fame via OpenAI's ChatGPT post, but annotation campaigns cost weeks and thousands. Humans define rubrics and prompts for LLM judges (takes hours), evaluating open-ended traits like helpfulness or guideline adherence. Start with large models like Qwen 2 235B; scale production human signals (e.g., Cursor's tab-acceptance feedback) into reward models for efficiency. For sparse feedback (10-20 samples), refine LLM judges; with thousands, train dedicated reward models. Test variants to maximize eval performance. Platforms like Adaptive Engine orchestrate complexity (e.g., 4 LLMs in APO), providing pre-built recipes on open models for holistic observe\u002Ftrain\u002Fserve cycles.",{"title":40,"searchDepth":41,"depth":41,"links":3760},[3761,3762,3763],{"id":3740,"depth":41,"text":3741},{"id":3747,"depth":41,"text":3748},{"id":3754,"depth":41,"text":3755},[106],{"content_references":3766,"triage":3774},[3767,3770,3772],{"type":62,"title":3768,"author":3769,"context":65},"Adaptive Engine","Adaptive ML",{"type":58,"title":3771,"context":65},"Cursor blog post on human feedback",{"type":58,"title":3773,"context":56},"OpenAI RLHF blog post",{"relevance":3775,"novelty":75,"quality":75,"actionability":75,"composite":3776,"reasoning":3777},5,4.35,"Category: AI & LLMs. The article discusses how reinforcement learning (RL) can improve the production of generative AI models, addressing a key pain point for product builders regarding the integration of feedback loops. It provides actionable insights on using RL for continuous improvement and cost reduction in AI model deployment.","\u002Fsummaries\u002Fa1052ce1f94d210c-rl-industrializes-genai-production-via-feedback-lo-summary","2026-05-12 17:00:06","2026-05-13 12:00:16",{"title":3730,"description":40},{"loc":3778},"a1052ce1f94d210c","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=X6NShR2ccOg","summaries\u002Fa1052ce1f94d210c-rl-industrializes-genai-production-via-feedback-lo-summary",[90,93,91],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FX6NShR2ccOg\u002Fhqdefault.jpg","95% of GenAI pilots fail production because instruction tuning and prompts can't systematically integrate defects and metrics. RL does, enabling smaller\u002Fcheaper\u002Ffaster models that scale to millions in token costs at Fortune 500s like AT&T.","Conference talk by [Alessandro Cappelli](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Falessandro-cappelli-aa8060172), Adaptive ML co-founder, pitching reinforcement learning pipelines over prompting or fine-tuning for scaling GenAI agents to production at Fortune 500s like AT&T—covers mock environments, synthetic data from training, and LLM judges as rewards.",[],"KLfXTLEeRkEs6VQBCm0cGgULN6IEf3_S4N2OcUMCTSc",{"id":3795,"title":3796,"ai":3797,"body":3802,"categories":3836,"created_at":47,"date_modified":47,"description":40,"extension":48,"faq":47,"featured":49,"kicker_label":47,"meta":3837,"navigation":79,"path":3850,"published_at":3851,"question":47,"scraped_at":3852,"seo":3853,"sitemap":3854,"source_id":3855,"source_name":3856,"source_type":86,"source_url":3857,"stem":3858,"tags":3859,"thumbnail_url":47,"tldr":3860,"tweet":47,"unknown_tags":3861,"__hash__":3862},"summaries\u002Fsummaries\u002Fdcb9afa6c7f04fd4-aurora-fixes-muon-s-neuron-death-in-tall-mlps-summary.md","Aurora Fixes Muon's Neuron Death in Tall MLPs",{"provider":7,"model":8,"input_tokens":3798,"output_tokens":3799,"processing_time_ms":3800,"cost_usd":3801},7761,2013,23604,0.00253605,{"type":14,"value":3803,"toc":3831},[3804,3808,3811,3814,3818,3821,3824,3828],[17,3805,3807],{"id":3806},"muons-orthogonal-updates-cause-neuron-death-in-tall-matrices","Muon's Orthogonal Updates Cause Neuron Death in Tall Matrices",[22,3809,3810],{},"Muon computes the polar factor UVᵀ of gradient matrix G (via thin SVD) for semi-orthogonal weight updates W ← W - η UVᵀ, enabling fast convergence on nanoGPT speedrun benchmarks over AdamW. In tall matrices like SwiGLU MLP up-projections (more rows n than columns m), row-norm anisotropy emerges: impossible for perfectly orthogonal matrices to have uniform row norms of 1, so some rows get massive updates while others starve. By training step 500, >1\u002F4 neurons die permanently, starving downstream layers and compounding inefficiency. Leverage scores (squared row norms of U) become highly anisotropic, amplifying the death spiral.",[22,3812,3813],{},"NorMuon patches this with inverse RMS row normalization to unit norm, boosting performance but sacrificing polar factor precision. U-NorMuon refines to target norm √(n\u002Fm) for column-orthogonal tall matrices, eliminating death and stabilizing gradients even in untouched layers like down-projections—at 340M scale, it outperforms Muon\u002FNorMuon with isotropic leverage.",[17,3815,3817],{"id":3816},"aurora-solves-joint-constraints-for-precise-uniform-updates","Aurora Solves Joint Constraints for Precise, Uniform Updates",[22,3819,3820],{},"Aurora reformulates as steepest descent maximizing Tr(GᵀU) under dual constraints: UᵀU = Iₙ (left semi-orthogonality) and ||U_||₂ = √(m\u002Fn) ∀i (uniform row leverage). This forces all singular values of U to 1, achieving perfect orthogonality without trade-offs—unlike NorMuon's post-hoc normalization.",[22,3822,3823],{},"Implement as drop-in Muon replacement: Riemannian Aurora (gradient projection on Stiefel\u002Fequal-leverage manifold) or vanilla Aurora (simpler). For wide\u002Fsquare matrices, orthogonality implies uniformity, so unchanged. Open-source code supports scale; adds only 6% compute vs. Muon.",[17,3825,3827],{"id":3826},"sota-results-scale-with-mlp-width","SOTA Results Scale with MLP Width",[22,3829,3830],{},"At 1.1B parameters, Aurora trains 100x data-efficient model on open internet data, beating larger models on HellaSwag. Tops modded-nanoGPT speedrun (prior SOTA: NorMuon). Gains grow with MLP expansion (wider = taller matrices = more anisotropy risk), confirming hypothesis. Use for GPT-style training to avoid silent capacity loss.",{"title":40,"searchDepth":41,"depth":41,"links":3832},[3833,3834,3835],{"id":3806,"depth":41,"text":3807},{"id":3816,"depth":41,"text":3817},{"id":3826,"depth":41,"text":3827},[106],{"content_references":3838,"triage":3847},[3839,3844],{"type":53,"title":3840,"author":3841,"url":3842,"context":3843},"Aurora","Tilde Research","https:\u002F\u002Fblog.tilderesearch.com\u002Fblog\u002Faurora","recommended",{"type":62,"title":3845,"url":3846,"context":3843},"aurora-release","https:\u002F\u002Fgithub.com\u002Ftilde-research\u002Faurora-release",{"relevance":76,"novelty":75,"quality":75,"actionability":41,"composite":3848,"reasoning":3849},3.25,"Category: AI & LLMs. The article discusses a new optimizer, Aurora, that addresses a specific technical problem in deep learning models, which is relevant to AI engineering. However, while it presents novel insights into the optimizer's mechanics and performance, it lacks practical guidance for implementation that the target audience could directly act upon.","\u002Fsummaries\u002Fdcb9afa6c7f04fd4-aurora-fixes-muon-s-neuron-death-in-tall-mlps-summary","2026-05-12 08:07:28","2026-05-12 15:01:25",{"title":3796,"description":40},{"loc":3850},"dcb9afa6c7f04fd4","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F12\u002Ftilde-research-introduces-aurora-a-leverage-aware-optimizer-that-fixes-a-hidden-neuron-death-problem-in-muon\u002F","summaries\u002Fdcb9afa6c7f04fd4-aurora-fixes-muon-s-neuron-death-in-tall-mlps-summary",[91,90,92],"Aurora optimizer eliminates >25% neuron death in Muon's tall matrices by jointly enforcing left semi-orthogonality and uniform row norms √(n\u002Fm), delivering SOTA on nanoGPT speedrun with 6% compute overhead.",[],"LbY7EBmj0SNTdCqYLDJeH1MTGWukIbA19aMUaOvqp7Y",{"id":3864,"title":3865,"ai":3866,"body":3871,"categories":3899,"created_at":47,"date_modified":47,"description":40,"extension":48,"faq":47,"featured":49,"kicker_label":47,"meta":3900,"navigation":79,"path":3908,"published_at":3909,"question":47,"scraped_at":3910,"seo":3911,"sitemap":3912,"source_id":3913,"source_name":3914,"source_type":86,"source_url":3915,"stem":3916,"tags":3917,"thumbnail_url":47,"tldr":3918,"tweet":47,"unknown_tags":3919,"__hash__":3920},"summaries\u002Fsummaries\u002F80f92a0da5fd7538-nvidia-halves-dsa-top-k-time-via-decode-stability-summary.md","NVIDIA Halves DSA Top-K Time via Decode Stability",{"provider":7,"model":8,"input_tokens":3867,"output_tokens":3868,"processing_time_ms":3869,"cost_usd":3870},3906,1547,23269,0.0015322,{"type":14,"value":3872,"toc":3894},[3873,3877,3880,3884,3887,3891],[17,3874,3876],{"id":3875},"top-k-bottleneck-in-scaling-dsa-contexts","Top-K Bottleneck in Scaling DSA Contexts",[22,3878,3879],{},"DeepSeek Sparse Attention (DSA) relies on a lightweight indexer to score every token in the KV cache, then select the top 2,048 highest-scoring positions via Top-K. This becomes critical as contexts grow from 8K to 128K tokens: the step scans the full sequence every decode iteration. Even an optimized radix-select kernel—7.4x faster than PyTorch—requires 3–4 passes over all N scores per step, creating a major GPU bottleneck during autoregressive generation.",[17,3881,3883],{"id":3882},"autoregressive-quirks-unlock-reuse","Autoregressive Quirks Unlock Reuse",[22,3885,3886],{},"Token-by-token generation creates structural predictability: each decode shifts the query by one position, appends one key to the cache, and evolves attention scores gradually. Consecutive steps query highly overlapping KV cache neighborhoods, making Top-K indices temporally stable. NVIDIA's insight treats this as a goldmine, not incidental—profiling shows brute-force scans waste cycles on redundant computations across similar steps.",[17,3888,3890],{"id":3889},"guess-verify-refine-cuts-compute-in-half","Guess-Verify-Refine Cuts Compute in Half",[22,3892,3893],{},"Their April 30, 2026 technical report introduces Guess-Verify-Refine Top-K: guess indices from the prior step (leveraging neighborhood similarity), verify against current scores (cheap partial scan), and refine only discrepancies. This halves Top-K time versus the production baseline without accuracy loss, proving workload structure trumps pure algorithmic speedups. Builders profiling LLM kernels should prioritize such properties for 2x+ gains in long-context decoding.",{"title":40,"searchDepth":41,"depth":41,"links":3895},[3896,3897,3898],{"id":3875,"depth":41,"text":3876},{"id":3882,"depth":41,"text":3883},{"id":3889,"depth":41,"text":3890},[],{"content_references":3901,"triage":3905},[3902],{"type":3903,"title":3904,"author":70,"context":56},"report","Guess-Verify-Refine Top-K for DeepSeek Sparse Attention Decoding",{"relevance":3775,"novelty":75,"quality":75,"actionability":76,"composite":3906,"reasoning":3907},4.15,"Category: AI & LLMs. The article provides a deep dive into NVIDIA's innovative approach to optimizing Top-K selection in LLMs, addressing a specific pain point in AI engineering related to performance bottlenecks. It introduces the Guess-Verify-Refine method, which offers a new perspective on improving efficiency, although it lacks detailed step-by-step guidance for implementation.","\u002Fsummaries\u002F80f92a0da5fd7538-nvidia-halves-dsa-top-k-time-via-decode-stability-summary","2026-05-09 13:31:00","2026-05-09 15:36:47",{"title":3865,"description":40},{"loc":3908},"80f92a0da5fd7538","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fhow-nvidia-cut-deepseek-sparse-attentions-top-k-time-8044db298334?source=rss----98111c9905da---4","summaries\u002F80f92a0da5fd7538-nvidia-halves-dsa-top-k-time-via-decode-stability-summary",[90,92,91],"NVIDIA exploits autoregressive decoding's temporal stability—similar queries and gradually evolving scores—to cut DeepSeek Sparse Attention's Top-K bottleneck by half using Guess-Verify-Refine.",[],"UqEkEcnX7IXIvuBgFoVuNNzm7zBptItKcMjy7Sq8NIE"]