[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-5c8a61f1aa3cea08-llm-scaling-works-via-strong-superposition-summary":3,"summaries-facets-categories":93,"summary-related-5c8a61f1aa3cea08-llm-scaling-works-via-strong-superposition-summary":3663},{"id":4,"title":5,"ai":6,"body":13,"categories":49,"created_at":50,"date_modified":50,"description":43,"extension":51,"faq":50,"featured":52,"kicker_label":50,"meta":53,"navigation":75,"path":76,"published_at":77,"question":50,"scraped_at":78,"seo":79,"sitemap":80,"source_id":81,"source_name":82,"source_type":83,"source_url":84,"stem":85,"tags":86,"thumbnail_url":50,"tldr":90,"tweet":50,"unknown_tags":91,"__hash__":92},"summaries\u002Fsummaries\u002F5c8a61f1aa3cea08-llm-scaling-works-via-strong-superposition-summary.md","LLM Scaling Works via Strong Superposition",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4549,1921,23559,0.00136345,{"type":14,"value":15,"toc":42},"minimark",[16,21,25,28,32,35,39],[17,18,20],"h2",{"id":19},"superposition-drives-predictable-error-reduction","Superposition Drives Predictable Error Reduction",[22,23,24],"p",{},"Language models represent tens of thousands of tokens in spaces with only thousands of dimensions by using superposition: squeezing multiple concepts into the same dimensions with slight overlaps. In the dominant 'strong superposition' regime, every token gets represented, and error stems from overlap noise, not dropped rare tokens. Doubling model width (m) halves error via the geometric 1\u002Fm relationship, yielding power-law scaling (exponent ~1) regardless of data distribution. Weak superposition, where only common tokens are stored cleanly, requires power-law token frequencies for scaling—less reliable for natural language's flatter distributions.",[22,26,27],{},"This mechanistic view outperforms prior assumptions: real LLMs don't discard rare tokens but overlap everything, matching theory with measured overlap strength shrinking at 1\u002Fm.",[17,29,31],{"id":30},"validation-across-real-models-matches-theory","Validation Across Real Models Matches Theory",[22,33,34],{},"Analysis of output layers in OPT, GPT-2, Qwen2.5, and Pythia (100M to 70B parameters) confirms strong superposition: all tokens represented with overlaps scaling at 1\u002Fm. Observed exponent of 0.91 aligns with theory's 1; DeepMind's Chinchilla data hits 0.88. Simplified models toggling overlap regimes prove scaling emerges directly from geometry, not just data power laws ('power law in, power law out').",[17,36,38],{"id":37},"limits-and-optimization-opportunities","Limits and Optimization Opportunities",[22,40,41],{},"Scaling halts when width equals vocabulary size—no more overlaps needed, error from superposition vanishes, breaking power laws. Natural language's even frequencies limit speedup, but uneven domains (e.g., specialized vocab) enable steeper curves. Architectures promoting denser packing, like Nvidia's nGPT (vectors on unit sphere), boost performance at fixed size. Trade-off: denser overlaps hinder mechanistic interpretability, complicating AI safety.",{"title":43,"searchDepth":44,"depth":44,"links":45},"",2,[46,47,48],{"id":19,"depth":44,"text":20},{"id":30,"depth":44,"text":31},{"id":37,"depth":44,"text":38},[],null,"md",false,{"content_references":54,"triage":70},[55,61,65],{"type":56,"title":57,"author":58,"url":59,"context":60},"paper","Toy Model of Superposition","Anthropic","https:\u002F\u002Ftransformer-circuits.pub\u002F2022\u002Ftoy_model\u002Findex.html","cited",{"type":56,"title":62,"author":63,"url":64,"context":60},"Chinchilla","DeepMind","https:\u002F\u002Fthe-decoder.com\u002Fdeepmind-artificial-intelligence-is-far-from-being-fed-up\u002F",{"type":56,"title":66,"author":67,"url":68,"context":69},"nGPT","Nvidia","https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.01131","mentioned",{"relevance":71,"novelty":72,"quality":72,"actionability":44,"composite":73,"reasoning":74},3,4,3.25,"Category: AI & LLMs. The article discusses the mechanics of LLM scaling through strong superposition, which is relevant to AI engineering. It presents new insights into how model width affects prediction error, but lacks practical applications or frameworks that the audience can directly implement.",true,"\u002Fsummaries\u002F5c8a61f1aa3cea08-llm-scaling-works-via-strong-superposition-summary","2026-05-03 08:42:45","2026-05-03 17:01:29",{"title":5,"description":43},{"loc":76},"5c8a61f1aa3cea08","The Decoder","article","https:\u002F\u002Fthe-decoder.com\u002Fmit-study-explains-why-scaling-language-models-works-so-reliably\u002F","summaries\u002F5c8a61f1aa3cea08-llm-scaling-works-via-strong-superposition-summary",[87,88,89],"llm","machine-learning","research","LLMs pack all tokens into limited dimensions via overlapping vectors (strong superposition), causing prediction error to halve when model width doubles—explaining reliable power-law scaling.",[],"TxCrmsO7g860jqMKD8Z7LhJqkiaTNkcDx-Z3AQT2GA0",[94,97,100,103,106,109,111,113,115,117,119,121,124,126,128,130,132,134,136,138,140,142,145,148,150,152,155,157,159,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661],{"categories":95},[96],"Developer Productivity",{"categories":98},[99],"Business & SaaS",{"categories":101},[102],"AI & LLMs",{"categories":104},[105],"AI Automation",{"categories":107},[108],"Product Strategy",{"categories":110},[102],{"categories":112},[96],{"categories":114},[99],{"categories":116},[],{"categories":118},[102],{"categories":120},[],{"categories":122},[123],"AI News & Trends",{"categories":125},[105],{"categories":127},[123],{"categories":129},[105],{"categories":131},[105],{"categories":133},[102],{"categories":135},[102],{"categories":137},[123],{"categories":139},[102],{"categories":141},[],{"categories":143},[144],"Design & Frontend",{"categories":146},[147],"Data Science & Visualization",{"categories":149},[123],{"categories":151},[],{"categories":153},[154],"Software Engineering",{"categories":156},[102],{"categories":158},[105],{"categories":160},[161],"Marketing & Growth",{"categories":163},[102],{"categories":165},[105],{"categories":167},[],{"categories":169},[],{"categories":171},[144],{"categories":173},[105],{"categories":175},[96],{"categories":177},[144],{"categories":179},[102],{"categories":181},[105],{"categories":183},[123],{"categories":185},[],{"categories":187},[],{"categories":189},[105],{"categories":191},[154],{"categories":193},[],{"categories":195},[99],{"categories":197},[],{"categories":199},[],{"categories":201},[105],{"categories":203},[105],{"categories":205},[102],{"categories":207},[],{"categories":209},[154],{"categories":211},[],{"categories":213},[],{"categories":215},[],{"categories":217},[102],{"categories":219},[161],{"categories":221},[144],{"categories":223},[144],{"categories":225},[102],{"categories":227},[105],{"categories":229},[102],{"categories":231},[102],{"categories":233},[105],{"categories":235},[105],{"categories":237},[147],{"categories":239},[123],{"categories":241},[105],{"categories":243},[161],{"categories":245},[105],{"categories":247},[108],{"categories":249},[],{"categories":251},[105],{"categories":253},[],{"categories":255},[105],{"categories":257},[154],{"categories":259},[144],{"categories":261},[102],{"categories":263},[],{"categories":265},[],{"categories":267},[105],{"categories":269},[],{"categories":271},[102],{"categories":273},[],{"categories":275},[96],{"categories":277},[154],{"categories":279},[99],{"categories":281},[123],{"categories":283},[102],{"categories":285},[],{"categories":287},[102],{"categories":289},[],{"categories":291},[154],{"categories":293},[147],{"categories":295},[],{"categories":297},[102],{"categories":299},[144],{"categories":301},[],{"categories":303},[144],{"categories":305},[105],{"categories":307},[],{"categories":309},[105],{"categories":311},[123],{"categories":313},[102],{"categories":315},[],{"categories":317},[105],{"categories":319},[102],{"categories":321},[108],{"categories":323},[],{"categories":325},[102],{"categories":327},[105],{"categories":329},[105],{"categories":331},[],{"categories":333},[147],{"categories":335},[102],{"categories":337},[],{"categories":339},[96],{"categories":341},[99],{"categories":343},[102],{"categories":345},[105],{"categories":347},[154],{"categories":349},[102],{"categories":351},[],{"categories":353},[],{"categories":355},[102],{"categories":357},[],{"categories":359},[144],{"categories":361},[],{"categories":363},[102],{"categories":365},[],{"categories":367},[105],{"categories":369},[102],{"categories":371},[144],{"categories":373},[],{"categories":375},[102],{"categories":377},[102],{"categories":379},[99],{"categories":381},[105],{"categories":383},[102],{"categories":385},[144],{"categories":387},[105],{"categories":389},[],{"categories":391},[],{"categories":393},[123],{"categories":395},[],{"categories":397},[102],{"categories":399},[99,161],{"categories":401},[],{"categories":403},[102],{"categories":405},[],{"categories":407},[],{"categories":409},[102],{"categories":411},[],{"categories":413},[102],{"categories":415},[416],"DevOps & Cloud",{"categories":418},[],{"categories":420},[123],{"categories":422},[144],{"categories":424},[],{"categories":426},[123],{"categories":428},[123],{"categories":430},[102],{"categories":432},[161],{"categories":434},[],{"categories":436},[99],{"categories":438},[],{"categories":440},[102,416],{"categories":442},[102],{"categories":444},[102],{"categories":446},[105],{"categories":448},[102,154],{"categories":450},[147],{"categories":452},[102],{"categories":454},[161],{"categories":456},[105],{"categories":458},[105],{"categories":460},[],{"categories":462},[105],{"categories":464},[102,99],{"categories":466},[],{"categories":468},[144],{"categories":470},[144],{"categories":472},[],{"categories":474},[],{"categories":476},[123],{"categories":478},[],{"categories":480},[96],{"categories":482},[154],{"categories":484},[102],{"categories":486},[144],{"categories":488},[105],{"categories":490},[154],{"categories":492},[123],{"categories":494},[144],{"categories":496},[],{"categories":498},[102],{"categories":500},[102],{"categories":502},[102],{"categories":504},[123],{"categories":506},[96],{"categories":508},[102],{"categories":510},[105],{"categories":512},[416],{"categories":514},[144],{"categories":516},[105],{"categories":518},[],{"categories":520},[],{"categories":522},[144],{"categories":524},[123],{"categories":526},[147],{"categories":528},[],{"categories":530},[102],{"categories":532},[102],{"categories":534},[99],{"categories":536},[102],{"categories":538},[102],{"categories":540},[123],{"categories":542},[],{"categories":544},[105],{"categories":546},[154],{"categories":548},[],{"categories":550},[102],{"categories":552},[102],{"categories":554},[105],{"categories":556},[],{"categories":558},[],{"categories":560},[102],{"categories":562},[],{"categories":564},[99],{"categories":566},[105],{"categories":568},[],{"categories":570},[96],{"categories":572},[102],{"categories":574},[99],{"categories":576},[123],{"categories":578},[],{"categories":580},[],{"categories":582},[],{"categories":584},[123],{"categories":586},[123],{"categories":588},[],{"categories":590},[],{"categories":592},[99],{"categories":594},[],{"categories":596},[],{"categories":598},[96],{"categories":600},[],{"categories":602},[161],{"categories":604},[105],{"categories":606},[99],{"categories":608},[105],{"categories":610},[],{"categories":612},[108],{"categories":614},[144],{"categories":616},[154],{"categories":618},[102],{"categories":620},[105],{"categories":622},[99],{"categories":624},[102],{"categories":626},[],{"categories":628},[],{"categories":630},[154],{"categories":632},[147],{"categories":634},[108],{"categories":636},[105],{"categories":638},[102],{"categories":640},[],{"categories":642},[416],{"categories":644},[],{"categories":646},[105],{"categories":648},[],{"categories":650},[],{"categories":652},[102],{"categories":654},[144],{"categories":656},[161],{"categories":658},[105],{"categories":660},[],{"categories":662},[96],{"categories":664},[],{"categories":666},[123],{"categories":668},[102,416],{"categories":670},[123],{"categories":672},[102],{"categories":674},[99],{"categories":676},[102],{"categories":678},[],{"categories":680},[99],{"categories":682},[],{"categories":684},[154],{"categories":686},[144],{"categories":688},[123],{"categories":690},[147],{"categories":692},[96],{"categories":694},[102],{"categories":696},[154],{"categories":698},[],{"categories":700},[],{"categories":702},[108],{"categories":704},[],{"categories":706},[102],{"categories":708},[],{"categories":710},[144],{"categories":712},[144],{"categories":714},[144],{"categories":716},[],{"categories":718},[],{"categories":720},[123],{"categories":722},[105],{"categories":724},[102],{"categories":726},[102],{"categories":728},[102],{"categories":730},[99],{"categories":732},[102],{"categories":734},[],{"categories":736},[154],{"categories":738},[154],{"categories":740},[99],{"categories":742},[],{"categories":744},[102],{"categories":746},[102],{"categories":748},[99],{"categories":750},[123],{"categories":752},[161],{"categories":754},[105],{"categories":756},[],{"categories":758},[144],{"categories":760},[],{"categories":762},[102],{"categories":764},[],{"categories":766},[99],{"categories":768},[105],{"categories":770},[],{"categories":772},[416],{"categories":774},[147],{"categories":776},[154],{"categories":778},[161],{"categories":780},[154],{"categories":782},[105],{"categories":784},[],{"categories":786},[],{"categories":788},[105],{"categories":790},[96],{"categories":792},[105],{"categories":794},[108],{"categories":796},[99],{"categories":798},[],{"categories":800},[102],{"categories":802},[108],{"categories":804},[102],{"categories":806},[102],{"categories":808},[161],{"categories":810},[144],{"categories":812},[105],{"categories":814},[],{"categories":816},[],{"categories":818},[416],{"categories":820},[154],{"categories":822},[],{"categories":824},[105],{"categories":826},[102],{"categories":828},[144,102],{"categories":830},[96],{"categories":832},[],{"categories":834},[102],{"categories":836},[96],{"categories":838},[144],{"categories":840},[105],{"categories":842},[154],{"categories":844},[],{"categories":846},[102],{"categories":848},[],{"categories":850},[96],{"categories":852},[],{"categories":854},[105],{"categories":856},[108],{"categories":858},[102],{"categories":860},[102],{"categories":862},[144],{"categories":864},[105],{"categories":866},[416],{"categories":868},[144],{"categories":870},[105],{"categories":872},[102],{"categories":874},[102],{"categories":876},[102],{"categories":878},[123],{"categories":880},[],{"categories":882},[108],{"categories":884},[105],{"categories":886},[144],{"categories":888},[105],{"categories":890},[154],{"categories":892},[144],{"categories":894},[105],{"categories":896},[123],{"categories":898},[],{"categories":900},[102],{"categories":902},[144],{"categories":904},[102],{"categories":906},[96],{"categories":908},[123],{"categories":910},[102],{"categories":912},[161],{"categories":914},[102],{"categories":916},[102],{"categories":918},[105],{"categories":920},[105],{"categories":922},[102],{"categories":924},[105],{"categories":926},[144],{"categories":928},[102],{"categories":930},[],{"categories":932},[],{"categories":934},[154],{"categories":936},[],{"categories":938},[96],{"categories":940},[416],{"categories":942},[],{"categories":944},[96],{"categories":946},[99],{"categories":948},[161],{"categories":950},[],{"categories":952},[99],{"categories":954},[],{"categories":956},[],{"categories":958},[],{"categories":960},[],{"categories":962},[],{"categories":964},[102],{"categories":966},[105],{"categories":968},[416],{"categories":970},[96],{"categories":972},[102],{"categories":974},[154],{"categories":976},[108],{"categories":978},[102],{"categories":980},[161],{"categories":982},[102],{"categories":984},[102],{"categories":986},[102],{"categories":988},[102,96],{"categories":990},[154],{"categories":992},[154],{"categories":994},[144],{"categories":996},[102],{"categories":998},[],{"categories":1000},[],{"categories":1002},[],{"categories":1004},[154],{"categories":1006},[147],{"categories":1008},[123],{"categories":1010},[144],{"categories":1012},[],{"categories":1014},[102],{"categories":1016},[102],{"categories":1018},[],{"categories":1020},[],{"categories":1022},[105],{"categories":1024},[102],{"categories":1026},[99],{"categories":1028},[],{"categories":1030},[96],{"categories":1032},[102],{"categories":1034},[96],{"categories":1036},[102],{"categories":1038},[154],{"categories":1040},[161],{"categories":1042},[102,144],{"categories":1044},[123],{"categories":1046},[144],{"categories":1048},[],{"categories":1050},[416],{"categories":1052},[144],{"categories":1054},[105],{"categories":1056},[],{"categories":1058},[],{"categories":1060},[],{"categories":1062},[],{"categories":1064},[154],{"categories":1066},[105],{"categories":1068},[105],{"categories":1070},[102],{"categories":1072},[102],{"categories":1074},[],{"categories":1076},[144],{"categories":1078},[],{"categories":1080},[],{"categories":1082},[105],{"categories":1084},[],{"categories":1086},[],{"categories":1088},[161],{"categories":1090},[161],{"categories":1092},[105],{"categories":1094},[],{"categories":1096},[102],{"categories":1098},[102],{"categories":1100},[154],{"categories":1102},[144],{"categories":1104},[144],{"categories":1106},[105],{"categories":1108},[96],{"categories":1110},[102],{"categories":1112},[144],{"categories":1114},[144],{"categories":1116},[105],{"categories":1118},[105],{"categories":1120},[102],{"categories":1122},[],{"categories":1124},[],{"categories":1126},[102],{"categories":1128},[105],{"categories":1130},[123],{"categories":1132},[154],{"categories":1134},[96],{"categories":1136},[102],{"categories":1138},[],{"categories":1140},[105],{"categories":1142},[105],{"categories":1144},[],{"categories":1146},[96],{"categories":1148},[102],{"categories":1150},[96],{"categories":1152},[96],{"categories":1154},[],{"categories":1156},[],{"categories":1158},[105],{"categories":1160},[105],{"categories":1162},[102],{"categories":1164},[102],{"categories":1166},[123],{"categories":1168},[147],{"categories":1170},[108],{"categories":1172},[123],{"categories":1174},[144],{"categories":1176},[],{"categories":1178},[123],{"categories":1180},[],{"categories":1182},[],{"categories":1184},[],{"categories":1186},[],{"categories":1188},[154],{"categories":1190},[147],{"categories":1192},[],{"categories":1194},[102],{"categories":1196},[102],{"categories":1198},[147],{"categories":1200},[154],{"categories":1202},[],{"categories":1204},[],{"categories":1206},[105],{"categories":1208},[123],{"categories":1210},[123],{"categories":1212},[105],{"categories":1214},[96],{"categories":1216},[102,416],{"categories":1218},[],{"categories":1220},[144],{"categories":1222},[96],{"categories":1224},[105],{"categories":1226},[144],{"categories":1228},[],{"categories":1230},[105],{"categories":1232},[105],{"categories":1234},[102],{"categories":1236},[161],{"categories":1238},[154],{"categories":1240},[144],{"categories":1242},[],{"categories":1244},[105],{"categories":1246},[102],{"categories":1248},[105],{"categories":1250},[105],{"categories":1252},[105],{"categories":1254},[161],{"categories":1256},[105],{"categories":1258},[102],{"categories":1260},[],{"categories":1262},[161],{"categories":1264},[123],{"categories":1266},[105],{"categories":1268},[],{"categories":1270},[],{"categories":1272},[102],{"categories":1274},[105],{"categories":1276},[123],{"categories":1278},[105],{"categories":1280},[],{"categories":1282},[],{"categories":1284},[],{"categories":1286},[105],{"categories":1288},[],{"categories":1290},[],{"categories":1292},[147],{"categories":1294},[102],{"categories":1296},[147],{"categories":1298},[123],{"categories":1300},[102],{"categories":1302},[102],{"categories":1304},[105],{"categories":1306},[102],{"categories":1308},[],{"categories":1310},[],{"categories":1312},[416],{"categories":1314},[],{"categories":1316},[],{"categories":1318},[96],{"categories":1320},[],{"categories":1322},[],{"categories":1324},[],{"categories":1326},[],{"categories":1328},[154],{"categories":1330},[123],{"categories":1332},[161],{"categories":1334},[99],{"categories":1336},[102],{"categories":1338},[102],{"categories":1340},[99],{"categories":1342},[],{"categories":1344},[144],{"categories":1346},[105],{"categories":1348},[99],{"categories":1350},[102],{"categories":1352},[102],{"categories":1354},[96],{"categories":1356},[],{"categories":1358},[96],{"categories":1360},[102],{"categories":1362},[161],{"categories":1364},[105],{"categories":1366},[123],{"categories":1368},[99],{"categories":1370},[102],{"categories":1372},[105],{"categories":1374},[],{"categories":1376},[102],{"categories":1378},[96],{"categories":1380},[102],{"categories":1382},[],{"categories":1384},[123],{"categories":1386},[102],{"categories":1388},[],{"categories":1390},[99],{"categories":1392},[102],{"categories":1394},[],{"categories":1396},[],{"categories":1398},[],{"categories":1400},[102],{"categories":1402},[],{"categories":1404},[416],{"categories":1406},[102],{"categories":1408},[],{"categories":1410},[102],{"categories":1412},[102],{"categories":1414},[102],{"categories":1416},[102,416],{"categories":1418},[102],{"categories":1420},[102],{"categories":1422},[144],{"categories":1424},[105],{"categories":1426},[],{"categories":1428},[105],{"categories":1430},[102],{"categories":1432},[102],{"categories":1434},[102],{"categories":1436},[96],{"categories":1438},[96],{"categories":1440},[154],{"categories":1442},[144],{"categories":1444},[105],{"categories":1446},[],{"categories":1448},[102],{"categories":1450},[123],{"categories":1452},[102],{"categories":1454},[99],{"categories":1456},[],{"categories":1458},[416],{"categories":1460},[144],{"categories":1462},[144],{"categories":1464},[105],{"categories":1466},[123],{"categories":1468},[105],{"categories":1470},[102],{"categories":1472},[],{"categories":1474},[102],{"categories":1476},[],{"categories":1478},[],{"categories":1480},[102],{"categories":1482},[102],{"categories":1484},[102],{"categories":1486},[105],{"categories":1488},[102],{"categories":1490},[],{"categories":1492},[147],{"categories":1494},[105],{"categories":1496},[],{"categories":1498},[102],{"categories":1500},[123],{"categories":1502},[],{"categories":1504},[144],{"categories":1506},[416],{"categories":1508},[123],{"categories":1510},[154],{"categories":1512},[154],{"categories":1514},[123],{"categories":1516},[123],{"categories":1518},[416],{"categories":1520},[],{"categories":1522},[123],{"categories":1524},[102],{"categories":1526},[96],{"categories":1528},[123],{"categories":1530},[],{"categories":1532},[147],{"categories":1534},[123],{"categories":1536},[154],{"categories":1538},[123],{"categories":1540},[416],{"categories":1542},[102],{"categories":1544},[102],{"categories":1546},[],{"categories":1548},[99],{"categories":1550},[],{"categories":1552},[],{"categories":1554},[102],{"categories":1556},[102],{"categories":1558},[102],{"categories":1560},[102],{"categories":1562},[],{"categories":1564},[147],{"categories":1566},[96],{"categories":1568},[],{"categories":1570},[102],{"categories":1572},[102],{"categories":1574},[416],{"categories":1576},[416],{"categories":1578},[],{"categories":1580},[105],{"categories":1582},[123],{"categories":1584},[123],{"categories":1586},[102],{"categories":1588},[105],{"categories":1590},[],{"categories":1592},[144],{"categories":1594},[102],{"categories":1596},[102],{"categories":1598},[],{"categories":1600},[],{"categories":1602},[416],{"categories":1604},[102],{"categories":1606},[154],{"categories":1608},[99],{"categories":1610},[102],{"categories":1612},[],{"categories":1614},[105],{"categories":1616},[96],{"categories":1618},[96],{"categories":1620},[],{"categories":1622},[102],{"categories":1624},[144],{"categories":1626},[105],{"categories":1628},[],{"categories":1630},[102],{"categories":1632},[102],{"categories":1634},[105],{"categories":1636},[],{"categories":1638},[105],{"categories":1640},[154],{"categories":1642},[],{"categories":1644},[102],{"categories":1646},[],{"categories":1648},[102],{"categories":1650},[],{"categories":1652},[102],{"categories":1654},[102],{"categories":1656},[],{"categories":1658},[102],{"categories":1660},[123],{"categories":1662},[102],{"categories":1664},[102],{"categories":1666},[96],{"categories":1668},[102],{"categories":1670},[123],{"categories":1672},[105],{"categories":1674},[],{"categories":1676},[102],{"categories":1678},[161],{"categories":1680},[],{"categories":1682},[],{"categories":1684},[],{"categories":1686},[96],{"categories":1688},[123],{"categories":1690},[105],{"categories":1692},[102],{"categories":1694},[144],{"categories":1696},[105],{"categories":1698},[],{"categories":1700},[105],{"categories":1702},[],{"categories":1704},[102],{"categories":1706},[105],{"categories":1708},[102],{"categories":1710},[],{"categories":1712},[102],{"categories":1714},[102],{"categories":1716},[123],{"categories":1718},[144],{"categories":1720},[105],{"categories":1722},[144],{"categories":1724},[99],{"categories":1726},[],{"categories":1728},[],{"categories":1730},[102],{"categories":1732},[96],{"categories":1734},[123],{"categories":1736},[],{"categories":1738},[],{"categories":1740},[154],{"categories":1742},[144],{"categories":1744},[],{"categories":1746},[102],{"categories":1748},[],{"categories":1750},[161],{"categories":1752},[102],{"categories":1754},[416],{"categories":1756},[154],{"categories":1758},[],{"categories":1760},[105],{"categories":1762},[102],{"categories":1764},[105],{"categories":1766},[105],{"categories":1768},[102],{"categories":1770},[],{"categories":1772},[96],{"categories":1774},[102],{"categories":1776},[99],{"categories":1778},[154],{"categories":1780},[144],{"categories":1782},[],{"categories":1784},[],{"categories":1786},[],{"categories":1788},[105],{"categories":1790},[144],{"categories":1792},[123],{"categories":1794},[102],{"categories":1796},[123],{"categories":1798},[144],{"categories":1800},[],{"categories":1802},[144],{"categories":1804},[123],{"categories":1806},[99],{"categories":1808},[102],{"categories":1810},[123],{"categories":1812},[161],{"categories":1814},[],{"categories":1816},[],{"categories":1818},[147],{"categories":1820},[102,154],{"categories":1822},[123],{"categories":1824},[102],{"categories":1826},[105],{"categories":1828},[105],{"categories":1830},[102],{"categories":1832},[],{"categories":1834},[154],{"categories":1836},[102],{"categories":1838},[147],{"categories":1840},[105],{"categories":1842},[161],{"categories":1844},[416],{"categories":1846},[],{"categories":1848},[96],{"categories":1850},[105],{"categories":1852},[105],{"categories":1854},[154],{"categories":1856},[102],{"categories":1858},[102],{"categories":1860},[],{"categories":1862},[],{"categories":1864},[],{"categories":1866},[416],{"categories":1868},[123],{"categories":1870},[102],{"categories":1872},[102],{"categories":1874},[102],{"categories":1876},[],{"categories":1878},[147],{"categories":1880},[99],{"categories":1882},[],{"categories":1884},[105],{"categories":1886},[416],{"categories":1888},[],{"categories":1890},[144],{"categories":1892},[144],{"categories":1894},[],{"categories":1896},[154],{"categories":1898},[144],{"categories":1900},[102],{"categories":1902},[],{"categories":1904},[123],{"categories":1906},[102],{"categories":1908},[144],{"categories":1910},[105],{"categories":1912},[123],{"categories":1914},[],{"categories":1916},[105],{"categories":1918},[144],{"categories":1920},[102],{"categories":1922},[],{"categories":1924},[102],{"categories":1926},[102],{"categories":1928},[416],{"categories":1930},[123],{"categories":1932},[147],{"categories":1934},[147],{"categories":1936},[],{"categories":1938},[],{"categories":1940},[],{"categories":1942},[105],{"categories":1944},[154],{"categories":1946},[154],{"categories":1948},[],{"categories":1950},[],{"categories":1952},[102],{"categories":1954},[],{"categories":1956},[105],{"categories":1958},[102],{"categories":1960},[],{"categories":1962},[102],{"categories":1964},[99],{"categories":1966},[102],{"categories":1968},[161],{"categories":1970},[105],{"categories":1972},[102],{"categories":1974},[154],{"categories":1976},[123],{"categories":1978},[105],{"categories":1980},[],{"categories":1982},[123],{"categories":1984},[105],{"categories":1986},[105],{"categories":1988},[],{"categories":1990},[99],{"categories":1992},[105],{"categories":1994},[],{"categories":1996},[102],{"categories":1998},[96],{"categories":2000},[123],{"categories":2002},[416],{"categories":2004},[105],{"categories":2006},[105],{"categories":2008},[96],{"categories":2010},[102],{"categories":2012},[],{"categories":2014},[],{"categories":2016},[144],{"categories":2018},[102,99],{"categories":2020},[],{"categories":2022},[96],{"categories":2024},[147],{"categories":2026},[102],{"categories":2028},[154],{"categories":2030},[102],{"categories":2032},[105],{"categories":2034},[102],{"categories":2036},[102],{"categories":2038},[123],{"categories":2040},[105],{"categories":2042},[],{"categories":2044},[],{"categories":2046},[105],{"categories":2048},[102],{"categories":2050},[416],{"categories":2052},[],{"categories":2054},[102],{"categories":2056},[105],{"categories":2058},[],{"categories":2060},[102],{"categories":2062},[161],{"categories":2064},[147],{"categories":2066},[105],{"categories":2068},[102],{"categories":2070},[416],{"categories":2072},[],{"categories":2074},[102],{"categories":2076},[161],{"categories":2078},[144],{"categories":2080},[102],{"categories":2082},[],{"categories":2084},[161],{"categories":2086},[123],{"categories":2088},[102],{"categories":2090},[102],{"categories":2092},[96],{"categories":2094},[],{"categories":2096},[],{"categories":2098},[144],{"categories":2100},[102],{"categories":2102},[147],{"categories":2104},[161],{"categories":2106},[161],{"categories":2108},[123],{"categories":2110},[],{"categories":2112},[],{"categories":2114},[102],{"categories":2116},[],{"categories":2118},[102,154],{"categories":2120},[123],{"categories":2122},[105],{"categories":2124},[154],{"categories":2126},[102],{"categories":2128},[96],{"categories":2130},[],{"categories":2132},[],{"categories":2134},[96],{"categories":2136},[161],{"categories":2138},[102],{"categories":2140},[],{"categories":2142},[144,102],{"categories":2144},[416],{"categories":2146},[96],{"categories":2148},[],{"categories":2150},[99],{"categories":2152},[99],{"categories":2154},[102],{"categories":2156},[154],{"categories":2158},[105],{"categories":2160},[123],{"categories":2162},[161],{"categories":2164},[144],{"categories":2166},[102],{"categories":2168},[102],{"categories":2170},[102],{"categories":2172},[96],{"categories":2174},[102],{"categories":2176},[105],{"categories":2178},[123],{"categories":2180},[],{"categories":2182},[],{"categories":2184},[147],{"categories":2186},[154],{"categories":2188},[102],{"categories":2190},[144],{"categories":2192},[147],{"categories":2194},[102],{"categories":2196},[102],{"categories":2198},[105],{"categories":2200},[105],{"categories":2202},[102,99],{"categories":2204},[],{"categories":2206},[144],{"categories":2208},[],{"categories":2210},[102],{"categories":2212},[123],{"categories":2214},[96],{"categories":2216},[96],{"categories":2218},[105],{"categories":2220},[102],{"categories":2222},[99],{"categories":2224},[154],{"categories":2226},[161],{"categories":2228},[],{"categories":2230},[123],{"categories":2232},[102],{"categories":2234},[102],{"categories":2236},[123],{"categories":2238},[154],{"categories":2240},[102],{"categories":2242},[105],{"categories":2244},[123],{"categories":2246},[102],{"categories":2248},[144],{"categories":2250},[102],{"categories":2252},[102],{"categories":2254},[416],{"categories":2256},[108],{"categories":2258},[105],{"categories":2260},[102],{"categories":2262},[123],{"categories":2264},[105],{"categories":2266},[161],{"categories":2268},[102],{"categories":2270},[],{"categories":2272},[102],{"categories":2274},[],{"categories":2276},[],{"categories":2278},[],{"categories":2280},[99],{"categories":2282},[102],{"categories":2284},[105],{"categories":2286},[123],{"categories":2288},[123],{"categories":2290},[123],{"categories":2292},[123],{"categories":2294},[],{"categories":2296},[96],{"categories":2298},[105],{"categories":2300},[123],{"categories":2302},[96],{"categories":2304},[105],{"categories":2306},[102],{"categories":2308},[102,105],{"categories":2310},[105],{"categories":2312},[416],{"categories":2314},[123],{"categories":2316},[123],{"categories":2318},[105],{"categories":2320},[102],{"categories":2322},[],{"categories":2324},[123],{"categories":2326},[161],{"categories":2328},[96],{"categories":2330},[102],{"categories":2332},[102],{"categories":2334},[],{"categories":2336},[154],{"categories":2338},[],{"categories":2340},[96],{"categories":2342},[105],{"categories":2344},[123],{"categories":2346},[102],{"categories":2348},[123],{"categories":2350},[96],{"categories":2352},[123],{"categories":2354},[123],{"categories":2356},[],{"categories":2358},[99],{"categories":2360},[105],{"categories":2362},[123],{"categories":2364},[123],{"categories":2366},[123],{"categories":2368},[123],{"categories":2370},[123],{"categories":2372},[123],{"categories":2374},[123],{"categories":2376},[123],{"categories":2378},[123],{"categories":2380},[123],{"categories":2382},[147],{"categories":2384},[96],{"categories":2386},[102],{"categories":2388},[102],{"categories":2390},[],{"categories":2392},[102,96],{"categories":2394},[],{"categories":2396},[105],{"categories":2398},[123],{"categories":2400},[105],{"categories":2402},[102],{"categories":2404},[102],{"categories":2406},[102],{"categories":2408},[102],{"categories":2410},[102],{"categories":2412},[105],{"categories":2414},[99],{"categories":2416},[144],{"categories":2418},[123],{"categories":2420},[102],{"categories":2422},[],{"categories":2424},[],{"categories":2426},[105],{"categories":2428},[144],{"categories":2430},[102],{"categories":2432},[],{"categories":2434},[],{"categories":2436},[161],{"categories":2438},[102],{"categories":2440},[],{"categories":2442},[],{"categories":2444},[96],{"categories":2446},[99],{"categories":2448},[102],{"categories":2450},[99],{"categories":2452},[144],{"categories":2454},[],{"categories":2456},[123],{"categories":2458},[],{"categories":2460},[144],{"categories":2462},[102],{"categories":2464},[161],{"categories":2466},[],{"categories":2468},[161],{"categories":2470},[],{"categories":2472},[],{"categories":2474},[105],{"categories":2476},[],{"categories":2478},[99],{"categories":2480},[96],{"categories":2482},[144],{"categories":2484},[154],{"categories":2486},[],{"categories":2488},[],{"categories":2490},[102],{"categories":2492},[96],{"categories":2494},[161],{"categories":2496},[],{"categories":2498},[105],{"categories":2500},[105],{"categories":2502},[123],{"categories":2504},[102],{"categories":2506},[105],{"categories":2508},[102],{"categories":2510},[105],{"categories":2512},[102],{"categories":2514},[108],{"categories":2516},[123],{"categories":2518},[],{"categories":2520},[161],{"categories":2522},[154],{"categories":2524},[105],{"categories":2526},[],{"categories":2528},[102],{"categories":2530},[105],{"categories":2532},[99],{"categories":2534},[96],{"categories":2536},[102],{"categories":2538},[144],{"categories":2540},[154],{"categories":2542},[154],{"categories":2544},[102],{"categories":2546},[147],{"categories":2548},[102],{"categories":2550},[105],{"categories":2552},[99],{"categories":2554},[105],{"categories":2556},[102],{"categories":2558},[102],{"categories":2560},[105],{"categories":2562},[123],{"categories":2564},[],{"categories":2566},[96],{"categories":2568},[102],{"categories":2570},[105],{"categories":2572},[102],{"categories":2574},[102],{"categories":2576},[],{"categories":2578},[144],{"categories":2580},[99],{"categories":2582},[123],{"categories":2584},[102],{"categories":2586},[102],{"categories":2588},[144],{"categories":2590},[161],{"categories":2592},[147],{"categories":2594},[102],{"categories":2596},[123],{"categories":2598},[102],{"categories":2600},[105],{"categories":2602},[416],{"categories":2604},[102],{"categories":2606},[105],{"categories":2608},[147],{"categories":2610},[],{"categories":2612},[105],{"categories":2614},[154],{"categories":2616},[144],{"categories":2618},[102],{"categories":2620},[96],{"categories":2622},[99],{"categories":2624},[154],{"categories":2626},[],{"categories":2628},[105],{"categories":2630},[102],{"categories":2632},[],{"categories":2634},[123],{"categories":2636},[],{"categories":2638},[123],{"categories":2640},[102],{"categories":2642},[105],{"categories":2644},[105],{"categories":2646},[105],{"categories":2648},[],{"categories":2650},[],{"categories":2652},[102],{"categories":2654},[102],{"categories":2656},[],{"categories":2658},[144],{"categories":2660},[105],{"categories":2662},[161],{"categories":2664},[96],{"categories":2666},[],{"categories":2668},[],{"categories":2670},[123],{"categories":2672},[154],{"categories":2674},[102],{"categories":2676},[102],{"categories":2678},[102],{"categories":2680},[154],{"categories":2682},[123],{"categories":2684},[144],{"categories":2686},[102],{"categories":2688},[102],{"categories":2690},[102],{"categories":2692},[123],{"categories":2694},[102],{"categories":2696},[123],{"categories":2698},[105],{"categories":2700},[105],{"categories":2702},[154],{"categories":2704},[105],{"categories":2706},[102],{"categories":2708},[154],{"categories":2710},[144],{"categories":2712},[],{"categories":2714},[105],{"categories":2716},[],{"categories":2718},[],{"categories":2720},[99],{"categories":2722},[102],{"categories":2724},[105],{"categories":2726},[96],{"categories":2728},[105],{"categories":2730},[161],{"categories":2732},[],{"categories":2734},[105],{"categories":2736},[],{"categories":2738},[96],{"categories":2740},[105],{"categories":2742},[],{"categories":2744},[105],{"categories":2746},[102],{"categories":2748},[123],{"categories":2750},[102],{"categories":2752},[105],{"categories":2754},[123],{"categories":2756},[105],{"categories":2758},[154],{"categories":2760},[144],{"categories":2762},[96],{"categories":2764},[],{"categories":2766},[105],{"categories":2768},[144],{"categories":2770},[123],{"categories":2772},[102],{"categories":2774},[144],{"categories":2776},[96],{"categories":2778},[],{"categories":2780},[105],{"categories":2782},[105],{"categories":2784},[102],{"categories":2786},[],{"categories":2788},[105],{"categories":2790},[108],{"categories":2792},[123],{"categories":2794},[105],{"categories":2796},[99],{"categories":2798},[],{"categories":2800},[102],{"categories":2802},[108],{"categories":2804},[102],{"categories":2806},[105],{"categories":2808},[123],{"categories":2810},[96],{"categories":2812},[416],{"categories":2814},[102],{"categories":2816},[102],{"categories":2818},[102],{"categories":2820},[123],{"categories":2822},[99],{"categories":2824},[102],{"categories":2826},[144],{"categories":2828},[123],{"categories":2830},[416],{"categories":2832},[102],{"categories":2834},[],{"categories":2836},[],{"categories":2838},[416],{"categories":2840},[147],{"categories":2842},[105],{"categories":2844},[105],{"categories":2846},[123],{"categories":2848},[102],{"categories":2850},[96],{"categories":2852},[144],{"categories":2854},[105],{"categories":2856},[102],{"categories":2858},[161],{"categories":2860},[102],{"categories":2862},[105],{"categories":2864},[],{"categories":2866},[102],{"categories":2868},[102],{"categories":2870},[123],{"categories":2872},[96],{"categories":2874},[],{"categories":2876},[102],{"categories":2878},[102],{"categories":2880},[154],{"categories":2882},[144],{"categories":2884},[102,105],{"categories":2886},[161,99],{"categories":2888},[102],{"categories":2890},[],{"categories":2892},[105],{"categories":2894},[],{"categories":2896},[154],{"categories":2898},[102],{"categories":2900},[123],{"categories":2902},[],{"categories":2904},[105],{"categories":2906},[],{"categories":2908},[105],{"categories":2910},[96],{"categories":2912},[105],{"categories":2914},[102],{"categories":2916},[416],{"categories":2918},[161],{"categories":2920},[99],{"categories":2922},[99],{"categories":2924},[96],{"categories":2926},[96],{"categories":2928},[102],{"categories":2930},[105],{"categories":2932},[102],{"categories":2934},[102],{"categories":2936},[96],{"categories":2938},[102],{"categories":2940},[161],{"categories":2942},[123],{"categories":2944},[102],{"categories":2946},[105],{"categories":2948},[102],{"categories":2950},[],{"categories":2952},[154],{"categories":2954},[],{"categories":2956},[105],{"categories":2958},[96],{"categories":2960},[],{"categories":2962},[416],{"categories":2964},[102],{"categories":2966},[],{"categories":2968},[123],{"categories":2970},[105],{"categories":2972},[154],{"categories":2974},[102],{"categories":2976},[105],{"categories":2978},[154],{"categories":2980},[105],{"categories":2982},[123],{"categories":2984},[96],{"categories":2986},[123],{"categories":2988},[154],{"categories":2990},[102],{"categories":2992},[144],{"categories":2994},[102],{"categories":2996},[102],{"categories":2998},[102],{"categories":3000},[102],{"categories":3002},[105],{"categories":3004},[102],{"categories":3006},[105],{"categories":3008},[102],{"categories":3010},[96],{"categories":3012},[102],{"categories":3014},[105],{"categories":3016},[144],{"categories":3018},[96],{"categories":3020},[105],{"categories":3022},[144],{"categories":3024},[],{"categories":3026},[102],{"categories":3028},[102],{"categories":3030},[154],{"categories":3032},[],{"categories":3034},[105],{"categories":3036},[161],{"categories":3038},[102],{"categories":3040},[123],{"categories":3042},[161],{"categories":3044},[105],{"categories":3046},[99],{"categories":3048},[99],{"categories":3050},[102],{"categories":3052},[96],{"categories":3054},[],{"categories":3056},[102],{"categories":3058},[],{"categories":3060},[96],{"categories":3062},[102],{"categories":3064},[105],{"categories":3066},[105],{"categories":3068},[],{"categories":3070},[154],{"categories":3072},[154],{"categories":3074},[161],{"categories":3076},[144],{"categories":3078},[],{"categories":3080},[102],{"categories":3082},[96],{"categories":3084},[102],{"categories":3086},[154],{"categories":3088},[96],{"categories":3090},[123],{"categories":3092},[123],{"categories":3094},[],{"categories":3096},[123],{"categories":3098},[105],{"categories":3100},[144],{"categories":3102},[147],{"categories":3104},[102],{"categories":3106},[],{"categories":3108},[123],{"categories":3110},[154],{"categories":3112},[99],{"categories":3114},[102],{"categories":3116},[96],{"categories":3118},[416],{"categories":3120},[96],{"categories":3122},[],{"categories":3124},[],{"categories":3126},[123],{"categories":3128},[],{"categories":3130},[105],{"categories":3132},[105],{"categories":3134},[105],{"categories":3136},[],{"categories":3138},[102],{"categories":3140},[],{"categories":3142},[123],{"categories":3144},[96],{"categories":3146},[144],{"categories":3148},[102],{"categories":3150},[123],{"categories":3152},[123],{"categories":3154},[],{"categories":3156},[123],{"categories":3158},[96],{"categories":3160},[102],{"categories":3162},[],{"categories":3164},[105],{"categories":3166},[105],{"categories":3168},[96],{"categories":3170},[],{"categories":3172},[],{"categories":3174},[],{"categories":3176},[144],{"categories":3178},[105],{"categories":3180},[102],{"categories":3182},[],{"categories":3184},[],{"categories":3186},[],{"categories":3188},[144],{"categories":3190},[],{"categories":3192},[96],{"categories":3194},[],{"categories":3196},[],{"categories":3198},[144],{"categories":3200},[102],{"categories":3202},[123],{"categories":3204},[],{"categories":3206},[161],{"categories":3208},[123],{"categories":3210},[161],{"categories":3212},[102],{"categories":3214},[],{"categories":3216},[],{"categories":3218},[105],{"categories":3220},[],{"categories":3222},[],{"categories":3224},[105],{"categories":3226},[102],{"categories":3228},[],{"categories":3230},[105],{"categories":3232},[123],{"categories":3234},[161],{"categories":3236},[147],{"categories":3238},[105],{"categories":3240},[105],{"categories":3242},[],{"categories":3244},[],{"categories":3246},[],{"categories":3248},[123],{"categories":3250},[],{"categories":3252},[],{"categories":3254},[144],{"categories":3256},[96],{"categories":3258},[],{"categories":3260},[99],{"categories":3262},[161],{"categories":3264},[102],{"categories":3266},[154],{"categories":3268},[96],{"categories":3270},[147],{"categories":3272},[99],{"categories":3274},[154],{"categories":3276},[],{"categories":3278},[],{"categories":3280},[105],{"categories":3282},[96],{"categories":3284},[144],{"categories":3286},[96],{"categories":3288},[105],{"categories":3290},[416],{"categories":3292},[105],{"categories":3294},[],{"categories":3296},[102],{"categories":3298},[123],{"categories":3300},[154],{"categories":3302},[],{"categories":3304},[144],{"categories":3306},[123],{"categories":3308},[96],{"categories":3310},[105],{"categories":3312},[102],{"categories":3314},[99],{"categories":3316},[105,416],{"categories":3318},[105],{"categories":3320},[154],{"categories":3322},[102],{"categories":3324},[147],{"categories":3326},[161],{"categories":3328},[105],{"categories":3330},[],{"categories":3332},[105],{"categories":3334},[102],{"categories":3336},[99],{"categories":3338},[],{"categories":3340},[],{"categories":3342},[102],{"categories":3344},[147],{"categories":3346},[102],{"categories":3348},[],{"categories":3350},[123],{"categories":3352},[],{"categories":3354},[123],{"categories":3356},[154],{"categories":3358},[105],{"categories":3360},[102],{"categories":3362},[161],{"categories":3364},[154],{"categories":3366},[],{"categories":3368},[123],{"categories":3370},[102],{"categories":3372},[],{"categories":3374},[102],{"categories":3376},[105],{"categories":3378},[102],{"categories":3380},[105],{"categories":3382},[102],{"categories":3384},[102],{"categories":3386},[102],{"categories":3388},[102],{"categories":3390},[99],{"categories":3392},[],{"categories":3394},[108],{"categories":3396},[123],{"categories":3398},[102],{"categories":3400},[],{"categories":3402},[154],{"categories":3404},[102],{"categories":3406},[102],{"categories":3408},[105],{"categories":3410},[123],{"categories":3412},[102],{"categories":3414},[102],{"categories":3416},[99],{"categories":3418},[105],{"categories":3420},[144],{"categories":3422},[],{"categories":3424},[147],{"categories":3426},[102],{"categories":3428},[],{"categories":3430},[123],{"categories":3432},[161],{"categories":3434},[],{"categories":3436},[],{"categories":3438},[123],{"categories":3440},[123],{"categories":3442},[161],{"categories":3444},[96],{"categories":3446},[105],{"categories":3448},[105],{"categories":3450},[102],{"categories":3452},[99],{"categories":3454},[],{"categories":3456},[],{"categories":3458},[123],{"categories":3460},[147],{"categories":3462},[154],{"categories":3464},[105],{"categories":3466},[144],{"categories":3468},[147],{"categories":3470},[147],{"categories":3472},[],{"categories":3474},[123],{"categories":3476},[102],{"categories":3478},[102],{"categories":3480},[154],{"categories":3482},[],{"categories":3484},[123],{"categories":3486},[123],{"categories":3488},[123],{"categories":3490},[],{"categories":3492},[105],{"categories":3494},[102],{"categories":3496},[],{"categories":3498},[96],{"categories":3500},[99],{"categories":3502},[],{"categories":3504},[102],{"categories":3506},[102],{"categories":3508},[],{"categories":3510},[154],{"categories":3512},[],{"categories":3514},[],{"categories":3516},[],{"categories":3518},[],{"categories":3520},[102],{"categories":3522},[123],{"categories":3524},[],{"categories":3526},[],{"categories":3528},[102],{"categories":3530},[102],{"categories":3532},[102],{"categories":3534},[147],{"categories":3536},[102],{"categories":3538},[147],{"categories":3540},[],{"categories":3542},[147],{"categories":3544},[147],{"categories":3546},[416],{"categories":3548},[105],{"categories":3550},[154],{"categories":3552},[],{"categories":3554},[],{"categories":3556},[147],{"categories":3558},[154],{"categories":3560},[154],{"categories":3562},[154],{"categories":3564},[],{"categories":3566},[96],{"categories":3568},[154],{"categories":3570},[154],{"categories":3572},[96],{"categories":3574},[154],{"categories":3576},[99],{"categories":3578},[154],{"categories":3580},[154],{"categories":3582},[154],{"categories":3584},[147],{"categories":3586},[123],{"categories":3588},[123],{"categories":3590},[102],{"categories":3592},[154],{"categories":3594},[147],{"categories":3596},[416],{"categories":3598},[147],{"categories":3600},[147],{"categories":3602},[147],{"categories":3604},[],{"categories":3606},[99],{"categories":3608},[],{"categories":3610},[416],{"categories":3612},[154],{"categories":3614},[154],{"categories":3616},[154],{"categories":3618},[105],{"categories":3620},[123,99],{"categories":3622},[147],{"categories":3624},[],{"categories":3626},[],{"categories":3628},[147],{"categories":3630},[],{"categories":3632},[147],{"categories":3634},[123],{"categories":3636},[105],{"categories":3638},[],{"categories":3640},[154],{"categories":3642},[102],{"categories":3644},[144],{"categories":3646},[],{"categories":3648},[102],{"categories":3650},[],{"categories":3652},[123],{"categories":3654},[96],{"categories":3656},[147],{"categories":3658},[],{"categories":3660},[154],{"categories":3662},[123],[3664,3747,3827,3906],{"id":3665,"title":3666,"ai":3667,"body":3672,"categories":3722,"created_at":50,"date_modified":50,"description":43,"extension":51,"faq":50,"featured":52,"kicker_label":50,"meta":3723,"navigation":75,"path":3734,"published_at":3735,"question":50,"scraped_at":3736,"seo":3737,"sitemap":3738,"source_id":3739,"source_name":3740,"source_type":83,"source_url":3741,"stem":3742,"tags":3743,"thumbnail_url":50,"tldr":3744,"tweet":50,"unknown_tags":3745,"__hash__":3746},"summaries\u002Fsummaries\u002F1dcaa9cf36eee656-blt-cuts-inference-bandwidth-50-92-via-diffusion-s-summary.md","BLT Cuts Inference Bandwidth 50-92% via Diffusion & Speculation",{"provider":7,"model":8,"input_tokens":3668,"output_tokens":3669,"processing_time_ms":3670,"cost_usd":3671},8589,2722,30748,0.00305615,{"type":14,"value":3673,"toc":3716},[3674,3678,3681,3685,3693,3696,3700,3703,3706,3709,3713],[17,3675,3677],{"id":3676},"blts-memory-bandwidth-bottleneck-in-byte-level-generation","BLT's Memory Bandwidth Bottleneck in Byte-Level Generation",[22,3679,3680],{},"Byte-level models like BLT avoid tokenization pitfalls—noise sensitivity, poor multilingual support, weak character\u002Fcode handling—by processing raw bytes via entropy-based patches (avg 4 bytes, max 8). Computation uses local encoder, global Transformer, local decoder on latent tokens. Inference slows because autoregressive decoder generates one byte\u002Fstep, vs. tokens covering multiple bytes. This multiplies memory loads for weights\u002FKV caches, the key serving bottleneck. BLT needs 4x more decoder passes than token models for equivalent text, hiking bandwidth costs.",[17,3682,3684],{"id":3683},"block-diffusion-enables-multi-byte-decoding-per-pass-blt-d","Block Diffusion Enables Multi-Byte Decoding per Pass (BLT-D)",[22,3686,3687,3688,3692],{},"BLT-D replaces byte-by-byte autoregression with discrete diffusion in fixed blocks (B=4\u002F8\u002F16 bytes). Training: corrupt blocks by masking bytes independently with prob t~U(0,1); loss combines next-byte prediction on clean seq + masked prediction on corrupted. Inference: start with ",[3689,3690,3691],"span",{},"MASK"," block, iteratively unmask multiple bytes\u002Fpass via confidence (prob>α) or entropy-bounded (cumulative entropy\u003Cγ) sampling. Encoder\u002Fglobal called once\u002Fblock, not per-patch; supports KV caching.",[22,3694,3695],{},"At 3B params on BLT-1T (1T tokens), BLT-D-4 matches BLT scores on FLORES-101 translation (French\u002FEnglish, German\u002FEnglish; 4-shot BLEU), nears on HumanEval\u002FMBPP coding (0\u002F3-shot pass@1). BLT-D-16 cuts bandwidth 87-92% but drops coding pass@1. Likelihoods (ARC-Easy\u002FChallenge, PIQA, HellaSwag, MMLU) near baseline via causal-masked decoder. Translation gains most; coding sensitive to block size. Entropy-bounded + top-p boosts diversity (higher type-token ratio) as NFEs rise.",[17,3697,3699],{"id":3698},"no-training-speculation-recycles-existing-decoder-blt-s-blt-dv","No-Training Speculation Recycles Existing Decoder (BLT-S, BLT-DV)",[22,3701,3702],{},"BLT-S uses lightweight decoder as self-drafter: generate k=8\u002F16 bytes ignoring patch boundaries, conditioning on last latent; verify via full encode\u002Fglobal\u002Fdecode, accept to first mismatch. Greedy decoding guarantees identical output to BLT (no quality loss); reduces encoder\u002Fglobal calls despite more decoder passes. At 3B\u002Fk=16, 77% bandwidth cut.",[22,3704,3705],{},"BLT-DV (on BLT-D weights): one-step diffusion drafts block, autoregressive verify accepts to mismatch. Single-step diffusion degrades alone but verification fixes it. At 3B, up to 81% bandwidth reduction.",[22,3707,3708],{},"All trained 1B:240k steps, 3B:480k on BLT-1T (public + Datacomp-LM subset). Efficiency proxies: decoder\u002Fencoder NFEs, GB bandwidth (16-bit, param\u002Fforward counts). Wall-clock needs optimized serving.",[17,3710,3712],{"id":3711},"practical-tradeoffs-for-production-deployment","Practical Tradeoffs for Production Deployment",[22,3714,3715],{},"BLT-D fastest (esp B=16) but coding tradeoffs; BLT-S zero-loss safest. All preserve autoregressive likelihoods\u002Freasoning. Bandwidth proxies predict real gains in memory-bound serving. Future: optimized inference impl. Byte-level now viable for production-scale speed without tokenizer fragility.",{"title":43,"searchDepth":44,"depth":44,"links":3717},[3718,3719,3720,3721],{"id":3676,"depth":44,"text":3677},{"id":3683,"depth":44,"text":3684},{"id":3698,"depth":44,"text":3699},{"id":3711,"depth":44,"text":3712},[102],{"content_references":3724,"triage":3732},[3725,3729],{"type":56,"title":3726,"url":3727,"context":3728},"Fast Byte Latent Transformer That Reduces Inference Memory Bandwidth by Over 50% Without Tokenization","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2605.08044","recommended",{"type":56,"title":3730,"url":3731,"context":60},"Byte Latent Transformer (BLT): A Tokenizer-Free Model That Scales Efficiently","https:\u002F\u002Fwww.marktechpost.com\u002F2024\u002F12\u002F13\u002Fmeta-ai-introduces-byte-latent-transformer-blt-a-tokenizer-free-model-that-scales-efficiently\u002F",{"relevance":71,"novelty":72,"quality":72,"actionability":44,"composite":73,"reasoning":3733},"Category: AI & LLMs. The article discusses a new approach to improving inference bandwidth in AI models, which is relevant to AI engineering. However, it lacks practical applications or frameworks that the audience can directly implement, focusing instead on theoretical advancements.","\u002Fsummaries\u002F1dcaa9cf36eee656-blt-cuts-inference-bandwidth-50-92-via-diffusion-s-summary","2026-05-11 17:52:15","2026-05-12 15:01:28",{"title":3666,"description":43},{"loc":3734},"1dcaa9cf36eee656","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F11\u002Fmeta-and-stanford-researchers-propose-fast-byte-latent-transformer-that-reduces-inference-memory-bandwidth-by-over-50-without-tokenization\u002F","summaries\u002F1dcaa9cf36eee656-blt-cuts-inference-bandwidth-50-92-via-diffusion-s-summary",[87,88,89],"Meta\u002FStanford researchers accelerate Byte Latent Transformer (BLT) inference with BLT-D (diffusion decoding), BLT-S (self-speculation), and BLT-DV (diffusion+verification), reducing memory bandwidth 50-92% at 3B params while nearing baseline performance on translation\u002Fcoding tasks.",[],"xMZyx1diuvh2XXZUy_NPhOgWy_XqDJeXjel738dmvjs",{"id":3748,"title":3749,"ai":3750,"body":3755,"categories":3797,"created_at":50,"date_modified":50,"description":43,"extension":51,"faq":50,"featured":52,"kicker_label":50,"meta":3798,"navigation":75,"path":3815,"published_at":50,"question":50,"scraped_at":3816,"seo":3817,"sitemap":3818,"source_id":3819,"source_name":3820,"source_type":83,"source_url":3821,"stem":3822,"tags":3823,"thumbnail_url":50,"tldr":3824,"tweet":50,"unknown_tags":3825,"__hash__":3826},"summaries\u002Fsummaries\u002Fd445780e74d7b6ed-llm-pretraining-scaling-fsdp-wins-until-comms-crat-summary.md","LLM Pretraining Scaling: FSDP Wins Until Comms Crater",{"provider":7,"model":8,"input_tokens":3751,"output_tokens":3752,"processing_time_ms":3753,"cost_usd":3754},8296,2378,19998,0.00282555,{"type":14,"value":3756,"toc":3791},[3757,3761,3764,3767,3771,3774,3778,3781,3785,3788],[17,3758,3760],{"id":3759},"fsdp-dominates-parallelism-until-scale-forces-pipeline-trade-offs","FSDP Dominates Parallelism Until Scale Forces Pipeline Trade-offs",[22,3762,3763],{},"Pretraining FLOPs = 6ND (2 forward + 4 backward per param-token). Data parallel (DP) copies weights across GPUs but hits HBM limits (B300: 288GB). Fully Sharded Data Parallel (FSDP) shards params per layer across GPUs, all-gathering full weights per layer (forward\u002Fbackward) while overlapping comms with compute since weights are layer-independent. FSDP comms: params×3 (all-gather forward\u002Fback + reduce-scatter backward), 50% over DP's params×2 all-reduce—achievable because all-gather is half an all-reduce. Use hierarchical collectives across NVLink domains: reduce-scatter intra-domain, all-reduce shards inter-domain, all-gather intra-domain to saturate IB bandwidth.",[22,3765,3766],{},"Comms time stays flat with GPU count (ring all-reduce chunks scale inversely with participants), but compute drops linearly, cratering MFU at 'crossover' (comms > compute). Delay crossover by larger batches (more compute\u002FGPU) or sparser models; TPUs excel with bigger domains. Batch size floors FSDP at ~1K GPUs (e.g., 10M-token batch, 10K seq len = 1K seqs). Add pipeline parallelism (PP) next, but it introduces bubbles (idle GPUs at batch start\u002Fend) unfillable in training due to per-batch gradient sync. PP constrains architecture (e.g., Kimi's cross-layer attention, mixed attention types cause stage imbalance), slowing research.",[17,3768,3770],{"id":3769},"distillation-remains-cheap-and-evasion-proof","Distillation Remains Cheap and Evasion-Proof",[22,3772,3773],{},"Frontier labs can't halt distillation: 1T tokens from Opus 4.6 costs $25M ($25\u002FMTok), commoditizing open models rapidly (cf. Fineweb 18.5T, OpenWebText 9B). Hiding chain-of-thought (CoT) fails—instruct no-think\u002Fdirect solve or RLVR on reconstructed CoT. Core value in local tool use (file edits, bash) evades cloud hiding; users resist workflow migration. Products atop APIs distill better: reward 'gold diffs' (final user-accepted code) over rejected intermediates from 10+ turn sessions.",[17,3775,3777],{"id":3776},"agentic-ai-shifts-cybersecurity-toward-defense","Agentic AI Shifts Cybersecurity Toward Defense",[22,3779,3780],{},"Mythos chains 5+ vulns into exploits (vs. prior single-vuln finds), but software is securer now despite human probing—sudden AI intelligence influx likely strengthens defense via industry patching (e.g., Glasswing reveals zero-days). AI excels at vuln finding over patching (XKCD: fixes break edge cases\u002Ffeatures). Solutions: LLM-port C to Rust; formal verification (e.g., seL4 proofs); patching mirrors LLM bug-finding in others' repos. Hoarding Mythos risky—build\u002Frelease classifiers rejecting cyberattack intents (Anthropic plans for 4.7). Evade classifiers by subproblems (harmless vulns). Patching own code routine for coding LLMs.",[17,3782,3784],{"id":3783},"pipeline-rl-fixes-stragglers-causalitybias-dooms-runs","Pipeline RL Fixes Stragglers; Causality\u002FBias Dooms Runs",[22,3786,3787],{},"RL responses grow in mean\u002Fvariance length, straggling GPU utilization. Pipeline RL does 'in-flight weight updates': swap generating model mid-trajectory post-training step, ensuring recent-model rollouts without full offline RL off-policyness.",[22,3789,3790],{},"Pretraining fails via causality breaks (MoE expert-choice routes token n+k affecting n; token-dropping ignores early for later matches—rumored Llama 4\u002FGemini 2 flops) or bias (FP16 collectives round large sums wrong, e.g., post-1024 granularity skips +1; GPT-4 initial bug). Bias compounds > variance. New scale unveils bespoke issues (numerics, kernels)—not 5 fixable failure modes. RL inference needs training-engine fidelity (numerical drift biases); enforce disciplined compute multipliers to avoid bug stacks. Kernel optimization AGI-hard (Nvidia took ages for Blackwell).",{"title":43,"searchDepth":44,"depth":44,"links":3792},[3793,3794,3795,3796],{"id":3759,"depth":44,"text":3760},{"id":3769,"depth":44,"text":3770},{"id":3776,"depth":44,"text":3777},{"id":3783,"depth":44,"text":3784},[],{"content_references":3799,"triage":3812},[3800,3804,3807],{"type":3801,"title":3802,"url":3803,"context":69},"podcast","Conversation with Michael Nielsen","https:\u002F\u002Fwww.dwarkesh.com\u002Fp\u002Fmichael-nielsen",{"type":56,"title":3805,"url":3806,"context":60},"Pipeline RL","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.19128",{"type":3808,"title":3809,"author":3810,"url":3811,"context":69},"other","Pretraining parallelisms lecture","Horace He","https:\u002F\u002Fhorace.io\u002F",{"relevance":72,"novelty":71,"quality":72,"actionability":44,"composite":3813,"reasoning":3814},3.4,"Category: AI & LLMs. The article discusses the practical application of Fully Sharded Data Parallel (FSDP) for scaling pretraining in LLMs, which addresses a specific pain point for AI developers regarding efficient model training. However, while it provides technical insights, it lacks concrete actionable steps that the audience could directly implement.","\u002Fsummaries\u002Fd445780e74d7b6ed-llm-pretraining-scaling-fsdp-wins-until-comms-crat-summary","2026-04-19 01:22:25",{"title":3749,"description":43},{"loc":3815},"d445780e74d7b6ed","Dwarkesh Patel","https:\u002F\u002Fwww.dwarkesh.com\u002Fp\u002Fwhat-i-learned-april-15","summaries\u002Fd445780e74d7b6ed-llm-pretraining-scaling-fsdp-wins-until-comms-crat-summary",[87,88,89],"Use FSDP as default for scaling pretraining (params×3 comms overhead) until GPU count hits comms crossover; distillation costs $25M\u002FT from frontier models, unstoppable via tool use; training fails from causality breaks and FP16 bias.",[],"UCftWL3lVDs_ij_juNq8mtYfE_yqIH5SLhHL1KTHG3s",{"id":3828,"title":3829,"ai":3830,"body":3835,"categories":3869,"created_at":50,"date_modified":50,"description":43,"extension":51,"faq":50,"featured":52,"kicker_label":50,"meta":3870,"navigation":75,"path":3893,"published_at":3894,"question":50,"scraped_at":3894,"seo":3895,"sitemap":3896,"source_id":3897,"source_name":3898,"source_type":83,"source_url":3899,"stem":3900,"tags":3901,"thumbnail_url":50,"tldr":3903,"tweet":50,"unknown_tags":3904,"__hash__":3905},"summaries\u002Fsummaries\u002Ffd797e93058cd1d0-parameter-golf-creativity-in-tiny-ml-models-summary.md","Parameter Golf: Creativity in Tiny ML Models",{"provider":7,"model":8,"input_tokens":3831,"output_tokens":3832,"processing_time_ms":3833,"cost_usd":3834},6948,2080,34202,0.00240695,{"type":14,"value":3836,"toc":3864},[3837,3841,3844,3847,3851,3854,3857,3861],[17,3838,3840],{"id":3839},"tight-constraints-spark-technical-innovation","Tight Constraints Spark Technical Innovation",[22,3842,3843],{},"Parameter Golf required minimizing held-out loss on FineWeb dataset within a 16 MB limit for model weights plus training code and 10 minutes on 8 H100s. This setup rewarded creativity: record-track leaders combined optimizer tuning (e.g., Muon weight decay, spectral embedding init, residual-mix scheduling in #60 by @notapplica), quantization (GPTQ-lite in #414 by @signalrush; full Hessian GPTQ in #1060 by @dexhunter), test-time adaptation (per-document LoRA in #77 by @samacqua; self-generated calibration in #1019 by @abaybektursun), and novel ideas like CaseOps tokenizer (#1729 by @romeerp), XSA attention (#265 by @unnir), SmearGate\u002FBigramHash features (#65 by @aquariouseworkman), and mini depth recurrence (#1204 by @msisovic). Nonrecord track saw alternatives like state-space models, JEPA, Designator attention, and byte-level H-Net beat the 1.22 BPB baseline, with top at 1.12 BPB, proving non-transformers viable under constraints.",[22,3845,3846],{},"These approaches show disciplined stacking of prior wins outperforms isolated changes, while pushing quantization and eval edges demands organizer scrutiny to stay rule-compliant.",[17,3848,3850],{"id":3849},"ai-coding-agents-transform-competitions","AI Coding Agents Transform Competitions",[22,3852,3853],{},"Agents slashed experimentation costs, enabling rapid setup, code inspection, and idea testing—most submitters used them, amplified by RunPod's $1M compute sponsorship. This lowered entry barriers, sped community progress (e.g., @notapplica's agent-run Live Updates bulletin explained leaderboards), and surfaced talent. Drawbacks: submission noise from agent-copied invalid tweaks, requiring a Codex-based triage bot to flag hundreds of daily PRs for review. Agents fostered community tools for rule-checking, but many top scores iterated small changes on leaders rather than breakthroughs.",[22,3855,3856],{},"Net effect: agents make open challenges more accessible and dynamic, shifting focus from implementation friction to taste and persistence, though they demand automated review scaling.",[17,3858,3860],{"id":3859},"implications-for-future-ml-research","Implications for Future ML Research",[22,3862,3863],{},"The 8-week event validated constrained problems for talent discovery and idea surfacing, with verified record-breakers spanning tuning to from-scratch features. Organizers reproduced all leaderboard entries, confirming timeliness. Alternatives held against transformers, hinting agents cheapen prototyping risky architectures. OpenAI plans more challenges; eligible participants can join via form for updates.",{"title":43,"searchDepth":44,"depth":44,"links":3865},[3866,3867,3868],{"id":3839,"depth":44,"text":3840},{"id":3849,"depth":44,"text":3850},{"id":3859,"depth":44,"text":3860},[123],{"content_references":3871,"triage":3890},[3872,3875,3878,3881,3884,3887],{"type":3808,"title":3873,"url":3874,"context":69},"Parameter Golf GitHub Repo","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fparameter-golf",{"type":3808,"title":3876,"url":3877,"context":69},"OpenAI Model Craft Parameter Golf Challenge Terms and Conditions","https:\u002F\u002Fcdn.openai.com\u002Fpdf\u002Fd5caec5a-ee81-419d-b0d7-39f1424d819c\u002FOpenAI%20Model%20Craft_%20Parameter%20Golf%20Challenge%20Terms%20and%20Conditions.pdf",{"type":3808,"title":3879,"url":3880,"context":3728},"Challenge Participant Form","https:\u002F\u002Fjobs.ashbyhq.com\u002Fopenai\u002Fform\u002Fopen-ai-challenge-parameter-golf",{"type":3808,"title":3882,"url":3883,"context":3728},"CiprianFlorim-Ifrim’s combination state-space model and JEPA submission","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fparameter-golf\u002Fblob\u002Fmain\u002Frecords\u002Ftrack_non_record_16mb\u002F2026-03-26_37M_LeWM_Jepa_Mamba2_10L_UNet_INT4FP8QAT_Brotli\u002FREADME.md",{"type":3808,"title":3885,"url":3886,"context":3728},"ddavidgao’s Designator\u002FGuided Attention submission","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fparameter-golf\u002Fblob\u002Fmain\u002Frecords\u002Ftrack_non_record_16mb\u002F2026-03-23_DGAttention_DavidGao\u002FREADME.md",{"type":3808,"title":3888,"url":3889,"context":3728},"DariusFeher’s Byte-Level H-Net submission","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fparameter-golf\u002Fblob\u002Fmain\u002Frecords\u002Ftrack_non_record_16mb\u002F2026-03-29_HNet_ByteVsSubword_Study\u002FREADME.md",{"relevance":72,"novelty":71,"quality":72,"actionability":71,"composite":3891,"reasoning":3892},3.6,"Category: AI & LLMs. The article discusses the Parameter Golf challenge, which highlights practical innovations in model optimization and the role of AI agents in enhancing research efficiency, addressing the audience's interest in actionable AI techniques. It provides specific examples of techniques used in the challenge, though it lacks a clear step-by-step guide for implementation.","\u002Fsummaries\u002Ffd797e93058cd1d0-parameter-golf-creativity-in-tiny-ml-models-summary","2026-05-13 12:01:01",{"title":3829,"description":43},{"loc":3893},"fd797e93058cd1d0","OpenAI News","https:\u002F\u002Fopenai.com\u002Findex\u002Fwhat-parameter-golf-taught-us","summaries\u002Ffd797e93058cd1d0-parameter-golf-creativity-in-tiny-ml-models-summary",[88,3902,89,87],"agents","OpenAI's 16MB\u002F10-min ML challenge drew 1,000+ participants and 2,000+ submissions, showcasing optimizations, quantization, novel architectures, and AI agents' role in accelerating research while creating review challenges.",[],"BTcH2ww5JGpqfKFVPggtTCqjhlqMca7zmRGWQP1Oiug",{"id":3907,"title":3908,"ai":3909,"body":3914,"categories":3999,"created_at":50,"date_modified":50,"description":43,"extension":51,"faq":50,"featured":52,"kicker_label":50,"meta":4000,"navigation":75,"path":4018,"published_at":4019,"question":50,"scraped_at":4020,"seo":4021,"sitemap":4022,"source_id":4023,"source_name":4024,"source_type":83,"source_url":4025,"stem":4026,"tags":4027,"thumbnail_url":50,"tldr":4029,"tweet":50,"unknown_tags":4030,"__hash__":4031},"summaries\u002Fsummaries\u002F0120dc1c893f4e5c-visual-primitives-solve-lmm-reference-gap-summary.md","Visual Primitives Solve LMM Reference Gap",{"provider":7,"model":8,"input_tokens":3910,"output_tokens":3911,"processing_time_ms":3912,"cost_usd":3913},8697,2172,36756,0.00280275,{"type":14,"value":3915,"toc":3993},[3916,3920,3923,3942,3945,3949,3952,3955,3958,3962,3965,3968,3971,3974,3977,3980,3983,3987,3990],[17,3917,3919],{"id":3918},"embed-coordinates-as-core-reasoning-units-to-eliminate-reference-gap","Embed Coordinates as Core Reasoning Units to Eliminate Reference Gap",[22,3921,3922],{},"Current large multimodal models (LMMs) suffer from a 'Reference Gap': natural language can't precisely pinpoint visual entities, causing failures in dense counting, multi-step spatial reasoning, and tracking. For example, asking 'What is the leftmost bird doing?' among 50 birds forces vague descriptions like 'gray bird near left edge,' collapsing logic chains.",[22,3924,3925,3926,3929,3930,3933,3934,3937,3938,3941],{},"DeepSeek's solution elevates bounding boxes (",[3689,3927,3928],{},"x1,y1,x2,y2",") and points (",[3689,3931,3932],{},"x,y",") from final outputs to 'visual primitives'—minimum units of thought. The model outputs coordinates inline during reasoning: 'I see a ",[3689,3935,3936],{},"452,23,804,411"," climbing a tree (exclude); ",[3689,3939,3940],{},"50,447,647,771"," on ground (include).' This anchors every step visually, mimicking human pointing while scanning, preventing lost tracks in dense scenes.",[22,3943,3944],{},"Built on DeepSeek-V4-Flash with DeepSeek-ViT vision encoder in LLaVA-style architecture (ViT features + LLM), it follows standard fusion but innovates in reasoning paradigm.",[17,3946,3948],{"id":3947},"achieve-7056x-token-compression-with-no-capability-loss","Achieve 7056x Token Compression with No Capability Loss",[22,3950,3951],{},"Processing an 800x800 image yields 2,916 patch tokens, bloating KV cache and slowing inference. DeepSeek applies two-stage compression: spatial (3x3 patches to 1 token, 2,916 → 324) + DeepSeek-V4-Flash's 4x Compressed Sparse Attention (324 → 81 tokens, ~90 KV slots total). Result: 7056x overall compression.",[22,3953,3954],{},"Comparisons: Gemma-4-31B (289 tokens), GPT-4o (740? note: likely GPT-4 variants), Claude-3.5-Sonnet (870? labeled 4.6), Gemini-1.5-Flash (1,100). DeepSeek uses 1\u002F10th of Claude's tokens.",[22,3956,3957],{},"Performance holds: 77.2% average across 7 benchmarks (counting, spatial reasoning, maze navigation, path tracking), beating GPT-4o (71.1%), Claude-3.5-Sonnet (65.3%), Gemini-1.5-Flash (76.5%). Excels in multi-step tasks: maze navigation 66.9% (vs GPT-4o 50.6%), path tracking 56.7% (vs 46.5%), Pixmo-Count 89.2% (vs Gemini 88.2%), fine-grained counting 88.7% (vs Qwen2-VL 87.2%).",[17,3959,3961],{"id":3960},"five-step-training-pipeline-yields-unified-spatial-expert","Five-Step Training Pipeline Yields Unified Spatial Expert",[22,3963,3964],{},"Pre-training: Crawl 97,984 bounding box sources (HuggingFace etc.), filter via Semantic Review (MLLM checks labels for nonsense\u002Fambiguity\u002Fharm) + Geometric Review (valid framing, no truncation\u002Fgiant boxes >90% area), retaining 31,701 sources → 40M+ samples.",[22,3966,3967],{},"SFT: Train separate box\u002Fpoint experts to avoid conflicts on small data.",[22,3969,3970],{},"RL: GRPO with 3 rewards—format (correct syntax, no duplicates\u002Floops), quality (LLM-judged reasoning), accuracy (task-specific: counting reward = 1 \u002F (1 + |pred - gt| \u002F (gt + 1)) with α=0.7, β=3 for dense tolerance; maze: causal progress ratio + completeness, truncating illegal wall-passes).",[22,3972,3973],{},"Rejection Fine-Tuning: Merge experts.",[22,3975,3976],{},"On-Policy Distillation: Experts teach student via full-vocab logits + reverse KL (peaks multimodal distributions, cuts hallucinations).",[22,3978,3979],{},"Evaluations span counting (coarse\u002Ffine-grained, anchor per object), spatial reasoning (multi-hop\u002Fembodied), mazes (grids\u002Fcircles\u002Fhoneycombs, including unsolvable), path tracking (curvature at colorless intersections).",[22,3981,3982],{},"Real tasks shine: distinguishes Chihuahuas from muffins via semantics + boxes; infers gummy bear heavier than cabinet from scale tilt; links Golden Gate box to Warriors NBA team; diagrams latte steps on espresso machine photo.",[17,3984,3986],{"id":3985},"outperforms-language-only-and-auxiliary-grounding-paradigms","Outperforms Language-Only and Auxiliary Grounding Paradigms",[22,3988,3989],{},"Text-only CoT (GPT-4V\u002FClaude3) fails on ambiguity. High-res cropping (InternVL) clarifies but can't cross-patch reference. Post-verification (GRIT\u002FDeepEyesV2) verifies linguistically. VGR aids but subordinates visuals.",[22,3991,3992],{},"DeepSeek makes primitives intrinsic: point-while-thinking drives reasoning, unlike Argus (2025 paper, arXiv:2505.23766) which explores architecture less deeply on data\u002Frewards.",{"title":43,"searchDepth":44,"depth":44,"links":3994},[3995,3996,3997,3998],{"id":3918,"depth":44,"text":3919},{"id":3947,"depth":44,"text":3948},{"id":3960,"depth":44,"text":3961},{"id":3985,"depth":44,"text":3986},[],{"content_references":4001,"triage":4016},[4002,4005,4008,4010,4012],{"type":56,"title":4003,"url":4004,"context":69},"Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.23766",{"type":4006,"title":4007,"context":69},"dataset","COCO",{"type":4006,"title":4009,"context":69},"Pixmo-Points",{"type":4006,"title":4011,"context":69},"Pixmo-Count",{"type":4013,"title":4014,"author":4015,"context":69},"tool","InternVL","Shanghai AI Laboratory",{"relevance":71,"novelty":72,"quality":72,"actionability":44,"composite":73,"reasoning":4017},"Category: AI & LLMs. The article discusses a novel approach to addressing the 'Reference Gap' in large multimodal models, which is relevant to AI product builders. However, while it presents interesting insights and performance metrics, it lacks specific actionable steps for implementation.","\u002Fsummaries\u002F0120dc1c893f4e5c-visual-primitives-solve-lmm-reference-gap-summary","2026-05-05 07:50:52","2026-05-05 16:09:35",{"title":3908,"description":43},{"loc":4018},"0120dc1c893f4e5c","Data and Beyond","https:\u002F\u002Fmedium.com\u002Fdata-and-beyond\u002Fwhats-inside-the-mysterious-paper-deepseek-withdrew-at-lightning-speed-4351004f7c69?source=rss----b680b860beb1---4","summaries\u002F0120dc1c893f4e5c-visual-primitives-solve-lmm-reference-gap-summary",[87,88,89,4028],"multimodal","DeepSeek's withdrawn paper introduces 'Thinking with Visual Primitives'—embedding bounding boxes and points into every reasoning step—to fix ambiguous referencing in multimodal models, achieving 77.2% on spatial benchmarks with 10x fewer tokens than rivals.",[4028],"sCDw8_LJ33P7jJSEOkmIPPRT33bjzUPRjkBA55ucT5E"]