[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-d26a3517eeb93944-ais-tackle-months-of-verifiable-swe-boosting-timel-summary":3,"summaries-facets-categories":348,"summary-related-d26a3517eeb93944-ais-tackle-months-of-verifiable-swe-boosting-timel-summary":5922},{"id":4,"title":5,"ai":6,"body":13,"categories":307,"created_at":309,"date_modified":309,"description":299,"extension":310,"faq":309,"featured":311,"kicker_label":309,"meta":312,"navigation":330,"path":331,"published_at":309,"question":309,"scraped_at":332,"seo":333,"sitemap":334,"source_id":335,"source_name":336,"source_type":337,"source_url":338,"stem":339,"tags":340,"thumbnail_url":309,"tldr":345,"tweet":309,"unknown_tags":346,"__hash__":347},"summaries\u002Fsummaries\u002Fd26a3517eeb93944-ais-tackle-months-of-verifiable-swe-boosting-timel-summary.md","AIs Tackle Months of Verifiable SWE, Boosting Timelines",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",9152,3207,28550,0.00341125,{"type":14,"value":15,"toc":298},"minimark",[16,21,25,28,31,34,37,41,44,74,77,80,84,87,139,142,145,156,159,162,166,169,172,262,265,268,272],[17,18,20],"h2",{"id":19},"esni-tasks-unlock-ais-iterative-superpowers","ESNI Tasks Unlock AI's Iterative Superpowers",[22,23,24],"p",{},"The core insight driving shorter timelines is AIs' exceptional performance on \"easy-and-cheap-to-verify SWE tasks that don't require much ideation\" (ESNI tasks). These are well-specified, CLI-focused software projects where success metrics are straightforward, like improving benchmarks or replicating existing tools. AIs excel by generating their own test suites, then iterating endlessly against them—fixing bugs, optimizing metrics, and recovering from errors autonomously.",[22,26,27],{},"Previously, the author expected only a 4x gap in 50% reliability time horizons between ESNI tasks and METR's benchmark suite; reality shows 20x (potentially >100x). This stems from two levels of verifiability: (1) AI labs optimize models via RL on these metrics, (2) runtime agents apply massive labor without human oversight. \"You can get the AI to develop a test suite \u002F benchmark set and then it can spend huge amounts of time making forward progress by optimizing its solution against this evaluation set.\"",[22,29,30],{},"This enters a \"superexponential progress\" regime: once generality allows error recovery, each doubling of time horizon gets easier. Lower generality suffices for ESNI vs. broader tasks, as mistakes are obvious and fixable iteratively. Tradeoff: ideation-heavy tasks (e.g., novel algorithms, distributed systems) resist this, favoring schlep work like infrastructure or replications.",[22,32,33],{},"Hierarchy of tasks clarifies: (1) ESNI (CLI, metric-driven) >> (2) Broader ES (harder verification) > (3) Hard-to-verify (research taste needed). Gap between 1-2 dwarfs 2-3, amplifying AI strengths.",[22,35,36],{},"\"A core thing I wasn't properly pricing in is that a task being easy-and-cheap-to-verify helps at two levels: it's both easier for AI companies to optimize... and it's easier for AIs themselves to just keep applying labor at runtime.\"",[17,38,40],{"id":39},"hands-on-experiments-reveal-massive-throughput","Hands-On Experiments Reveal Massive Throughput",[22,42,43],{},"Testing an agent orchestrator on Opus 4.5\u002F4.6, the author ran fully autonomous projects:",[45,46,47,62,68],"ul",{},[48,49,50,54,55,58,59,61],"li",{},[51,52,53],"strong",{},"Two massive SWE replications",": AIs completed 3-12 months of human-equivalent work. One nears beating complex closed-source software on key metrics (with bugs\u002Funimplemented features); the other trails top open-source but impresses. Started with 1-2 hours human guidance on metrics\u002Finfra (amortized). Code quality low initially, but scaffolding fixes it to \"mostly OK.\"",[56,57],"br",{},"AIs falter on prioritization (declaring \"done\" prematurely), code cleanup, and big-picture errors—but iteration compensates. Misalignment causes incomplete tasks, patched by orchestration. Human tips every ~day (15 mins) yield big gains; AIs incorporate advice mediocrely.",[56,60],{},"Especially strong at \"software replication tasks\" (drop-in replacements with speed\u002Fsecurity edges). Forthcoming METR\u002FEpoch AI confirms, amplified by scaffolding.",[48,63,64,67],{},[51,65,66],{},"AI R&D optimization",": On a well-optimized target, AI made days-to-week of expert progress. Bottlenecks: poor idea generation, experiment selection, resource inefficiency (e.g., waiting on runs). Tweaking dominates over breakthroughs.",[48,69,70,73],{},[51,71,72],{},"Cyber tasks",": Strong with scaffolding, leveraging domain knowledge.",[22,75,76],{},"Safety automation attempts hit taste\u002Fjudgment walls: AIs skip thoroughness, make bad calls, but thoroughness compensates (e.g., weeks of work via low-value grind). Mundane misalignment persists, soon patchable for well-specified safety research.",[22,78,79],{},"\"I found that the AI successfully completed what looks like many months (3-12 months) of useful work in the SWE projects.\"",[17,81,83],{"id":82},"blockers-temper-acceleration-on-ai-rd","Blockers Temper Acceleration on AI R&D",[22,85,86],{},"ESNI covers limited AI R&D: ML experiments need expensive evals or taste (idea setup, interpretation); infra\u002Fefficiency closer but not pure. Examples:",[88,89,90,103],"table",{},[91,92,93],"thead",{},[94,95,96,100],"tr",{},[97,98,99],"th",{},"Potentially ESNI?",[97,101,102],{},"Why\u002FWhy Not",[104,105,106,115,123,131],"tbody",{},[94,107,108,112],{},[109,110,111],"td",{},"Hyperparameter sweeps",[109,113,114],{},"Yes, metric-driven.",[94,116,117,120],{},[109,118,119],{},"Efficiency tooling",[109,121,122],{},"Borderline, some taste.",[94,124,125,128],{},[109,126,127],{},"Ablation studies",[109,129,130],{},"No, design judgment.",[94,132,133,136],{},[109,134,135],{},"Small-scale experiments",[109,137,138],{},"Yes, if verifiable.",[22,140,141],{},"Naive speedup moderate; humans bottleneck elsewhere. But superhuman ESNI enables massive gains via efficient small experiments—if taste improves for resource use. Current AIs mimic fast-but-low-taste engineers, running months autonomously post-human ideas.",[22,143,144],{},"Counter-evidence:",[45,146,147,150,153],{},[48,148,149],{},"METR benchmarks not just under-elicited; task distribution (checkability\u002Fiterability) drives gap. Scaffolding helps moderately now, hugely soon for large-context tasks.",[48,151,152],{},"Poor \"taste\u002Fjudgment\" (instincts on non-straightforward calls) lags agentic gains, RL\u002Fpretraining-driven, 2-3x slower.",[48,154,155],{},"Stupid errors\u002Fmisalignment in empirical research.",[22,157,158],{},"Yet 2026 pretraining surges possible; blockers erodible.",[22,160,161],{},"\"By default, not that much of currently done AI R&D is straightforwardly an ESNI task... but AI companies might figure out better ways to leverage AIs doing something ESNI-like.\"",[17,163,165],{"id":164},"shorter-timelines-reflect-compounding-speedups","Shorter Timelines Reflect Compounding Speedups",[22,167,168],{},"Updates: ~30% full AI R&D automation EOY 2028 (from 15%); 50% ESNI reliability over years by EOY 2026 (90% in hours\u002Fdays). 2026 progress >2025 despite prior slowdown expectation. Useful AIs accelerate R&D recursively.",[22,170,171],{},"Forecasts (parity: better firing all humans than 2020 AI):",[88,173,174,193],{},[91,175,176],{},[94,177,178,181,184,187,190],{},[97,179,180],{},"Milestone",[97,182,183],{},"EOY 2026",[97,185,186],{},"2027",[97,188,189],{},"2028",[97,191,192],{},"Median",[104,194,195,212,229,246],{},[94,196,197,200,203,206,209],{},[109,198,199],{},"AI R&D Parity",[109,201,202],{},"7%",[109,204,205],{},"19%",[109,207,208],{},"30%",[109,210,211],{},"Early 2031",[94,213,214,217,220,223,226],{},[109,215,216],{},"AI Stack + Conflict Parity",[109,218,219],{},"3%",[109,221,222],{},"9%",[109,224,225],{},"17%",[109,227,228],{},"Late 2034",[94,230,231,234,237,240,243],{},[109,232,233],{},"Automated Coder (AC)",[109,235,236],{},"11%",[109,238,239],{},"27%",[109,241,242],{},"39%",[109,244,245],{},"Mid 2031",[94,247,248,251,254,257,259],{},[109,249,250],{},"Top-Expert-Dominating AI (TEDAI)",[109,252,253],{},"4%",[109,255,256],{},"12%",[109,258,205],{},[109,260,261],{},"Mid 2032",[22,263,264],{},"Compares favorably to Cotra (AI Research Parity early 2030). Medians right-skewed; conditional gaps shorter (e.g., R&D to TEDAI ~1.75yrs). Views unstable; recent drift longer.",[22,266,267],{},"\"We're well into the superexponential progress on 50% reliability time-horizon regime for these ESNI tasks: because sufficient generality and error recovery allows for infinite time horizon.\"",[17,269,271],{"id":270},"key-takeaways","Key Takeaways",[45,273,274,277,280,283,286,289,292,295],{},[48,275,276],{},"Prioritize ESNI tasks for AI agents: Generate tests, iterate metrics—yields months of work autonomously.",[48,278,279],{},"Expect 20x+ time horizons on verifiable CLI SWE vs. benchmarks; scaffold replications for wins.",[48,281,282],{},"Taste\u002Fjudgment lags: Compensate with thoroughness, human nudges; watch pretraining for catch-up.",[48,284,285],{},"AI R&D speedup indirect but recursive: Use for infra\u002Fexperiments, pair with human ideas.",[48,287,288],{},"Timelines compress: 30% AI R&D parity 2028; plan for 2026 surges in agentic SWE.",[48,290,291],{},"Scaffolding critical for large tasks; misalignment mundane but fixable.",[48,293,294],{},"Replicate via 1-2hr setup + iteration; 15min daily tips boost 2x+.",[48,296,297],{},"Superexponential on ESNI: Low generality unlocks endless horizons.",{"title":299,"searchDepth":300,"depth":300,"links":301},"",2,[302,303,304,305,306],{"id":19,"depth":300,"text":20},{"id":39,"depth":300,"text":40},{"id":82,"depth":300,"text":83},{"id":164,"depth":300,"text":165},{"id":270,"depth":300,"text":271},[308],"AI & LLMs",null,"md",false,{"content_references":313,"triage":325},[314,320],{"type":315,"title":316,"author":317,"url":318,"context":319},"other","Six Milestones for AI Automation","Cotra","https:\u002F\u002Fwww.planned-obsolescence.org\u002Fp\u002Fsix-milestones-for-ai-automation","cited",{"type":315,"title":321,"author":322,"url":323,"context":324},"Talk on AI and Cyber Capabilities","Nicholas Carlini","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1sd26pWhfmg","mentioned",{"relevance":326,"novelty":327,"quality":326,"actionability":327,"composite":328,"reasoning":329},4,3,3.6,"Category: AI & LLMs. The article discusses how AIs can autonomously complete software engineering tasks, which is relevant to AI engineering and automation. It provides insights into the performance of AIs on specific types of tasks, but lacks detailed frameworks or actionable steps for implementation.",true,"\u002Fsummaries\u002Fd26a3517eeb93944-ais-tackle-months-of-verifiable-swe-boosting-timel-summary","2026-04-14 14:32:52",{"title":5,"description":299},{"loc":331},"d26a3517eeb93944","__oneoff__","article","https:\u002F\u002Fwww.lesswrong.com\u002Fposts\u002FdKpC6wHFqDrGZwnah\u002Fais-can-now-often-do-massive-easy-to-verify-swe-tasks-and-i","summaries\u002Fd26a3517eeb93944-ais-tackle-months-of-verifiable-swe-boosting-timel-summary",[341,342,343,344],"llm","agents","automation","coding","Author updates to 30% chance of AI R&D parity by 2028 after AIs autonomously complete 3-12 months of easy-to-verify SWE tasks, revealing 20x longer time horizons than benchmarks like METR's.",[],"jDinHXo1hsNRL2FvEe6Ypb8u6Um2VzoSwmfdRwSJxVs",[349,352,355,357,360,363,365,367,370,372,374,376,378,381,383,385,387,389,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,429,432,434,436,438,440,442,444,446,448,450,452,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,486,488,490,492,494,496,498,500,502,504,506,508,510,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,572,574,576,578,580,582,584,586,588,590,593,595,597,599,601,603,605,607,609,611,613,615,618,620,622,624,626,628,630,632,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,684,686,688,691,693,695,697,699,701,703,705,707,709,711,713,715,717,720,722,724,726,728,730,732,734,736,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,978,980,982,984,987,989,991,993,995,997,999,1001,1003,1005,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2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The bottleneck is not raw model capability but the lack of a management layer to specify, verify, and reuse work. Raymond Weitekamp argues that the solution lies in treating agents as \"mismanaged geniuses\" that require better orchestration rather than more raw intelligence.",[17,5942,5944],{"id":5943},"recursive-language-models-rlms-as-reasoning-engines","Recursive Language Models (RLMs) as Reasoning Engines",[22,5946,5947],{},"RLMs represent a paradigm shift in inference-time compute by marrying tool calling with reasoning. Unlike standard prompting, where a model reads a static context, an RLM treats the context as a variable to be manipulated symbolically.",[22,5949,5950],{},"Key characteristics of an RLM include:",[45,5952,5953,5959,5965],{},[48,5954,5955,5958],{},[51,5956,5957],{},"Externalized Context:"," The prompt is not fully loaded into the context window; instead, the model uses tools to explore it symbolically.",[48,5960,5961,5964],{},[51,5962,5963],{},"Recursive Decomposition:"," The model breaks down a problem into sub-tasks, delegating them to sub-agents (or sub-RLMs) that operate independently.",[48,5966,5967,5970],{},[51,5968,5969],{},"Executable Environment:"," The reasoning process is unified with code execution, allowing the agent to \"reason through tool calling.\"",[22,5972,5973],{},"This approach allows smaller models (e.g., Qwen 2.5 7B) to outperform frontier models on long-reasoning tasks by maintaining a thread of execution through recursive calls that exceed the limitations of a single context window.",[17,5975,5977],{"id":5976},"implementing-recursive-coding-agents","Implementing Recursive Coding Agents",[22,5979,5980],{},"To transform standard coding agents into RLMs, developers must implement a harness that allows the agent to call itself or other agents recursively.",[45,5982,5983,5989,5995],{},[48,5984,5985,5988],{},[51,5986,5987],{},"Pi (Coding Agent):"," A minimal, extensible agent that now supports pure recursive extensions, allowing for deep, nested task execution.",[48,5990,5991,5994],{},[51,5992,5993],{},"Open Pros:"," A markdown-based programming language that allows users to declare sub-agent workflows. It enables developers to define explicit dependencies (skills\u002Ftools) for sub-agents, ensuring they are configured correctly before executing their portion of a contract.",[48,5996,5997,6000],{},[51,5998,5999],{},"Dynamic Workflows:"," Tools like Anthropic's Claude Code now support dynamic workflows, which effectively turn them into RLMs by enabling recursive, multi-step agentic loops.",[17,6002,6004],{"id":6003},"capturing-reliability","Capturing Reliability",[22,6006,6007],{},"Reliability is achieved by moving from \"one-off\" prompts to reusable workflows. Weitekamp demonstrates that agents can be instructed to deconstruct a \"golden session\"—a successful execution path—into a reusable program (such as an Open Pros markdown file). This allows developers to codify successful reasoning patterns, ensuring that the agent can replicate high-performance results consistently across different tasks, such as bug sweeps, large-scale refactors, or adversarial audits.",{"title":299,"searchDepth":300,"depth":300,"links":6009},[6010,6011,6012,6013],{"id":5936,"depth":300,"text":5937},{"id":5943,"depth":300,"text":5944},{"id":5976,"depth":300,"text":5977},{"id":6003,"depth":300,"text":6004},[308],{"content_references":6016,"triage":6034},[6017,6022,6025,6028,6031],{"type":6018,"title":6019,"url":6020,"context":6021},"tool","recursivecodingagents.com","https:\u002F\u002Frecursivecodingagents.com","recommended",{"type":6018,"title":6023,"url":6024,"context":6021},"Open Pros","https:\u002F\u002Fgithub.com\u002Fopenprose\u002Fopenprose",{"type":6018,"title":6026,"url":6027,"context":324},"Pi","https:\u002F\u002Fgithub.com\u002Fmario-pi\u002Fpi",{"type":6018,"title":6029,"url":6030,"context":324},"Claude Code","https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-code",{"type":315,"title":6032,"author":6033,"context":319},"Recursive Language Models paper","Alex Zang, Zed Lee, Omar Khattab",{"relevance":6035,"novelty":326,"quality":326,"actionability":326,"composite":6036,"reasoning":6037},5,4.35,"Category: AI & LLMs. The article discusses Recursive Language Models (RLMs) and their application in managing AI agents, addressing the pain point of unreliable outcomes in AI coding tasks. It provides actionable insights on implementing RLMs, including specific tools like Pi and Open Pros, which developers can use to enhance their coding agents.","\u002Fsummaries\u002F6a2df319877f8e98-recursive-coding-agents-managing-ai-geniuses-summary","2026-06-25 21:00:06","2026-06-26 12:56:24",{"title":5925,"description":299},{"loc":6038},"6a2df319877f8e98","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3hXJI2q0Jz8","summaries\u002F6a2df319877f8e98-recursive-coding-agents-managing-ai-geniuses-summary",[342,341,344,343],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F3hXJI2q0Jz8\u002Fhqdefault.jpg","Recursive Language Models (RLMs) improve agent reliability by treating context as an object of computation, allowing agents to decompose complex tasks into recursive sub-agent calls that verify and execute work symbolically.","This talk provides a high-level overview of Recursive Language Models (RLMs) and how their \"inference-time compute\" patterns can be applied to coding agents. The speaker explains the core concept—using symbolic manipulation and recursive sub-agent calls to bypass context window limits—and demonstrates how to implement this recursion using [Pi](https:\u002F\u002Fgithub.com\u002Fmario-p-i\u002Fpi) extensions.",[],"jbAZocQGq7eJMiiP0ekIxxnj1m7I5jbExCuu0lGEdbA",{"id":6055,"title":6056,"ai":6057,"body":6062,"categories":6099,"created_at":309,"date_modified":309,"description":299,"extension":310,"faq":309,"featured":311,"kicker_label":309,"meta":6100,"navigation":330,"path":6120,"published_at":309,"question":309,"scraped_at":6121,"seo":6122,"sitemap":6123,"source_id":6124,"source_name":336,"source_type":337,"source_url":6125,"stem":6126,"tags":6127,"thumbnail_url":309,"tldr":6128,"tweet":309,"unknown_tags":6129,"__hash__":6130},"summaries\u002Fsummaries\u002F5dc48731884521dd-glm-5-1-excels-in-long-horizon-agentic-coding-summary.md","GLM-5.1 Excels in Long-Horizon Agentic Coding",{"provider":7,"model":8,"input_tokens":6058,"output_tokens":6059,"processing_time_ms":6060,"cost_usd":6061},7818,1969,9224,0.00252545,{"type":14,"value":6063,"toc":6094},[6064,6068,6071,6074,6077,6081,6084,6087,6091],[17,6065,6067],{"id":6066},"sustains-optimization-over-extended-horizons-for-superior-results","Sustains Optimization Over Extended Horizons for Superior Results",[22,6069,6070],{},"GLM-5.1 breaks through short-session limits by maintaining productivity across hundreds of iterations and thousands of tool calls, revising strategies based on benchmarks and self-analysis. On VectorDBBench, it optimizes a Rust vector database for SIFT-1M (Recall ≥95%), starting from a skeleton with HTTP endpoints. Over 655 iterations and 6,000+ tool calls, it hits 21.5k QPS—6x the prior 3.5k QPS best from Claude Opus 4.6 in 50 turns. Progress follows a staircase: incremental tuning plateaus, then structural shifts like IVF probing with f16 compression (iteration ~90, 6.4k QPS) or u8 prescoring + f16 reranking (iteration ~240, 13.4k QPS) unlock jumps, with temporary Recall dips during exploration.",[22,6072,6073],{},"On KernelBench Level 3 (50 full-model optimizations like MobileNet, VGG), GLM-5.1 delivers 3.6x geometric mean speedup vs. PyTorch baseline (torch.compile max-autotune: 1.49x), sustaining gains over 1,200 turns per problem in isolated H100 Docker. It outlasts GLM-5 (early plateau) and Claude Opus 4.5, trailing only Opus 4.6 (4.2x) but showing more headroom. Audits confirm no benchmark exploits.",[22,6075,6076],{},"For open-ended tasks without metrics, GLM-5.1 builds a Linux desktop web app from scratch over 8 hours: starts with taskbar\u002Fwindows, iterates to add file browser, terminal, editor, monitor, calculator, games—polishing UI, interactions, and edge cases via self-review loops.",[17,6078,6080],{"id":6079},"tops-coding-and-agentic-benchmarks-with-precise-judgment","Tops Coding and Agentic Benchmarks with Precise Judgment",[22,6082,6083],{},"GLM-5.1 leads SWE-Bench Pro at 58.4% (vs. GLM-5 55.1%, GPT-5.4 57.7%), NL2Repo at 42.7% (vs. GLM-5 35.9%), Terminal-Bench 2.0 Terminus-2 at 63.5%, and CyberGym at 68.7% (Claude Code harness). In agentic evals: BrowseComp w\u002F Context Manage 79.3%, τ³-Bench 70.6%, MCP-Atlas 71.8%, Tool-Decathlon 40.7%, Vending Bench 2 $5,634 revenue. Reasoning holds strong: HLE w\u002F Tools 52.3%, AIME 2026 95.3%, GPQA-Diamond 86.2%. Settings emphasize long contexts (up to 202k tokens) and tool use without hacking (rule + model detection).",[22,6085,6086],{},"Trade-offs: Higher quota use (2-3x), challenges remain in escaping local optima, trace coherence over thousands of calls, and metric-free self-eval.",[17,6088,6090],{"id":6089},"deploy-immediately-for-agentic-workflows","Deploy Immediately for Agentic Workflows",[22,6092,6093],{},"Open-source (MIT) on GitHub\u002FHuggingFace\u002FModelScope; infer with vLLM\u002FSGLang. API on api.z.ai\u002FBigModel.cn; compatible with Claude Code\u002FOpenClaw. GLM Coding Plan: update to \"GLM-5.1\" (1-3x quota, promo 1x off-peak); GUI via Z Code for multi-agent SSH\u002Fphone tasks. Z.ai chat rollout soon.",{"title":299,"searchDepth":300,"depth":300,"links":6095},[6096,6097,6098],{"id":6066,"depth":300,"text":6067},{"id":6079,"depth":300,"text":6080},{"id":6089,"depth":300,"text":6090},[308],{"content_references":6101,"triage":6117},[6102,6105,6109,6112,6115],{"type":315,"title":6103,"url":6104,"context":319},"VectorDBBench","https:\u002F\u002Fgithub.com\u002FKCORES\u002Fvector-db-bench",{"type":6106,"title":6107,"url":6108,"context":319},"paper","KernelBench","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.10517v1",{"type":315,"title":6110,"url":6111,"context":319},"Vending Bench 2","https:\u002F\u002Fandonlabs.com\u002Fevals\u002Fvending-bench-2",{"type":6018,"title":6113,"url":6114,"context":324},"GLM-5.1","https:\u002F\u002Fgithub.com\u002Fzai-org\u002FGLM-5",{"type":6018,"title":6113,"url":6116,"context":324},"https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-5.1",{"relevance":326,"novelty":327,"quality":326,"actionability":300,"composite":6118,"reasoning":6119},3.4,"Category: AI & LLMs. The article discusses the performance of GLM-5.1 in coding and agentic benchmarks, which is relevant to AI engineering and software development. However, while it provides insights into the model's capabilities, it lacks concrete, actionable steps for developers looking to implement these findings in their own projects.","\u002Fsummaries\u002F5dc48731884521dd-glm-5-1-excels-in-long-horizon-agentic-coding-summary","2026-04-16 03:09:20",{"title":6056,"description":299},{"loc":6120},"5dc48731884521dd","https:\u002F\u002Fz.ai\u002Fblog\u002Fglm-5.1","summaries\u002F5dc48731884521dd-glm-5-1-excels-in-long-horizon-agentic-coding-summary",[341,342,344],"GLM-5.1 tops SWE-Bench Pro at 58.4% and sustains gains over 600+ iterations on VectorDBBench (21.5k QPS, 6x prior best) and 1,000+ turns on KernelBench (3.6x speedup), enabling complex builds like a full Linux desktop in 8 hours.",[],"9o9PWiYh9qZfKw3Vzn915SRQ8On2LwxyJS9rRH8Td78",{"id":6132,"title":6133,"ai":6134,"body":6139,"categories":6172,"created_at":309,"date_modified":309,"description":299,"extension":310,"faq":309,"featured":311,"kicker_label":309,"meta":6173,"navigation":330,"path":6197,"published_at":309,"question":309,"scraped_at":6198,"seo":6199,"sitemap":6200,"source_id":6201,"source_name":336,"source_type":337,"source_url":6202,"stem":6203,"tags":6204,"thumbnail_url":309,"tldr":6205,"tweet":309,"unknown_tags":6206,"__hash__":6207},"summaries\u002Fsummaries\u002F68ffcf2c57450e50-claude-opus-4-1-reaches-74-5-on-swe-bench-for-supe-summary.md","Claude Opus 4.1 Reaches 74.5% on SWE-bench for Superior Coding",{"provider":7,"model":8,"input_tokens":6135,"output_tokens":6136,"processing_time_ms":6137,"cost_usd":6138},4521,1887,10018,0.00182505,{"type":14,"value":6140,"toc":6167},[6141,6145,6148,6152,6155,6159],[17,6142,6144],{"id":6143},"coding-gains-target-production-workflows","Coding Gains Target Production Workflows",[22,6146,6147],{},"Claude Opus 4.1 achieves 74.5% on SWE-bench Verified using only bash and file-editing tools—no planning tool—scoring across all 500 problems, outperforming prior models on multi-file refactoring. This setup equips the model for real codebase edits via string replacements, enabling precise fixes without unnecessary changes or bugs. Rakuten Group uses it for everyday debugging in large codebases, preferring its pinpoint accuracy. Windsurf's junior developer benchmark shows a one standard deviation leap over Opus 4, matching the Sonnet 3.7 to 4 jump, proving reliable junior-level code handling.",[17,6149,6151],{"id":6150},"agentic-tasks-and-research-boosted-by-extended-thinking","Agentic Tasks and Research Boosted by Extended Thinking",[22,6153,6154],{},"Opus 4.1 enhances detail tracking, in-depth research, and data analysis via agentic search. On TAU-bench (Airline\u002FRetail agents), scores improve with a prompt addendum encouraging explicit reasoning during extended thinking up to 64K tokens and 100 steps (most under 30). This leverages hybrid reasoning for multi-turn trajectories, separating thoughts from actions. Benchmarks like GPQA Diamond, MMMLU, MMMU, and AIME use extended thinking; SWE-bench and Terminal-Bench do not. GitHub reports broad gains over Opus 4, especially refactoring.",[17,6156,6158],{"id":6157},"immediate-upgrade-path-delivers-value","Immediate Upgrade Path Delivers Value",[22,6160,6161,6162,6166],{},"Switch to Opus 4.1 for all tasks via API model ",[6163,6164,6165],"code",{},"claude-opus-4-1-20250805",", Claude Code, Amazon Bedrock, or Vertex AI at Opus 4 pricing. Larger upgrades follow soon. Feedback drives iterations; check system card, model page, pricing, and docs for details. Hybrid reasoning maximizes scores, balancing tool use with chain-of-thought for complex problems.",{"title":299,"searchDepth":300,"depth":300,"links":6168},[6169,6170,6171],{"id":6143,"depth":300,"text":6144},{"id":6150,"depth":300,"text":6151},{"id":6157,"depth":300,"text":6158},[391],{"content_references":6174,"triage":6195},[6175,6179,6182,6186,6189,6192],{"type":6176,"title":6177,"url":6178,"context":319},"dataset","SWE-bench Verified","https:\u002F\u002Fwww.swebench.com\u002F",{"type":315,"title":6180,"url":6181,"context":319},"o3 launch post","https:\u002F\u002Fopenai.com\u002Findex\u002Fintroducing-o3-and-o4-mini\u002F",{"type":6183,"title":6184,"url":6185,"context":319},"report","o3-and-o4-mini-system-card.pdf","https:\u002F\u002Fcdn.openai.com\u002Fpdf\u002F2221c875-02dc-4789-800b-e7758f3722c1\u002Fo3-and-o4-mini-system-card.pdf",{"type":6183,"title":6187,"url":6188,"context":319},"2.5 Pro model card","https:\u002F\u002Fstorage.googleapis.com\u002Fmodel-cards\u002Fdocuments\u002Fgemini-2.5-pro.pdf",{"type":315,"title":6190,"url":6191,"context":319},"Sonnet 3.7 launch post","https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-3-7-sonnet",{"type":315,"title":6193,"url":6194,"context":319},"Claude 4 launch post","https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-4",{"relevance":326,"novelty":327,"quality":326,"actionability":327,"composite":328,"reasoning":6196},"Category: AI & LLMs. The article discusses the capabilities of Claude Opus 4.1 in coding tasks, which is relevant to AI engineering and software development. It provides specific performance metrics and use cases, such as its application in debugging large codebases, which addresses practical concerns for developers. However, while it offers insights into the model's performance, it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002F68ffcf2c57450e50-claude-opus-4-1-reaches-74-5-on-swe-bench-for-supe-summary","2026-04-16 03:07:34",{"title":6133,"description":299},{"loc":6197},"68ffcf2c57450e50","https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-opus-4-1","summaries\u002F68ffcf2c57450e50-claude-opus-4-1-reaches-74-5-on-swe-bench-for-supe-summary",[341,342,344],"Claude Opus 4.1 upgrades agentic tasks, coding, and reasoning to 74.5% on SWE-bench Verified, with gains in multi-file refactoring and precise debugging; available now at same pricing.",[],"MhwMncz4IS_mgOHa_l1HYHmRbXKI5_VPHszvHw54SpI",{"id":6209,"title":6210,"ai":6211,"body":6216,"categories":6252,"created_at":309,"date_modified":309,"description":299,"extension":310,"faq":309,"featured":311,"kicker_label":309,"meta":6253,"navigation":330,"path":6257,"published_at":309,"question":309,"scraped_at":6258,"seo":6259,"sitemap":6260,"source_id":6261,"source_name":6262,"source_type":337,"source_url":6263,"stem":6264,"tags":6265,"thumbnail_url":309,"tldr":6266,"tweet":309,"unknown_tags":6267,"__hash__":6268},"summaries\u002Fsummaries\u002F6ff41910e72e599f-5-llm-pitfalls-engineers-hit-building-agents-summary.md","5 LLM Pitfalls Engineers Hit Building Agents",{"provider":7,"model":8,"input_tokens":6212,"output_tokens":6213,"processing_time_ms":6214,"cost_usd":6215},9923,1337,7742,0.00263045,{"type":14,"value":6217,"toc":6246},[6218,6222,6225,6229,6232,6236,6239,6243],[17,6219,6221],{"id":6220},"budget-context-windows-like-ram-to-avoid-gradual-degradation","Budget Context Windows Like RAM to Avoid Gradual Degradation",[22,6223,6224],{},"Context windows are bounded buffers holding system prompts, conversation history, tool outputs, prior responses, and retrieval chunks—not just documents. Exceeding them truncates silently without errors, making agents lose access to key info. A coding agent reading three medium files plus tool responses burns 30-50K tokens before real work, even on 200K models. Design agents to fetch on-demand rather than stuffing everything in: prioritize what the model needs now. This scales demos to production; ignoring it causes toy-task success but real-work failure.",[17,6226,6228],{"id":6227},"tokenize-workloads-precisely-for-cost-and-limits","Tokenize Workloads Precisely for Cost and Limits",[22,6230,6231],{},"Tokens are sub-word fragments from the model's tokenizer, not words\u002Fcharacters. Code eats tokens fast—brackets, underscores, indentation, identifiers push a 200-line Python file to ~3K tokens, not 2K. Non-English (Japanese, Arabic, Hindi) uses 2-4x more tokens than English for same meaning, breaking English-based estimates. JSON\u002FXML schemas add overhead vs. prose. Always run representative samples through the exact tokenizer pre-launch: word-count guesses underestimate costs severely.",[17,6233,6235],{"id":6234},"tune-temperature-for-reproducibility-ground-hallucinations-systematically","Tune Temperature for Reproducibility, Ground Hallucinations Systematically",[22,6237,6238],{},"Temperature controls low-probability token sampling, trading reproducibility for variety—use 0 for tool-calling, extraction, classification, spec'd code gen where correctness trumps creativity. Higher suits diverse generation (variants, brainstorming) but invites irreproducible bugs. Even temperature=0 isn't fully deterministic due to API routing\u002Fbatching; true control needs seeded inference. View hallucinations as pattern continuation from training data, not recall errors: counter with retrieval grounding, output schemas\u002Ffunction calls, and post-generation validation (critical for actions). In coding agents, models invent APIs confidently—prompting\u002Ffeedback loops fail; validate rigorously.",[17,6240,6242],{"id":6241},"engineer-rag-retrieval-not-just-the-llm","Engineer RAG Retrieval, Not Just the LLM",[22,6244,6245],{},"RAG indexes data, retrieves chunks at query time for context. The LLM is easy; retrieval decides success—tune chunking, embeddings, hybrid search, reranking, query rewriting. Diagnose with recall@10 on held-out eval sets: poor retrieval, not the model, causes most failures. Teams blame LLMs without measuring this.",{"title":299,"searchDepth":300,"depth":300,"links":6247},[6248,6249,6250,6251],{"id":6220,"depth":300,"text":6221},{"id":6227,"depth":300,"text":6228},{"id":6234,"depth":300,"text":6235},{"id":6241,"depth":300,"text":6242},[],{"content_references":6254,"triage":6255},[],{"relevance":6035,"novelty":326,"quality":326,"actionability":326,"composite":6036,"reasoning":6256},"Category: AI & LLMs. The article directly addresses common pitfalls engineers face when building AI agents, providing practical insights on managing context windows and tokenization, which are critical for production-ready AI features. It offers actionable advice on tuning parameters and validating outputs, making it highly relevant for developers looking to implement LLMs effectively.","\u002Fsummaries\u002F6ff41910e72e599f-5-llm-pitfalls-engineers-hit-building-agents-summary","2026-04-19 01:22:12",{"title":6210,"description":299},{"loc":6257},"6ff41910e72e599f","Generative AI","https:\u002F\u002Fgenerativeai.pub\u002Ffive-llm-concepts-i-keep-explaining-to-engineers-shipping-their-first-agents-87c502f3c378?source=rss----440100e76000---4","summaries\u002F6ff41910e72e599f-5-llm-pitfalls-engineers-hit-building-agents-summary",[341,342,344],"Context windows act like RAM—budget system prompts, history, tools, and retrieval tightly or agents degrade silently. Tokenize code\u002Fnon-English workloads early; set temperature=0 for reproducibility; ground hallucinations with RAG\u002Fschemas\u002Fvalidation; measure RAG recall@10.",[],"VfRNIenBuwdRZlBDaBdU7mmrUrZCRezKKYwx6VA-caQ"]