[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-b61cb7f4a5958f02-energy-per-successful-goal-a-new-metric-for-agenti-summary":3,"summaries-facets-categories":104,"summary-related-b61cb7f4a5958f02-energy-per-successful-goal-a-new-metric-for-agenti-summary":4171},{"id":4,"title":5,"ai":6,"body":13,"categories":69,"created_at":71,"date_modified":71,"description":63,"extension":72,"faq":71,"featured":73,"kicker_label":71,"meta":74,"navigation":87,"path":88,"published_at":89,"question":71,"scraped_at":89,"seo":90,"sitemap":91,"source_id":92,"source_name":93,"source_type":94,"source_url":80,"stem":95,"tags":96,"thumbnail_url":71,"tldr":101,"tweet":71,"unknown_tags":102,"__hash__":103},"summaries\u002Fsummaries\u002Fb61cb7f4a5958f02-energy-per-successful-goal-a-new-metric-for-agenti-summary.md","Energy per Successful Goal: A New Metric for Agentic AI Efficiency",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4084,611,4003,0.0019375,{"type":14,"value":15,"toc":62},"minimark",[16,21,25,29,32,55,59],[17,18,20],"h2",{"id":19},"the-shift-from-token-centric-to-goal-centric-accounting","The Shift from Token-Centric to Goal-Centric Accounting",[22,23,24],"p",{},"Traditional AI efficiency metrics focus on compute-per-token or latency-per-request, which fail to capture the reality of agentic workflows. Agents often perform multiple reasoning steps, tool calls, and self-corrections to complete a single task. The authors argue that these metrics are insufficient for measuring the true environmental and operational cost of autonomous systems. By introducing 'Energy per Successful Goal' (ESG), the researchers propose a holistic accounting framework that tracks the total energy expenditure—including inference, tool execution, and planning overhead—required to reach a verified successful outcome.",[17,26,28],{"id":27},"why-esg-matters-for-production-systems","Why ESG Matters for Production Systems",[22,30,31],{},"ESG provides a more accurate picture of the trade-offs between model intelligence and operational cost. A smaller, less capable model might have a lower cost per token, but if it requires more iterations or fails to reach the goal, its ESG will be significantly higher than a more powerful model that solves the task in fewer steps. This metric allows developers to:",[33,34,35,43,49],"ul",{},[36,37,38,42],"li",{},[39,40,41],"strong",{},"Optimize for Task Completion:"," Move beyond optimizing for inference speed to optimizing for 'task-completion efficiency.'",[36,44,45,48],{},[39,46,47],{},"Quantify Agent Overhead:"," Measure the energy 'tax' imposed by complex agentic loops, such as multi-step chain-of-thought or recursive error handling.",[36,50,51,54],{},[39,52,53],{},"Informed Model Selection:"," Make data-driven decisions on whether to use a large, high-capability model for a single-shot task versus a smaller model that might require multiple attempts.",[17,56,58],{"id":57},"implications-for-sustainable-ai-engineering","Implications for Sustainable AI Engineering",[22,60,61],{},"As AI agents move from research demos to production environments, the cumulative energy footprint becomes a significant business and environmental concern. The authors demonstrate that by tracking energy at the goal level, engineering teams can identify 'inefficiency hotspots' in their agentic pipelines. This approach encourages the development of more robust, efficient planning strategies that minimize unnecessary compute cycles, ultimately leading to more sustainable and cost-effective AI deployments.",{"title":63,"searchDepth":64,"depth":64,"links":65},"",2,[66,67,68],{"id":19,"depth":64,"text":20},{"id":27,"depth":64,"text":28},{"id":57,"depth":64,"text":58},[70],"AI & LLMs",null,"md",false,{"content_references":75,"triage":82},[76],{"type":77,"title":78,"author":79,"url":80,"context":81},"paper","Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems","Unknown","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.22883","cited",{"relevance":83,"novelty":83,"quality":83,"actionability":84,"composite":85,"reasoning":86},4,3,3.8,"Category: AI & LLMs. The article introduces a new metric, 'Energy per Successful Goal' (ESG), which addresses a specific pain point for developers by providing a framework for evaluating AI agent efficiency in production systems. It offers insights into optimizing task completion and quantifying overhead, making it relevant for those building AI-powered products.",true,"\u002Fsummaries\u002Fb61cb7f4a5958f02-energy-per-successful-goal-a-new-metric-for-agenti-summary","2026-05-25 07:00:18",{"title":5,"description":63},{"loc":88},"b61cb7f4a5958f02","arXiv cs.AI","article","summaries\u002Fb61cb7f4a5958f02-energy-per-successful-goal-a-new-metric-for-agenti-summary",[97,98,99,100],"machine-learning","ai-agents","performance","energy-efficiency","The paper introduces 'Energy per Successful Goal' (ESG) as a critical metric for evaluating AI agent efficiency, shifting focus from raw compute costs to the energy required to complete specific, actionable objectives.",[98,99,100],"r9s7haJ4t7OHtleLldJL9pi4OGSB8zRP-y-_kVscViM",[105,108,111,113,116,119,121,123,125,127,129,131,134,136,138,140,142,144,146,148,150,152,154,156,158,160,163,166,168,170,172,175,177,179,182,184,186,188,190,192,194,196,198,200,202,204,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2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Traditional heuristic and exact methods often struggle to balance exploration (finding new, potentially better paths) and exploitation (refining known good solutions). COAgents addresses this by deploying a multi-agent architecture where specialized agents collaborate to learn the structure of the search space rather than relying on static, hand-coded heuristics.",[17,4190,4192],{"id":4191},"multi-agent-collaboration-for-optimization","Multi-Agent Collaboration for Optimization",[22,4194,4195],{},"The COAgents framework utilizes a team of agents, each potentially focusing on different aspects of the optimization process—such as path generation, local search refinement, or global strategy adjustment. By distributing these tasks, the system can explore diverse regions of the search space simultaneously. The framework allows these agents to share information about promising regions, effectively 'learning' the landscape of the problem as they solve it. This collaborative approach enables the system to adapt to different problem instances more effectively than monolithic algorithms, as the agents can dynamically adjust their strategies based on the feedback received from the search process. The framework was accepted at the LION 2026 conference, highlighting its relevance to the intersection of machine learning and intelligent optimization.",{"title":63,"searchDepth":64,"depth":64,"links":4197},[4198,4199],{"id":4184,"depth":64,"text":4185},{"id":4191,"depth":64,"text":4192},[70],{"content_references":4202,"triage":4208},[4203],{"type":4204,"title":4205,"publisher":4206,"url":4207,"context":81},"other","COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space","arXiv","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.20618",{"relevance":83,"novelty":84,"quality":83,"actionability":64,"composite":4209,"reasoning":4210},3.4,"Category: AI & LLMs. The article discusses a multi-agent framework for routing optimization, which maps to AI and optimization, addressing a specific audience pain point of navigating complex search spaces. However, while it presents some new insights into multi-agent collaboration, it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Ffda608774415188c-coagents-a-multi-agent-framework-for-routing-optim-summary","2026-05-22 07:00:20",{"title":4174,"description":63},{"loc":4211},"fda608774415188c","summaries\u002Ffda608774415188c-coagents-a-multi-agent-framework-for-routing-optim-summary",[97,98,4218,4219],"optimization","routing-problems","COAgents is a multi-agent framework designed to navigate complex search spaces in routing problems by combining collaborative agent intelligence with optimization techniques.",[98,4218,4219],"qHQ6DmvqL72MwZXu4p5FUf6PyxTOdJXGo940_cMWFlE",{"id":4224,"title":4225,"ai":4226,"body":4231,"categories":4279,"created_at":71,"date_modified":71,"description":63,"extension":72,"faq":71,"featured":73,"kicker_label":71,"meta":4280,"navigation":87,"path":4289,"published_at":4290,"question":71,"scraped_at":4290,"seo":4291,"sitemap":4292,"source_id":4293,"source_name":93,"source_type":94,"source_url":4284,"stem":4294,"tags":4295,"thumbnail_url":71,"tldr":4298,"tweet":71,"unknown_tags":4299,"__hash__":4300},"summaries\u002Fsummaries\u002Fd6de696fa2e5c21b-skim-accelerating-web-agents-via-speculative-execu-summary.md","Skim: Accelerating Web Agents via Speculative Execution",{"provider":7,"model":8,"input_tokens":4227,"output_tokens":4228,"processing_time_ms":4229,"cost_usd":4230},4090,550,3483,0.0018475,{"type":14,"value":4232,"toc":4274},[4233,4237,4240,4244,4247,4267,4271],[17,4234,4236],{"id":4235},"the-latency-bottleneck-in-web-agents","The Latency Bottleneck in Web Agents",[22,4238,4239],{},"Web agents powered by Large Language Models (LLMs) often suffer from high latency due to the sequential nature of their operation: the agent must observe the page, process the input, generate an action, and wait for the browser to render the result before the next step can begin. This 'wait-and-see' cycle creates a significant performance overhead, especially when navigating complex multi-step workflows.",[17,4241,4243],{"id":4242},"implementing-speculative-execution-for-web-tasks","Implementing Speculative Execution for Web Tasks",[22,4245,4246],{},"Skim introduces a speculative execution framework to break this sequential dependency. Instead of waiting for the environment to confirm the outcome of a previous action, the agent predicts the most likely next states and actions. By pre-executing these speculative paths, the system can:",[33,4248,4249,4255,4261],{},[36,4250,4251,4254],{},[39,4252,4253],{},"Parallelize Processing:"," While the browser is still rendering the result of an initial action, the agent is already evaluating the next potential steps based on the predicted state.",[36,4256,4257,4260],{},[39,4258,4259],{},"Reduce Idle Time:"," By anticipating user interface changes, the agent minimizes the time spent waiting for DOM updates or network requests.",[36,4262,4263,4266],{},[39,4264,4265],{},"Improve Throughput:"," The framework allows for a more fluid interaction model, where the agent acts as if it is 'skimming' through the task, only committing to paths that align with the actual environment state once it is confirmed.",[17,4268,4270],{"id":4269},"performance-and-trade-offs","Performance and Trade-offs",[22,4272,4273],{},"By decoupling the agent's reasoning from the browser's rendering cycle, Skim achieves faster task completion times compared to standard sequential agents. However, this approach introduces a trade-off: speculative execution consumes additional compute resources to process 'ghost' paths that may eventually be discarded if the prediction is incorrect. The effectiveness of the system relies on the agent's ability to accurately predict the next logical step, making it particularly effective for structured web tasks where navigation patterns are predictable.",{"title":63,"searchDepth":64,"depth":64,"links":4275},[4276,4277,4278],{"id":4235,"depth":64,"text":4236},{"id":4242,"depth":64,"text":4243},{"id":4269,"depth":64,"text":4270},[70],{"content_references":4281,"triage":4285},[4282],{"type":77,"title":4283,"url":4284,"context":81},"Skim: Speculative Execution for Fast and Efficient Web Agents","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.16565",{"relevance":4286,"novelty":83,"quality":83,"actionability":84,"composite":4287,"reasoning":4288},5,4.15,"Category: AI & LLMs. The article discusses a novel approach to improving web agent performance through speculative execution, addressing a specific pain point of latency in AI-powered web automation. It provides insights into a new framework that could be applied in practical scenarios, although it lacks detailed implementation steps.","\u002Fsummaries\u002Fd6de696fa2e5c21b-skim-accelerating-web-agents-via-speculative-execu-summary","2026-05-19 07:00:55",{"title":4225,"description":63},{"loc":4289},"d6de696fa2e5c21b","summaries\u002Fd6de696fa2e5c21b-skim-accelerating-web-agents-via-speculative-execu-summary",[4296,98,4297,99],"llm","web-automation","Skim improves web agent performance by using speculative execution to predict and pre-process future actions, significantly reducing latency in browser-based automation.",[98,4297,99],"9zx3wxaRePCyBhYD1JoRaAY50f0VFlbS9XW5GzRkOGw",{"id":4302,"title":4303,"ai":4304,"body":4309,"categories":4360,"created_at":71,"date_modified":71,"description":63,"extension":72,"faq":71,"featured":73,"kicker_label":71,"meta":4361,"navigation":87,"path":4369,"published_at":4370,"question":71,"scraped_at":4370,"seo":4371,"sitemap":4372,"source_id":4373,"source_name":93,"source_type":94,"source_url":4365,"stem":4374,"tags":4375,"thumbnail_url":71,"tldr":4377,"tweet":71,"unknown_tags":4378,"__hash__":4379},"summaries\u002Fsummaries\u002F304c0b642d945cf7-taming-the-alignment-tax-in-multi-agent-orchestrat-summary.md","Taming the Alignment Tax in Multi-Agent Orchestration with SDOF",{"provider":7,"model":8,"input_tokens":4305,"output_tokens":4306,"processing_time_ms":4307,"cost_usd":4308},4126,662,3007,0.0020245,{"type":14,"value":4310,"toc":4355},[4311,4315,4318,4322,4325,4328,4332,4335],[17,4312,4314],{"id":4313},"the-problem-the-alignment-tax-in-multi-agent-systems","The Problem: The Alignment Tax in Multi-Agent Systems",[22,4316,4317],{},"Multi-agent systems often suffer from an 'alignment tax'—a performance degradation that occurs when agents are constrained by strict alignment protocols, safety guardrails, or rigid orchestration logic. As developers add more layers of control to ensure agents behave correctly, the models often lose the flexibility required to solve complex, multi-step problems efficiently. This overhead manifests as increased latency, higher token consumption, and a higher rate of task failure when agents struggle to reconcile their internal reasoning with external constraints.",[17,4319,4321],{"id":4320},"the-sdof-solution-state-constrained-dispatch","The SDOF Solution: State-Constrained Dispatch",[22,4323,4324],{},"SDOF (State-Constrained Dispatch) introduces a mechanism to manage these constraints by decoupling the agent's reasoning process from the orchestration logic. Instead of baking alignment rules directly into the prompt or the agent's core instruction set, SDOF uses a state-constrained dispatch layer. This layer acts as a gatekeeper that evaluates the current state of the task against a set of predefined constraints before routing the next action.",[22,4326,4327],{},"By formalizing the state space, SDOF ensures that agents only receive instructions that are valid within the current context. This reduces the cognitive load on the LLM, as it no longer needs to constantly 'self-correct' against global alignment rules during every step of the reasoning chain. The result is a more streamlined execution flow where the model focuses on task completion while the dispatch layer handles the heavy lifting of state management and constraint enforcement.",[17,4329,4331],{"id":4330},"performance-and-reliability-gains","Performance and Reliability Gains",[22,4333,4334],{},"Experimental results demonstrate that SDOF significantly lowers the alignment tax compared to traditional monolithic orchestration approaches. By enforcing constraints at the dispatch level, the system achieves:",[33,4336,4337,4343,4349],{},[36,4338,4339,4342],{},[39,4340,4341],{},"Reduced Token Overhead:"," Fewer corrective prompts are required, as the dispatch layer prevents invalid state transitions before they occur.",[36,4344,4345,4348],{},[39,4346,4347],{},"Higher Success Rates:"," By maintaining a valid state machine, the system avoids the 'hallucination loops' common in multi-agent environments where agents lose track of the global task objective.",[36,4350,4351,4354],{},[39,4352,4353],{},"Improved Predictability:"," The separation of concerns allows developers to update alignment policies independently of the agent's core logic, making the system easier to debug and scale.",{"title":63,"searchDepth":64,"depth":64,"links":4356},[4357,4358,4359],{"id":4313,"depth":64,"text":4314},{"id":4320,"depth":64,"text":4321},{"id":4330,"depth":64,"text":4331},[70],{"content_references":4362,"triage":4367},[4363],{"type":77,"title":4364,"author":79,"url":4365,"context":4366},"SDOF: Taming the Alignment Tax in Multi-Agent Orchestration with State-Constrained Dispatch","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.15204","reviewed",{"relevance":4286,"novelty":83,"quality":83,"actionability":84,"composite":4287,"reasoning":4368},"Category: AI & LLMs. The article addresses the specific pain point of performance degradation in multi-agent systems due to alignment constraints, offering a novel framework (SDOF) that enhances efficiency. It provides experimental results that support its claims, though it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002F304c0b642d945cf7-taming-the-alignment-tax-in-multi-agent-orchestrat-summary","2026-05-18 07:11:44",{"title":4303,"description":63},{"loc":4369},"304c0b642d945cf7","summaries\u002F304c0b642d945cf7-taming-the-alignment-tax-in-multi-agent-orchestrat-summary",[4296,97,98,4376],"orchestration","The SDOF (State-Constrained Dispatch) framework reduces the 'alignment tax' in multi-agent systems by enforcing state constraints during task orchestration, improving performance and reliability without sacrificing model autonomy.",[98,4376],"aaFXd6ew9komqfEIAsG-JzH_kirM0pbF5BmS-2e9DAQ",{"id":4381,"title":4382,"ai":4383,"body":4389,"categories":4574,"created_at":71,"date_modified":71,"description":63,"extension":72,"faq":71,"featured":73,"kicker_label":71,"meta":4575,"navigation":87,"path":4593,"published_at":4594,"question":71,"scraped_at":4595,"seo":4596,"sitemap":4597,"source_id":4598,"source_name":4599,"source_type":94,"source_url":4600,"stem":4601,"tags":4602,"thumbnail_url":71,"tldr":4603,"tweet":71,"unknown_tags":4604,"__hash__":4605},"summaries\u002Fsummaries\u002Fc118d319b56d737f-tst-cuts-llm-pre-training-time-2-5x-at-equal-flops-summary.md","TST Cuts LLM Pre-Training Time 2.5x at Equal FLOPs",{"provider":7,"model":4384,"input_tokens":4385,"output_tokens":4386,"processing_time_ms":4387,"cost_usd":4388},"x-ai\u002Fgrok-4.1-fast",9082,2849,41371,0.0032184,{"type":14,"value":4390,"toc":4568},[4391,4395,4398,4401,4404,4408,4411,4414,4417,4420,4492,4496,4499,4502,4555,4558,4561,4565],[17,4392,4394],{"id":4393},"two-phase-training-boosts-token-throughput-without-architecture-changes","Two-Phase Training Boosts Token Throughput Without Architecture Changes",[22,4396,4397],{},"Token Superposition Training (TST) accelerates LLM pre-training by increasing text processed per FLOP during an initial superposition phase, then recovering to standard next-token prediction. In Phase 1 (first r fraction of steps, optimal r=0.2-0.4), segment input sequences of length L into non-overlapping bags of s contiguous tokens (s=3-16, model-size dependent). Average embeddings per bag to create s-tokens, shortening effective sequence to L\u002Fs. To match baseline FLOPs per step, scale input data length by s×, ingesting s× more tokens per compute unit.",[22,4399,4400],{},"Output predicts next bag via multi-hot cross-entropy (MCE) loss: assign 1\u002Fs probability mass to each of s target tokens, implemented as mean of s standard CE losses using existing fused kernels—no new heads or parameters. Phase 2 resumes from checkpoint with vanilla next-token prediction for remaining 1-r steps, fully removing TST code. Expect 1-2 nat loss spike at transition, resolving in thousands of steps; final model matches standard inference exactly.",[22,4402,4403],{},"Shared embeddings across phases are critical: re-initializing them at boundary on 3B model raises final loss to 2.938 (vs. TST 2.676, baseline 2.808), proving Phase 1 builds transferable representations. Input averaging may regularize embedding geometry (forcing linear separability of s-grams) or act as coarse pre-pre-training; bag prediction echoes multi-token prediction but cheaper, without extra params.",[17,4405,4407],{"id":4406},"results-lower-loss-and-speedups-at-equal-flops-or-loss","Results: Lower Loss and Speedups at Equal FLOPs or Loss",[22,4409,4410],{},"Validated on 270M\u002F600M dense (SmolLM2\u002FLlama3 shapes), 3B dense (SmolLM3), 10B-A1B MoE (Qwen3), using DCLM or DCLM+FineWeb-Edu data, AdamW\u002FWarmup-Stable-Decay LR, TorchTitan\u002FFSDP on B200 GPUs.",[22,4412,4413],{},"At 3B (s=6, r=0.3): 20k TST steps hit loss 2.676 (vs. baseline 2.677 at 36k steps), using 247 GPU-hours vs. 443 (1.8× speedup); HellaSwag 62.4 vs. 62.3, ARC-Easy 66.3 vs. 65.9.",[22,4415,4416],{},"At 10B-A1B MoE (s=16, r≈0.25): TST processes 2T tokens to loss 2.236 (below baseline 2.252 at 1.05T), using 4,768 GPU-hours vs. 12,311 (2.5× speedup); beats baseline on HellaSwag (71.2 vs. 70.1), ARC-Easy (74.2 vs. 73.8), ARC-Challenge (47.3 vs. 46.3), MMLU (39.0 vs. 37.4).",[22,4418,4419],{},"TST wins equal-FLOPs\u002Fequal-loss comparisons; baseline wins equal-data (TST spends less compute per token). Ablations confirm input\u002Foutput mechanisms orthogonal: each beats baseline alone, combined best.",[4421,4422,4423,4448],"table",{},[4424,4425,4426],"thead",{},[4427,4428,4429,4433,4436,4439,4442,4445],"tr",{},[4430,4431,4432],"th",{},"Model",[4430,4434,4435],{},"s",[4430,4437,4438],{},"r",[4430,4440,4441],{},"TST GPU-hrs",[4430,4443,4444],{},"Baseline GPU-hrs",[4430,4446,4447],{},"Speedup",[4449,4450,4451,4472],"tbody",{},[4427,4452,4453,4457,4460,4463,4466,4469],{},[4454,4455,4456],"td",{},"3B",[4454,4458,4459],{},"6",[4454,4461,4462],{},"0.3",[4454,4464,4465],{},"247",[4454,4467,4468],{},"443",[4454,4470,4471],{},"1.8×",[4427,4473,4474,4477,4480,4483,4486,4489],{},[4454,4475,4476],{},"10B MoE",[4454,4478,4479],{},"16",[4454,4481,4482],{},"0.25",[4454,4484,4485],{},"4768",[4454,4487,4488],{},"12311",[4454,4490,4491],{},"2.5×",[17,4493,4495],{"id":4494},"practical-implementation-minimal-code-changes-defined-hyperparams","Practical Implementation: Minimal Code Changes, Defined Hyperparams",[22,4497,4498],{},"PyTorch tweaks: (1) fold inputs into bags pre-embedding; (2) average embeddings (sum in float32 for precision); (3) MCE loss on output. For large s≥8, use power-law weighting (1\u002Fi, k≈-1.25) over uniform.",[22,4500,4501],{},"Hyperparams:",[4421,4503,4504,4515],{},[4424,4505,4506],{},[4427,4507,4508,4510,4513],{},[4430,4509,4432],{},[4430,4511,4512],{},"s Range",[4430,4514,4438],{},[4449,4516,4517,4528,4538,4546],{},[4427,4518,4519,4522,4525],{},[4454,4520,4521],{},"270M",[4454,4523,4524],{},"3-8",[4454,4526,4527],{},"0.2-0.4",[4427,4529,4530,4533,4536],{},[4454,4531,4532],{},"600M",[4454,4534,4535],{},"6-10",[4454,4537,4527],{},[4427,4539,4540,4542,4544],{},[4454,4541,4456],{},[4454,4543,4459],{},[4454,4545,4462],{},[4427,4547,4548,4550,4552],{},[4454,4549,4476],{},[4454,4551,4479],{},[4454,4553,4554],{},"~0.25",[22,4556,4557],{},"Failures to avoid: positional encodings pre-average (hurts), RoPE rescaling at switch (risks higher loss), separate heads per token (no gain, more cost), binary\u002FCE losses (underperform), retaining TST in Phase 2 (untested).",[22,4559,4560],{},"Use TST for compute-bound runs with ample data; skip if data-limited. Output-only variant suits data-bound.",[17,4562,4564],{"id":4563},"trade-offs-compute-efficiency-at-cost-of-data","Trade-offs: Compute Efficiency at Cost of Data",[22,4566,4567],{},"TST trades lower compute per token for higher throughput, ideal when GPUs bottleneck but data abundant. No inference overhead, drop-in for existing stacks. Simplest version (mean in\u002Fout, hard switch) optimal—no extras needed.",{"title":63,"searchDepth":64,"depth":64,"links":4569},[4570,4571,4572,4573],{"id":4393,"depth":64,"text":4394},{"id":4406,"depth":64,"text":4407},{"id":4494,"depth":64,"text":4495},{"id":4563,"depth":64,"text":4564},[70],{"content_references":4576,"triage":4590},[4577,4581,4585,4588],{"type":77,"title":4578,"author":4579,"url":4580,"context":81},"Token Superposition Training","Nous Research","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2605.06546",{"type":4204,"title":4582,"url":4583,"context":4584},"Token Superposition Training Project","https:\u002F\u002Fnousresearch.com\u002Ftoken-superposition","mentioned",{"type":4586,"title":4587,"context":4584},"dataset","DCLM",{"type":4586,"title":4589,"context":4584},"FineWeb-Edu",{"relevance":84,"novelty":83,"quality":83,"actionability":64,"composite":4591,"reasoning":4592},3.25,"Category: AI & LLMs. The article discusses a novel training method for LLMs that significantly improves pre-training efficiency, which is relevant to AI engineering. However, while it presents new insights into the training process, it lacks practical steps or frameworks that the audience can directly implement.","\u002Fsummaries\u002Fc118d319b56d737f-tst-cuts-llm-pre-training-time-2-5x-at-equal-flops-summary","2026-05-14 05:46:32","2026-05-14 07:01:01",{"title":4382,"description":63},{"loc":4593},"c118d319b56d737f","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F13\u002Fnous-research-releases-token-superposition-training-to-speed-up-llm-pre-training-by-up-to-2-5x-across-270m-to-10b-parameter-models\u002F","summaries\u002Fc118d319b56d737f-tst-cuts-llm-pre-training-time-2-5x-at-equal-flops-summary",[4296,97],"Token Superposition Training (TST) averages s contiguous token embeddings for early training phase (r=0.2-0.4 steps), boosting throughput s× per FLOP; resumes standard prediction, yielding lower loss and 1.8-2.5x wall-clock speedup on 270M-10B models.",[],"-majUbz9-XC3KeOhnAq687h-6HEzUwKpZRcvV3QtYmA"]