[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-f6adfc8e9acbe4c6-building-embodied-foundation-models-with-perceptiv-summary":3,"summaries-facets-categories":113,"summary-related-f6adfc8e9acbe4c6-building-embodied-foundation-models-with-perceptiv-summary":7819},{"id":4,"title":5,"ai":6,"body":13,"categories":71,"created_at":73,"date_modified":73,"description":64,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":76,"navigation":92,"path":93,"published_at":94,"question":73,"scraped_at":95,"seo":96,"sitemap":97,"source_id":98,"source_name":99,"source_type":100,"source_url":101,"stem":102,"tags":103,"thumbnail_url":108,"tldr":109,"tweet":110,"unknown_tags":111,"__hash__":112},"summaries\u002Fsummaries\u002Ff6adfc8e9acbe4c6-building-embodied-foundation-models-with-perceptiv-summary.md","Building Embodied Foundation Models with Perceptive Objectives",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",8315,731,3645,0.00317525,{"type":14,"value":15,"toc":63},"minimark",[16,21,25,29,32,49,53,56,60],[17,18,20],"h2",{"id":19},"unifying-perception-reasoning-and-control","Unifying Perception, Reasoning, and Control",[22,23,24],"p",{},"Perceptron AI advocates for moving away from the fragmented landscape of Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models toward unified \"embodied foundation models.\" The goal is to create a single architecture capable of perceiving, reasoning, and acting in the physical world. By training jointly on text, images, video, and trajectories (from desktop use to video games), these models achieve frontier-level performance in embodied reasoning at a fraction of the cost of larger proprietary models.",[17,26,28],{"id":27},"solving-data-sparsity-and-context-bloat","Solving Data Sparsity and Context Bloat",[22,30,31],{},"Training on video data presents two major challenges: lack of ground truth and context bloat.",[33,34,35,43],"ul",{},[36,37,38,42],"li",{},[39,40,41],"strong",{},"Perceptive Objectives:"," Rather than predicting every pixel—which treats background noise with the same weight as critical contact points—Perceptron uses a learned \"perceptive objective.\" This allows the model to automatically identify and prioritize the percepts that matter for future actions, such as the tip of a robotic gripper.",[36,44,45,48],{},[39,46,47],{},"Data-Sparse Mixture of Experts:"," To handle the massive context of \"always-on\" cameras, the team employs a router-based architecture that decides per layer which tokens to process and which to skip. This allows the model to dynamically allocate compute based on the task (e.g., zooming into a graph or segmenting specific objects), effectively managing context bloat without hard-coded architectural priors.",[17,50,52],{"id":51},"new-scaling-laws-for-robotics","New Scaling Laws for Robotics",[22,54,55],{},"One of the most significant findings is a new scaling law for embodied agents: video pre-training can effectively substitute for expensive teleoperation data. The team observed that 10x more video pre-training data can compensate for 10x less teleop data. This is a critical breakthrough, as teleop data costs approximately $100 per hour to collect. By leveraging this trade-off, the model can perform complex, multi-step tasks—such as reading book titles and sorting them into bins—zero-shot, by emitting control tokens directly from its reasoning process.",[17,57,59],{"id":58},"agentic-detection-and-robustness","Agentic Detection and Robustness",[22,61,62],{},"By framing detection as an agentic task rather than a static classification problem, the model can actively interact with its input. It can tile images, increase contrast, and propose boxes until it successfully identifies hard-to-find objects. This joint modeling of perception and control also leads to higher robustness against environmental changes, such as shifting backgrounds or lighting conditions, compared to traditional VLA models that often fail when the visual context is slightly altered.",{"title":64,"searchDepth":65,"depth":65,"links":66},"",2,[67,68,69,70],{"id":19,"depth":65,"text":20},{"id":27,"depth":65,"text":28},{"id":51,"depth":65,"text":52},{"id":58,"depth":65,"text":59},[72],"AI & LLMs",null,"md",false,{"content_references":77,"triage":87},[78,83],{"type":79,"title":80,"author":81,"context":82},"tool","Momo","AI2","mentioned",{"type":79,"title":84,"author":85,"context":86},"Gemini 3.1 Pro","Google DeepMind","cited",{"relevance":88,"novelty":89,"quality":89,"actionability":65,"composite":90,"reasoning":91},3,4,3.25,"Category: AI & LLMs. The article discusses the development of embodied foundation models, which is relevant to AI engineering and addresses the challenge of context bloat in AI applications. However, it lacks specific actionable steps or frameworks that the audience could implement directly.",true,"\u002Fsummaries\u002Ff6adfc8e9acbe4c6-building-embodied-foundation-models-with-perceptiv-summary","2026-09-23 16:00:30","2026-09-24 03:20:02",{"title":5,"description":64},{"loc":93},"f6adfc8e9acbe4c6","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZZcE0HeO-Hc","summaries\u002Ff6adfc8e9acbe4c6-building-embodied-foundation-models-with-perceptiv-summary",[104,105,106,107],"llm","agents","ai-tools","robotics","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FZZcE0HeO-Hc\u002Fhqdefault.jpg","Perceptron AI is moving beyond traditional VLMs by unifying perception, reasoning, and control into a single 'embodied foundation model' that uses data-sparse mixture-of-experts to handle context bloat and learns task-relevant percepts automatically.","This presentation outlines [Perceptron's](https:\u002F\u002Fperceptron.inc) research approach to building embodied foundation models, focusing on two specific technical hurdles: creating self-supervised objectives that prioritize task-relevant visual tokens and using data-sparse mixture-of-experts to manage context bloat. The speaker, [Armen Aghajanyan](https:\u002F\u002Fx.com\u002FArmenAgha), argues that these architectural choices allow for more efficient scaling by substituting expensive teleoperation data with large-scale video pretraining.",[107],"L5P39lhMQewFI6qcWKibme-xblA2mV_GLJ0iisIIKVE",[114,116,119,121,124,126,129,132,134,136,138,140,143,146,148,150,152,154,157,159,161,163,165,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,411,413,415,417,419,421,423,425,427,429,431,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,467,469,471,473,475,477,479,481,483,485,487,489,491,493,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,565,567,569,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,613,615,617,619,621,623,625,627,629,631,633,635,637,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,957,959,961,963,965,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,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A critical challenge was the original Qwen3-TTS implementation, which failed to meet the latency requirements for real-time agents.",[22,7845,7846],{},"Andres Marafioti achieved a 5.8x real-time performance boost (reducing time-to-first-audio to under 200ms) by addressing three primary bottlenecks:",[33,7848,7849,7855,7861],{},[36,7850,7851,7854],{},[39,7852,7853],{},"Streaming:"," Implementing streaming to avoid waiting for the full audio generation.",[36,7856,7857,7860],{},[39,7858,7859],{},"CPU-GPU Overhead:"," Eliminating the constant data transfer between CPU and GPU by compiling the model for direct GPU execution.",[36,7862,7863,7866],{},[39,7864,7865],{},"KV Cache Management:"," Replacing the dynamic KV cache with a static one, which allowed for the use of CUDA graph captures to accelerate generation.",[17,7868,7870],{"id":7869},"infrastructure-and-deployment-strategy","Infrastructure and Deployment Strategy",[22,7872,7873],{},"The team manages a large fleet of robots using a load-balanced architecture that separates LLM inference from conversation nodes. This separation is vital because conversation nodes handle varying user activity levels; by decoupling the LLM endpoints, the system can scale compute resources efficiently based on actual conversation volume rather than just the number of connected devices. The system also performs echo cancellation and partial transcription (every 150ms) to allow the robot to react mid-sentence, ensuring a responsive, natural user experience.",{"title":64,"searchDepth":65,"depth":65,"links":7875},[7876,7877,7878],{"id":7832,"depth":65,"text":7833},{"id":7839,"depth":65,"text":7840},{"id":7869,"depth":65,"text":7870},[128],{"content_references":7881,"triage":7891},[7882,7885,7888],{"type":79,"title":7883,"url":7884,"context":82},"Reachy Mini","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Freachy-mini",{"type":79,"title":7886,"url":7887,"context":82},"Qwen3-TTS","https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen2-Audio",{"type":79,"title":7889,"url":7890,"context":82},"Parakeet","https:\u002F\u002Fhuggingface.co\u002Fnvidia\u002Fparakeet-rnnt-1.1b",{"relevance":7892,"novelty":89,"quality":89,"actionability":89,"composite":7893,"reasoning":7894},5,4.35,"Category: AI & LLMs. The article provides in-depth insights into optimizing voice AI for real-time interaction, addressing specific pain points like latency in voice agents, which is crucial for product builders. It details practical optimizations made to the Qwen3-TTS model, offering actionable strategies that developers can implement in their own AI projects.","\u002Fsummaries\u002F7bba90a7c7e3e107-optimizing-open-source-robotics-and-real-time-voic-summary","2026-05-29 17:00:06","2026-05-30 14:00:27",{"title":7822,"description":64},{"loc":7895},"7bba90a7c7e3e107","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0jeZfjJMfmo","summaries\u002F7bba90a7c7e3e107-optimizing-open-source-robotics-and-real-time-voic-summary",[104,105,106,107],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F0jeZfjJMfmo\u002Fhqdefault.jpg","Andres Marafioti details how Hugging Face is democratizing robotics with the $300 Reachy Mini, and explains how he optimized Qwen3-TTS to achieve 5.8x real-time performance for low-latency voice interaction.","This talk covers the technical optimization of a text-to-speech pipeline for the [Reachy Mini](https:\u002F\u002Freachy.pollen-robotics.com\u002F), an open-source, modular robot kit. The speaker details how they improved Qwen3-TTS latency by implementing a static KV cache and CUDA graph captures, alongside a high-level overview of the robot's voice-agent architecture.",[107],"x7goukxPFyMd9yyMdmTrusJpL8EQIJ01_HtCuVyeghM",{"id":7910,"title":7911,"ai":7912,"body":7918,"categories":7957,"created_at":73,"date_modified":73,"description":64,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":7958,"navigation":92,"path":7984,"published_at":7985,"question":73,"scraped_at":7986,"seo":7987,"sitemap":7988,"source_id":7989,"source_name":7990,"source_type":7991,"source_url":7992,"stem":7993,"tags":7994,"thumbnail_url":73,"tldr":7995,"tweet":73,"unknown_tags":7996,"__hash__":7997},"summaries\u002Fsummaries\u002Fafcbccfc90adeba9-gemini-s-push-to-agentic-browser-robots-and-skill-summary.md","Gemini's Push to Agentic Browser, Robots, and Skill Eval",{"provider":7,"model":7913,"input_tokens":7914,"output_tokens":7915,"processing_time_ms":7916,"cost_usd":7917},"x-ai\u002Fgrok-4.1-fast",6619,1978,15418,0.00180595,{"type":14,"value":7919,"toc":7951},[7920,7924,7927,7930,7934,7937,7941,7944,7948],[17,7921,7923],{"id":7922},"browser-level-agents-via-reusable-skills-and-enterprise-tabs","Browser-Level Agents via Reusable Skills and Enterprise Tabs",[22,7925,7926],{},"Chrome's new Gemini Skills (rolled out April 14, 2024 on Mac\u002FWindows\u002FChromeOS, English US only) let you save prompts as reusable workflows, triggered by slash or + button, running instantly on current or multiple tabs. This eliminates retyping for tasks like ingredient analysis or spec comparison—open five product pages, trigger once, get unified output. Edit\u002Fcustomize skills anytime; Google's pre-made library covers gift picking (budget\u002Fpreferences) and document scanning. Safety gates confirm high-impact actions (e.g., email\u002Fcalendar), using Chrome's red-teaming\u002Fauto-updates. Trade-off: Browser-templated prompts democratize LangChain-style libraries for non-devs but tie to Gemini's ecosystem.",[22,7928,7929],{},"Enterprise Gemini tests an Agent tab with 'New Task'\u002F'Inbox' for multi-step execution: side panel tracks goals, agents, apps\u002Ffiles, human-review toggle. Mirrors Claude Projects—define goal, grant tool access, execute autonomously. Signals full desktop agents, potentially via upcoming AI Studio app, evolving from chat to workspace for persistent, tool-using workflows.",[17,7931,7933],{"id":7932},"notebooklm-evolves-into-visual-data-hub","NotebookLM Evolves into Visual Data Hub",[22,7935,7936],{},"Tested Canvas turns sources into timelines, interactive pages, or visualizers, shifting from summaries to structured apps. Connectors pull external data (Google ecosystem first), plus autolabeling for large datasets, fixing navigation pain in multi-source analysis. Builds central research layer, enabling dynamic experiences from static uploads.",[17,7938,7940],{"id":7939},"robotics-er-16-enables-reliable-real-world-tasks","Robotics-ER 1.6 Enables Reliable Real-World Tasks",[22,7942,7943],{},"Paired with VLA (direct robot control), ER 1.6 reasons\u002Fplans: boosted spatial skills (pointing\u002Fcounting\u002Fobject relations, pixel-accurate paths\u002Fconstraints) avoid hallucinations (e.g., correctly IDs hammers\u002Fscissors). Multi-view success detection handles occlusions\u002Fdynamics for autonomous retry\u002Fdecide. New: instrument reading (gauges\u002Fmeters\u002Fdisplays) via agentic vision—zoom\u002Fanalyze\u002Frun code\u002Fapply knowledge. On Boston Dynamics Spot: ER 1.5 at 23%, Gemini 3.0 Flash 67%, ER 1.6 86%, +agentic 93%. Unlocks facility navigation\u002Finterpretation without humans.",[17,7945,7947],{"id":7946},"vantage-llms-score-durable-human-skills-accurately","Vantage: LLMs Score 'Durable' Human Skills Accurately",[22,7949,7950],{},"Executive LLM steers AI personas to probe collaboration\u002Fcreativity\u002Fcritical thinking (e.g., inject conflict for resolution tests), outperforming independent agents. In 188 participants\u002F373 convos: project mgmt evidence 92.4%, conflict 85%; scoring matches humans (Cohen's Kappa 0.45-0.64). Creativity on 180 student works: 0.88 Pearson vs. experts. Simulates skill levels pre-human studies (lower error, matches real patterns). Outputs skills maps linking scores to convo snippets for interpretability—scales beyond knowledge tests.",{"title":64,"searchDepth":65,"depth":65,"links":7952},[7953,7954,7955,7956],{"id":7922,"depth":65,"text":7923},{"id":7932,"depth":65,"text":7933},{"id":7939,"depth":65,"text":7940},{"id":7946,"depth":65,"text":7947},[167],{"content_references":7959,"triage":7981},[7960,7964,7967,7970,7973,7977],{"type":7961,"title":7962,"url":7963,"context":86},"other","Skills in Chrome","https:\u002F\u002Fblog.google\u002Fproducts-and-platforms\u002Fproducts\u002Fchrome\u002Fskills-in-chrome\u002F",{"type":7961,"title":7965,"url":7966,"context":86},"Google is quietly testing Gemini agents with a brand new Agent tab","https:\u002F\u002Fwww.testingcatalog.com\u002Fgoogle-is-quietly-testing-gemini-agents-with-a-brand-new-agent-tab\u002F",{"type":7961,"title":7968,"url":7969,"context":86},"Google tests Canvas and Connectors on NotebookLM","https:\u002F\u002Fwww.testingcatalog.com\u002Fgoogle-tests-canvas-and-connectors-on-notebooklm\u002F",{"type":7961,"title":7971,"url":7972,"context":86},"Gemini Robotics-ER 1.6","https:\u002F\u002Fdeepmind.google\u002Fblog\u002Fgemini-robotics-er-1-6\u002F",{"type":7974,"title":7975,"url":7976,"context":86},"paper","Toward Scalable Measurement of Durable Skills","https:\u002F\u002Fservices.google.com\u002Ffh\u002Ffiles\u002Fmisc\u002Ftoward_scalable_measurement_of_durable_skills.pdf",{"type":79,"title":7978,"url":7979,"context":7980},"Seedance 2.0 on Higgsfield","https:\u002F\u002Fhiggsfield.ai\u002Fs\u002Fseedance-2-0-airevolutionx-yDYwTG","recommended",{"relevance":88,"novelty":88,"quality":89,"actionability":65,"composite":7982,"reasoning":7983},3.05,"Category: AI & LLMs. The article discusses new features in Chrome's Gemini Skills that enable reusable workflows for AI agents, which is relevant to AI engineering. However, while it presents some new functionalities, it lacks detailed actionable steps for implementation, making it less practical for immediate application.","\u002Fsummaries\u002Fafcbccfc90adeba9-gemini-s-push-to-agentic-browser-robots-and-skill-summary","2026-04-15 21:57:46","2026-04-19 03:36:52",{"title":7911,"description":64},{"loc":7984},"afcbccfc90adeba9","AI Revolution","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5TA0Ul2eS_k","summaries\u002Fafcbccfc90adeba9-gemini-s-push-to-agentic-browser-robots-and-skill-summary",[104,105,106,107],"Chrome's Gemini Skills enable reusable multi-tab prompts (e.g., compare products across tabs), Enterprise tests agent workspaces with human review, Robotics-ER 1.6 hits 93% gauge-reading accuracy on Spot, Vantage uses executive LLMs to score human creativity\u002Fconflict resolution at 0.88 correlation with experts.",[107],"RXQymNqsM_23wtoGzF3qpjKnpyBRUHS9rVLoEO7RPoo",{"id":7999,"title":8000,"ai":8001,"body":8006,"categories":8026,"created_at":73,"date_modified":73,"description":64,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":8027,"navigation":92,"path":8036,"published_at":8037,"question":73,"scraped_at":8037,"seo":8038,"sitemap":8039,"source_id":8040,"source_name":8041,"source_type":7991,"source_url":8032,"stem":8042,"tags":8043,"thumbnail_url":73,"tldr":8044,"tweet":73,"unknown_tags":8045,"__hash__":8046},"summaries\u002Fsummaries\u002Fcedf57bb6ac50ed2-agentao-a-governed-local-first-runtime-for-ai-agen-summary.md","Agentao: A Governed Local-First Runtime for AI Agents",{"provider":7,"model":8,"input_tokens":8002,"output_tokens":8003,"processing_time_ms":8004,"cost_usd":8005},4039,405,2227,0.00161725,{"type":14,"value":8007,"toc":8022},[8008,8012,8015,8019],[17,8009,8011],{"id":8010},"the-shift-toward-local-first-agent-governance","The Shift Toward Local-First Agent Governance",[22,8013,8014],{},"Agentao addresses the critical security and control challenges inherent in deploying tool-using LLM agents. Current agentic workflows often rely on cloud-based runtimes where the execution environment is opaque, making it difficult to audit agent behavior, restrict tool access, or ensure data privacy. Agentao proposes a local-first runtime architecture that places the agent's execution within a governed, local environment. This approach allows developers to define strict boundaries for what tools an agent can access and how it interacts with local system resources, effectively mitigating the risks of unauthorized tool execution or data exfiltration.",[17,8016,8018],{"id":8017},"architecture-for-secure-tool-interaction","Architecture for Secure Tool Interaction",[22,8020,8021],{},"The core value of Agentao lies in its governance model. By moving the runtime to a local-first paradigm, the framework enables developers to implement fine-grained access control policies that are enforced at the execution layer rather than the prompt layer. This ensures that even if an LLM is prompted to perform an unauthorized action, the runtime environment acts as a hard constraint, preventing the execution of restricted tools or unauthorized file system access. This design is particularly relevant for multi-agent systems where individual agents may have varying levels of trust and permission scopes, providing a standardized way to manage these interactions without sacrificing the flexibility of LLM-based decision-making.",{"title":64,"searchDepth":65,"depth":65,"links":8023},[8024,8025],{"id":8010,"depth":65,"text":8011},{"id":8017,"depth":65,"text":8018},[72],{"content_references":8028,"triage":8033},[8029],{"type":7961,"title":8030,"author":8031,"url":8032,"context":82},"Agentao: A Governed Local-First Runtime for Tool-Using LLM Agents","Unknown","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.13574",{"relevance":7892,"novelty":89,"quality":89,"actionability":88,"composite":8034,"reasoning":8035},4.15,"Category: AI & LLMs. The article discusses a local-first runtime for AI agents, addressing security and governance, which is highly relevant for developers integrating AI into products. It presents a novel approach to managing agent interactions and governance, though it lacks specific implementation details that would enhance actionability.","\u002Fsummaries\u002Fcedf57bb6ac50ed2-agentao-a-governed-local-first-runtime-for-ai-agen-summary","2026-08-18 03:10:24",{"title":8000,"description":64},{"loc":8036},"cedf57bb6ac50ed2","arXiv cs.AI","summaries\u002Fcedf57bb6ac50ed2-agentao-a-governed-local-first-runtime-for-ai-agen-summary",[105,104,106],"Agentao is a proposed local-first runtime designed to provide governance and security for tool-using LLM agents, shifting control from cloud-based black boxes to local execution environments.",[],"WWKqUt7tRqK8cLeNO9hxy6EPoNBwZ8lC4CIFsNSnBDc",{"id":8048,"title":8049,"ai":8050,"body":8055,"categories":8104,"created_at":73,"date_modified":73,"description":64,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":8105,"navigation":92,"path":8113,"published_at":8114,"question":73,"scraped_at":8114,"seo":8115,"sitemap":8116,"source_id":8117,"source_name":8041,"source_type":7991,"source_url":8109,"stem":8118,"tags":8119,"thumbnail_url":73,"tldr":8120,"tweet":73,"unknown_tags":8121,"__hash__":8122},"summaries\u002Fsummaries\u002F21fc8eb6f77f5baa-shape-your-feed-agentic-conversational-recommendat-summary.md","Shape Your Feed: Agentic Conversational Recommendation Systems",{"provider":7,"model":8,"input_tokens":8051,"output_tokens":8052,"processing_time_ms":8053,"cost_usd":8054},4037,586,3156,0.00188825,{"type":14,"value":8056,"toc":8099},[8057,8061,8064,8068,8071,8092,8096],[17,8058,8060],{"id":8059},"moving-beyond-static-recommendation-feeds","Moving Beyond Static Recommendation Feeds",[22,8062,8063],{},"Traditional recommendation systems often rely on static, batch-processed algorithms that struggle to incorporate immediate user feedback or nuanced preferences. The 'Shape Your Feed' framework introduces an agentic approach to this problem, leveraging Large Language Models (LLMs) to act as an intermediary between the user and the recommendation engine. By treating the feed as a conversational interface, the system allows users to refine their content streams through natural language, effectively turning the recommendation process into a dynamic, iterative dialogue.",[17,8065,8067],{"id":8066},"the-agentic-architecture","The Agentic Architecture",[22,8069,8070],{},"The core of the system is an agentic loop that manages the interaction between user intent and content retrieval. Instead of a single-shot prediction, the agent performs three critical functions:",[8072,8073,8074,8080,8086],"ol",{},[36,8075,8076,8079],{},[39,8077,8078],{},"Intent Interpretation:"," The LLM parses natural language input to extract specific constraints, preferences, or feedback on existing items.",[36,8081,8082,8085],{},[39,8083,8084],{},"Dynamic Retrieval Strategy:"," Rather than relying solely on pre-computed embeddings, the agent translates user intent into actionable queries that can adjust the retrieval parameters or re-rank the current feed.",[36,8087,8088,8091],{},[39,8089,8090],{},"Feedback Integration:"," The system maintains a stateful context of the conversation, allowing it to learn from negative feedback (e.g., 'I don't like this type of content') and positive reinforcement in real-time, significantly reducing the 'cold start' friction for new user preferences.",[17,8093,8095],{"id":8094},"practical-implications-for-industrial-systems","Practical Implications for Industrial Systems",[22,8097,8098],{},"Accepted for the RecSys 2026 Industrial Track, this approach addresses the scalability and latency challenges typically associated with LLM-integrated systems. By offloading complex reasoning to the agent while keeping the heavy-lifting retrieval tasks optimized, the system demonstrates that conversational recommendation can be performant enough for production environments. The primary trade-off is the increased complexity in managing state and context across long-running sessions, but the benefit is a significantly higher degree of user agency and satisfaction compared to traditional 'black-box' recommendation models.",{"title":64,"searchDepth":65,"depth":65,"links":8100},[8101,8102,8103],{"id":8059,"depth":65,"text":8060},{"id":8066,"depth":65,"text":8067},{"id":8094,"depth":65,"text":8095},[72],{"content_references":8106,"triage":8111},[8107],{"type":7974,"title":8108,"url":8109,"context":8110},"Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.06632","reviewed",{"relevance":7892,"novelty":89,"quality":89,"actionability":88,"composite":8034,"reasoning":8112},"Category: AI & LLMs. The article presents a novel framework for transforming static recommendation systems into interactive, conversational experiences using LLMs, addressing a specific pain point of traditional systems. It provides insights into the architecture and functions of the agentic loop, though it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002F21fc8eb6f77f5baa-shape-your-feed-agentic-conversational-recommendat-summary","2026-08-11 03:21:37",{"title":8049,"description":64},{"loc":8113},"21fc8eb6f77f5baa","summaries\u002F21fc8eb6f77f5baa-shape-your-feed-agentic-conversational-recommendat-summary",[104,105,106],"The 'Shape Your Feed' framework uses LLM-based agents to transform static recommendation feeds into interactive, conversational experiences that adapt to user feedback in real-time.",[],"aKrsHmqYkbUpdCuUSqvmWdXdBuKzCpBoCJsDP8jQ7rk"]