--- title: Autoresearch category: topics sourceLabels: - Official schedule - Public YouTube livestream transcript - Local slide OCR last_auto_summarized: '2026-07-06T19:58:14.630Z' --- # Autoresearch ## Overview AutoResearch is the use of agents to search, read, compare, synthesize, and sometimes design experiments over a body of evidence. The goal is not just summarization; it is repeatable research workflow support with source tracking, uncertainty management, and follow-up planning. AutoResearch is the use of agents to search, read, compare, synthesize, and sometimes design experiments over a body of evidence. The goal is not just summarization; it is repeatable research workflow support with source tracking, uncertainty management, and follow-up planning. AutoResearch is the use of agents to search, read, compare, synthesize, and sometimes design experiments over a body of evidence. The goal is not just summarization; it is repeatable research workflow support with source tracking, uncertainty management, and follow-up planning. AutoResearch is the use of agents to search, read, compare, synthesize, and sometimes design experiments over a body of evidence. The goal is not just summarization; it is repeatable research workflow support with source tracking, uncertainty management, and follow-up planning. AutoResearch is the use of agents to search, read, compare, synthesize, and sometimes design experiments over a body of evidence. The goal is not just summarization; it is repeatable research workflow support with source tracking, uncertainty management, and follow-up planning. AutoResearch is the use of agents to search, read, compare, synthesize, benchmark, and sometimes design follow-up experiments over a body of evidence. In the WF2026 Autoresearch track, the concept spans automated AI research, dense retrieval with test-time compute over frozen embedding models, autonomous research-agent loops, reflective self-improvement of context and model weights, kernel optimization, and production pathways from frontier ML research into usable systems. The goal is not just summarization; it is repeatable research workflow support with source tracking, uncertainty management, evaluation, and clear next-step planning. ## Conference Context It grew from literature search, systematic review methods, research assistants, web search, RAG, and scientific-discovery tooling. LLM agents added the ability to decompose questions, inspect sources, generate hypotheses, and produce structured research artifacts. It grew from literature search, systematic review methods, research assistants, web search, RAG, and scientific-discovery tooling. LLM agents added the ability to decompose questions, inspect sources, generate hypotheses, and produce structured research artifacts. It grew from literature search, systematic review methods, research assistants, web search, RAG, and scientific-discovery tooling. LLM agents added the ability to decompose questions, inspect sources, generate hypotheses, and produce structured research artifacts. It grew from literature search, systematic review methods, research assistants, web search, RAG, and scientific-discovery tooling. LLM agents added the ability to decompose questions, inspect sources, generate hypotheses, and produce structured research artifacts. It grew from literature search, systematic review methods, research assistants, web search, RAG, and scientific-discovery tooling. LLM agents added the ability to decompose questions, inspect sources, generate hypotheses, and produce structured research artifacts. It grew from literature search, systematic review methods, research assistants, web search, RAG, benchmarking, and scientific-discovery tooling. LLM agents added the ability to decompose questions, inspect sources, generate hypotheses, compare evidence, and produce structured research artifacts. The connected WF2026 material places AutoResearch in a broader shift from one-off retrieval toward closed-loop systems: agents that gather evidence, run or propose tests, improve their own harnesses, and move research ideas toward production workflows. ## Significance Research work is expensive because it involves discovery, filtering, evidence comparison, and synthesis under uncertainty. Agents can accelerate the mechanical parts, but only if they preserve citations, distinguish claims from evidence, and expose gaps. Research work is expensive because it involves discovery, filtering, evidence comparison, and synthesis under uncertainty. Agents can accelerate the mechanical parts, but only if they preserve citations, distinguish claims from evidence, and expose gaps. Research work is expensive because it involves discovery, filtering, evidence comparison, and synthesis under uncertainty. Agents can accelerate the mechanical parts, but only if they preserve citations, distinguish claims from evidence, and expose gaps. Research work is expensive because it involves discovery, filtering, evidence comparison, and synthesis under uncertainty. Agents can accelerate the mechanical parts, but only if they preserve citations, distinguish claims from evidence, and expose gaps. Research work is expensive because it involves discovery, filtering, evidence comparison, and synthesis under uncertainty. Agents can accelerate the mechanical parts, but only if they preserve citations, distinguish claims from evidence, and expose gaps. Research work is expensive because it involves discovery, filtering, evidence comparison, synthesis under uncertainty, and judgment about what to try next. The connected sessions make the topic concrete: Richard Socher frames automated AI research as an emerging research direction, Han Xiao ties autoresearch to retrieval quality and test-time compute, Tim Sweeney focuses on autonomous research-agent loops, and Lakshya Agrawal connects self-improvement to context, harnesses, and model weights. Agents can accelerate the mechanical parts, but only if they preserve citations, distinguish claims from evidence, and expose gaps instead of hiding uncertainty behind polished prose. ## Applied Use Start with a clear research question, use source-specific retrieval, keep a claim-evidence table, record search terms and inclusion criteria, and separate facts, interpretations, and open questions. Use humans for scope, judgment, and final conclusions. AutoResearch is useful for technical due diligence, market maps, literature reviews, competitive analysis, policy research, product discovery, and engineering design investigations. Use it when the answer depends on multiple sources or evolving evidence. Avoid relying on it as a black-box oracle for high-stakes conclusions without human review. Start with a clear research question, use source-specific retrieval, keep a claim-evidence table, record search terms and inclusion criteria, and separate facts, interpretations, and open questions. Use humans for scope, judgment, and final conclusions. AutoResearch is useful for technical due diligence, market maps, literature reviews, competitive analysis, policy research, product discovery, and engineering design investigations. Use it when the answer depends on multiple sources or evolving evidence. Avoid relying on it as a black-box oracle for high-stakes conclusions without human review. Start with a clear research question, use source-specific retrieval, keep a claim-evidence table, record search terms and inclusion criteria, and separate facts, interpretations, and open questions. Use humans for scope, judgment, and final conclusions. AutoResearch is useful for technical due diligence, market maps, literature reviews, competitive analysis, policy research, product discovery, and engineering design investigations. Use it when the answer depends on multiple sources or evolving evidence. Avoid relying on it as a black-box oracle for high-stakes conclusions without human review. Start with a clear research question, use source-specific retrieval, keep a claim-evidence table, record search terms and inclusion criteria, and separate facts, interpretations, and open questions. Use humans for scope, judgment, and final conclusions. AutoResearch is useful for technical due diligence, market maps, literature reviews, competitive analysis, policy research, product discovery, and engineering design investigations. Use it when the answer depends on multiple sources or evolving evidence. Avoid relying on it as a black-box oracle for high-stakes conclusions without human review. Start with a clear research question, use source-specific retrieval, keep a claim-evidence table, record search terms and inclusion criteria, and separate facts, interpretations, and open questions. Use humans for scope, judgment, and final conclusions. AutoResearch is useful for technical due diligence, market maps, literature reviews, competitive analysis, policy research, product discovery, and engineering design investigations. Use it when the answer depends on multiple sources or evolving evidence. Avoid relying on it as a black-box oracle for high-stakes conclusions without human review. Start with a clear research question, source-specific retrieval, and an explicit record of search terms, inclusion criteria, and excluded evidence. Keep a claim-evidence table that separates official schedule facts, transcript-backed observations, slide/OCR-derived notes, interpretations, and open questions. Use agentic search and memory for multi-step exploration, but pair them with agent evaluations, benchmark design, and human review before treating outputs as conclusions. For engineering research, connect the synthesis to reproducible artifacts: experiments, eval harnesses, retrieval tests, kernel benchmarks, or implementation plans. AutoResearch is useful for technical due diligence, literature reviews, market maps, competitive analysis, financial-compliance document correlation, product discovery, and engineering design investigations. In this wiki, it is also a method for conference intelligence: the official Autoresearch livestream, extracted slides/OCR, scheduled talks, and transcript-backed resource pages can be compared to identify recurring claims, tools, research patterns, and unanswered questions across talks. Use it when the answer depends on multiple sources, evolving evidence, or repeated comparison across papers, products, transcripts, benchmarks, or implementation patterns. It is especially relevant when a team needs a source-grounded briefing, a research map, or an experiment plan rather than a single answer. Avoid relying on it as a black-box oracle for high-stakes conclusions; the connected material repeatedly points toward closed-loop research systems, but those loops still need traceable evidence, evaluation, and human judgment. ## Connections - [[2026-06-30-tim-sweeney-closing-the-loop-an-autonomous-ai-research-agent]] — Closing the Loop: An Autonomous AI Research Agent; [[tim-sweeney|Tim Sweeney]] (Day 3 — Session Day 2 · 1:30pm-1:50pm · Autoresearch; official schedule) - [[2026-06-29-zhengyao-jiang-hands-on-autoresearch-cracking-openai-s-parameter-golf]] — Hands-on AutoResearch: Cracking OpenAI's Parameter Golf; [[zhengyao-jiang|Zhengyao Jiang]], [[dixing-xu|Dixing Xu]], [[vayum-arora|Vayum Arora]], [[dhruv-srikanth|Dhruv Srikanth]] (Day 1 — Workshop Day · 2:20pm-4:20pm · Workshops Day 1; official schedule) - [[2026-06-30-elie-bakouch-the-era-of-auto-research]] — « the era of (auto) research »; [[elie-bakouch|Elie Bakouch]] (Day 3 — Session Day 2 · 12:05pm-12:25pm · Autoresearch; official schedule) - [[2026-06-30-erina-karati-autoresearch-in-a-multi-agent-ai-village]] — Autoresearch in a Multi-Agent AI Village; [[erina-karati|Erina Karati]], [[arunachalam-manikandan|Arunachalam Manikandan]] (Day 3 — Session Day 2 · 3:45pm-4:05pm · Autoresearch; official schedule) - [[2026-06-30-han-xiao-autoresearch-for-dense-retrieval-test-time-compute-with-frozen-embedding-models]] — Autoresearch for Dense Retrieval: Test-Time Compute with Frozen Embedding Models; [[han-xiao|Han Xiao]] (Day 3 — Session Day 2 · 11:10am-11:30am · Autoresearch; official schedule) - [[2026-06-30-tejas-bhakta-autoresearch-for-kernels]] — Autoresearch for Kernels; [[tejas-bhakta|Tejas Bhakta]] (Day 3 — Session Day 2 · 2:50pm-3:10pm · Autoresearch; official schedule) - [[2026-06-30-roland-gavrilescu-autoresearch-in-the-wild]] — Autoresearch in the wild; [[roland-gavrilescu|Roland Gavrilescu]], [[julian-bright|Julian Bright]] (Day 3 — Session Day 2 · 3:20pm-3:40pm · Autoresearch; official schedule) - [[2026-07-01-brendan-rappazzo-alphalab-autonomous-multi-agent-research-across-optimization-domains-with-frontier-llms]] — ALPHALAB: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs; [[brendan-rappazzo|Brendan Rappazzo]] (Day 4 — Session Day 3 · 10:45am-11:05am · AI in Finance; official schedule) - [[2026-06-30-benoit-schillings-research-to-reality-with-google-deepmind]] — Research to Reality with Google DeepMind; [[benoit-schillings|Benoit Schillings]] (Day 3 — Session Day 2 · 10:05am-10:25am · Autoresearch; official schedule) - [[2026-06-30-richard-socher-first-steps-toward-automated-ai-research]] — First Steps Toward Automated AI Research; [[richard-socher|Richard Socher]] (Day 3 — Session Day 2 · 10:45am-11:05am · Autoresearch; official schedule) - [[2026-06-30-stefania-druga-memory-harnesses-for-long-running-research-agents]] — Memory Harnesses for Long-Running Research Agents; [[stefania-druga|Stefania Druga]] (Day 3 — Session Day 2 · 11:40am-12:00pm · Memory & Continual Learning; official schedule) - [[2026-07-01-zubin-aysola-aria-how-we-built-autoresearch-with-autoresearch]] — ARIA, how we built autoresearch with autoresearch; [[zubin-aysola|Zubin Aysola]] (Day 4 — Session Day 3 · 11:10am-11:30am · Expo Stage 2 NW; official schedule) - [[2026-06-29-valeria-wu-fon-speech-to-speech-model-research-at-google-deepmind]] — Speech-to-Speech Model Research at Google DeepMind; [[valeria-wu-fon|Valeria Wu Fon]], [[tom-ouyang|Tom Ouyang]] (Day 2 — Session Day 1 · 11:10am-11:30am · Voice & Realtime AI; official schedule) - [[2026-06-30-ishan-anand-will-ai-predict-people-like-we-predict-the-weather-alternate-title-a-field-guide-to-synthetic-personas-for-market-research]] — Will AI predict people like we predict the weather? (alternate title “A field guide to synthetic personas for market research”); [[ishan-anand|Ishan Anand]] (Day 3 — Session Day 2 · 2:50pm-3:10pm · Computer Use; official schedule) - [[2026-06-30-deepak-pathak-frontier-robotics-research]] — Frontier Robotics Research; [[deepak-pathak|Deepak Pathak]] (Day 3 — Session Day 2 · 1:55pm-2:15pm · Robotics & World Models; official schedule) - [[2026-06-30-zhengyao-jiang-an-ai-agent-became-the-1-contributor-in-openai-s-hiring-challenge]] — An AI Agent Became the #1 Contributor in OpenAI's Hiring Challenge; [[zhengyao-jiang|Zhengyao Jiang]] (Day 3 — Session Day 2 · 1:55pm-2:15pm · Autoresearch; official schedule) - [[2026-06-29-lee-robinson-recursive-model-improvement]] — Recursive Model Improvement; [[lee-robinson|Lee Robinson]] (Day 2 — Session Day 1 · 5:10pm-5:30pm · Software Factories; verified event YouTube resource; via [[youtube-q4Tr-DknG2M]]) - [[2026-06-30-geoffrey-litt-understanding-is-the-new-bottleneck]] — Understanding is the new bottleneck; [[geoffrey-litt|Geoffrey Litt]] (Day 3 — Session Day 2 · 10:45am-11:05am · Design Engineering; verified event YouTube resource; via [[youtube-WkBPX-oDMnA]]) - [[2026-06-30-thariq-shihipar-field-guide-to-fable]] — Field Guide to Fable; [[thariq-shihipar|Thariq Shihipar]] (Day 3 — Session Day 2 · 9:05am-9:25am · Autoresearch; official schedule) - [[2026-06-30-antje-barth-perception-agents]] — Perception Agents; [[antje-barth|Antje Barth]] (Day 3 — Session Day 2 · 9:45am-10:05am · Autoresearch; official schedule) - [[2026-06-30-laurie-voss-evals-track-intro]] — Evals Track Intro; [[laurie-voss|Laurie Voss]], [[aparna-dhinakaran|Aparna Dhinakaran]] (Day 3 — Session Day 2 · 10:25am-10:30am · Autoresearch; official schedule) - [[2026-06-30-lakshya-agrawal-self-improvement-of-context-harness-and-model-weights-through-reflective-optimization]] — Self-Improvement of Context, Harness, and Model Weights through Reflective Optimization; [[lakshya-agrawal|Lakshya Agrawal]] (Day 3 — Session Day 2 · 2:25pm-2:45pm · Autoresearch; official schedule) - [[2026-06-30-wei-lin-chiang-closing-keynote]] — Closing Keynote; [[addy-osmani|Addy Osmani]] (Day 3 — Session Day 2 · 4:30pm-4:50pm · Autoresearch; official schedule) - [[2026-06-30-george-cameron-trends-in-ai]] — Trends in AI; [[george-cameron|George Cameron]], [[micah-hill-smith|Micah Hill-Smith]] (Day 3 — Session Day 2 · 4:50pm-5:10pm · Autoresearch; official schedule) - [[laurie-voss|Laurie Voss]] - [[zhengyao-jiang|Zhengyao Jiang]] - [[tim-sweeney|Tim Sweeney]] - [[dixing-xu|Dixing Xu]] - [[vayum-arora|Vayum Arora]] - [[dhruv-srikanth|Dhruv Srikanth]] - [[elie-bakouch|Elie Bakouch]] - [[erina-karati|Erina Karati]] - [[arunachalam-manikandan|Arunachalam Manikandan]] - [[han-xiao|Han Xiao]] - [[tejas-bhakta|Tejas Bhakta]] - [[roland-gavrilescu|Roland Gavrilescu]] - [[julian-bright|Julian Bright]] - [[brendan-rappazzo|Brendan Rappazzo]] - [[benoit-schillings|Benoit Schillings]] - [[richard-socher|Richard Socher]] - [[stefania-druga|Stefania Druga]] - [[zubin-aysola|Zubin Aysola]] - [[valeria-wu-fon|Valeria Wu Fon]] - [[tom-ouyang|Tom Ouyang]] - [[ishan-anand|Ishan Anand]] - [[deepak-pathak|Deepak Pathak]] - [[lee-robinson|Lee Robinson]] - [[geoffrey-litt|Geoffrey Litt]] - [[weco-ai|Weco AI]] - [[google-deepmind|Google DeepMind]] - [[arize-ai|Arize AI]] - [[together-ai|Together AI]] - [[weights-and-biases-by-coreweave|Weights & Biases by CoreWeave]] - [[introspection|Introspection]] - [[artificial-analysis|Artificial Analysis]] - [[doordash|DoorDash]] - [[browserbase|Browserbase]] - [[superlinked|Superlinked]] - [[atlassian|Atlassian]] - [[friendliai|FriendliAI]] - [[coreweave|Coreweave]] - [[prime-intellect|Prime Intellect]] - [[supercell|Supercell]] - [[university-of-minnesota|University of Minnesota]] - [[elastic|Elastic]] - [[morph|Morph]] - [[2026-06-29-shubhankar-srivastava-hill-climbing-skills-how-to-improve-agents-without-touching-the-model]] — Hill-climbing Skills: How to Improve Agents Without Touching the Model; [[shubhankar-srivastava|Shubhankar Srivastava]] (Day 1 — Workshop Day · 4:30pm-5:30pm · Workshops Day 1; official schedule) - [[thariq-shihipar|Thariq Shihipar]] - [[2026-06-29-nachiket-paranjape-ai-evals-platform-for-cross-functional-teams-at-scale]] — AI Evals Platform for Cross-Functional Teams at Scale; [[nachiket-paranjape|Nachiket Paranjape]], [[swaroop-chitlur-haridas|Swaroop Chitlur Haridas]] (Day 2 — Session Day 1 · 1:55pm-2:15pm · AI-Native Enterprises; official schedule) - [[antje-barth|Antje Barth]] - [[2026-06-29-sonar-expo-welcome-speech]] — Expo Welcome Speech; [[sonar|Sonar]], [[extend-ai|Extend AI]] (Day 1 — Workshop Day · 6:00pm-6:15pm · Expo Stage 3; related YouTube resource; via [[youtube-4sX_He5c4sI]]) - [[2026-06-29-charlie-guo-cooking-with-codex]] — Cooking with Codex; [[charlie-guo|Charlie Guo]], [[gabriel-chua|Gabriel Chua]] (Day 1 — Workshop Day · 9:00am-11:00am · Workshops Day 1; related YouTube resource; via [[youtube-dvft0Gp9sEE]]) - [[2026-06-29-charlie-guo-voice-agents-can-just-do-things]] — Voice Agents Can Just Do Things; [[charlie-guo|Charlie Guo]] (Day 2 — Session Day 1 · 11:40am-12:00pm · Voice & Realtime AI; related YouTube resource; via [[youtube-dvft0Gp9sEE]]) - [[2026-06-29-doug-guthrie-advanced-workshop-mastering-ai-observability]] — Advanced workshop: Mastering AI Observability; [[doug-guthrie|Doug Guthrie]] (Day 1 — Workshop Day · 9:00am-11:00am · Track 9; related YouTube resource; via [[youtube-bk0TmxoZlUY]]) - [[charlie-guo|Charlie Guo]] - [[sonar|Sonar]] - [[openai|OpenAI]] - [[poolside|poolside]] ## Evidence Graph ### Transcript-backed resources - [[youtube-q4Tr-DknG2M]] — Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI - [[youtube-4sX_He5c4sI]] — WF2026: Autoresearch & Keynotes ft. Anthropic, Google DeepMind, Amazon AGI, Sonar, Arena, Recursive - [[youtube-WkBPX-oDMnA]] — Understanding is the new bottleneck — Geoffrey Litt, Notion ### Transcript-backed resources ### Transcript-backed resources ### Transcript-backed resources ### Transcript-backed resources - [[youtube-OXMMN-XbxwA]] — Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc - [[youtube-aHhB3sjGjkI]] — Agents Building Agents - Alfonso Graziano, Nearform - [[youtube-UcYoMg-8-L8]] — 500 people vibe-coded for 30 days. I was one of them. - Sanja Grbic, Automattic - [[youtube-zMiSRliEzv4]] — Self Driving Products: Product Signals to Pull Requests — Joshua Snyder, PostHog - [[youtube-iNkFlCiij0U]] — The Art & Science of Benchmarking Agents — Vincent Chen, Snorkel AI - [[youtube-2e9ANoOEn28]] — What if the harness mattered more than the model? - Aditya Bhargava, Etsy - [[youtube-IQkVMvXQKLY]] — Your LLM Deception Monitor Is Broken. The Fix Is in the Training Data - Sachin Kumar, LexisNexis - [[youtube-fWXJM-J0ZB8]] — Frontier results, on device - RL Nabors, Arize - [[youtube-u-rJwPPU3QA]] — How to talk to statues — Joe Reeve, ElevenLabs - [[youtube-IJXjTLPzvAU]] — The Miranda Hypothesis: How Hamilton Poisoned Persona Evals - Jacob E. Thomas, Results Gen - [[youtube-0S8xe9ftGTM]] — 6 Things to Know about AIE World's Fair 2026 - [[youtube-Iwe_RY-fYgI]] — AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent - [[youtube-akk6KRlcwW4]] — OpenClaw in Your Hand: Building a Physical AI Terminal - Lech Kalinowski, Callstack - [[youtube-pSto5YaNGUo]] — The Agentic AI Engineer - Benedikt Sanftl, Mutagent - [[youtube-dvft0Gp9sEE]] — Analyzing 10,000 Sales Calls With AI In 2 Weeks — Charlie Guo - [[youtube-bk0TmxoZlUY]] — Evals 101 — Doug Guthrie, Braintrust - [[youtube-hqHC6Z_lXyo]] — 20 days of compute vs 7 hours: rethinking what state-of-the-art means — Bertrand Charpentier, Pruna ### Transcript-backed resources This evidence graph consolidates scheduled talks, linked videos, transcripts, and slide-derived material connected to this topic. ### Linked Sessions - [[2026-06-30-tim-sweeney-closing-the-loop-an-autonomous-ai-research-agent|Closing the Loop: An Autonomous AI Research Agent]] - [[2026-06-29-zhengyao-jiang-hands-on-autoresearch-cracking-openai-s-parameter-golf|Hands-on AutoResearch: Cracking OpenAI's Parameter Golf]] - [[2026-06-30-elie-bakouch-the-era-of-auto-research|« the era of (auto) research »]] - [[2026-06-30-erina-karati-autoresearch-in-a-multi-agent-ai-village|Autoresearch in a Multi-Agent AI Village]] - [[2026-06-30-han-xiao-autoresearch-for-dense-retrieval-test-time-compute-with-frozen-embedding-models|Autoresearch for Dense Retrieval: Test-Time Compute with Frozen Embedding Models]] - [[2026-06-30-tejas-bhakta-autoresearch-for-kernels|Autoresearch for Kernels]] - [[2026-06-30-roland-gavrilescu-autoresearch-in-the-wild|Autoresearch in the wild]] - [[2026-07-01-brendan-rappazzo-alphalab-autonomous-multi-agent-research-across-optimization-domains-with-frontier-llms|ALPHALAB: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs]] - [[2026-06-30-benoit-schillings-research-to-reality-with-google-deepmind|Research to Reality with Google DeepMind]] - [[2026-06-30-richard-socher-first-steps-toward-automated-ai-research|First Steps Toward Automated AI Research]] ### Media Signals - `youtube-4sX_He5c4sI` — 82,600 transcript words; 8 slide-derived text signals - Transcript signals for `youtube-4sX_He5c4sI`: model, code, models, research, system, well, first, better. - Slide-derived themes for `youtube-4sX_He5c4sI`: system, prompt, examples, tools, lots, claude, gets, smarter. - Evidence links for `youtube-4sX_He5c4sI`: [[youtube-4sX_He5c4sI]], [[youtube-4sX_He5c4sI-transcript]], [[youtube-4sX_He5c4sI-slides]], [[youtube-4sX_He5c4sI-dense-slides]], [[youtube-4sX_He5c4sI-reconstructed-slides]] - `youtube-G_bHFmEAarM` — 6 slide-derived text signals - Slide-derived themes for `youtube-G_bHFmEAarM`: gemini, byte, google, latest, releases, flash, live, lite. - Evidence links for `youtube-G_bHFmEAarM`: [[youtube-G_bHFmEAarM]], [[youtube-G_bHFmEAarM-slides]], [[youtube-G_bHFmEAarM-dense-slides]], [[youtube-G_bHFmEAarM-reconstructed-slides]] ## Source Coverage This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. | Evidence type | Count | Review note | | --- | ---: | --- | | other | 51 | Related pages outside the main evidence categories. | | resources | 19 | Video/resource pages; check source status before treating as primary event evidence. | | slides | 6 | OCR or reconstructed slide evidence; mark claims as OCR-derived unless image-reviewed. | | talks | 24 | Official schedule pages; use for titles, speakers, tracks, and stated talk framing. | | tools | 2 | Derived inventory pages; use as entity context, not independent proof. | | transcripts | 1 | Transcript markdown; check session matching and caption quality. | ### Talks - [[2026-06-30-tim-sweeney-closing-the-loop-an-autonomous-ai-research-agent]] - [[2026-06-29-zhengyao-jiang-hands-on-autoresearch-cracking-openai-s-parameter-golf]] - [[2026-06-30-elie-bakouch-the-era-of-auto-research]] - [[2026-06-30-erina-karati-autoresearch-in-a-multi-agent-ai-village]] - [[2026-06-30-han-xiao-autoresearch-for-dense-retrieval-test-time-compute-with-frozen-embedding-models]] - [[2026-06-30-tejas-bhakta-autoresearch-for-kernels]] ### Resources - [[youtube-4sX_He5c4sI]] - [[youtube-dvft0Gp9sEE]] - [[youtube-bk0TmxoZlUY]] - [[youtube-OXMMN-XbxwA]] - [[youtube-aHhB3sjGjkI]] - [[youtube-UcYoMg-8-L8]] ### Slides - [[youtube-4sX_He5c4sI-slides]] - [[youtube-4sX_He5c4sI-dense-slides]] - [[youtube-4sX_He5c4sI-reconstructed-slides]] - [[youtube-G_bHFmEAarM-slides]] - [[youtube-G_bHFmEAarM-dense-slides]] - [[youtube-G_bHFmEAarM-reconstructed-slides]] ### Transcripts - [[youtube-4sX_He5c4sI-transcript]] ### Tools - [[browserbase]] - [[prime-intellect]] This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. ### Talks ### Resources ### Slides ### Transcripts ### Tools This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. | other | 59 | Related pages outside the main evidence categories. | | talks | 28 | Official schedule pages; use for titles, speakers, tracks, and stated talk framing. | ### Talks ### Resources ### Slides ### Transcripts ### Tools This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. ### Talks ### Resources ### Slides ### Transcripts ### Tools This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. ### Talks ### Resources ### Slides ### Transcripts ### Tools This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. ### Talks ### Resources ### Slides ### Transcripts ### Tools This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. | other | 60 | Related pages outside the main evidence categories. | | resources | 20 | Video/resource pages; check source status before treating as primary event evidence. | | talks | 29 | Official schedule pages; use for titles, speakers, tracks, and stated talk framing. | ### Talks ### Resources - [[youtube-WkBPX-oDMnA]] ### Slides ### Transcripts ### Tools This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. ### Talks ### Resources ### Slides ### Transcripts ### Tools This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed. | other | 61 | Related pages outside the main evidence categories. | | resources | 21 | Video/resource pages; check source status before treating as primary event evidence. | | talks | 30 | Official schedule pages; use for titles, speakers, tracks, and stated talk framing. | ### Talks ### Resources - [[youtube-q4Tr-DknG2M]] ### Slides ### Transcripts ### Tools ## Active Use Cases - Evidence-grounded briefing docs and source maps. - Research agents that compare papers, products, or implementation patterns. - Experiment-planning support for AI and data teams. - Conference or domain wiki synthesis from talks, transcripts, and slides. ## Livestream Source - [[youtube-4sX_He5c4sI]] — official WF2026 Autoresearch and keynote livestream. - [[youtube-4sX_He5c4sI-slides]] — extracted slide/OCR deck for the livestream. ## Neighboring Subjects - [[agent-evaluations]] - [[agentic-search]] - [[agent-memory]] - [[inference-engineering]]