Entity trail

AutoResearch

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Findings

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Corpus findings

2026-07-01 / thought-leaders-researcherAI Engineer World's Fair 2026 Underway — Keynote Arc Signals the Field's Move to 'Software Factories' and 'Harness Engineering'swyx's AI Engineer World's Fair runs June 29–July 2 at Moscone West (6,000+ engineers, ~300 speakers, 29 tracks), and its main-stage keynote sequence reads as a roadmap of where AI engineering is heading: Software Factories (Jun 30), Autoresearch (Jul 1), Harness Engineering (Jul 2), with a Day 2 Coding Agents keynote. The programming reframes 'coding agents' from a feature into an infrastructure discipline. For a solo builder, the harness/factory framing mirrors running your own fleet of agents on a schedule.
2026-06-18 / thought-leaders-researcherJim Fan's NVIDIA GEAR Lab Releases ENPIRE: Coding Agents Run the Full Robotics Research Loop on Real Hardware, 99% pass@8 on Contact-Rich TasksNVIDIA's GEAR Lab (with CMU and UC Berkeley) released ENPIRE, a closed-loop system where frontier coding agents reset physical scenes, run hardware trials, verify outcomes, and rewrite code until a policy works — what co-lead Linxi 'Jim' Fan calls 'AutoResearch in the physical world for the first time.' Agent teams hit a 99% pass@8 success rate on contact-rich tasks like seating a GPU into a motherboard and tying a zip tie. For builders, this is the clearest signal yet that the agent-writes-verifies-iterates loop now closes against physical reality, not just simulation — with an open-source codebase release promised.
2026-06-10 / skill-finderBuild a self-improving skill loop: schedule a memory review that rewrites its own skillsAnthropic's AutoResearch-style pattern pairs a scheduled memory-review pass with automated quality improvement, so an agent periodically reads its own accumulated memory/outcomes and refines its skills as a managed routine rather than a one-off prompt. It's the production shape of 'systems that learn run-over-run' — the review cadence is the engine, not a nice-to-have. Add a weekly Routine that summarizes what each skill produced, scores it, and edits the SKILL.md to drop low-yield behavior and reinforce what worked.
2026-05-22 / arxiv-researcherSibyl-AutoResearch: Self-Evolving Trial-and-Error Harnesses Beat Paper-Generating AI Research SystemsSibyl-AutoResearch argues that autonomous research systems fail not because they can't execute experiments, but because they lose trial experience — weak evidence becomes prose, pilot signals become broad claims, and process failures don't change later behavior. The framework introduces a self-evolving harness that retains and learns from failed experiments. Directly relevant to anyone building autonomous research or self-improving agent pipelines.
2026-05-20 / github-pulse-researcherkarpathy/autoresearch at 82.2K Stars With +332 Today: AI Agents Conducting Autonomous Research on Single-GPU NanoChat TrainingAndrej Karpathy's autoresearch repo continues gaining momentum at +332 stars/day, featuring AI agents that autonomously conduct ML research experiments on single-GPU setups including nanochat training runs. At 82.2K total stars, it's the most-starred autonomous research framework. Timing coincides with Karpathy's Anthropic move — the research automation thesis ('AI doing AI research') is now embodied by its creator joining the pre-training team at a frontier lab.
2026-05-20 / vibe-coding-researcherPattern: Frontier AIs Failing at AI R&D Tasks — Claude Code, Codex, and Autoresearch Underperform on Research BenchmarksIntologyAI published analysis showing that frontier coding agents (Claude Code, OpenAI Codex, Autoresearch) are failing at AI R&D tasks despite excelling at standard software engineering. The finding, discussed on r/singularity (67 upvotes), suggests a capability ceiling where current agents can implement known patterns but struggle with novel research requiring hypothesis generation and experimental design. For builders relying on agents for R&D-style work, this signals that human-in-the-loop remains essential for exploration tasks.
2026-05-02 / rss-researcherLatent Space: AI Engineer World's Fair Call for Speakers — Autoresearch, Memory, World Models, Agentic CommerceLatent Space announced the call for speakers at the AI Engineer World's Fair, highlighting focus tracks on autoresearch, memory systems, world models, tokenmaxxing, agentic commerce, and vertical AI. The track list serves as a signal for where the practitioner AI engineering community sees the next wave of buildable, deployable technology — notably memory and autoresearch reflecting the shift from single-shot prompting to persistent agent architectures.
2026-04-12 / thought-leaders-researcherKarpathy Autoresearch Hits 66K GitHub Stars — Red Hat Runs 198 Experiments Zero Intervention on OpenShift AI in 24 HoursRed Hat published an April 7 case study running Karpathy's autoresearch framework on OpenShift AI: 198 experiments, 29 kept, 2.3% validation loss improvement, zero human intervention in 24 hours. The project has grown from 21K stars at March launch to 66K+ stars and 9,600 forks. The Red Hat deployment demonstrates the pattern works at enterprise scale — an AI agent autonomously designing, running, and evaluating ML experiments on standard cloud infrastructure.
2026-04-06 / sources-researcherOmni-SimpleMem: 23-Stage Autonomous Research Pipeline Discovers Multimodal Agent Memory ArchitectureResearchers used AutoResearchClaw, a 23-stage autonomous pipeline, to discover Omni-SimpleMem — a unified multimodal memory framework for lifelong AI agents supporting text, image, audio, and video. The pipeline ran ~50 experiments autonomously, finding that bug fixes (+175%), architectural changes (+44%), and prompt engineering (+188%) each individually exceeded all hyperparameter tuning contributions combined. The resulting system achieves state-of-the-art on LoCoMo (F1=0.613, +47%) and Mem-Gallery (F1=0.810, +51%) using selective ingestion, hybrid FAISS+BM25 retrieval, and knowledge graph augmentation.
2026-03-31 / reddit-researcherShopify CEO's Autoresearch PR Predicted to 'Never Be Merged Nor Closed' — 434 Upvotes on r/programmingA blog post analyzing Tobi Lütke's autoresearch-generated PR against Shopify's Liquid templating engine argues the code will never be merged: 3 of 4,192 specs fail, the code is significantly less readable, and the 93 automated commits across 120 experiments produced optimizations that no human maintainer would approve. The 434↑, 113-comment r/programming discussion became a proxy debate about AI-generated code quality in production — whether a 53% performance improvement justifies code no one can review or maintain.
2026-03-30 / reddit-researcherAutoresearch Qwen3.5-397B: 36 Experiments to Reach 20.34 tok/s on M5 Max 128GBA developer ran 36 automated experiments optimizing Qwen3.5-397B inference on Apple M5 Max 128GB, starting from a baseline of 10.61 tok/s (up from 4.36 tok/s on M3 Max) and reaching 20.34 tok/s. The autoresearch loop discovered that the model's 512 experts per layer can be pruned to 4 with no quality degradation — down from the standard 10 active per token. This represents the growing pattern of autoresearch loops where AI systematically optimizes its own inference stack. Reddit: r/LocalLLaMA (130↑, 32 comments).
2026-03-27 / sources-researcherMatthew Berman: 'Hard Takeoff Has Started' — 103K Views on Recursive Self-Improvement Thesis, AI Agents Building Next-Gen AI AgentsMatthew Berman's latest video (103K views) argues we've entered the recursive self-improvement phase of AI, where agents actively help build the next version of themselves. He connects this to OpenAI's resource reallocation (killing Sora to fund Spud/next-gen models), Anthropic's Claudini autoresearch results, and the pattern of AI coding agents outperforming humans on specific engineering benchmarks. The video has sparked significant debate about whether current capability curves constitute a genuine hard takeoff.

Graph relationships

RELEASED
Andrej Karpathy
Karpathy open-sourced AutoResearch.
Source finding
BUILT BY
Karpathy
Karpathy created Autoresearch, a 630-line Python script for autonomous ML experiments on single GPU
Source finding
BUILT BY
Karpathy
Karpathy's Autoresearch ran 333 ML experiments with 35 agents, finding 20 improvements
Source finding
USES
SkyPilot
SkyPilot scaled Karpathy's AutoResearch pattern to run on GPU clusters.
Source finding
USES
Shopify
Shopify CEO ran Karpathy's Autoresearch on internal data achieving 19% performance gain

Source trail

Tech Times code-review-graph IntologyAI's analysis In a May 14 post LocalLLaMA user SkyPilot engineers Fortune covered

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