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SoL-Pi introduces a method for recursively scaling auto-research loops in coding agents, achieving significant token and cost reductions while maintaining performance on benchmarks.
Agora is a shared memory system for autonomous AI research agents that uses Git to record research as an append-only DAG, enabling collaborative discovery. In a 12-day experiment, 13 agents improved a model's performance by 62% towards a trained baseline.
LabAgent is an AI agent system designed to customize research hubs for scientific discoveries, enabling reproducible and continuous laboratory work across various biological domains and outperforming commercial generalist agents.
SoL-Pi is a standalone extension for Pi agents that enhances efficiency by reducing token traffic and inference work through mechanisms like action fusion and context compaction, with all features being opt-in and preserving original evidence.
Enhanced the TUI of ouroboros, a CLI tool for self-evolving auto-research systems, and deleted 2.79 million lines of vestigial code to improve usability.
The paper introduces a framework for personalized auto-research systems that condition every stage of the research process on individual scientist representations, arguing that personalization is essential for AI to serve as true co-scientists rather than generic instruments.
A blog post detailing how the author used Codex to optimize a kernel in a GPU Mode contest, achieving a 232x speedup in QR decomposition and sharing learnings on auto-research.
This paper introduces ARAC-Bench, a benchmark for evaluating the alignment, logical coherence, and completeness of Auto-Research systems' research processes against human methodology. Experiments on 11 state-of-the-art frameworks show a best alignment score of only 67.9/100, highlighting a significant gap in simulating rigorous human research behavior.
Hosting an auto-research hackathon this Saturday in SF at a mansion. Interested participants can DM or comment to join.
GigaWorld-Policy-0.5 is an enhanced World Action Model for robot control that improves training and inference efficiency through a Mixed Action-Conditioned World Modeling strategy and a Mixture-of-Transformers architecture, achieving 85ms latency on a local RTX 4090.
The author shares observations from auto-research experiments in algebra, noting that AI models can generate code and discover novel abstract rules, leading to potentially alien mathematics that humans struggle to understand.
A blog post describes using Codex to automatically iterate and optimize GPU kernels, achieving a 212x speedup over baseline. The post highlights how expertise amplifies AI's utility, turning unknown unknowns into known unknowns through a looped experimental workflow.
Blog post by Sankalp detailing how he used Codex to achieve a 232x faster GPU kernel for QR decomposition in GPU Mode's contest, outlining his auto-research methodology.
Introduce a tool that by changing 'Github' to 'ARGithub' in any repo URL, deploys an agent to orient itself on the codebase, resolve setup issues, and run experiments.
Deli Chen open sources his AutoResearch SKILL tool and releases a survey paper on Self-play, inspired by AlphaZero.
SIQ-1 Qwen3.6 is a new AI model designed for automated research and autonomous agency tasks, extending the Qwen family with enhanced agentic capabilities.
PseudoBench is a benchmark to evaluate whether LLM-based agentic auto-research systems can resist pseudoscientific narratives. Testing seven state-of-the-art agents reveals they readily produce persuasive pseudoscientific reports with near-zero refusal rates, calling for scientific alignment before deployment.
NVIDIA GEAR lab introduces ENPIRE, a system that uses 8 Codex agents to autonomously control a robot fleet for physical tasks like tying zip-ties and installing GPUs, demonstrating self-improving robotics research and a new 'physical scaling' phenomenon.
A curated GitHub resource that maps AI-assisted scientific research tools and papers across the full research lifecycle, from idea generation to dissemination.
Yacine conducted a 1.5-hour in-depth interview with the founders of Paradigma, discussing how to use DAG (Directed Acyclic Graph) as the underlying infrastructure for autonomous research, covering core topics such as Agent operation, building large-scale public DAGs, and avoiding bad DAGs.