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A prominent AI paper from the week addresses whether self-improving agents are truly discovering new knowledge or merely remixing existing information.
Weekly summary of notable AI research papers from May 31 to June 7, including LEAP, AutoLab, and scaling laws for agent harnesses.
A weekly roundup of notable AI papers covering self-revising discovery systems from MIT, disentangling agent self-evolution, and Google's LEAP for formal mathematics using agentic scaffolds.
A roundup of three notable AI papers: SkillOpt treats skill documents as trainable parameters to optimize frozen agents; a new method compiles agentic workflows into model weights for 100x cost reduction; and AutoScientists introduces a decentralized agent team for long-running science without a central planner.
A curated roundup of top AI papers from May 25-31, covering multi-agent world modeling, vision-language-action models, agent skill optimization, and alignment frameworks.
Niels from Hugging Face announces the revival of PapersWithCode as paperswithcode.co, a platform that parses high-impact AI papers at scale and automatically generates leaderboards and benchmarks, incorporating features like trending papers, domain categorization, and external paper support.
A personal collection of 1,400 hand-curated Arxiv whitepapers on reasoning, RL, alignment, and more, now online with semantic search and browsable conceptual connections, built using Obsidian and Claude.
A curated list of the top AI papers from May 11-17, featuring papers on AEvo, δ-mem, AutoTTS, AI Co-Mathematician, Lighthouse Attention, and others.
A roundup of the top AI papers from May 11-17, covering Lighthouse Attention for long-context pretraining, a comparison of grep vs embedding retrieval for coding agents, and mechanistic interpretability work revealing a geometric calculator in LLMs.
A weekly roundup of top AI research papers covering topics such as Conductor, HeavySkill, Horizon Generalization, synthetic computers, self-improving pretraining, and AlphaZero for Connect Four.
TraceScope provides an interactive web-based tool for exploring semantic flows of recent AI papers from arXiv, with an open-source library available on GitHub.