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Introducing autoresearch with GPT 5.6 We had GPT 5.6 Sol reproduce the key findings from “Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in LLM Finetuning” Compared to GPT-5.5 and even Fable 5, GPT-5.6 stayed more focused on a few, critical experiments and spent less time on peripheral details. It also asked fewer “clarification” questions and independently resolved ambiguities instead of pushing them back to us OpenAI pushing the boundaries of the automated research loop with models that don’t have handcuffs
alphaXiv83,327 views • 5 days ago

Introducing GLM 5.2 for autoresearch GLM 5.2 is the first open weights model we've tried on our autoresearch pipeline that's proven capable for real research tasks. With Fable 5's restrictions on research, having an open weights alternative is a huge win for open source Watch it carry out fully async vs colocated sync RL training on Harbor code contests across two 8xH100 nodes on top of SkyRL. Resolves setup issues, tracks runs to completion, and produces a full comparison of throughput and reward stability
alphaXiv269,670 views • 26 days ago

Introducing autoresearch for GitHub repos Change 'Github' to 'ARGithub' in any repo URL Research artifacts extend beyond papers. Autoresearch is especially useful for experimenting on existing codebases that move fast and outpace their own publications. With one URL change you can now deploy an agent to orient itself on the codebase, resolve setup issues, and iterate on experiments.
alphaXiv72,227 views • 24 days ago

Introducing Deep Research for arXiv Ask questions like 'What are the latest breakthroughs in RL fine-tuning?' and get comprehensive lit reviews with trending papers automatically included Turn hours of literature searches into seconds with AI-powered research context ⚡
alphaXiv372,737 views • 1 year ago

1997: Deep Blue defeats Kasparov at chess 2016: AlphaGo masters the game of Go 2025: Stanford researchers crack Among Us Trending on alphaXiv 📈 Remarkable new work trains LLMs to master strategic social deduction through multi-agent RL, doubling win rates over standard RL.
alphaXiv210,063 views • 1 year ago