alphaXiv ACM CAIS We’re excited to announce our partnership with the ACM Conference on AI and Agentic Systems! alphaXiv will serve as the complimentary research hub for accepted papers and system demos, helping the community discover, understand, and build on top of the
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AutoResearchClaw: Autonomous AI Research with Collaboration
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AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration As real-world science is iterative, this paper introduces AutoResearchClaw, a multi-agent system that debates ideas, self-corrects experiments, decides whether to pivot or refine, verifies data, and
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Analysis of RoPE Limitations in Long-Context LLMs
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“RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts” RoPE is the default way LLMs track position, but this paper proves it breaks down as context gets long. Attention becomes close to random at telling near from far, and can even assign the same score to different
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New Research Paper Proposes Exploration Checkpoint Coverage for LLM Agents
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new longcat paper! “Look Before You Leap” LLM agents often fail because they act before they understand the environment. So this paper introduces Exploration Checkpoint Coverage, a verifiable reward for discovering key states, objects, affordances, and constraints. With
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Technical Analysis of Optimal Mixture-of-Experts Model Configuration
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"Slicing and Dicing: Configuring Optimal MoE" MoE models have a huge design space, but most choices are overcomplicated. This paper runs 2,000+ pretraining runs up to 6.6B params and finds the key knobs are mostly expert count and expert granularity. Adding more inactive
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Research on Steered LLM Activations and Manifold Reachability
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"Steered LLM Activations are Non-Surjective" Activation steering can make an LLM behave very differently, but that doesn't mean any prompt could have caused the same internal state. This paper shows steering pushes activations off the prompt-reachable manifold into
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LLM traces masquent un planificateur myope
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“Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning” Reasoning models can write traces that look like real tree search, but this paper shows their decisions are mostly driven by shallow one-step evaluation. They extract search trees from LLM CoT in
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Oxford AI4Science research on LLM long-horizon reasoning
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Long-horizon reasoning is one of the largest obstacles in LLMs. In our latest AI4Science talk, Sumeet (
@sumeetrm
) and Charlie (
@CharlieLondon02
) from Oxford discussed H1 and LongCoT, two projects focused on measuring and improving how models reason over long chains of steps. -
SlimQwen: Technical Research on Pruning and Distillation for MoE Models
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“SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training” This new Qwen paper shows that pruning a pretrained MoE is much better than training the smaller MoE from scratch. All you need to do is prune depth, width, and experts, preserve some experts
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SANA-WM Introduces Efficient Minute-Scale World Modeling
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"SANA-WM Efficient Minute-Scale World Modeling" Most world models can generate short controlled clips, but minute-long 720p rollouts usually need huge models, massive private data, and multi-GPU inference. This paper makes long-horizon world modeling much more practical by