Another cool research on Looped Transformers They ask the question: "Can we loop a frozen, off-the-shelf checkpoint directly at inference time without any modifications?" So naive repetition pushes hidden states outside the distribution later layers expect, so performance
@askalphaxiv
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Language Models Sleep: Context Replay for Deep Reasoning
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"Language Models Need Sleep" Instead of thinking longer at answer time, this paper makes LLMs sleep before forgetting. They replay old context, write it into fast weights, clear the KV cache, and answer later at normal speed. More sleep improves deep reasoning over long and
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One-click VPO template for Qwen from openresearch.sh
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Check out the one-click template of VPO here! https://
openresearch.sh/templates/qwen
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OpenResearch sneak peek: train VPO on ToolRL in one click
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Here’s an early sneak peak of OpenResearch, our brand new feature for reproducing and experimenting on top of papers
— alphaXiv (@askalphaxiv) 26 mai 2026
We put together a template so you can train VPO on ToolRL in one click on a single gpu
Vector Policy Optimization trains models to generate diverse answer sets… pic.twitter.com/MEaGQ1HIYMHere’s an early sneak peak of OpenResearch, our brand new feature for reproducing and experimenting on top of papers We put together a template so you can train VPO on ToolRL in one click on a single gpu Vector Policy Optimization trains models to generate diverse answer sets
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Fast and High-Resolution Latent Decoding with Pixel Diffusion
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Image models usually VAE decode first, then super-res later, which is slow and mostly reconstructs rather than creates detail. This NVIDIA research makes the decoder itself a pixel diffusion model, so a 512^2
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DeepMind LLMs Lean Proof-Search Agents Solve Open Problems
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This new DeepMind research turns LLMs into Lean proof-search agents, so every step must compile and the final proof is mechanically verified. Under this setup, they solved 9 open Erdős problems, proved 44 OEIS conjectures, and helped advance actual research in optimization,
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ConvexTok: Linear Programming for Optimal LLM Tokenization
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"Tokenisation via Convex Relaxations" Most LLM tokenizers still use BPE, a greedy merge algorithm that can waste vocab slots on locally good but globally suboptimal tokens. This paper turns tokenizer training into a linear program, then rounds the solution into ConvexTok. This
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Code as Agent Harness: Meta Paper Reframes AI Systems
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"Code as Agent Harness" Agents are becoming less like chatbots that write code and more like systems that run on code. This new Meta paper reframes code as the harness around an agent, the executable layer for reasoning, acting, memory, verification, and coordination. The key
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HRM Outperforms Transformers 7x Its Size
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HRM beats Transformers seven times its size in language modeling!? "HRM-Text: Efficient Pretraining Beyond Scaling" This paper introduces the Hierarchical Recurrent Model (HRM), which incorporates slow planning layers and fast execution layers to enhance planning and recurrence. The model was trained directly on…
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Stochastic Tiny Recursive Models améliorent PPBench
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“Probabilistic Tiny Recursive Model” This paper makes Tiny Recursive Models stochastic at test time by adding Gaussian noise, running parallel rollouts, and using the existing Q head to pick the best answer. With no retraining and no task-specific tricks, its PPBench jumps from