We've been motivated by an ambition to propose an innovative approach to publishing in AI, highlighting the prime importance of claims and evidence in assessing scientific work. Looking back, we've accomplished a lot!
MACHINE LEARNING
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TMLR Leadership Transition Announced New Editorial Team
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A little under 4 years ago, @RaiaHadsell
, @kchonyc and I launched TMLR. I am SO proud of what we've achieved since then, and I'm particularly happy to leave TMLR to a remarkable team of EICs, with @thegautamkamath
, @NailaMurray
, Nihar B. Shah and @lcharlin . -
Base Models Trained on CoT Data: Finding the Silver Lining
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Yeah, there is a silver lining. But I think it gets a bit muddled since most base models are trained on CoT data.
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Optimal RL Balance in Training Reasoning Models
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Another really interesting paper from my 2025 bookmarked papers: On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models (https://arxiv.org/abs/2512.07783). In short, RL is most effective when applied to data that is neither too close to nor too far from the
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FlowBlending: Stage-Aware Multi-Model Sampling for Video Generation
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FlowBlending Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Generation
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Dream2Flow: Video Generation with 3D Object Flow for Manipulation
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Dream2Flow
— AK (@_akhaliq) 2 janvier 2026
Bridging Video Generation and Open-World Manipulation with 3D Object Flow pic.twitter.com/5m09xRzjG0Dream2Flow Bridging Generation and Open-World Manipulation with 3D Object Flow
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Hypergraph Memory Enhances Multi-step RAG Long-Context Modeling
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Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling
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DiffThinker: Generative Multimodal Reasoning with Diffusion Models
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DiffThinker Towards Generative Multimodal Reasoning with Diffusion Models
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Dynamic Large Concept Models with Latent Reasoning in Adaptive Semantic Space
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Dynamic Large Concept Models Latent Reasoning in an Adaptive Semantic Space
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JavisGPT: Multi-modal LLM for Video Understanding and Generation
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JavisGPT A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation