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MAST achieves targeted forgetting in RLVR-induced reasoning with minimal impact on performance, preserving critical task accuracy while effectively unlearning unwanted knowledge.
LatentSkill achieves a 21.4-point increase in task success while slashing prefill token usage by over 64%, revolutionizing how LLM agents utilize skills.
Decentralized debate among LLM agents doesn't just select the best solution for optimization modeling; it structurally enables agents to refine flawed candidates and even recover correct formulations through interaction.
Evaluating web coding LLMs with real-world fidelity reveals that even state-of-the-art models still struggle with aesthetics and framework-specific nuances.
LLM agent progress increasingly hinges on better external cognitive infrastructure, not just stronger models.
Stop 3D models from hallucinating structural outliers: Dehallu3D leverages cyclic view consistency to generate more faithful 3D meshes from single images.
