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研究Safe Domain Adaptation for Physics: Overcoming Nuisances, Label Shifts, and Simulation Priors(arxiv.org)
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研究Accelerating Visual On-Policy Distillation with Batched Speculative Jacobi Rollouts(arxiv.org)
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研究Entropy-Constrained Adaptive Stochastic Quantization(arxiv.org)
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研究H$^2$EDL: Hyper Evidential Deep Learning for Hierarchical Classification(arxiv.org)
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研究Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models(arxiv.org)
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研究Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions(arxiv.org)
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研究The Download: AI’s self-improvement problem, and what’s driving the heat(technologyreview.com)
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研究Child-monitoring apps might need a reboot(technologyreview.com)
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研究Toward Personal Intelligence Through Cooperative Observation(arxiv.org)
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研究Fool's Gold: Defensive Deception Against Safety-Removal Attacks on Open-Weight Models(arxiv.org)
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研究Memory Is Communication: The Frontier Between Remembering and Signaling(arxiv.org)
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研究KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn(arxiv.org)
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研究Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language Models(arxiv.org)
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研究The Problem Is the Problem: Towards Scalable Mathematical Discovery(arxiv.org)
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研究KernelArc: A Multi-Agent Framework for GPU Kernel Optimization(arxiv.org)
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研究GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents(arxiv.org)
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研究DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization(arxiv.org)
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研究The Download: how people really use AI, and Flock’s design choices(technologyreview.com)
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研究The Sequence Knowledge - Issue 916: From Thinking Longer to Learning Better(thesequence.substack.com)
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研究We still don’t know how people are really using AI(technologyreview.com)
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研究AI’s recursive self-improvement might not come so quickly after all(technologyreview.com)
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研究The role of the astronaut is in flux(technologyreview.com)
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研究Large Language Models Show Metacognitive Sensitivity in Medical Reasoning(arxiv.org)
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研究FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment(arxiv.org)
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研究Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws(arxiv.org)
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研究When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL(arxiv.org)
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研究Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review(arxiv.org)
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研究From Doyle to AGM: A Survey and an Implementation Roadmap for Belief Change(arxiv.org)
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研究Position: AI Lock-In Is in Progress, and We Must Be Prepared(arxiv.org)
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研究What Flock’s defenders are missing(technologyreview.com)
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