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研究SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models(arxiv.org)
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研究Learning to Discover Interesting Mathematics(arxiv.org)
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研究Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents(arxiv.org)
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研究Reinforcement Learning with Verifiable Rewards for Small Search Agents(arxiv.org)
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研究Agent Memory with Episodic Retrieval for Financial Decision-Making(arxiv.org)
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研究Continual learning might make your blocking monitors nearly useless(alignmentforum.org)
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研究America Is Behind on Memory Chips—and Tariffs Threaten to Make Things Harder(cset.georgetown.edu)
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研究The Download: a bid to scrap the virtual wall and AI hits Climate Week(technologyreview.com)
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研究AI is dominating the conversation at Climate Week(technologyreview.com)
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研究Math Reasoning in LLMs is Organized by Approach, Not Topic(arxiv.org)
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研究Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations(arxiv.org)
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研究Are Stated Reasoning Steps Causally Load-Bearing?(arxiv.org)
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研究Escaping Python Dependency Hell: A Hybrid Replay-and-Repair Pipeline for Python Dependency Resolution(arxiv.org)
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研究Reinforcement Learning with Decomposed Subtasks(arxiv.org)
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研究Harness as a Language: A Minimalist Agent Framework With Maximal Expressivity(arxiv.org)
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研究TwinCheck: Evidence-Grounded Negative-Twin Verification for Stateful Tool Agents(arxiv.org)
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研究Toner: There’s a Lot of Upside To AI if We Do It Right(cset.georgetown.edu)
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研究A congressional representative just proposed killing America’s border tower program(technologyreview.com)
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研究The Download: India’s smart glasses menace and AI’s trillion-dollar gamble(technologyreview.com)
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研究The Sequence Learning Loop - Issue 938: Learn About the Amazing Jev, Gemini and Paper2Agent(thesequence.substack.com)
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研究Smart glasses are already causing havoc in India(technologyreview.com)
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研究The AI Hype Index: AI loves cheating(technologyreview.com)
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研究WorkspaceBench: Evaluating Interpretability Methods for the Global Workspace(alignmentforum.org)
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研究Trains but Doesn't Learn: A Post-Training Delivery Benchmark for LLM Agents as Forward-Deployed Engineers(arxiv.org)
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研究Mitigating Sequential Reappearance in Diffusion Data-Point Unlearning(arxiv.org)
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研究IntLawNER: A Named Entity Recognition Dataset and Benchmark in International Law(arxiv.org)
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研究Splitting Documents at Lower Cost: Multi-Split Boundary Decisions for LLM-Based Page Stream Segmentation(arxiv.org)
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研究GaitVista: Reliability-Aware AI Measurement toward Accessible Longitudinal Gait Assessment(arxiv.org)
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研究Exposing Blind Spots in Deep Imbalanced Regression Evaluation(arxiv.org)
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研究AutoGym: Blueprint-First Generation of Verifiable Agent Gyms(arxiv.org)
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