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研究I wrote an AI textbook — how long until AI can do it better?(interconnects.ai)
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研究The Download: our 35 young innovators and the “censorship-industrial complex”(technologyreview.com)
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研究The Sequence Chat - Issue 912: NVIDIA’s Chris Alexiuk Talks About Nemotron, GPUs and Agentic AI(thesequence.substack.com)
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研究How we picked 35 of the world’s top young scientists and engineers(technologyreview.com)
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研究CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation(arxiv.org)
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研究Post-Hoc Sparse Coding of Latent Communication Between Vision-Language Model Agents(arxiv.org)
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研究Closed-Loop LLM Co-Pilots for Digital Agriculture(arxiv.org)
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研究Automating and Scaling Behavioral Scientific Research on AI Agents(arxiv.org)
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研究SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning(arxiv.org)
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研究ReCBM: Uncertainty-Gated Relational Reasoning for Concept Bottleneck Models(arxiv.org)
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研究Generating Attacks for LLMs with GFlowNets(arxiv.org)
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研究Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons(arxiv.org)
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研究Transformer Geometry Observatory TGO-IV: Developmental Topology Observatory(arxiv.org)
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研究MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis(arxiv.org)
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研究Edge Phoneme Recognition for Children's Speech through Age-Aware Training(arxiv.org)
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研究Mitigating Bus Bunching with Reinforcement Learning Enhanced by Semantic Stop Embedding(arxiv.org)
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研究DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents(arxiv.org)
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研究Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification(arxiv.org)
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研究FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows(arxiv.org)
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研究UserToolBench: A User-Profile-Hidden Benchmark for Personalized Decision Making in Tool-Use LLMs(arxiv.org)
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研究ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models(arxiv.org)
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研究TRACE: Trustworthy Retrieval-Augmented Conversational Engine(arxiv.org)
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研究Procedural Fairness Failures in RLHF from Preference Averaging(arxiv.org)
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研究SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks(arxiv.org)
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研究Sheaf-Based Federated Representation Learning(arxiv.org)
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研究SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents(arxiv.org)
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研究Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review(arxiv.org)
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研究An anytime algorithm for mixing the computable measures(alignmentforum.org)
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研究How the “censorship-industrial complex” is changing the Internet and US policy(technologyreview.com)
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研究The Download: the next big thing in LLMs and how AI academic research is shifting(technologyreview.com)
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