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AI+医疗机器人教育金融能源健康娱乐思考
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研究Calibrating the Evaluator: Does Probability Calibration Mitigate Preference Coupling in LLM Agent Feedback Loops?(arxiv.org)
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182
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arxiv.org
研究Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models(arxiv.org)
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183
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arxiv.org
研究LLM-Driven Personalities for Decision Making in Emergency Simulations(arxiv.org)
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184
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arxiv.org
研究STEB: Style Text Embedding Benchmark(arxiv.org)
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185
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研究SwiftAudio: Data-Efficient Caption-Only Distillation for One-Step Text-to-Audio Diffusion-based Generation(arxiv.org)
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研究PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding(arxiv.org)
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187
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arxiv.org
研究Citation Discipline in Spec-Driven Development: A Cross-Model Empirical Study of Output Determinism and Automated Hallucination Detection in LLM-Generated Code(arxiv.org)
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188
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研究DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers(arxiv.org)
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189
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研究Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks(arxiv.org)
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190
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研究CLIMB: Centroid-Based Hierarchical Memory for Online Continual Self-Supervised Learning(arxiv.org)
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191
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研究When to Re-Plan: Subgoal Persistence in Hierarchical Latent Reasoning(arxiv.org)
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192
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arxiv.org
研究Knowledge Distillation from Large Reasoning Models to Compact Student Models: A Case Study on the John O Bryan Mathematics Competition(arxiv.org)
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193
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arxiv.org
研究OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models(arxiv.org)
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研究LUMOS: A Semantic Operating-System Layer for Accessibility-Grounded AI Agents(arxiv.org)
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研究ECHO: Prune to act, trace to learn with selective turn memory in agentic RL(arxiv.org)
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196
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arxiv.org
研究Learning Video Dynamics with Predictive Differentiable Rendering(arxiv.org)
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197
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arxiv.org
研究An AI-Based Solution for Secure Service Provisioning in IoT(arxiv.org)
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198
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研究ATRIA: Adaptive Traceable ECG Reporting with Iterative Agents(arxiv.org)
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199
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arxiv.org
研究Mapping the Artificial Intelligence Divide in Africa: Infrastructure, Accessibility and Capacity(arxiv.org)
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200
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arxiv.org
研究ALM2Vec: Learning Audio Embeddings for Universal Audio Retrieval with Large Audio-Language Models(arxiv.org)
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