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Regret Pre-training: Bridging Prior and Posterior Views for Enhanced Knowledge Grounding
arXiv:2606.03080v1 Announce Type: new Abstract: Causal language models factorize sequence probabilities using only preceding context, leaving future information unexploited during training despite its availability in the training data. This paper introduces Regret Pre-training, a self-supervised framework grounded in the Learning Using Privileged Information (LUPI) paradigm. The framework employs a dual-view architecture in which a single model generates both a causal Student distribution...
PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
arXiv:2606.08938v1 Announce Type: new Abstract: Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show strong medical reasoning ability, but single-paradigm or naively mixed dialogue supervision makes these paradigms difficult to learn without interference. We propose \textbf{PACT} (Periodic Anchor Consensus Training), a framework that couples supervised multi-paradigm dialogue synthesis with...
CAPF: Guiding Search-Agent Rollouts with Credit-Attenuated Privileged Feedback
arXiv:2606.01830v1 Announce Type: new Abstract: Recent LLM search agents use reinforcement learning with verifiable rewards (RLVR) to learn search-augmented reasoning from outcome rewards. On hard problems, these agents rarely sample end-to-end successful rollouts, leaving outcome-only RLVR with few positive-reward trajectories.
Decentralized End-to-End Multi-AAV Pursuit Using Predictive Spatio-Temporal Observation via Deep Reinforcement Learning
arXiv:2603.24238v2 Announce Type: replace Abstract: Decentralized cooperative pursuit in cluttered environments is challenging for autonomous aerial swarms, especially under partial and noisy perception. Existing methods often rely on abstracted geometric features or privileged ground-truth states, and therefore sidestep perceptual uncertainty in real-world settings. We propose a decentralized end-to-end multi-agent reinforcement learning (MARL) framework that maps raw LiDAR observations...
Pathway-Structured Privileged Distillation for Deployable Computational Pathology
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CoFiDA-M: Concept-Aware Feature Modulation for Cross-Domain Adaptation with Image-Only Inference
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Commentary: The pitfalls of seeking legal advice from AI chatbots
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The Painful Truth About Long Covid
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Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization
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Actually, the SAT Was Necessary After All
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