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This paper introduces AUTOPILOT-VQA, a novel benchmark designed to evaluate vision-language models (VLMs) on their ability to understand incident-centric.
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This paper addresses the challenge of compressing large language model (LLM) weights to extremely low bit rates—around 2 bits per weight—without relying.
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To enable training with explicit turn-level retrieval supervision, the authors develop a Unified Scenario Engine that programmatically generates.
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DexVerse addresses a critical gap in robotic dexterous manipulation research by introducing a large-scale, modular benchmark that spans 100 diverse tasks.
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This paper addresses the challenge of detecting AI-generated images (AIGI) across diverse generative models, including GANs and diffusion models.
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This paper investigates the question of how capable and biased large language model (LLM) judges must be to reliably evaluate citation quality in.
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DocMaster addresses the challenge of analyzing complex documents such as academic papers, technical manuals, and financial reports by preserving their.
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This paper presents DominoTree, a novel training-free speculative decoding method that builds a best-first tree of draft token sequences scored.
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This paper addresses the challenging problem of exact permutation recovery between two sets of noisy feature vectors when the underlying true features.
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This paper studies how formal mechanisms can sustain market stability and cooperation among self-interested LLM-driven agents in a multi-agent trading.
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This paper addresses a central challenge in the Noisy Intermediate-Scale Quantum (NISQ) era: the difficulty in objectively and comparably characterizing.
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This paper provides a comprehensive and unified treatment of a recently introduced class of quantum divergences known as hockey stick f-divergences, which.
