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This paper addresses the problem of adversarial manipulation of ranked recommendations produced by web-augmented large language models (LLMs).
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This paper addresses a core challenge in self-evolutionary training of large language models (LLMs): balancing open-ended task diversity with reliable.
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This paper investigates why adding procedural skills to large language model (LLM) agents can both help and hurt task performance, a phenomenon termed the.
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This paper addresses the challenge of routing large language model (LLM) requests for agentic AI workloads, where each user task consists of a long.
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This paper addresses the challenge that existing vision-language models (VLMs), primarily built on 2D visual input, lack fine-grained 3D spatial.
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This technical report addresses the challenge of systematically measuring the alignment—or legislative-geographic congruence—between U.S.
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This paper addresses a key bottleneck in longitudinal health and social science research—data preparation—by evaluating the capabilities of open-weight.
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This article presents a practice-centered vision for integrating artificial intelligence (AI) into science education by embedding AI as scientific.
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AXIS addresses the core bottleneck in scalable robot manipulation learning: the difficulty of collecting large-scale, diverse, and high-quality.
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This work addresses the challenging problem of neutron source identification from recoil spectroscopy measurements, which is crucial for nuclear.
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This paper addresses a critical gap in Embodied Question Answering (EQA) research: the common episodic evaluation paradigm treats each question as an.
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This paper investigates the nuanced process by which large language models (LLMs) revise their moral judgments in response to external viewpoints, moving.
