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전체 전문분석자료
arXiv:2603.19313v1 Announce Type: new Abstract: A core challenge for faithful LLM role-playing is sustaining consistent characterization throughout long, open-ended dialogues, as models frequently fail to …
arXiv:2603.19321v1 Announce Type: new Abstract: Entity Matching (EM) is an important task that determines the logical relationship between two entities, such as Same, Different, or …
arXiv:2603.19415v1 Announce Type: new Abstract: Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance …
arXiv:2603.19426v1 Announce Type: new Abstract: Prior work uses linear probes on benchmark prompts as evidence of evaluation awareness in large language models. Because evaluation context …
arXiv:2603.19427v1 Announce Type: new Abstract: Why do some languages like Czech permit free word order, while others like English do not? We address this question …
arXiv:2603.19453v1 Announce Type: new Abstract: We study LLM policy synthesis: using a large language model to iteratively generate programmatic agent policies for multi-agent environments. Rather …
arXiv:2603.19519v1 Announce Type: new Abstract: We address a not-widely-recognized subset of exploratory search, where a user sets out on a typically long "search quest" for …
arXiv:2603.19532v1 Announce Type: new Abstract: Large Language Models (LLMs) are fluent but prone to hallucinations, producing answers that appear plausible yet are unsupported by available …
arXiv:2603.19539v1 Announce Type: new Abstract: We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assessment, using the U.S. …
arXiv:2603.19558v1 Announce Type: new Abstract: Eliciting explicit, step-by-step reasoning traces from large language models (LLMs) has emerged as a dominant paradigm for enhancing model capabilities. …
arXiv:2603.19635v1 Announce Type: new Abstract: The exponential expansion of context windows in LLMs has unlocked capabilities for long-document understanding but introduced severe bottlenecks in inference …
arXiv:2603.19294v1 Announce Type: new Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled …