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arXiv:2603.10396v1 Announce Type: new Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not …
arXiv:2603.09988v1 Announce Type: cross Abstract: Mechanistic interpretability identifies internal circuits responsible for model behaviors, yet translating these findings into human-understandable explanations remains an open problem. …
arXiv:2603.10002v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly tasked with producing and manipulating structured artifacts. We consider the task of end-to-end spreadsheet …
arXiv:2603.09997v1 Announce Type: cross Abstract: When OpenAI deprecated GPT-4o in early 2026, thousands of users protested under #keep4o, claiming newer models had "lost their empathy." …
arXiv:2603.10359v1 Announce Type: new Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitation of rejection sampling. …
arXiv:2603.10891v1 Announce Type: new Abstract: Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. …
arXiv:2603.09990v1 Announce Type: cross Abstract: In business-to-business relations, it is common to establish NonDisclosure Agreements (NDAs). However, these documents exhibit significant variation in format, structure, …
arXiv:2603.10009v1 Announce Type: cross Abstract: Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training …
arXiv:2603.10384v1 Announce Type: new Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework …
arXiv:2603.09991v1 Announce Type: cross Abstract: The rapid growth of the global poultry industry, driven by rising demand for affordable animal protein, has intensified public discourse …
arXiv:2603.09980v1 Announce Type: cross Abstract: LLM unlearning is essential for mitigating safety, copyright, and privacy concerns in pre-trained large language models (LLMs). Compared to preference …
arXiv:2603.09987v1 Announce Type: cross Abstract: Feature Transformation (FT) is a core data-centric AI task that improves feature space quality to advance downstream predictive performance. However, …