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arXiv:2603.22744v1 Announce Type: new Abstract: Large language models excel on objectively verifiable tasks such as math and programming, where evaluation reduces to unit tests or …
arXiv:2603.22566v1 Announce Type: new Abstract: The 2022 U.S. Supreme Court decision in Dobbs v. Jackson Women's Health Organization reshaped the reproductive rights landscape, introducing new …
arXiv:2603.22576v1 Announce Type: new Abstract: We introduce CAPITU, a benchmark for evaluating instruction-following capabilities of Large Language Models (LLMs) in Brazilian Portuguese. Unlike existing benchmarks …
arXiv:2603.22752v1 Announce Type: new Abstract: Automated classification of clinical transcriptions into medical specialties is essential for routing, coding, and clinical decision support, yet prior work …
arXiv:2603.22877v1 Announce Type: new Abstract: Efficient solutions for satisfiability modulo theories (SMT) are integral in industrial applications such as hardware verification and design automation. Existing …
arXiv:2603.22651v1 Announce Type: new Abstract: The adoption of large language models (LLMs) for structured information extraction from financial documents has accelerated rapidly, yet production deployments …
arXiv:2603.22347v1 Announce Type: new Abstract: While Landauer's principle establishes the fundamental thermodynamic floor for information erasure and Fisher Information provides a metric for local curvature …
arXiv:2603.22306v1 Announce Type: new Abstract: Affective judgment in real interaction is rarely a purely local prediction problem. Emotional meaning often depends on prior trajectory, accumulated …
arXiv:2603.23004v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated great capabilities across diverse natural language tasks; yet their ability to solve abstraction and …
arXiv:2603.22735v1 Announce Type: new Abstract: Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet …
arXiv:2603.22754v1 Announce Type: new Abstract: Large language models (LLMs) solve complex problems by generating multi-step reasoning traces. Yet these traces are typically analyzed from only …
arXiv:2603.22582v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has been proposed as a transparency mechanism for large language models in safety-critical deployments, yet its effectiveness …
