1071 papers
This paper introduces a transformer architecture combining adaptive per-layer looping and gated memory banks, demonstrating that while looping primarily enhances mathematical reasoning and memory aids commonsense tasks, their integration yields a model that outperforms significantly deeper baselines on math benchmarks.
This paper introduces QUORUM, a unified evaluation framework, and COACH, an LLM-driven pipeline, to generate and assess personalized health counseling for cancer patients, demonstrating that while stakeholders converge on the system's relevance and quality, they diverge on nuances like tone and error sensitivity, thereby highlighting the critical need for multi-perspective evaluation in trustworthy patient-centered NLP systems.
This paper introduces Token-Conditioned Reinforcement Learning (ToCoRL), a framework that leverages the intrinsic behavioral plasticity of Large Language Models to internalize and stabilize inference-time adaptations, enabling precise control over behavioral modes like switching from reasoning to direct answering without degrading overall capabilities.
The paper introduces Sandpiper, a mixed-initiative system that integrates interactive researcher dashboards with agentic LLMs to enable scalable, privacy-preserving, and rigorous qualitative analysis of large-scale educational discourse while mitigating hallucinations and ensuring methodological consistency.
This paper challenges the stability of human and AI preferences in Reinforcement Learning from Human Feedback (RLHF) by demonstrating that both are susceptible to "choice blindness," where preferences are easily manipulated by context and shallow cues, leading to undetected reward signal corruption and downstream policy degradation.
This paper proposes a method to equip LLM agents with native retrieval capabilities by projecting their hidden states directly into the embedding space via a lightweight head, thereby eliminating the need for a separate embedding model while retaining 97% of baseline retrieval quality.
This paper introduces VET-Bench, a diagnostic benchmark revealing that current Vision-Language Models fail at tracking visually identical objects due to an over-reliance on static features, and proposes Spatiotemporal Grounded Chain-of-Thought (SGCoT) to achieve over 90% accuracy by explicitly generating object trajectories as intermediate reasoning steps.
This prospective feasibility study demonstrates that a conversational AI system (AMIE) can safely and effectively conduct clinical history-taking and generate diagnostic suggestions in a real-world urgent care setting, achieving high patient satisfaction and diagnostic accuracy comparable to primary care providers while requiring no real-time human intervention.
This paper introduces the first freely available English-to-Swedish dataset designed to benchmark language models' tendency to prefer literal "translationese" over idiomatic phrasing, revealing that exposure to source text biases models toward unnatural translations even when context is removed.
LycheeCluster is a novel KV cache management method that employs structure-aware chunking and hierarchical indexing to transform cache retrieval into a logarithmic-time process, achieving up to a 3.6x inference speedup with minimal performance degradation for long-context LLMs.
Fanar-Sadiq is a bilingual multi-agent system that addresses hallucination and source misattribution in Islamic queries by routing diverse requests to specialized modules for grounded retrieval, exact scripture lookup, and deterministic legal calculations, demonstrating high effectiveness and widespread public adoption.
The paper introduces Drift2Act, a controller that reframes distribution drift monitoring as constrained decision-making by combining sensing with online risk certificates to dynamically select cost-effective interventions or safety-preserving escalations, thereby achieving near-zero safety violations and rapid recovery under realistic resource constraints.
The paper introduces OfficeQA Pro, a challenging enterprise benchmark using a massive corpus of U.S. Treasury Bulletins to demonstrate that current frontier AI agents struggle significantly with grounded, multi-document reasoning, achieving low accuracy even with direct document access and benefiting notably from structured document representations.
This paper introduces CODA, a method that optimizes adaptive reasoning by dynamically allocating inference-time compute based on estimated instance difficulty, significantly reducing token costs on simple tasks while enhancing deliberation on complex ones without requiring external annotations.
This paper provides a comprehensive theoretical and empirical analysis of unsupervised reinforcement learning with verifiable rewards (URLVR), revealing that intrinsic reward methods are fundamentally limited by a confidence-correctness alignment ceiling that causes model collapse, while suggesting that external rewards grounded in computational asymmetries may offer a scalable alternative.
The paper proposes Agentic Critical Training (ACT), a reinforcement learning paradigm that enhances large language model agents by rewarding their ability to autonomously judge the quality of actions among alternatives, thereby fostering genuine self-reflection and outperforming traditional imitation learning and knowledge distillation methods across various benchmarks.
This study demonstrates that ChatGPT outperformed two medical students in answering challenging cardiology and vascular pathology questions, achieving a 92.10% accuracy rate on a 190-question dataset, thereby highlighting its potential as a valuable tool for medical education.
This paper reveals a significant performance disparity where Large Language Models excel at generation tasks but struggle with evaluation, often producing unfaithful judgments even in areas where they lack competence, thereby challenging the assumption that generative proficiency guarantees evaluative reliability.
This paper proposes using ChatGPT to perform sentiment analysis on citations within scientific articles, aiming to uncover nuanced reception patterns and detect potential biases or conflicts of interest to enhance the integrity of scholarly research.
This paper argues that while Large Language Models lack the autonomy required for genuine agency due to their absence of self-generated norms, goals, and embodied interaction, they function as transformative "linguistic automata" that, through a unique human-machine coupling, enable new "midtended" forms of intentional agency.