11579 papers
This study reveals that while the correctness of LLM-generated reasoning is the primary driver of user accuracy in critical thinking tasks, medium-length explanations uniquely enhance users' ability to detect errors when the AI's reasoning is incorrect, suggesting that response length plays a nuanced role in shaping human critical evaluation.
This paper introduces a Physics-Informed Neural Operator (PINO) surrogate model that accelerates the retention analysis of Ferroelectric Vertical NAND devices by over 10,000 times compared to traditional TCAD simulations while maintaining physical accuracy, thereby enabling efficient optimization of device designs against charge detrapping and ferroelectric depolarization.
This paper proposes a Distributed Legal Infrastructure (DLI) framework comprising five interlocking layers—ranging from soulbound agent identities to decentralized adjudication—to establish interoperable protocols that ensure accountability, contestability, and rule-of-law principles within the emerging autonomous agentic web.
This study demonstrates that a hybrid machine learning model combining Bi-LSTM and GRU algorithms, trained on diverse patient data, achieves a 97.07% accuracy in detecting Coronary Artery Disease, significantly outperforming traditional diagnostic methods.
This paper introduces SELSM, a training-free framework that enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic logical rules, thereby significantly improving zero-shot FHIR-based task performance and achieving a 100% completion rate on the MedAgentBench without compromising data privacy.
MindfulAgents is a large language model-driven multi-agent system that personalizes mindfulness meditation through expert-aligned script generation and real-time adaptation, significantly improving user engagement, self-awareness, and stress reduction in both short-term and long-term studies.
This study demonstrates that DNA foundation models (DNABERT-2, Evo 2, and NTv2) are vulnerable to model inversion attacks, where adversaries can reconstruct sensitive genomic sequences from shared embeddings with high accuracy, particularly for shorter sequences and per-token representations, thereby highlighting critical privacy risks in Embeddings-as-a-Service frameworks.
This paper establishes that while policy gradient methods can achieve near-optimal performance in post-training autoregressive models when staying within the base model's support, they face an exponential query barrier to generalize beyond it unless process rewards are utilized to leverage token-level likelihood quantiles.
This paper presents a principled analysis of why imitation learning is effective for quadruped locomotion with limited data and proposes a new offline method that enables robots to learn robust walking policies from just a few seconds of demonstration.
This paper introduces Elenchus, a dialogue system that leverages prover-skeptic interactions between a human expert and an LLM to construct knowledge bases grounded in inferentialist semantics, mapping dialectical states to formal logic to explicitly capture and verify the inferential relationships and design rationales of complex ontologies like PROV-O.
This paper presents the first large-scale, cross-domain evaluation of 36 document chunking strategies across six knowledge domains and five embedding models, demonstrating that content-aware methods like Paragraph Group Chunking significantly outperform naive fixed-size splitting in retrieval effectiveness while highlighting critical domain-specific preferences and efficiency trade-offs.
This paper introduces NePPO, a novel multi-agent reinforcement learning pipeline that computes approximate Nash equilibria in general-sum games by learning a player-independent potential function to transform the mixed cooperative-competitive environment into an approximating cooperative game.
The paper introduces Diffusion Controller (DiffCon), a unified control-theoretic framework that models reverse diffusion sampling as a state-only stochastic control problem within LS-MDPs, enabling the derivation of practical fine-tuning algorithms and a lightweight side-network architecture that outperforms existing gray-box and white-box adaptation methods.
This paper demonstrates that optimizing for objectives like profit or crime reduction while maintaining a zero Average Treatment Effect (ATE) can mask significant unfairness driven by confounding, thereby arguing that fairness regulations must shift from evaluating aggregate decision-level outcomes to scrutinizing model-level causal mechanisms.
This paper introduces a lightweight, probabilistic world model built on a pretrained vision foundation model that generates uncertainty-based runtime monitors to accurately detect anomalous failures in bimanual manipulators, outperforming existing baselines while requiring significantly fewer trainable parameters.
This study analyzes how 80 student design teams integrated generative AI into their creative process, revealing that while AI serves as a cognitive accelerator for early-stage tasks like brainstorming, human competencies in agency, domain knowledge, imagination, and taste remain essential for interpreting context, validating outputs, and refining design solutions.
This paper introduces Self-MOA, a fully automated framework that aligns small language models using weak supervision from automated evaluators to achieve significant safety improvements with minimal training data while preserving helpfulness.
The paper introduces \textsc{ReSched}, a minimalist deep reinforcement learning framework that simplifies the Flexible Job Shop Scheduling Problem by condensing the state space to four essential features and utilizing a modified Transformer architecture, achieving superior performance and generalization across various scheduling variants compared to existing methods.
Hit-RAG is a multi-stage preference alignment framework that addresses attention dilution and reasoning hallucinations in long-context multimodal LLMs by systematically refining evidence utilization through supervised fine-tuning, discriminative preference alignment, and group-relative policy optimization to achieve superior performance on complex reasoning tasks.
This paper proposes the Hierarchical Memory Tree (HMT), a structured framework that decouples high-level task logic from site-specific action details through a three-level abstraction hierarchy, thereby significantly enhancing the generalization and robustness of large language model-based web agents in unseen environments.