4195 papers
This paper proposes a lightweight hybrid framework for the Game of the Amazons that integrates Graph Attention Autoencoders, Stochastic Graph Genetic Algorithms, and GPT-4o-mini to overcome resource constraints, achieving decision accuracy improvements of 15%–56% over baselines and outperforming its teacher model by effectively denoising LLM outputs through structural graph reasoning.
The paper introduces IH-Challenge, a reinforcement learning dataset designed to enhance instruction hierarchy robustness in frontier LLMs, which significantly improves their ability to prioritize instructions against conflicts and adversarial attacks while maintaining helpfulness and minimizing capability regression.
This paper presents a Multi-Agent Reinforcement Learning framework using Proximal Policy Optimization to coordinate UAV fleets for time-critical medical supply delivery, demonstrating that classical PPO outperforms asynchronous and sequential strategies in dynamically prioritizing tasks and reallocating resources under uncertain conditions using real-world geographic data.
This study evaluates 11 promptable foundation models for musculoskeletal CT segmentation across four anatomical regions, revealing that while specific models like SAM and nnInteractive perform best under ideal conditions, all models exhibit significant sensitivity to human prompting variations, leading to performance drops and highlighting the challenge of selecting robust models for real-world clinical applications.
The paper introduces SCORE, a lightweight deep learning architecture that replaces traditional layer stacking with a weight-shared, ODE-inspired contractive recurrent update mechanism to improve training stability, accelerate convergence, and reduce parameter counts across various model types.
This paper introduces a novel language-guided framework that leverages pretrained vision-language models and a specialized adapter to achieve zero-shot, generative detection and localization of subsurface defects in carbon fiber-reinforced polymers using active infrared thermography, thereby eliminating the need for costly, task-specific training datasets while significantly improving signal-to-noise ratios and detection accuracy.
This paper proposes a novel self-finetuning framework that enables Generative AI agents to autonomously learn continuous control for dynamic Radio Access Network slicing by distilling long-horizon experiences into model parameters via a bi-perspective reflection mechanism, thereby outperforming traditional Reinforcement Learning and standard LLM-based agents in sample efficiency and multi-objective optimization without relying on handcrafted reward signals.
This paper presents CUAAudit, a large-scale meta-evaluation demonstrating that while Vision-Language Models can serve as autonomous auditors for Computer-Use Agents with strong accuracy and calibration, their significant performance degradation in complex environments and notable inter-model disagreement reveal fundamental limitations that necessitate explicit accounting for evaluator reliability and uncertainty in real-world deployments.
This paper empirically demonstrates that contrary to the hypothesis that moral reasoning alignment requires diversity-seeking algorithms, standard reward-maximizing RLVR methods are equally or more effective because high-reward moral responses exhibit a concentrated distribution in semantic space similar to logical reasoning tasks.
This paper establishes a mathematical framework called Gradient Flow Drifting that proves the equivalence between the recently proposed Drifting Model and the Wasserstein gradient flow of the forward KL divergence under KDE approximation, while extending the approach to a mixed-divergence strategy on Riemannian manifolds to simultaneously mitigate mode collapse and blurring.
This paper proposes the Progressive Retrospective Framework (PRF), a plug-and-play method that utilizes a cascade of retrospective units and a rolling-start training strategy to effectively address the challenge of variable-length trajectory prediction in autonomous driving by progressively aligning features from incomplete observations with complete ones.
This paper introduces a novel framework for self-improving LLM agents that automatically extracts structured learnings from execution trajectories—categorizing them into strategy, recovery, and optimization tips—and injects them via adaptive memory retrieval to significantly boost task completion rates, particularly on complex scenarios.
This paper proposes Conditional Expectation Reward (CER), a novel reinforcement learning method that utilizes the large language model itself as an implicit verifier to provide soft, graded reward signals, thereby overcoming the limitations of rule-based verification and enabling effective reasoning training across both mathematical and general free-form answer domains.
This paper proposes a novel, explainable approach to detect and eliminate neural network backdoors by analyzing active paths within the model, demonstrating its effectiveness through experiments on intrusion detection systems.
This paper proposes a novel framework that interleaves task scheduling and motion planning through an incremental learning loop, where symbolic feedback from motion feasibility checks guides the scheduler to generate efficient, collision-free plans for multi-object navigation in shared workspaces.
This paper introduces ROVA, a training framework that enhances video reasoning models' robustness against real-world disturbances like weather and occlusion through difficulty-aware adaptive training and a robustness-aware consistency reward, validated by the new PVRBench benchmark which demonstrates significant performance improvements over existing models.
The paper introduces FAME, a novel abductive explanation method for large neural networks that utilizes dedicated perturbation domains and LiRPA-based bounds to efficiently generate formal abstract minimal explanations, demonstrating superior performance in explanation size and runtime compared to VERIX+.
The paper introduces DxEvolve, a self-evolving diagnostic agent that emulates clinician cognition through an interactive deep clinical research workflow, autonomously requisitioning examinations and externalizing experience to achieve superior diagnostic accuracy and governed continual improvement compared to existing AI models.
This paper presents a platform-agnostic Digital Human Modelling framework that integrates OpenBCI Galea biosensing with a reproducible SuperTux game environment to provide structured, temporally aligned multimodal data for future AI-driven, ethics-approved research in accessibility and inclusive interaction design.
This paper introduces Contract And Conquer (CAC), a black-box adversarial attack method that combines knowledge distillation on an expanding dataset with precise search space contraction to provably compute adversarial examples within a fixed number of iterations, outperforming existing state-of-the-art approaches on ImageNet.