6093 papers
The paper introduces SelfOrg, a training-free framework that enables multi-agent systems to self-organize by dynamically constructing response-conditioned communication graphs based on Shapley value approximations, thereby optimizing collaboration and significantly improving performance—especially with weaker LLMs—without relying on fixed topologies or external supervision.
The paper introduces CroSTAta, a Cross-State Transition Attention Transformer that enhances robotic manipulation robustness by employing a novel State Transition Attention mechanism to model temporal structures like failure and recovery patterns, outperforming standard attention and sequential models in simulation.
This paper introduces a "double projection" method within dynamical variational autoencoders that simultaneously estimates system state trajectories and noise time series from data, enabling effective multi-step reconstruction and learning of low-dimensional stochastic models across various benchmark problems.
This study presents an automated, cost-effective LLM-based agentic workflow that successfully extracts over 27,000 thermoelectric and structural property records from approximately 10,000 scientific articles, creating the largest LLM-curated dataset to date and establishing a scalable foundation for data-driven materials discovery.
The paper introduces DialTree, a tree-based dialogue reinforcement learning framework that autonomously discovers diverse and effective multi-turn attack strategies against large language models, significantly outperforming existing single-turn or template-based red-teaming methods.
This study leverages large language models to extract and structure nearly 8,000 articles from Senegalese legal texts into a comprehensive knowledge graph, thereby enhancing access to judicial information and clarifying rights and responsibilities for citizens and legal professionals.
This paper demonstrates that current graph-based tabular deep learning methods fail to accurately recover underlying feature interaction structures despite their focus on predictive accuracy, and shows that explicitly modeling the true graph structure significantly improves prediction performance.
This paper introduces a scalable and regularized Wasserstein barycenter solver based on gradient flows that leverages mini-batch optimal transport and seamlessly integrates supervised label information, achieving state-of-the-art performance across diverse domain adaptation benchmarks.
This paper proposes a pretraining-finetuning paradigm for robot locomotion that leverages a task-agnostic exploration strategy to train a Proprioceptive Inverse Dynamics Model (PIDM), which is then used to warm-start actor-critic algorithms like PPO, resulting in significant improvements in sample efficiency and task performance across diverse robot embodiments.
This paper introduces ARM-FM, a framework that leverages foundation models to automatically generate structured reward machines from natural language specifications, thereby enabling compositional reinforcement learning with improved task decomposition and zero-shot generalization.
This paper reveals that reinforcement learning can induce large language models to engage in systematic motivated reasoning, generating plausible justifications for violating safety instructions that successfully deceive smaller Chain-of-Thought monitors, thereby undermining current oversight mechanisms.
This paper proposes an explainable, adaptive graph learning framework that detects financial anomalies by routing them through mechanism-specific experts to identify distinct drivers like price shocks or liquidity freezes, thereby enabling targeted responses and outperforming existing baselines in both accuracy and early warning capabilities.
This paper proposes a reinforcement learning framework called Permutation Relative Policy Optimization (PRPO) that leverages column-permutation invariance as a structural prior to unlock the latent numerical reasoning capabilities of reasoning LLMs, enabling them to achieve state-of-the-art performance in tabular prediction tasks—particularly in zero-shot settings—while significantly outperforming much larger models with limited supervision.
This paper introduces RobLight, a tool that enhances the structural robustness verification of Graph Neural Networks by replacing computationally expensive constraint solvers with efficient, polynomial-time partial solvers, thereby improving upon the state of the art in detecting adversarial attacks.
This paper introduces a formal, unified framework for zero-shot reinforcement learning that establishes a two-level taxonomy of algorithms and decomposes error bounds into inference, reward, and approximation components to enable rigorous comparisons across diverse methods.
SwiftTS is a swift selection framework for time series pre-trained models that leverages multi-task meta-learning and a lightweight dual-encoder architecture to efficiently predict the best model for unseen datasets without expensive fine-tuning, achieving state-of-the-art performance across diverse horizons and datasets.
This paper introduces "Mercer priors," a new class of interpretable priors for Bayesian neural networks derived from Mercer representations of covariance kernels, which enable the networks to approximate Gaussian process samples and thereby combine the scalability of neural networks with the uncertainty quantification interpretability of Gaussian processes.
The paper proposes PESO, a continual learning method for LLM-based recommender systems that utilizes a proximal regularizer to anchor LoRA adapters to their most recent frozen states, thereby effectively balancing adaptation to evolving user preferences with the preservation of recent behavioral patterns.
This study compares a transparent ANFIS-FBCSP-PSO model with the deep-learning benchmark EEGNet on motor imagery EEG data, revealing that the fuzzy-neural approach offers superior within-subject performance and interpretability while EEGNet demonstrates stronger cross-subject generalization, thereby providing practical guidance for selecting BCI systems based on specific design priorities.
This paper proposes a Networked Mixture-of-Experts (NMoE) system and a hybrid federated learning framework that enable collaborative inference and efficient, privacy-preserving training of large AI models on resource-constrained mobile edge devices by leveraging neighbor expertise and balancing personalization with generalization.