3672 papers
This paper demonstrates that in multi-agent LLM systems, simple communication protocols are a more robust mechanism for achieving cooperation in social dilemmas than curriculum learning, which can inadvertently induce "learned pessimism" and reduce alignment depending on the sequence of training games.
This paper presents the first systematic evaluation of self-supervised learning for label-efficient sleep staging using wearable EEG, demonstrating that a specialized SSL pipeline significantly outperforms supervised baselines and general-purpose foundation models by achieving clinical-grade accuracy with only 5–10% of labeled data.
This paper distinguishes between geopolitical and geoeconomic shocks by demonstrating that while geopolitical risks directly reprice sovereign default risk, geoeconomic shocks transmit through monetary policy and the global financial cycle, creating a "scissors pattern" in sovereign CDS spreads that necessitates different policy responses for liquidity provision versus persistent risk premia.
The paper proposes HyWA, a novel Personalized Voice Activity Detection (PVAD) approach that utilizes a hypernetwork to generate personalized weights for selected layers of a standard VAD model, demonstrating consistent performance improvements and enhanced deployment flexibility compared to existing speaker-conditioning methods.
This paper introduces "Reveal-to-Revise," an explainable, bias-aware generative framework that unifies cross-modal attention, Grad-CAM++ attribution, and iterative feedback to achieve state-of-the-art performance and fairness in multimodal image generation and text classification tasks.
This paper introduces novel absolute cluster validity indices that quantify the compactness and separability of clusters to determine the true number of clusters, demonstrating their effectiveness across synthetic and real-world datasets compared to existing relative indices.
This paper introduces the Hermite eigenstructure ansatz (HEA), a theoretical framework that accurately predicts kernel regression learning curves on real datasets using only the empirical data covariance and target function decomposition, by approximating kernel eigenstructures as Hermite polynomials and demonstrating that MLPs in the feature-learning regime follow similar learning patterns.
This paper presents a unified geometry-based analysis demonstrating that Value Iteration achieves geometric convergence in both discounted and average-reward settings under a unique unichain optimal policy assumption, thereby resolving the discrepancy between classical theoretical bounds and observed empirical performance.
KVTC is a lightweight, model-agnostic transform coder that achieves up to 20 (or higher) compression of Key-Value caches for large language models by combining PCA-based decorrelation, adaptive quantization, and entropy coding, thereby enabling memory-efficient serving with reusable caches while maintaining high reasoning and long-context accuracy.
This paper introduces Augmented Time Series Causal Models (ATSCM), a novel framework that integrates neural causal discovery with counterfactual reasoning to dynamically model complex, time-varying causal relationships in energy markets and enable interpretable scenario analysis for electricity price formation.
This paper proposes the Time-series Neural Causal Model VAE (TNCM-VAE), a novel framework that integrates variational autoencoders with structural causal models to generate synthetic financial time series that preserve both temporal dependencies and causal relationships, thereby enabling robust counterfactual analysis and risk assessment.
This paper proposes the Dual-Graph Embedding with Transformer (DGET) framework, a multi-task learning architecture combining Graph Neural Networks and Transformers, to efficiently solve the NP-hard resource allocation problem in hybrid RF-OWC IoT networks under partial observability, achieving near-optimal throughput and Age of Information performance with significantly lower computational complexity than traditional optimization methods.
This paper introduces STREAM-VAE, a dual-path variational autoencoder that separates slow drifts and fast spikes in vehicle telemetry data to overcome the limitations of standard reconstruction-based methods and achieve robust anomaly detection across diverse operating modes.
This paper proposes a hierarchical dual-strategy framework that achieves precise selective unlearning of privacy-sensitive medical knowledge in large language models while preserving fundamental competencies, demonstrated by high forgetting and preservation rates on clinical datasets with minimal parameter modification.
This paper introduces CostNav, the first physics-grounded navigation benchmark that evaluates autonomous agents using real-world economic data to reveal that current methods, despite varying in hardware and architecture, all fail to achieve economic viability due to negative contribution margins.
This paper introduces Partially Equivariant Reinforcement Learning, a framework that mitigates error propagation in symmetry-breaking environments by selectively applying group-invariant or standard Bellman backups based on local symmetry, thereby achieving superior sample efficiency and generalization compared to existing methods.
This paper presents a co-design framework that simultaneously optimizes robotic hand morphology and control policies to enable the end-to-end design, fabrication, and deployment of task-specific dexterous hands in under 24 hours.
The paper introduces AlphaQubit 2, a scalable neural-network decoder that achieves near-optimal logical error rates for both surface and color codes while enabling real-time decoding faster than 1 microsecond per cycle on commercial accelerators, thereby overcoming previous limitations in speed and accuracy for fault-tolerant quantum computing.
The paper introduces Trio, a closed-loop molecular generation framework that integrates fragment-based language modeling, reinforcement learning, and Monte Carlo tree search to produce chemically valid, diverse, and pharmacologically optimized ligands with significantly improved binding affinity, drug-likeness, and synthetic accessibility compared to state-of-the-art methods.
This paper introduces computationally efficient and statistically consistent Random Forest variants that minimize the maximum risk across diverse environments to improve out-of-distribution generalization, offering novel guarantees for mean squared error, negative reward, and regret-based risks.