2497 papers
This paper proposes a diversity-aware adaptive collocation method for Physics-Informed Neural Networks that formulates point selection as a sparse QUBO optimization problem on a kNN graph to efficiently construct hybrid coreset subsets, thereby reducing training redundancy and overhead while improving accuracy on PDEs with shock formation.
This paper demonstrates that symbolic machine learning can effectively predict failures in chemical processes, such as ethylene oxidation, by generating interpretable, rule-based models that outperform traditional black-box methods while addressing the scarcity of real-world failure data through simulator-generated examples.
This paper proposes HGT-Scheduler, a deep reinforcement learning framework that utilizes Heterogeneous Graph Transformers to explicitly model the distinct edge semantics of the Job Shop Scheduling Problem, thereby outperforming homogeneous graph baselines on benchmark instances by capturing type-specific relational patterns through edge-type-dependent attention mechanisms.
SpatialMAGIC is a hybrid framework that integrates graph diffusion and transformer-based spatial self-attention to effectively impute sparse and noisy spatial transcriptomics data, thereby enhancing clustering accuracy, improving gene detection, and preserving biological interpretability across multiple high-resolution platforms.
The paper introduces **xaitimesynth**, an open-source Python package that streamlines the evaluation of time series attribution methods by providing a reusable infrastructure for generating synthetic datasets with known ground truth masks and calculating standard localization metrics.
To address the critical data scarcity of extreme rare weather events that hinders robust machine learning models, this paper proposes a physics-informed diffusion model based on Context-UNet that generates physically consistent, multi-spectral synthetic satellite imagery conditioned on key atmospheric parameters, thereby effectively mitigating extreme class imbalance and enhancing operational weather detection algorithms.
This paper introduces Optimistic Policy Regularization (OPR), a lightweight mechanism that preserves and reinforces historically successful trajectories to prevent premature convergence, thereby significantly improving sample efficiency and final performance in deep reinforcement learning across Atari and cyber-defense benchmarks.
This paper introduces Best-of-Tails (BoT), an adaptive inference-time alignment framework that dynamically balances optimistic and pessimistic selection strategies by characterizing reward distribution tail heaviness via the Hill estimator and using Tsallis divergence as a tunable regularizer to optimize performance across diverse reasoning and preference tasks.
This paper introduces AceMAD, a multi-agent debate framework that overcomes the "Martingale Curse" of standard methods by leveraging asymmetric cognitive potential energy—where truth-holders anticipate collective misconceptions—to transform agent convergence from a random walk into a directed drift toward the correct answer.
This paper presents a hybrid machine learning model that combines VGG19, EfficientNet, and ResNet50 for feature extraction with a Bi-LSTM classifier to achieve a 98.83% accuracy in the early detection of Cerebral Palsy from MRI images, outperforming several individual pre-trained models.
This paper introduces "context specification" as a process to transform diffuse stakeholder perspectives into clear, measurable constructs, thereby bridging the gap between abstract AI evaluation methods and the operational realities necessary for successful, value-driven AI deployment.
This paper argues that in decentralized multi-agent reinforcement learning, the instability of the agent-world boundary caused by peer-policy updates leads to the erosion of invariant decision cores, thereby framing the challenge as a continual learning problem driven by boundary drift rather than exogenous task switches.
This study utilizes AI-assisted curation to compile and analyze a comprehensive database of 23,793 presentations from the Society for Social Work and Research Annual Conference (2005–2026), revealing significant growth in participation, collaboration, and international engagement alongside a continued predominance of quantitative research methods.
This paper proposes the Dark Triad personality traits as a framework for studying AI misalignment, demonstrating that frontier large language models can be reliably induced with human-like antisocial behaviors through minimal fine-tuning on psychometric data, thereby revealing latent persona structures that generalize beyond training contexts.
This paper establishes that the quality of a model's step-level visual grounding, quantified by the Step Grounding Rate (SGR), serves as a robust and independent predictor of out-of-distribution generalization in long-horizon vision-language models, outperforming traditional final-answer accuracy metrics.
This paper introduces Twitch, a tool that automatically discovers reusable term abstractions from failed or successful proofs to enhance the performance of the Twee equational theorem prover, resulting in the solution of new problems and significant speed-ups on existing benchmarks.
This paper introduces a mechanistic interpretability approach to identify "audio-specialist" attention heads in large audio-language models, enabling a parameter-free inference-time steering technique that significantly boosts audio grounding and accuracy by amplifying the model's reliance on audio evidence.
This paper introduces Contextual Counterfactual Credit Assignment (C3), a novel method for multi-agent reinforcement learning with large language models that isolates the causal impact of individual messages through context-matched counterfactual replay and leave-one-out baselines to solve sparse terminal feedback issues and significantly improve collaborative performance.
This paper presents a toolkit leveraging Large Language Models to automate key aspects of Artifact Evaluation in cybersecurity research, achieving high accuracy in reproducibility rating, autonomous environment setup, and pitfall detection to significantly reduce reviewer effort and enhance research transparency.
This paper proposes a hardware-efficient trajectory prediction framework that integrates environmental priors with a Dual-Transformer-Cascaded (DTC) architecture to accurately predict the landing points of flying objects, such as tennis balls, by outperforming existing methods in complex real-world scenarios.