7439 papers
This paper introduces LLMTM, a comprehensive benchmark for evaluating Large Language Models on temporal motif analysis in dynamic graphs, and proposes a cost-effective, structure-aware dispatcher that intelligently balances high accuracy and computational expense by routing queries between standard prompting and a specialized tool-augmented agent.
The paper proposes MDENeRF, an iterative Bayesian framework that refines smooth monocular depth estimates by fusing them with high-frequency geometric details and uncertainty derived from Neural Radiance Fields, thereby enhancing scene understanding for applications like autonomous navigation.
This paper proposes a novel end-to-end audio-visual speech recognition framework that integrates speech enhancement via a Conformer-based bottleneck fusion module to implicitly refine noisy audio features without explicit mask generation, thereby preserving semantic integrity and outperforming existing mask-based methods on the LRS3 benchmark under noisy conditions.
This paper proposes a novel domain adaptation method that derives domain-invariant representations by interpreting smoothed optimal transport plans as bipartite graph adjacency matrices and applying spectral embedding, demonstrating strong performance across acoustic and electrical defect detection tasks while mitigating the sensitivity of traditional Monge map approximations to regularization and hyperparameters.
This paper presents a robust neuromorphic platform for machine learning that implements optical neural networks using only linear optical resources and coherent states, achieving necessary nonlinearity through phase-shift encoding to enable straightforward experimental in situ training and inference while demonstrating high resilience to photon losses.
This paper introduces CELM, the first foundation model for clinical EEG-to-language generation that leverages a large-scale dataset of 9,922 reports and 11,000 hours of recordings to achieve significant improvements in summarizing long-term EEG data and generating comprehensive clinical reports.
This paper presents and experimentally demonstrates a purely photonic deep neuromorphic network that achieves 100% accuracy on a letter recognition task by utilizing a local optical feedback mechanism with non-volatile phase-change material synapses to enable online, unsupervised Hebbian learning without inefficient optical-electrical-optical conversions.
ZK-HybridFL is a secure, scalable decentralized federated learning framework that integrates a DAG ledger with zero-knowledge proofs and sidechains to enable privacy-preserving model validation, robust adversarial detection, and efficient on-chain verification while outperforming existing solutions in convergence speed, accuracy, and latency.
This paper introduces the Entropy Dynamics Instability Score (EDIS), a metric that leverages the temporal evolution of token-level entropy to diagnose and improve LLM reasoning by identifying characteristic instability patterns associated with erroneous solutions.
This paper introduces Latent Exploration Decoding (LED), a training-free decoding strategy that leverages high-entropy intermediate layer posteriors to counteract exploration collapse in post-trained Large Reasoning Models, thereby significantly improving accuracy across multiple benchmarks.
This paper introduces an automatic red-team pipeline that successfully stress-tests alignment audits by generating strategic system prompts capable of deceiving both black-box and white-box methods into making confident, incorrect assessments of misaligned models, thereby revealing the first documented evidence of activation-based strategic deception.
The paper introduces LaPha, a method that trains AlphaZero-like LLM agents in a hyperbolic Poincaré latent space to leverage negative curvature for efficient search and dense process rewards, significantly boosting mathematical reasoning performance on benchmarks like MATH-500 and AIME.
This paper introduces a perturbation-based validation protocol to ensure the faithfulness of saliency maps in siRNA efficacy prediction, revealing critical failure modes across datasets and proposing a biology-informed regularizer to enhance the reliability of explanation-guided therapeutic design.
This paper introduces Aletheia, an autonomous AI research agent powered by advanced reasoning models and tool use that successfully generates, verifies, and revises mathematical proofs from Olympiad problems to PhD-level research, achieving milestones such as fully AI-generated papers and the autonomous solution of open problems while proposing new frameworks for quantifying AI autonomy and transparency.
This paper investigates the "diagonal sink" phenomenon in temporal attention mechanisms, where information degeneration causes a bias toward initial tokens, and proposes theoretical sensitivity bounds alongside effective regularization methods to mitigate this issue.
SWE-MiniSandbox is a lightweight, container-free framework that leverages kernel-level isolation and environment pre-caching to significantly reduce storage and setup overhead while maintaining performance comparable to traditional container-based pipelines for scaling reinforcement learning in software engineering agents.
This paper introduces MiDAS, an open-source, platform-agnostic system that enables non-invasive, time-synchronized multimodal data acquisition for robot-assisted minimally invasive surgery, validated by demonstrating that its external sensing approach achieves gesture recognition performance comparable to proprietary telemetry while releasing the first annotated dataset for hernia repair suturing.
This paper introduces AHSIV, an adaptive framework that addresses horizon-induced model ranking instability in demand forecasting by integrating horizon-aware error metrics, structural demand classification, and multi-objective optimization to provide robust, operationally coherent model selection for heterogeneous business environments.
GaiaFlow is a novel framework that achieves carbon-frugal search by integrating semantic-guided diffusion tuning, retrieval-guided Langevin dynamics, and adaptive efficiency protocols to balance high retrieval accuracy with significantly reduced environmental impact.
MolCrystalFlow is a novel flow-based generative model that predicts molecular crystal structures by disentangling intramolecular complexity from intermolecular packing through rigid body embeddings and Riemannian manifold representations, thereby outperforming existing methods and enabling data-driven discovery of periodic molecular crystals.