9817 papers
This paper presents a novel framework for the simultaneous acquisition of real-time MRI, EEG, and surface EMG to capture brain, muscle, and articulatory activity during speech, featuring a specialized artifact suppression pipeline to overcome technical challenges and enable unprecedented insights into speech neuroscience.
This paper introduces a training-free, non-parametric approach to multi-step theorem prediction that overcomes the scalability limitations of vanilla in-context learning by leveraging Theorem Precedence Graphs to encode temporal dependencies and impose topological constraints, achieving state-of-the-art accuracy on the FormalGeo7k benchmark without gradient-based optimization.
This paper introduces ReCouPLe, a lightweight framework that leverages natural language rationales as causal guidance to train reward models that are robust to spurious correlations and capable of zero-shot transfer to novel tasks, significantly outperforming baselines in reward accuracy and downstream policy performance under distribution shifts.
K-Gen is an interpretable multimodal framework that leverages Multimodal Large Language Models to generate reasoning-guided keypoints from rasterized maps and text, which are then refined into realistic trajectories, outperforming existing baselines on autonomous driving benchmarks.
SEA-TS is a self-evolving agent framework that autonomously generates and optimizes time series forecasting code through innovations like Metric-Advantage MCTS and global steerable reasoning, achieving superior accuracy and discovering novel algorithmic patterns that outperform state-of-the-art methods and human-engineered baselines.
This paper presents a scalable, interpretable framework that achieves 80.4% accuracy in classifying eight professional baseball pitch types using only monocular 3D body kinematics, revealing that upper-body mechanics—particularly wrist position and trunk tilt—are the primary predictors while establishing an empirical ceiling for grip-based distinctions.
This paper proposes DeformTrace, a hybrid architecture combining State Space Models with deformable dynamics and relay tokens to achieve state-of-the-art temporal forgery localization by addressing challenges in boundary ambiguity, sparse forgeries, and long-range modeling.
To address the fidelity-efficiency dilemma in infinite-horizon dialogue streams where existing memory mechanisms fail to support ad-hoc recall, this paper introduces STEM-Bench, a new benchmark for evaluation, and ProStream, a proactive hierarchical memory framework that achieves bounded-state inference with high reasoning fidelity through multi-granular distillation and adaptive spatiotemporal optimization.
FedAFD is a unified multimodal federated learning framework that enhances both client and server performance by employing a bi-level adversarial alignment and granularity-aware fusion for personalized local learning, alongside a similarity-guided ensemble distillation mechanism to effectively handle model heterogeneity and modality discrepancies.
This paper proposes a training-free, low-cost intervention for Diffusion Language Models that sequentially repels intermediate samples in a batch to enhance generative diversity and improve Pass@ performance on reasoning tasks without significant computational overhead.
This paper introduces DP-MTV, the first framework that enables differentially private many-shot multimodal in-context learning for vision-language models by aggregating hundreds of demonstrations into compact task vectors, thereby overcoming the token-scaling limitations of existing methods while preserving significant performance gains under strict privacy constraints.
This paper proposes AoD-IP, a novel framework for Vision-Language Models that enhances intellectual property protection by introducing a lightweight dynamic authorization module and a dual-path inference mechanism to enable flexible, user-controlled, legality-aware domain adaptation in dynamic environments.
EvoTool is a self-evolving framework that optimizes modular LLM agent tool-use policies through a gradient-free evolutionary process featuring blame-aware mutation and diversity-aware selection, significantly outperforming existing baselines in accuracy, efficiency, and transferability.
This paper introduces AgentSCOPE, a benchmark and Privacy Flow Graph framework that reveals how agentic systems frequently violate contextual privacy at intermediate pipeline stages—particularly during tool responses—demonstrating that current output-focused evaluations significantly underestimate the true privacy risks of multi-step AI workflows.
This paper presents four preregistered studies demonstrating that safety alignment interventions in large language models can produce a "language-dependent backfire" effect, where alignment reduces collective pathology in English but amplifies it in other languages (particularly Japanese) due to cultural-linguistic constraints, thereby revealing that English-centric safety validations do not generalize and may induce iatrogenic dissociation in multi-agent systems.
This paper presents a deterministic, rule-based Python workflow that processes administrative academic data to generate reproducible cost-per-student reports enriched with an interpretable fuzzy banding system for classifying school-year performance into Low, Medium, or High categories.
This paper introduces VPWEM, a non-Markovian visuomotor policy that combines a sliding window of recent observations with a Transformer-based episodic memory compressor to efficiently retain long-term context for robotic control, achieving significant performance improvements over state-of-the-art baselines on memory-intensive manipulation tasks while maintaining constant computational costs.
The paper introduces EVMbench, a benchmarking framework that evaluates the capabilities of frontier AI agents in detecting, patching, and exploiting smart contract vulnerabilities within a realistic local Ethereum environment, revealing their ability to successfully execute end-to-end attacks against live blockchain instances.
This paper introduces BandPO, a novel reinforcement learning algorithm that replaces PPO's fixed clipping mechanism with a dynamic, probability-aware operator to resolve the exploration bottleneck and entropy collapse caused by suppressing high-advantage low-probability actions, thereby achieving superior stability and performance across diverse models.
This paper proposes KBD, a novel bid optimization method that integrates human expertise as inductive biases and employs a dual-process control mechanism combining a fast rule-based PID system with a Decision Transformer to overcome the limitations of black-box models in data-sparse, long-term, and out-of-distribution scenarios.