6178 papers
This paper presents the first systematic study of cross-persistence diagram density, establishing its theoretical foundations and introducing a novel machine learning framework that leverages these diagrams to effectively distinguish point clouds from different manifolds, with experiments demonstrating that adding noise can further enhance classification performance.
MedPruner is a training-free, model-agnostic hierarchical token pruning framework that eliminates anatomical redundancy in 3D medical vision-language models through inter-slice filtering and dynamic token selection, enabling significant computational efficiency while maintaining or improving performance with fewer than 5% of visual tokens.
The paper introduces VisDoT, a framework that enhances visual reasoning in large vision-language models by formalizing human-like perceptual grounding tasks and employing a Decomposition-of-Thought prompting strategy to separate visual perception from logical reasoning, achieving state-of-the-art performance on chart understanding benchmarks.
The paper introduces HouseMind, a multimodal large language model that unifies the understanding, generation, and editing of architectural floor plans by employing discrete room-instance tokens to bridge visual layouts with symbolic reasoning, thereby achieving superior geometric validity and controllability.
The paper proposes IDRL, an individual-aware multimodal framework that disentangles representations into depression-related and unrelated spaces while dynamically fusing features based on individual differences to overcome inter-modal inconsistencies and improve robust depression diagnosis.
This paper introduces Stable Spike, a hardware-friendly method that employs bitwise AND operations to decouple stable spike skeletons from noisy multi-timestep maps and enforce dual consistency, thereby significantly improving the accuracy and generalization of Spiking Neural Networks for ultra-low latency neuromorphic object recognition.
This perspective paper argues that effective human-agent collaboration requires shifting from reactive, step-by-step control to a "simulation-in-the-loop" paradigm, which empowers users to explore simulated future trajectories and make informed decisions based on foresight rather than just immediate correction.
This paper proposes an agentic pipeline that leverages Large Language Models to automatically synthesize programmatic rubrics for transforming complex, heterogeneous clinical data into standardized formats, thereby enabling sample-efficient supervised learning that outperforms traditional models and larger foundation models while offering significant advantages in auditability and deployment cost.
This paper argues that standard policy gradient algorithms inadvertently reduce trajectory diversity by lowering entropy during training, and proposes new entropy-preserving methods like REPO and ADAPO to actively control entropy, thereby maintaining diversity and improving both final performance and the capacity for sequential learning.
This paper proposes a novel causal prosody mediation framework for FastSpeech2 that employs counterfactual training and structural causal modeling to disentangle emotional prosody from linguistic content, thereby enabling improved controllability, expressiveness, and emotion rendering in text-to-speech synthesis.
The paper introduces SemBench, a scalable and language-independent framework that automatically generates synthetic benchmarks using dictionary definitions and sentence encoders to efficiently evaluate the semantic understanding of large language models across different languages.
This paper proposes an Explicit Logic Channel that runs in parallel with black-box Multimodal Large Language Models to perform explicit logical reasoning and probabilistic inference, enabling zero-shot model validation, selection, and performance enhancement through a Consistency Rate metric without requiring ground-truth annotations.
STAIRS-Former is a novel transformer architecture for offline multi-task multi-agent reinforcement learning that leverages spatio-temporal attention, an interleaved recursive structure, and token dropout to effectively handle varying agent populations and long-horizon dependencies, achieving state-of-the-art performance across diverse benchmarks.
This paper introduces OSCBench, a novel benchmark derived from instructional cooking data that evaluates the ability of text-to-video models to generate accurate and temporally consistent object state changes, revealing that current models struggle significantly with this capability despite their progress in other areas.
This paper introduces the "Agent Scaling Law" and the AgentProfile framework to demonstrate that the capabilities of educational AI agents scale predictably with structured profile richness—specifically role clarity, skill depth, tool completeness, runtime capability, and educator expertise—rather than solely through increased model size, as validated by the EduClaw platform's deployment of over 330 agent profiles.
This paper introduces a new dataset and demonstrates that surface electromyography (sEMG) signals from facial and neck muscles can reliably decode affective states, particularly frustration, during both phonated and silent speech, highlighting their potential for affect-aware silent speech interfaces.
This paper proposes an architecture for an "Agentic Operating System for Hospital" that adapts the OpenClaw framework to safely deploy LLM agents in clinical environments by integrating a restricted execution environment, document-centric interactions, page-indexed long-term memory, and a curated medical skills library to ensure reliability, security, and auditability in dynamic workflows.
This paper introduces Transfer Aware Dijkstra (TAD), an optimized algorithm that outperforms the state-of-the-art RAPTOR-based MR method in public transit routing by correctly handling buffer times through trip-sequence scanning while maintaining optimal results and achieving over a two-fold speed-up.
This study evaluates the potential for gender bias in generative AI-assisted recruitment by analyzing how a state-of-the-art model (GPT-5) suggests occupations for simulated Italian graduates, revealing that while job recommendations remain neutral, the model perpetuates gender stereotypes by attributing emotional traits to women and analytical traits to men.
The paper introduces CINDI, an unsupervised probabilistic framework based on conditional normalizing flows that unifies anomaly detection and imputation into a single end-to-end system to effectively restore data integrity in noisy multivariate time series, such as those found in power grids.