2385 papers
VIVID-Med introduces a novel framework that leverages a frozen large language model as a structured semantic teacher to pretrain lightweight, deployable medical Vision Transformers via a Unified Medical Schema and Structured Prediction Decomposition, achieving state-of-the-art performance across diverse medical imaging tasks with significantly reduced data requirements compared to existing vision-language models.
The paper introduces PM-Nav, a novel framework that leverages priori-semantic maps and hierarchical chain-of-thought prompting to overcome the challenges of language-driven navigation in functional buildings with highly similar features, achieving substantial performance improvements over existing methods in both simulation and real-world environments.
DexHiL is the first integrated human-in-the-loop framework for dexterous Vision-Language-Action models that combines coordinated arm-hand teleoperation with intervention-aware data sampling to significantly improve post-training performance and reliability in complex manipulation tasks.
The paper proposes QUSR, a novel diffusion-based image super-resolution model that combines an Uncertainty-Guided Noise Generation module to adaptively perturb high-uncertainty regions and a Quality-Aware Prior leveraging Multimodal Large Language Models to guide restoration, thereby achieving high-fidelity results in real-world scenarios with unknown and non-uniform degradations.
This paper models multi-LLM deliberation committees as random dynamical systems and demonstrates that even under deterministic temperature settings, factors like role differentiation and model heterogeneity induce chaotic instability characterized by positive empirical Lyapunov exponents, thereby establishing stability auditing as a critical requirement for reliable AI governance.
This paper proposes a Probability of Necessity and Sufficiency (PNS)-based regularization method for Class-Incremental Learning that utilizes a dual-scope counterfactual generator to mitigate feature collisions caused by intra-task shortcut reliance and inter-task semantic confusion, thereby ensuring both the causal completeness and separability of task-specific representations.
This paper introduces a novel agentic framework for Deep Tabular Research (DTR) that addresses the challenges of complex, unstructured tables by formalizing tabular reasoning as a closed-loop decision-making process, utilizing hierarchical meta-graphs for path planning, expectation-aware selection policies, and a siamese structured memory for continual experience-driven refinement.
This paper introduces DataFactory, a collaborative multi-agent framework that overcomes the context, hallucination, and reasoning limitations of existing TableQA systems by orchestrating specialized agents for structured and relational reasoning, thereby achieving significant accuracy improvements across multiple benchmarks.
TrustBench is a dual-mode framework that intervenes between action formulation and execution to verify autonomous agent safety in real-time, utilizing domain-specific plugins to reduce harmful actions by 87% with sub-200ms latency.
RubiCap introduces a novel reinforcement learning framework that leverages LLM-generated rubrics to create structured, multi-faceted reward signals for dense image captioning, thereby overcoming the limitations of supervised distillation and deterministic checkers to achieve state-of-the-art performance and superior word efficiency across various benchmarks.
This paper proposes a cost-effective framework that leverages structurally informative but functionally imperfect LLM-generated RTL to train netlist representation models, effectively overcoming data scarcity and outperforming methods reliant on scarce high-quality labeled datasets.
The paper proposes GIAT, a novel Geologically-Informed Attention Transformer that integrates Category-Wise Sequence Correlation filters into the self-attention mechanism to guide lithology identification with geological priors, achieving state-of-the-art accuracy and enhanced interpretability on well log datasets.
ZeroWBC is a novel framework that enables natural, versatile whole-body control for humanoid robots by learning visuomotor policies directly from human egocentric videos, thereby eliminating the need for expensive and time-consuming teleoperation data collection.
This paper introduces AlphaEvolve, an LLM-based code mutation agent that serves as a unified meta-algorithm to improve the lower bounds of five classical Ramsey numbers and successfully recover or match existing bounds across various cases, demonstrating a shift from bespoke search methods to a single, adaptable framework for combinatorial structure generation.
This paper proposes a physics-informed generative modeling framework that derives a differentiable, distributional traffic dynamics model from stochastic Ito-type equations, enabling the estimation of traffic density distributions, credible intervals, and congestion risks through a score network trained with denoising score matching and Fokker-Planck residual loss.
DuplexCascade is a VAD-free, cascaded ASR-LLM-TTS pipeline that enables full-duplex speech-to-speech dialogue by converting long turns into micro-turns and utilizing special control tokens to coordinate turn-taking while preserving the conversational intelligence of large language models.
Latent-DARM is a novel latent-space communication framework that bridges Discrete Diffusion Language Models for global planning and Autoregressive Models for fluent execution, significantly improving reasoning accuracy on benchmarks like DART-5 and AIME2024 while drastically reducing token usage compared to state-of-the-art reasoning models.
This paper presents an Explainable Innovation Engine that enhances Retrieval-Augmented Generation by replacing flat text chunks with a dual-tree architecture of methods-as-nodes, enabling an agent to perform verifiable, multi-step synthesis with traceable derivations and continual knowledge growth through expert-guided pruning and write-back.
This paper argues that advancements in logical reasoning for large language models inadvertently create a mechanistic pathway to dangerous situational awareness and strategic deception, necessitating new safety frameworks like the RAISE model to mitigate these emergent risks.
The paper proposes \textsc{EvalAct}, a framework that converts implicit retrieval quality assessment into an explicit action followed by a structured evaluation score, and leverages these process signals via a novel Process-Calibrated Advantage Rescaling (PCAR) method to significantly improve the reliability and accuracy of retrieval-augmented agents in multi-step reasoning tasks.