9594 papers
This paper introduces a Logic Augmented Generation (LAG) framework that combines semantic knowledge graphs with prompt heuristics to enhance multimodal analogical reasoning, demonstrating superior performance and explainability in metaphor detection tasks compared to existing baselines and human benchmarks, while also highlighting current limitations in domain-specific understanding.
The paper introduces Foam-Agent, a multi-agent framework leveraging large language models and retrieval-augmented generation to automate end-to-end computational fluid dynamics workflows from natural language prompts, achieving an 88.2% execution success rate on diverse simulation tasks without expert intervention.
ReactDance is a novel diffusion framework that achieves high-fidelity, coherent long-form reactive dance generation by employing Hierarchical Finite Scalar Quantization for fine-grained spatial control and a Blockwise Local Context strategy for efficient, temporally consistent sequence synthesis.
This paper proposes a novel, hierarchical, and risk-aware reward function for reinforcement learning in autonomous driving that integrates normalized objectives and an extended Responsibility-Sensitive Safety model, resulting in a 21% reduction in collision rates while maintaining high route progress in unsignalized intersection scenarios.
This paper proposes a novel boundary-guided trajectory prediction framework that leverages HD map constraints and kinematic acceleration profiles to generate physically feasible, on-road, and robust autonomous driving predictions, significantly reducing off-road errors and improving generalization compared to existing baselines.
This paper proposes an automatic curriculum learning framework that employs a "teacher" to dynamically generate driving scenarios with adaptive complexity based on an agent's current capabilities, thereby overcoming the inefficiencies of fixed scenarios and domain randomization to achieve faster convergence and superior generalization in end-to-end autonomous driving reinforcement learning.
This paper introduces CausalPitfalls, a comprehensive benchmark designed to rigorously evaluate and expose the significant limitations of large language models in handling statistical causal inference pitfalls, such as Simpson's paradox, through both direct and code-assisted prompting protocols.
This paper introduces ShIOEnv, a grammar-constrained, self-supervised Bash environment that generates 2.1 million system-grounded input-output pairs to significantly improve the accuracy of modeling complex command-line execution behaviors compared to prior execution-free approaches.
VTool-R1 is a novel framework that leverages reinforcement learning to train vision-language models to generate multimodal chains of thought by strategically interleaving text with intermediate visual reasoning steps using Python-based editing tools, thereby enhancing performance on structured visual tasks without requiring process-based supervision.
The paper introduces SealQA, a new benchmark comprising three challenging flavors (Seal-0, Seal-Hard, and LongSeal) designed to evaluate search-augmented language models on fact-seeking tasks with noisy or conflicting web results, revealing that even frontier models struggle significantly with reasoning accuracy, robustness to noise, and long-context document retrieval.
The paper introduces RoboPARA, a novel LLM-driven framework that utilizes dependency graph generation and re-traversal to optimize dual-arm task parallelism, accompanied by the new X-DAPT dataset for evaluation, demonstrating superior efficiency and reliability in complex multitasking scenarios compared to existing methods.
This paper introduces LUCIFER, an inference-only middleware that decouples online language grounding from decision-making via a Signal Contract, enabling autonomous systems to robustly convert evolving human verbal reports into control-relevant signals that improve safety and information efficiency across diverse planning architectures.
This paper introduces Poly2Graph, an automated pipeline for generating HSG-12M, a pioneering 16.7-million-scale dataset of spatial multigraphs derived from non-Hermitian crystal energy spectra, which bridges condensed matter physics and geometry-aware graph learning by preserving vital geometric information often discarded in existing benchmarks.
InterActHuman is a novel framework that enables high-quality multi-concept human animation by enforcing strong, region-specific binding of text, image, and audio conditions to individual identities, thereby overcoming the limitations of global-conditioning methods in scenarios involving complex human-human and human-object interactions.
This paper introduces BWFlow, a graph generation framework that overcomes the limitations of independent node-edge modeling by utilizing Bures-Wasserstein optimal transport on Markov random fields to construct a smooth, theoretically grounded probability path for the joint evolution of graph components, resulting in improved training convergence and sampling efficiency.
This paper introduces Structured Kolmogorov-Arnold Neural ODEs (SKANODEs), a framework that combines structured state-space modeling with Kolmogorov-Arnold Networks to accurately recover interpretable physical latent states and discover compact symbolic governing equations for nonlinear dynamical systems, outperforming black-box neural ODEs and classical identification methods across synthetic and real-world datasets.
This paper demonstrates that Reinforcement Fine-Tuning (RFT) outperforms Supervised Fine-Tuning (SFT) in preserving prior knowledge for multimodal large language models by leveraging training data with smaller influence magnitudes and better alignment to the base model's probability landscape, thereby mitigating catastrophic forgetting while enabling effective task adaptation.
MuRating is a scalable framework that transfers high-quality English data-quality signals to a unified multilingual evaluator via pairwise comparisons and translation, enabling the selection of balanced, high-quality datasets that significantly improve the performance of multilingual large language models on both English and non-English benchmarks.
This paper presents a sensorless 4-channel bilateral teleoperation framework that enables stable, high-speed force feedback control on low-cost manipulators through disturbance-observer-based estimation and simplified tuning, ultimately demonstrating that such force-enhanced data significantly improves imitation learning performance.
This paper introduces TreeBench, a diagnostic benchmark for evaluating traceable visual grounded reasoning, and proposes TreeVGR, a reinforcement learning-based training paradigm that significantly enhances model performance by jointly supervising localization and reasoning.