9602 papers
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.
Overtone is a unified framework for transformer-based PDE surrogates that employs cyclic patch size modulation via architecture-agnostic modules to dynamically distribute harmonic errors and adapt computational costs, achieving significantly lower long-term rollout errors and flexible efficiency compared to static-patch baselines.
This paper presents the first systematic study of in-training safeguards against emergent misalignment in fine-tuned language models, demonstrating that interleaving training examples selected by the perplexity gap between aligned and misaligned models effectively prevents broad misalignment while preserving task performance and coherence.
Vevo2 is a unified framework for controllable speech and singing voice generation that employs novel audio tokenizers and a two-stage modeling approach with specialized training strategies to achieve flexible control over content, prosody, style, and timbre while demonstrating strong generalization across diverse synthesis tasks.
The paper introduces LHM-Humanoid, a unified learning framework and benchmark that employs reinforcement learning and policy distillation to enable humanoid agents to perform robust, long-horizon loco-manipulation tasks across diverse, cluttered environments without relying on pre-trained skill libraries or environment resets.
This paper attributes the performance degradation of GNN-based SAT solvers on difficult instances to inherent negative graph Ricci curvature in formula representations, which causes oversquashing by creating local connectivity bottlenecks that hinder the compression of long-range dependencies.
This paper proposes TSPC, a novel two-stage phoneme-centric architecture that leverages an extended Vietnamese phoneme set as an intermediate representation to significantly improve Vietnamese-English code-switching speech recognition accuracy while maintaining computational efficiency.
This paper presents a reinforcement learning-based quadrotor navigation method that utilizes privileged time-of-arrival maps and a yaw alignment loss to successfully navigate around large obstacles in cluttered environments, achieving an 86% success rate in simulation and demonstrating collision-free flight in real-world outdoor conditions.
This paper introduces Diffusion-Based Impedance Learning, a framework that combines a Transformer-based diffusion model with energy-consistent impedance control to enable robots to learn and adapt contact-rich manipulation behaviors from teleoperated demonstrations, achieving high-precision performance and robust generalization in tasks like peg-in-hole insertion.
This paper reveals that SpeechLLM backbones struggle with conversational disfluencies due to a bias toward semantic abstraction over structural fidelity, with performance varying by architecture and fine-tuning often compromising generalization despite achieving state-of-the-art results.
This paper introduces Complexity-Regularized Proximal Policy Optimization (CR-PPO), a novel algorithm that replaces standard entropy regularization with a self-regulating complexity term—defined as the product of Shannon entropy and disequilibrium—to maintain beneficial stochasticity while reducing sensitivity to hyperparameter tuning and avoiding the overriding of reward signals.
BridgeDrive introduces a novel anchor-guided diffusion bridge policy that ensures theoretical consistency between forward and reverse processes to transform coarse expert trajectories into refined, safe, and reactive closed-loop plans, achieving state-of-the-art performance on the Bench2Drive benchmark.