4352 papers
This paper introduces the Robot Control Stack (RCS), a lean and modular software ecosystem designed to bridge the gap between large-scale Vision-Language-Action model training and real-world robot deployment by unifying simulation and physical control, while validating its effectiveness through extensive evaluations of policies like Octo, OpenVLA, and Pi Zero.
This paper introduces Kuramoto Orientation Diffusion Models, a score-based generative framework that leverages stochastic Kuramoto dynamics on periodic domains to effectively model orientation-rich images by replacing standard isotropic diffusion with synchronization-based forward processes and desynchronization-based reverse generation.
This paper demonstrates that AI-driven code mutation agents, specifically AlphaEvolve, can significantly advance complexity theory by discovering new gadget reductions and extremal graph constructions that yield improved hardness of approximation results for MAX-CUT, MAX-Independent Set, MAX-k-CUT, and the metric Traveling Salesman Problem.
The paper proposes VLCE, a knowledge-enhanced framework that integrates external semantic knowledge from ConceptNet and WordNet into a two-stage vision-language pipeline to generate more accurate, domain-specific, and actionable image descriptions for disaster assessment, outperforming general-purpose models on satellite and UAV benchmarks.
This paper introduces ZeroSiam, an efficient asymmetric Siamese architecture that prevents model collapse during test-time entropy minimization by employing asymmetric divergence alignment, thereby enhancing adaptation and reasoning performance across diverse vision and language tasks with negligible overhead.
This paper introduces General Policy Composition (GPC), a training-free method that enhances diffusion and flow-based robot policies by theoretically and empirically demonstrating that convexly combining the distributional scores of multiple pre-trained policies at test time yields superior performance and adaptability across diverse tasks.
This paper proposes a hybrid control framework that combines Deep Reinforcement Learning (DRL) with robust model-independent bounded extremum seeking to enhance the stability and adaptability of controlling nonlinear time-varying systems, demonstrating its effectiveness through numerical simulations and the automatic tuning of a particle accelerator.
The Latent Speech-Text Transformer (LST) improves the efficiency and performance of auto-regressive speech-text models by aggregating speech tokens into latent patches, which aligns sequence granularity with text, reduces computational costs, and achieves significant accuracy gains across speech and text benchmarks.
AlphaApollo is an agentic reasoning system that enhances foundation models' performance on complex, long-horizon tasks by orchestrating multi-turn agentic reasoning, turn-level reinforcement learning for tool-use optimization, and a propose-judge-update evolution loop with verification.
This paper addresses the challenge of LiDAR-based 3D semantic segmentation under noisy labels and domain shifts by introducing the DGLSS-NL task, establishing a new benchmark, and proposing DuNe, a dual-view framework that achieves state-of-the-art robustness across multiple datasets.
The paper introduces RECODE, an agentic framework that enhances visual question answering by reverse-engineering structured visuals into executable code through iterative generation and selection, thereby transforming ambiguous perceptual tasks into verifiable symbolic reasoning problems that significantly outperform existing methods.
This paper introduces REAP, a novel expert pruning method that outperforms existing merging techniques for compressing Mixture-of-Experts models in generative tasks by leveraging router gate-values and activation norms to minimize reconstruction error, achieving near-lossless compression even at 50% expert reduction.
RL-100 is a unified real-world reinforcement learning framework that combines diffusion visuomotor policies with a clipped PPO objective and consistency distillation to achieve 100% success across 1,000 diverse robotic manipulation trials, matching or surpassing human experts while demonstrating robust zero-shot generalization and continuous deployment in dynamic environments.
This paper introduces Bradley-Terry Policy Optimization (BTPO), a novel framework that derives a consistent Monte Carlo gradient estimator to effectively train large language models with chain-of-thought reasoning on non-verifiable pairwise preference tasks, overcoming the limitations of existing heuristic RL approaches.
This paper introduces AffPCL, a personalized collaborative learning framework that utilizes affinity-based variance reduction to automatically adapt to unknown heterogeneity among agents, achieving sample complexity improvements that seamlessly interpolate between independent learning and linear speedup without requiring prior knowledge of system similarity.
FALCON addresses the spatial reasoning limitations of existing 2D-based vision-language-action models by leveraging spatial foundation models to inject rich 3D geometric priors directly into the action head, achieving state-of-the-art performance across diverse simulation and real-world tasks without requiring architectural changes or specialized sensors.
The paper proposes GraphKeeper, a novel framework for Graph Domain-Incremental Learning that addresses catastrophic forgetting through knowledge disentanglement and deviation-free preservation, achieving state-of-the-art performance across multiple graph domains while remaining compatible with various graph foundation models.
This paper proposes a structured matrix scaling approach for multi-class calibration that leverages theoretical insights from logistic regression, combined with structured regularization and robust optimization, to effectively manage the bias-variance tradeoff and achieve substantial performance gains over existing methods while providing an open-source implementation.
This paper proposes LTSV, a lightweight and efficient method for valuing time series data in foundation models by leveraging in-context finetuning and temporal block aggregation to overcome the computational bottlenecks and temporal dependency limitations of traditional approaches.
This paper introduces a novel in-context learning approach using Time-Series Foundation Models (TSFMs) to classify bearing health status from vibration data without fine-tuning, enabling scalable, zero-shot maintenance solutions across varying operational conditions.