9495 papers
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 MiCo, a hierarchical language agent framework that leverages large language models to design adaptive heuristics for solving the complex Online Dynamic Multidimensional Bin Packing problem in cloud VM scheduling, achieving a 96.9% competitive ratio in large-scale, real-world scenarios.
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.
This paper presents an attribute-efficient PAC learning algorithm for sparse halfspaces that achieves robustness against a constant malicious noise rate using samples by applying simple variants to hinge loss minimization under specific concentration and margin conditions.
This paper introduces a novel framework that enables direct finetuning of large language models using multi-kernel Boolean parameters without latent weights, significantly reducing complexity while outperforming existing ultra low-bit quantization and binarization techniques.
This paper introduces Continuous Chain of Thought (CoT2), a framework that replaces discrete token sampling with continuously-valued tokens to enable parallel exploration of multiple reasoning traces, offering theoretical guarantees for solving combinatorial problems and demonstrating improved performance through novel supervision and policy optimization strategies.
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.
This systematic review analyzes 68 experiments deploying machine learning models on FPGAs for Earth Observation, introducing dual taxonomies for model architectures and implementation strategies to address the challenges of onboard processing in the NewSpace era.
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.
This paper introduces EDINET-Bench, a challenging open-source benchmark derived from ten years of Japanese financial reports to evaluate LLMs on complex tasks like fraud detection and earnings forecasting, revealing that current models struggle significantly without specialized scaffolding and highlighting the need for more realistic evaluation frameworks.
The paper introduces SPEED-RL, an adaptive online curriculum learning method that selectively samples intermediate-difficulty prompts to theoretically and empirically accelerate reinforcement learning training for reasoning models by 2x to 6x without compromising accuracy.
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 proposes ExSUL, a novel online learning framework that enables selective generation for large language models to robustly control the False Discovery Rate (FDR) and achieve optimal regret bounds in non-stationary and adversarial environments by converting bandit regret into FDR guarantees and unlocking additional learning signals from partial user feedback.
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 introduces Presymplectification Networks (PSNs), a novel framework that restores non-degenerate symplectic geometry for constrained and dissipative mechanical systems by learning a symplectification lift via Dirac structures, thereby enabling accurate, structure-preserving long-term prediction of complex multibody dynamics like those of the ANYmal quadruped robot.
This paper introduces a dual-stress framework combining synaptic filtering and adversarial attacks to classify and quantify the fragility, robustness, and antifragility of parameters in PPO-trained RL agents, revealing that targeted filtering can enhance policy adaptability in continuous control environments.
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.
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.