7919 papers
This paper challenges the reliability of language model micro-benchmarks by demonstrating that they often fail to consistently rank models with small performance differences, frequently requiring as many as 250 examples to achieve accuracy comparable to random sampling, thereby offering actionable guidance on the trade-off between evaluation efficiency and reliability.
CanvasMAR enhances masked autoregressive video prediction by introducing a global "canvas" prior and a motion-aware curriculum to generate high-fidelity, coherent videos with fewer sampling steps, achieving performance that rivals advanced diffusion-based methods.
This paper demonstrates that injecting external verification into synthetic data retraining can prevent model collapse and yield near-term improvements, though theoretical analysis and experiments across linear regression, VAEs, and LLMs show that long-term performance ultimately converges to the verifier's knowledge center and may plateau or decline if the verifier is imperfect.
This paper proposes a novel, computationally efficient interval-based reachability analysis method for Neural ODEs that leverages continuous-time mixed monotonicity to trade off tightness for scalability, making it particularly suitable for high-dimensional and safety-critical real-time applications.
This paper presents a real-time online framework that utilizes modified sliding-window Hankel Dynamic Mode Decomposition with singular-value hard thresholding and Cadzow projection to denoise partial measurements and construct predictive models for dynamic obstacle motion, enabling stable, variance-aware forecasting suitable for robotic motion planning.
The paper introduces KLASS, a training-free, KL-divergence-guided sampling method that significantly accelerates inference in masked diffusion models by unmasking multiple stable tokens per iteration, achieving state-of-the-art speed and performance across text, image, and molecular generation tasks.
The paper proposes CADM, a cluster-customized adaptive distance metric that dynamically adjusts distance measurements based on the unique attribute distributions within each cluster to improve categorical and mixed data clustering performance.
The paper introduces FireScope, a novel VLM-based framework and accompanying FireScope-Bench dataset that leverage chain-of-thought reasoning to significantly improve the generalization, interpretability, and accuracy of cross-continental wildfire risk prediction by integrating visual, climatic, and geographic factors.
The paper introduces EgoCogNav, a multimodal framework that predicts perceived path uncertainty to jointly forecast egocentric trajectories and head motion, supported by the new Cognition-aware Egocentric Navigation (CEN) dataset to better model human cognitive factors in navigation.
The paper proposes SPINE, a token-selective test-time reinforcement learning framework that improves reasoning model performance by updating only high-entropy decision-critical tokens with entropy-band regularization, thereby preventing response collapse and enhancing stability without requiring external labels or reward models.
The paper introduces DAISI, a scalable data assimilation algorithm that leverages flow-based generative models with a novel inverse-sampling step to integrate forecast information and guide conditional sampling, enabling accurate filtering in complex, nonlinear systems where traditional Gaussian-based methods fail.
This paper introduces Soft Q-based Diffusion Finetuning (SQDF), a novel KL-regularized reinforcement learning method that employs a reparameterized policy gradient of a training-free soft Q-function, enhanced by discount factors, consistency models, and off-policy replay buffers, to effectively align diffusion models with downstream objectives while mitigating reward over-optimization and preserving sample diversity.
This paper proposes a novel training framework that leverages the -divergence family to explicitly filter incorrect answers and control the precision-diversity trade-off, thereby overcoming the diversity loss inherent in standard Reinforcement Learning and achieving state-of-the-art performance on the Lean theorem-proving benchmark.
The paper introduces A-3PO, a method that accelerates asynchronous LLM training by 1.8x by approximating the computationally expensive proximal policy in Decoupled PPO through simple interpolation, thereby eliminating the need for extra forward passes while maintaining comparable performance.
DFIR-DETR is a transformer-based small object detector that addresses key limitations in standard architectures by introducing Dynamic Content-Feature Aggregation for adaptive attention, a norm-preserving Dynamic Feature Pyramid Network for detail recovery, and a Frequency-domain Iterative Refinement module to preserve high-frequency boundaries, achieving state-of-the-art performance on NEU-DET and VisDrone benchmarks with high efficiency.
This paper introduces a novel two-dimensional visualization framework that maps reaction trajectories onto a permutation-corrected RMSD plane with gradient-enhanced Gaussian Process energy interpolation, enabling more effective comparison and validation of optimization histories across different computational methods for complex chemical reactions.
This paper addresses the degradation of existing subset-based visual explanation methods under out-of-distribution conditions by introducing a training-free framework that integrates layer-wise uncertainty estimation with submodular optimization to generate robust, diverse, and informative attributions.
This paper proposes a novel global sensitivity analysis method based on Individual Conditional Expectation (ICE) curves that overcomes the limitations of traditional Partial Dependence Plots (PDPs) in capturing input interactions, offering a mathematically proven, more informative metric for explainable machine learning in engineering design.
This paper introduces a novel patch-based Topological Data Analysis approach for 3D CT imaging that significantly outperforms traditional 3D cubical complex methods and radiomic features in both classification accuracy and computational efficiency, accompanied by the release of a Python package to facilitate its adoption.
This paper addresses the optimization challenges in hyperbolic deep reinforcement learning by identifying the destabilizing effects of large-norm embeddings and introducing Hyper++, a new agent that employs feature regularization, categorical value loss, and improved layer formulations to achieve stable, faster, and superior performance compared to existing Euclidean and hyperbolic baselines.