7594 papers
The paper introduces ScaleZero, a multi-task planning model that combines a Mixture-of-Experts architecture to mitigate gradient conflicts and an online Dynamic Parameter Scaling strategy to efficiently allocate capacity, achieving performance comparable to specialized single-task agents with significantly reduced sample complexity.
This paper demonstrates that deep neural network ensembles enable accurate, real-time quantum parameter estimation with well-calibrated uncertainty quantification and drift detection, offering a faster alternative to traditional Bayesian inference methods without sacrificing accuracy.
This paper introduces "Answer-Then-Check," a novel safety alignment method that enhances LLM robustness against jailbreak attacks by training models to generate direct answers internally and then critically evaluate their safety before responding, achieving superior protection with reduced over-refusal while maintaining general reasoning capabilities through the newly constructed 80K-sample ReSA dataset.
VEGA is an electric vehicle navigation system that combines a physics-informed neural operator for real-time vehicle parameter estimation with a Proximal Policy Optimization agent for efficient, charge-aware route and charging stop planning, demonstrating superior inference speed and generalization across international road networks compared to traditional energy-aware baselines.
This paper introduces the Tensor Atomic Cluster Expansion (TACE), a unified atomistic machine learning framework that employs irreducible Cartesian tensors to efficiently model both scalar and tensorial observables without complex angular-momentum coupling, demonstrating robust accuracy and scalability across diverse chemical systems and tasks.
This paper proposes C²Prompt, a novel federated continual learning method that mitigates temporal and spatial forgetting by introducing a local class distribution compensation mechanism and a class-aware prompt aggregation scheme to enhance class-wise knowledge coherence across distributed clients.
This paper proposes a computationally efficient dual-network architecture combining an auto-regressive U-Net and a CNN to predict time-dependent full-field damage evolution and key mechanical properties in concrete, thereby enabling insights into aggregate effects and optimizing mix designs for improved durability.
This paper introduces GPHDM, a novel framework that extends Gaussian Process Dynamical Models to hyperbolic manifolds to generate physically consistent, human-like robot motions by preserving the hierarchical taxonomy and temporal dynamics of movement.
This paper addresses the training-inference mismatch in diffusion language models caused by planner-based sampling strategies by deriving a new Planned Evidence Lower Bound (P-ELBO) and introducing Planner Aware Path Learning (PAPL), a simple training modification that aligns training with planned inference to achieve significant performance gains across protein, text, and code generation tasks.
The paper introduces Diffusion Alignment as Variational Expectation-Maximization (DAV), an iterative framework that alternates between test-time search for diverse, reward-aligned samples and model refinement to optimize diffusion models for downstream objectives while mitigating reward over-optimization and mode collapse.
This paper addresses the online minimization of polarization and disagreement in the Friedkin-Johnsen opinion dynamics model under incomplete information by proposing a two-stage low-rank matrix bandit algorithm that achieves a cumulative regret of through subspace estimation and linear bandit optimization.
This paper introduces Self-Speculative Masked Diffusions, a novel generative model for discrete data that leverages causal attention and integrated speculative sampling to produce non-factorized predictions, thereby achieving a roughly 2x reduction in neural network forward passes compared to standard masked diffusion models.
This paper proposes TCR-EML, an explainable-by-design model that integrates prototype layers based on known biochemical binding mechanisms into protein-language model backbones to achieve competitive TCR-pMHC binding prediction accuracy while providing interpretable rationales superior to existing black-box approaches.
This paper demonstrates that while standard decoder-only models underperform compared to encoder-only architectures in cross-modal adaptation for partial differential equations, introducing novel bidirectionality-mimicking techniques like Parallel Flipping and Sequence Doubling effectively closes this performance gap.
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.