2316 papers
This paper proposes a data-driven robust Markov decision process framework for Borel spaces with unknown disturbance distributions, utilizing ambiguity sets defined by distance functions to establish finite-sample performance guarantees, probabilistic convergence rates, and out-of-distribution bounds that empirical MDPs fail to provide.
MAPLE introduces a unified training paradigm that enhances medical large language models by integrating Test-Time Reinforcement Learning with expert-aligned Med-RPMs to replace unreliable majority voting with fine-grained process rewards, thereby significantly improving clinical reasoning accuracy and reliability across multiple benchmarks.
This book reinterprets generative AI, specifically through flow matching, as a statistical framework for nonparametric distribution learning that enables principled inference for tasks like missing-data imputation and causal analysis by integrating generative models with double/debiased machine learning techniques to ensure inferential validity.
This paper introduces Normalized Flow Matching (NFM), a novel method that distills quasi-deterministic couplings from pretrained auto-regressive normalizing flow models to train student flow models, achieving superior performance over both traditional flow matching approaches and the teacher models themselves.
This paper proposes an exact, parameterization-aware flatness measure tailored to the geometric structure of convolutional neural networks with global average pooling, demonstrating its effectiveness as a robust proxy for estimating and comparing generalization performance across various CNN architectures.
This paper introduces CALIPER, a data-only, detector-agnostic test that determines the sufficient post-drift data size for stable model retraining by analyzing the trend of a one-step proxy error against a locality parameter, thereby bridging the gap between drift detection and effective adaptation in streaming learning.
The paper introduces EPIC, a hardware- and physics-co-guided distributed scientific machine learning framework that significantly reduces communication latency and energy consumption while preserving physical fidelity by performing lightweight local encoding and physics-aware decoding with cross-attention for tasks like full-waveform inversion.
SCALAR is a bidirectional framework that couples LLM-guided symbolic planning with deep RL to iteratively refine skill specifications through execution feedback, significantly outperforming prior methods in complex environments like Craftax by correcting initial planning errors and improving sample efficiency.
This paper presents FlexServe, a high-performance and secure LLM serving system for mobile devices that leverages a novel Flexible Resource Isolation mechanism to overcome the significant overhead of ARM TrustZone, achieving up to 10.05× faster time-to-first-token and 24.30× faster multi-model workflow execution compared to baseline designs.
The paper introduces Sentinel, an autonomous AI agent that achieves reliable, scalable clinical triage for remote patient monitoring by outperforming individual clinicians in sensitivity and consistency while maintaining a clinically defensible overtriage profile at a negligible cost.
The paper proposes Sim2Act, a robust simulation-to-decision framework that enhances policy reliability in mission-critical domains by combining an adversarial calibration mechanism to align simulation fidelity with decision impact and a group-relative perturbation strategy to stabilize learning without overly conservative constraints.
This paper introduces Quality over Quantity (QoQ), a systematic framework that leverages influence functions to automatically curate high-quality robot learning demonstrations by quantifying each sample's contribution to reducing validation loss, thereby significantly improving policy performance over manual or heuristic data selection methods.
This paper proposes a novel integrated online reliability prediction framework for satellite electronics that combines a Wiener process-based degradation model with a two-stage adaptive active learning strategy to significantly improve prediction accuracy while reducing data requirements under limited and variable operational conditions.
This paper introduces DynaME, a novel hybrid framework for online time series forecasting that redefines concept drift into recurring and emergent types, utilizing specialized historical experts for the former and a stable general expert for the latter to significantly outperform existing baselines.
This paper proposes learning lightweight, reinforcement-trained decoding adapters that dynamically select sampling strategies at both the sequence and token levels based on prompt features and compute budgets, significantly improving the accuracy-efficiency tradeoff on math and coding benchmarks compared to fixed hyperparameter baselines.
This paper verifies that persistent observers in causally invariant hypergraph substrates satisfy the Conant-Ashby Good Regulator Theorem, thereby necessitating internal models that lead to natural gradient descent as the unique learning rule and yielding a model-dependent closed-form formula for Vanchurin's regime parameter with a quantum-classical threshold at .
The paper introduces Exclusive Self Attention (XSA), a modification that constrains attention to information orthogonal to a token's own value vector, thereby improving Transformer performance in language modeling tasks, particularly as sequence length increases.
This paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing framework that utilizes a Proximal Policy Optimization (PPO) and Linear Programming (LP) hybrid scheme to jointly optimize offloading ratios, semantic symbols, and RIS phase shifts, achieving a 40–50% reduction in end-to-end latency compared to existing methods.
This study demonstrates that integrating sentiment scores derived from a finetuned Qwen3 model analyzing English and Chinese news significantly enhances aluminum price forecasting accuracy and economic utility, particularly during periods of high market volatility, compared to traditional tabular data models.
This paper proposes a unified taxonomy and evaluation framework for latent world models in automated driving, organizing design choices by latent representations and structural priors while identifying key internal mechanics and research directions to enhance robustness, generalization, and deployability.