5060 papers
The paper introduces LaMer, a Meta-RL framework that enhances language agents' ability to actively explore and adapt to novel environments at test time through cross-episode training and in-context policy reflection, significantly outperforming standard RL baselines across diverse tasks.
Re-Depth Anything is a test-time self-supervised framework that enhances monocular depth estimation by fusing foundation models with large-scale 2D diffusion priors to perform label-free refinement via generative re-lighting and Score Distillation Sampling, achieving state-of-the-art results without direct depth tensor optimization.
This paper proposes three complementary strategies—pre-training tailored to Newton-Kaczmarz updates, training on disjoint data subsets with model merging, and FPGA-based parallelization—to overcome the sequential bottlenecks in Kolmogorov-Arnold network training and significantly accelerate convergence.
The paper proposes "Latent Sculpting," a hierarchical two-stage architecture that combines a Transformer-based encoder with a Binary Latent Sculpting loss and a Masked Autoregressive Flow to enforce explicit geometric boundaries on benign data, achieving robust zero-shot generalization and high detection rates for out-of-distribution cyberattacks on the CIC-IDS-2017 benchmark.
This paper proposes FedORA, a primal-dual optimization framework that enables efficient and theoretically certified sample and label unlearning in Vertical Federated Learning by introducing a novel uncertainty-promoting loss function and adaptive strategies to minimize computational overhead while preserving model utility.
This paper proposes a process mining-based method that analyzes online gaming network traffic to unsupervisedly characterize device states as interpretable Petri nets and accurately classify different video games, demonstrating its effectiveness through the UPSIDE case study involving Clash Royale and Rocket League.
This paper proposes a parameter-free, equivariant topological spatial graph coarsening method that reduces graph size by collapsing short edges while preserving key topological features through a novel triangle-aware graph filtration adapted from persistent diagrams.
This paper proposes actor-critic methods with sparse robust estimator oracles to achieve the first non-vacuous guarantees for learning near-optimal policies in high-dimensional sparse offline reinforcement learning under strong data corruption and single-policy concentrability, overcoming the limitations of traditional Least Square Value Iteration approaches in such regimes.
This paper proposes weaker constraints on filters for group convolutional neural networks that reduce the required number of nodes while resolving incompatibilities with non-compact stabilizers and generalizing results to non-transitive group actions and non-unimodular groups.
This paper introduces an operator-legible evaluation framework centered on under-prediction risk to demonstrate that standard accuracy metrics fail to capture safety-critical grid forecasting needs, revealing that while explicit weather integration improves reliability, unconstrained probabilistic models often induce "fake safety" through excessive inflation, a problem solved by new Bias/OPR-constrained objectives.
This paper proposes an amortized Bayesian inference framework for graph-structured data that combines permutation-invariant summary networks with neural posterior estimators to enable fast, likelihood-free inference on node, edge, and graph-level parameters, demonstrating its effectiveness through evaluations on synthetic benchmarks and real-world applications in biology and logistics.
DevBench is a realistic, telemetry-driven benchmark comprising 1,800 instances across six languages that evaluates LLMs on code completion tasks with a focus on ecological validity, contamination-free assessment, and detailed diagnostic insights to guide practical model selection and development.
This paper presents the first component-level survey systematically reviewing the bidirectional interactions between Large Language Models and Multi-Armed Bandits, highlighting how MAB algorithms optimize LLM workflows while LLMs redefine core MAB components to enhance adaptive decision-making.
ELSA is a novel framework that integrates split learning and hierarchical federated learning with client clustering, dynamic model splitting, and privacy-preserving communication sketches to enable efficient, robust, and privacy-aware fine-tuning of large language models on resource-constrained edge networks.
This paper proposes a novel data-rate-aware continuous-flow architecture for CNN inference on FPGAs that mitigates hardware underutilization caused by data reduction in pooling and strided convolution layers by interleaving signals and sharing resources, thereby enabling the high-throughput implementation of complex models like MobileNet on a single device.
MeanCache is a training-free framework that accelerates Flow Matching inference by replacing instantaneous velocity caching with an average-velocity approach using cached Jacobian-vector products and a trajectory-stability scheduling strategy, achieving significant speedups (up to 4.56X) while maintaining high generation quality across models like FLUX.1 and HunyuanVideo.
PASS is a scalable framework for pairwise-constrained k-means clustering that optimizes a small working subset while formally certifying the feasibility of cannot-link constraints via list-coloring, thereby enabling efficient classical and quantum solutions with verifiable repair mechanisms for infeasible instances.
This paper presents a systematic empirical comparison demonstrating that model-free neural estimators, particularly state-space models, achieve state estimation performance comparable to strong nonlinear Kalman filters in nonlinear dynamical systems while offering significantly higher inference throughput.
TimeSliver is a novel explainable deep learning framework for time-series classification that jointly leverages raw data and symbolic abstraction to linearly encode temporal segment contributions, achieving superior attribution accuracy and competitive predictive performance compared to existing methods.
This paper proposes TGCC, a novel causal-invariance-based graph dataset condensation method that extracts domain-invariant features and injects them via spectral contrastive learning to significantly improve performance in cross-task and cross-domain scenarios while maintaining state-of-the-art results in single-task settings.