Search: “papers”

Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification

arXiv:2605.11460v1 Announce Type: new Abstract: System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlin

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Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models

DGX agent

arXiv:2605.11726v1 Announce Type: new Abstract: Recently, reinforcement learning (RL) has been widely applied during post-training for diffusion large language models (dLLMs) to enhance reasoning with

model-releasesarxiv-cs-lg

13 May 2026

Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters

model-releasesarxiv-cs-cv

arXiv:2605.11960v1 Announce Type: new Abstract: Vision Large Language Models (VLLMs) have achieved remarkable success in modern text-rich visual understanding. However, their perceptual robustness in

Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner

arXiv:2510.03206v2 Announce Type: replace-cross Abstract: Diffusion language models, especially masked discrete diffusion models, have achieved great success recently. While there are some theoretical

applicationsarxiv-cs-cl

Combining On-Policy Optimization and Distillation for Long-Context Reasoning in Large Language Models

arXiv:2605.12227v1 Announce Type: new Abstract: Adapting large language models (LLMs) to long-context tasks requires post-training methods that remain accurate and coherent over thousands of tokens. E

safetyarxiv-cs-cl

Compositional Neural Operators for Multi-Dimensional Fluid Dynamics

arXiv:2605.11691v1 Announce Type: new Abstract: Partial differential equations (PDEs) govern diverse physical phenomena, yet high-fidelity numerical solutions are computationally expensive and Machine

Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification

arXiv:2509.20899v3 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but exte

Agents

Control Charts for Multi-agent Systems

arXiv:2605.11135v1 Announce Type: cross Abstract: Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal

agentsarxiv-cs-lg

Control of Fully Actuated Aerial Vehicles: A Comparison of Model-based and Sensor-based Dynamic Inversion

model-releasesarxiv-cs-ro

arXiv:2605.12071v1 Announce Type: new Abstract: Fully actuated multirotor platforms decouple translational force generation from vehicle attitude, enabling independent control of position and orientat

DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift

arXiv:2605.11237v1 Announce Type: new Abstract: Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-

Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning

arXiv:2605.11430v1 Announce Type: new Abstract: Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with clas

Local Ai

Distributed Quantum Gaussian Processes for Multi-Agent Systems

arXiv:2602.15006v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale re

local-aiarxiv-cs-lg

Agents

DORA: Dynamic Online Reinforcement Agent for Token Merging in Vision Transformers

arXiv:2605.11683v1 Announce Type: new Abstract: Vision Transformers (ViTs) incur significant computational overhead due to the quadratic complexity of self-attention relative to the token sequence len

agentsarxiv-cs-cv

Dynamic Execution Commitment of Vision-Language-Action Models

arXiv:2605.11567v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models predominantly adopt action chunking, i.e., predicting and committing to a short horizon of consecutive low-level act

applicationsarxiv-cs-cv

Efficient and Adaptive Human Activity Recognition via LLM Backbones

arXiv:2605.12019v1 Announce Type: new Abstract: Human Activity Recognition (HAR) is a core task in pervasive computing systems, where models must operate under strict computational constraints while r

Efficient LLM-based Advertising via Model Compression and Parallel Verification

arXiv:2605.11582v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable potential in advertising scenarios such as ad creative generation and targeted advertising. However,

applicationsarxiv-cs-cl

Hardware

Efficient Remote KV Cache Reuse with GPU-native Video Codec

arXiv:2602.09725v3 Announce Type: replace-cross Abstract: Remote KV cache reuse fetches KV cache for identical contexts from remote storage, avoiding recomputation, accelerating LLM inference. While i

hardwarearxiv-cs-lg

EgoForce: Forearm-Guided Camera-Space 3D Hand Pose from a Monocular Egocentric Camera

arXiv:2605.12498v1 Announce Type: new Abstract: Reconstructing the absolute 3D pose and shape of the hands from the user's viewpoint using a single head-mounted camera is crucial for practical egocent

Enhancing Target-Guided Proactive Dialogue Systems via Conversational Scenario Modeling and Intent-Keyword Bridging

arXiv:2605.11964v1 Announce Type: new Abstract: A target-guided proactive dialogue system aims to steer conversations proactively toward pre-defined targets, such as designated keywords or specific to

safetyarxiv-cs-cl

Enriching and Controlling Global Semantics for Text Summarization

arXiv:2109.10616v2 Announce Type: replace Abstract: Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Ne

researcharxiv-cs-cl

EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models

arXiv:2605.11859v1 Announce Type: new Abstract: Robot navigation is a crucial task with applications to social robots in dynamic human environments. While Reinforcement Learning (RL) has shown great p

safetyarxiv-cs-ro

Express Your Doubts -- Probabilistic World Modeling Should not be Based on Token logprobs

arXiv:2505.02072v2 Announce Type: replace Abstract: Language modeling has shifted in recent years from a distribution over strings to prediction models with textual inputs and outputs for general-purp

researcharxiv-cs-cl

Fair Conformal Classification via Learning Representation-Based Groups

arXiv:2605.12195v1 Announce Type: new Abstract: Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to accoun

Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning

arXiv:2605.11815v1 Announce Type: new Abstract: Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for join

Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

arXiv:2605.11752v1 Announce Type: new Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods ass

Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

model-releasesarxiv-cs-ro

arXiv:2603.28561v2 Announce Type: replace Abstract: The growing deployment of small Unmanned Aerial Systems (sUASs) in low-altitude airspaces has increased the need for reliable tactical deconfliction

ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching

arXiv:2605.11048v1 Announce Type: new Abstract: Existing imitation learning methods enable robots to interact autonomously with the physical environment. However, contact-rich manipulation tasks remai

safetyarxiv-cs-ro

G^2TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models

arXiv:2605.12309v1 Announce Type: new Abstract: The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. I

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation

arXiv:2605.11853v1 Announce Type: cross Abstract: Reinforcement learning has become a widely used post-training approach for LLM agents, where training commonly relies on outcome-level rewards that pr

safetyarxiv-cs-cl

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models

arXiv:2605.12327v1 Announce Type: new Abstract: A major recent advance in quantization is given by microscaled 4-bit formats such as NVFP4 and MXFP4, quantizing values into small groups sharing a scal

Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes

model-releasesarxiv-cs-cl

arXiv:2605.06152v2 Announce Type: replace-cross Abstract: Deep neural networks exhibit periodic loss spikes during unregularized long-term training, a phenomenon known as the 'Slingshot Mechanism.' Ex

Tutorials

GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization

arXiv:2605.12369v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Exist

tutorialsarxiv-cs-ro

HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench

model-releasesarxiv-cs-cl

arXiv:2601.20255v2 Announce Type: replace-cross Abstract: SWE-bench has emerged as the premier benchmark for evaluating Large Language Models on complex software engineering tasks. While these capabil

Local Ai

Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity

arXiv:2605.11509v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control an

local-aiarxiv-cs-lg

Hypernetworks for Dynamic Feature Selection

arXiv:2605.12278v1 Announce Type: new Abstract: Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constrai

Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications

arXiv:2605.11855v1 Announce Type: new Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term de

Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning

arXiv:2605.11889v1 Announce Type: new Abstract: Collaborative machine learning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, exis

Information theoretic underpinning of self-supervised learning by clustering

arXiv:2605.11870v1 Announce Type: new Abstract: Self-supervised learning (SSL) is recognized as an essential tool for building foundation models for Artificial Intelligence applications. The advances

Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessment

arXiv:2605.11439v1 Announce Type: new Abstract: Rapid and accurate situational awareness is essential for effective response during natural disasters, where delays in analysis can significantly hinder

applicationsarxiv-cs-cv

Local Ai

Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models

arXiv:2605.12258v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress, yet the object hallucination remains a critical challenge for reliable deplo

local-aiarxiv-cs-lg

Intention-Conditioned Flow Occupancy Models

arXiv:2506.08902v4 Announce Type: replace Abstract: Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then c

Interactive State Space Model with Cross-Modal Local Scanning for Depth Super-Resolution

arXiv:2605.11934v1 Announce Type: new Abstract: Guided depth super-resolution (GDSR) reconstructs HR depth maps from LR inputs with HR RGB guidance. Existing methods either model each modality indepen

Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning

arXiv:2605.11235v1 Announce Type: new Abstract: In LLM Reinforcement Fine-Tuning (RFT), curriculum learning drives both efficiency and performance. Yet, current methods externalize curriculum judgment

Interpretability Can Be Actionable

arXiv:2605.11161v1 Announce Type: new Abstract: Interpretability aims to explain the behavior of deep neural networks. Despite rapid growth, there is mounting concern that much of this work has not tr

applicationsarxiv-cs-lg

Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS

arXiv:2605.12200v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance, often outperforming supervised models in zero-shot settings.

Latent Chain-of-Thought Improves Structured-Data Transformers

arXiv:2605.11262v1 Announce Type: new Abstract: Chain-of-thought and more broadly test-time compute are known to augment the expressive capabilities of language models and have led to major innovation

Learnable Multi-level Discrete Wavelet Transforms for 3D Gaussian Splatting Frequency Modulation

model-releasesarxiv-cs-cv

arXiv:2602.14199v2 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful approach for novel view synthesis. However, the number of Gaussian primitives often gro

Tutorials

Learning density ratios in causal inference using Bregman-Riesz regression

arXiv:2510.16127v2 Announce Type: replace-cross Abstract: The ratio of two probability density functions is a fundamental quantity that appears in many areas of statistics and machine learning, includ

tutorialsarxiv-cs-lg