Search: “benchmark”

Simpler and Stronger Baselines for All-in-One Image Restoration

arXiv:2512.10581v2 Announce Type: replace Abstract: Current methods for all-in-one image restoration increasingly rely on complex mechanisms, such as Mixture-of-Experts or prompt learning, to handle d

Local Ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Simplifying and Improving On-Policy Distillation for Few-Step Diffusion

DGX agent

arXiv:2610.18472v1 Announce Type: new Abstract: Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialize

local-aiarxiv-cs-cv

14 Oct 2026

Smartphone-Based, Field-Deployed AI Inspection System for Power Distribution Infrastructure

arXiv:2610.17620v1 Announce Type: new Abstract: Routine inspection of power distribution infrastructure remains challenging for utilities operating geographically dispersed networks, where conventiona

SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair

arXiv:2610.15204v1 Announce Type: cross Abstract: Large language models are increasingly used in computer science education. However, the teaching abilities of even the most powerful LLMs are limited:

applicationsarxiv-cs-lg

Solvating machine-learning interatomic potentials for solution-phase chemistry

arXiv:2610.17226v1 Announce Type: cross Abstract: Most machine-learning interatomic potentials (MLIPs) are trained on gas-phase data, which limits their use for chemistry in solution. Here, we introdu

SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis

arXiv:2610.17808v1 Announce Type: new Abstract: Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential. However, existing AL strategies fa

Spectral Costs of Multinomial and Residual Resampling in High-Dimensional Particle Ensembles: A Free-Probability Analysis

arXiv:2610.16680v1 Announce Type: cross Abstract: Resampling is the standard remedy against weight degeneracy in particle filters, yet its effect on the spectrum of the particle second-moment matrix i

SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering

arXiv:2610.14405v1 Announce Type: cross Abstract: Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. Howev

SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy

arXiv:2610.17323v1 Announce Type: new Abstract: Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent. Generator-based protec

Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection

arXiv:2606.19774v2 Announce Type: replace Abstract: Action chunking enables robot policies to produce temporally coherent behavior, but generating multi-step action sequences with flow-based policies

safetyarxiv-cs-ro

Stereo-Planner: Learning Stereo-to-Trajectory Planning for Zero-Shot Sim-to-Real Quadrotor Navigation

model-releasesarxiv-cs-ro

arXiv:2610.15560v1 Announce Type: new Abstract: Existing navigation systems commonly rely on dedicated depth sensors or explicit depth reconstruction, while learning to generate trajectories directly

Hardware

Swarm Telepathy: Prefill-Free KV-Cache Transfer Across Families

arXiv:2610.15095v1 Announce Type: new Abstract: LLM multi-agent systems split work across agents that communicate through text. An agent summarizes its findings, another agent receives the text. This

hardwarearxiv-cs-lg

Agents

TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency

arXiv:2610.17540v1 Announce Type: cross Abstract: Textual reasoning before acting has improved vision-language-action (VLA) models, but at the cost of latency: every action conditioned on the reasonin

agentsarxiv-cs-ro

Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling

arXiv:2610.14440v1 Announce Type: new Abstract: Asynchronous reinforcement-learning post-training of large language models is computationally efficient but inherently off-policy: the learner updates o

TartanMatch: Towards Universal Dense Correspondence Across Modalities

arXiv:2610.14513v1 Announce Type: new Abstract: Robotic platforms combine multi-modal sensors to operate across diverse conditions, but most dense correspondence methods are limited to RGB-RGB or a sm

applicationsarxiv-cs-cv

Teach Me After You Learn It: Lookahead Self-Distillation for RLVR

arXiv:2610.18256v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a popular training paradigm for LLMs, providing dense, token-level supervision by aligning a model's o

Testing Central Structure for Complex Networks

arXiv:2610.15659v1 Announce Type: new Abstract: Centrality measures rank nodes even when the observed differences can be explained by ordinary structural variation. We therefore study a preceding ques

The Optimal Decoder is Tilted Temperature

arXiv:2610.15058v1 Announce Type: new Abstract: Larger sample budgets benefit from higher sampling temperatures in LLMs, but what temperature tuning optimizes and why it works remain poorly understood

The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors

arXiv:2610.16977v1 Announce Type: new Abstract: Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends c

Agents

Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models

arXiv:2610.16165v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning

agentsarxiv-cs-lg

Thinking with Images without Coordinates

arXiv:2610.14265v1 Announce Type: new Abstract: Recent advances in multi-modal large language models have shifted toward the 'thinking with images' paradigm to improve reasoning faithfulness, yet exis

Through the PRISM: Preference Representation in Intermediate States of Video Diffusion Models

arXiv:2606.20310v2 Announce Type: replace Abstract: Video reward models are central to aligning video diffusion models with human preferences, yet existing ones are external vision-language models tha

TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings

arXiv:2610.17065v1 Announce Type: new Abstract: Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical

Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning

arXiv:2610.16921v1 Announce Type: cross Abstract: Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misalign

To Cache or Not to Cache: Understanding and Mitigating Safety Incompatibility in Cross-Model KV Cache Reuse

arXiv:2610.15721v1 Announce Type: new Abstract: Cross-model KV cache sharing reduces prefill computation by allowing a receiver model to reuse prefix KV states generated by a related sender model. Exi

TokenPB: Enabling VLA Action Self-Verification via VLM Token Probability Bucketing

arXiv:2603.18091v2 Announce Type: replace Abstract: Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candi

applicationsarxiv-cs-cv

TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization

arXiv:2603.25063v2 Announce Type: replace-cross Abstract: Recent agentic systems demonstrate that large language models can generate complex codebases from natural language prompts. Despite this progr

Totem: Object-Centric 3D Memory for World-Consistent Video Generation

arXiv:2610.15807v1 Announce Type: new Abstract: Generating coherent videos grounded in a unified underlying world remains a major challenge, particularly under large viewpoint changes, where existing

Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification

arXiv:2602.24111v2 Announce Type: replace Abstract: Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagno

Toward Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups

arXiv:2601.02759v2 Announce Type: replace Abstract: Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring target-domain retraining, fine-tun

Towards a Systematic Study of Embodied System 2 over Full Task Execution Cycles

model-releasesarxiv-cs-ro

arXiv:2610.17657v1 Announce Type: new Abstract: An embodied brain, or System 2, must turn an abstract goal into actions while discovering relevant scene conditions and checking whether executed skills

Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference

arXiv:2610.17531v1 Announce Type: new Abstract: While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact

Towards Privacy-Preserving Thermal Human Perception from Dataset to Deployment

arXiv:2610.14846v1 Announce Type: new Abstract: Indoor human perception is the foundation for smart homes and healthcare. However, RGB cameras raise privacy concerns in private indoor environments, wh

applicationsarxiv-cs-cv

TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation

arXiv:2610.15365v1 Announce Type: cross Abstract: Large language models (LLMs) can produce harmful content while generating a response. A useful safeguard should not only detect this risk during gener

TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training

arXiv:2610.17726v1 Announce Type: new Abstract: Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new sk

UEPS: Robust and Efficient MRI Reconstruction

arXiv:2603.18572v2 Announce Type: replace-cross Abstract: Deep unrolled models (DUM) have become the state-of-the-art for accelerated MRI reconstruction, yet their robustness under domain shift remain

Understanding Hidden Spatial Preferences in Bayesian Optimization

arXiv:2610.14696v1 Announce Type: new Abstract: Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral. On bounded domains, Gaussian

Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing

arXiv:2601.17673v2 Announce Type: replace Abstract: Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object loc

UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning

arXiv:2610.12941v2 Announce Type: replace Abstract: Image deblurring is a fundamental task in computer vision, yet existing methods mainly specialize in isolated scenarios. Consequently, the deblurrin

Tutorials

Universal Incremental Learning: Mitigating Confusion from Inter- and Intra-task Distribution Randomness

arXiv:2503.07035v3 Announce Type: replace Abstract: Existing incremental learning (IL) methods rely on strong assumptions: incoming tasks either introduce exclusively new classes or new domains (i.e.,

tutorialsarxiv-cs-cv

UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity

arXiv:2511.13714v2 Announce Type: replace Abstract: The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity rem

Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

arXiv:2204.09334v4 Announce Type: replace-cross Abstract: Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle

Value-Preserving Action Quantization

arXiv:2610.17252v1 Announce Type: cross Abstract: Search-based policy learning, such as Monte Carlo tree search (MCTS), offers the flexibility to trade runtime computation for data efficiency and adap

Variable Input Masked Autoencoders for Wrist-Worn Accelerometry

arXiv:2610.17383v1 Announce Type: new Abstract: Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing

Agents

VideoSlate: Stateful Commit Control for Temporally Grounded Agentic Video Reasoning

arXiv:2610.16357v1 Announce Type: new Abstract: Agentic video reasoning systems gather and assess video evidence across stages, selecting relevant spans as visual evidence for answering questions. New

agentsarxiv-cs-cv

Visual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP

arXiv:2610.17902v1 Announce Type: new Abstract: Visual question answering models often produce plausible responses even when the available visual evidence is insufficient to support a valid answer. Al

Local Ai

Visual Tokens on Demand: Visual Budgeting for Scalable and Efficient Multi-Image Reasoning in Multimodal LLMs

arXiv:2610.18858v1 Announce Type: new Abstract: Multimodal large language models have advanced rapidly, but most remain optimized for single-image inputs. Multi-image reasoning is costly because each

local-aiarxiv-cs-cv

Tutorials

We Have Been Interpreting Diffusion Transformer Backbone Behaviors Wrong

arXiv:2610.14517v1 Announce Type: new Abstract: During a network forward call, diffusion backbones naturally produce many intermediate activations. If we evaluate each activation against an identical

tutorialsarxiv-cs-cv