Search: “benchmark”

SeamCam: Quantifying Seamless Camouflage via Multi-Cue Visual Detectability

arXiv:2605.16515v1 Announce Type: new Abstract: Animals are described as effectively camouflaged when they blend seamlessly with their surrounding, yet no standardized quantitative measure of this sea

local-aiarxiv-cs-cv

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SEDualVLN: A Spatially-Enhanced Dual-System for Vision-Language Navigation

DGX agent

arXiv:2605.17249v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) approaches have currently followed two primary paradigms: the end-to-end Vision-Language Model (VLM) policy fine-tuned

local-aiarxiv-cs-ro

19 May 2026

See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding

arXiv:2605.18018v1 Announce Type: cross Abstract: We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understandi

local-aiarxiv-cs-ai

Seeing Together:Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models

arXiv:2605.18431v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made substantial progress in egocentric video understanding, but their ability to reason cooperatively fro

Self-Evolving Spatial Reasoning in Vision Language Models via Geometric Logic Consistency

arXiv:2605.18162v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile: models that answer an original input correctly

safetyarxiv-cs-ai

Self-Supervised On-Policy Distillation for Reasoning Language Models

arXiv:2605.17497v1 Announce Type: new Abstract: GRPO-style RLVR trains reasoning models from multiple on-policy attempts per prompt, but typically uses these attempts only through terminal rewards. We

SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning

arXiv:2605.17101v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is widely employed to mitigate risks such as hallucinations and knowledge obsolescence in medical question answer

Semantic Generative Tuning for Unified Multimodal Models

arXiv:2605.18714v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing tr

researcharxiv-cs-ai

Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares

arXiv:2605.18156v1 Announce Type: new Abstract: Lens flare removal is challenging due to the large spatial extent of flare artifacts and their entanglement with scene structures, while existing method

researcharxiv-cs-cv

Setting the Stage: Text-Driven Scene-Consistent Image Generation

arXiv:2512.12598v3 Announce Type: replace Abstract: We focus on the foundational task of Scene Staging: given a reference scene image and a text condition specifying an actor category to be generated

Shallow Deep Learning Can Still Excel in Fine-Grained Few-Shot Learning

arXiv:2507.22041v2 Announce Type: replace Abstract: Deep learning has witnessed the extensive utilization across a wide spectrum of domains, including fine-grained few-shot learning (FGFSL) which heav

researcharxiv-cs-cv

SHED: Style-Homogenized Embedding Alignment for Domain Generalization

arXiv:2605.16973v1 Announce Type: new Abstract: Domain generalization aims to enhance model robustness against unseen domains with embedding distribution shifts. While large-scale vision-language mode

safetyarxiv-cs-cv

Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures

arXiv:2605.17850v1 Announce Type: cross Abstract: iffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve sampl

safetyarxiv-cs-cv

Tutorials

Single Image Reflection Removal with Patch Reflectance Prior

arXiv:2312.03798v2 Announce Type: replace Abstract: Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface d

tutorialsarxiv-cs-cv

Sketch Then Paint: Hierarchical Reinforcement Learning for Diffusion Multi-Modal Large Language Models

arXiv:2605.16842v1 Announce Type: new Abstract: Diffusion Multi-Modal Large Language Models (dMLLMs) are powerful for image generation, but optimizing them through reinforcement learning (RL) remains

local-aiarxiv-cs-ai

SKG-Eval: Stateful Evaluation of Multi-Turn Dialogue via Incremental Semantic Knowledge Graphs

arXiv:2605.16650v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue systems remains challenging because response quality depends not only on the current prompt, but also on previously est

local-aiarxiv-cs-ai

Skim: Speculative Execution for Fast and Efficient Web Agents

arXiv:2605.16565v1 Announce Type: new Abstract: Skim is a speculative execution framework for web agents that exploits the predictable structure of purpose-built websites. Today's web-agent expense is

SkyNative: A Native Multimodal Framework for Remote Sensing Visual Evidence Reasoning

arXiv:2605.17949v1 Announce Type: new Abstract: Remote sensing vision-language models commonly rely on pretrained visual encoders to convert images into semantic features before language-model reasoni

Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules

arXiv:2604.08432v2 Announce Type: replace-cross Abstract: Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, rei

Sometin Beta Pass Notin (SBPN): Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation

model-releasesarxiv-cs-cl

arXiv:2605.17710v1 Announce Type: new Abstract: Although modern multilingual Automatic Speech Recognition (ASR) systems support several Nigerian languages, their performance consistently lags behind h

Hardware

Sparse Mamba Decoder for Quantum Error Correction: Efficient Defect-Centric Processing of Surface Code Syndromes

arXiv:2605.17156v1 Announce Type: cross Abstract: Quantum error correction (QEC) is essential for building fault-tolerant quantum computers, requiring decoders that are simultaneously accurate, fast,

hardwarearxiv-cs-lg

Sparse Training of Neural Networks based on Multilevel Mirror Descent

arXiv:2602.03535v2 Announce Type: replace Abstract: We introduce a dynamic sparse training algorithm based on linearized Bregman iterations / mirror descent that exploits the naturally incurred sparsi

SparseSAM: Structured Sparsification of Activations in Segment Anything Models

arXiv:2605.17633v1 Announce Type: cross Abstract: The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory.

researcharxiv-cs-ai

SPATIOROUTE: Dynamic Prompt Routing for Zero-Shot Spatial Reasoning

arXiv:2605.18209v1 Announce Type: cross Abstract: Spatial question answering over egocentric video is a challenging task that requires Vision-Language Models (VLMs) to reason about 3D object positions

Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception

arXiv:2601.09413v2 Announce Type: replace-cross Abstract: We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult

Spherical Steering: Geometry-Aware Activation Rotation for Language Models

arXiv:2602.08169v2 Announce Type: replace-cross Abstract: Inference-time steering offers a promising way to control language models (LMs) without retraining. However, standard approaches typically rel

researcharxiv-cs-cl

SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning

arXiv:2510.16416v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown remarkable abilities by integrating large language models with visual inputs. However, they often fai

safetyarxiv-cs-ai

ST-BCP: Tightening Coverage Bound for Backward Conformal Prediction via Non-Conformity Score Transformation

arXiv:2602.01733v2 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guaran

researcharxiv-cs-lg

Stable Routing for Mixture-of-Experts in Class-Incremental Learning

arXiv:2605.17571v1 Announce Type: new Abstract: Class-incremental learning (CIL) requires models to learn new classes sequentially while preserving prior knowledge. Recently, approaches that combine p

safetyarxiv-cs-cv

StableHand: Quality-Aware Flow Matching for World-Space Dual-Hand Motion Estimation from Egocentric Video

arXiv:2605.18553v1 Announce Type: cross Abstract: Recovering world space 4D motion of two interacting hands from egocentric video is a fundamental capability for supervising robot policy learning, whe

safetyarxiv-cs-ai

Tutorials

Starve to Perceive: Taming Lazy Perception in VLMs with Constrained Visual Bandwidth

arXiv:2605.18603v1 Announce Type: new Abstract: Vision-Language Models (VLMs) deployed as situated agents in high-resolution visual environments require active perception -- the ability to dynamically

tutorialsarxiv-cs-cv

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning

arXiv:2605.18656v1 Announce Type: cross Abstract: Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-

Step-wise Rubric Rewards for LLM Reasoning

arXiv:2605.17291v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in large language models, but rewards only final-answer correc

researcharxiv-cs-lg

Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models

arXiv:2605.17672v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance by generating long chains of thought (CoT), but often overthink, continuing to reason after a s

safetyarxiv-cs-cl

Strategic Over-Parameterization for Generalizable Low-Rank Adaptation

arXiv:2605.16470v1 Announce Type: cross Abstract: Adapting large language models (LLMs) to downstream tasks via full fine-tuning is increasingly impractical due to its computational and memory demands

STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery

arXiv:2605.17790v1 Announce Type: new Abstract: LLM-based equation discovery offers a promising route to recovering symbolic laws from data, but many systems still rely on generation-centered loops th

StrLoRA: Towards Streaming Continual Visual Instruction Tuning for MLLMs

arXiv:2605.16353v1 Announce Type: cross Abstract: Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models to incrementally acquire new abilities. However, existing CVIT met

Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving

arXiv:2506.05442v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) offer a promising approach to end-to-end autonomous driving due to their human-like reasoning capabilities. Howe

Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling

arXiv:2605.17568v1 Announce Type: new Abstract: Multi-class event streams arise in numerous real-world applications, where uncovering structured, interpretable inter-event relationships, together with

Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting

arXiv:2511.19953v2 Announce Type: replace Abstract: Accurate nuclear instance segmentation is a pivotal task in computational pathology, supporting data-driven clinical insights and facilitating downs

Supervising the search process produces reliable and generalizable information-seeking agents

arXiv:2502.13957v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming web search by shifting from document ranking to synthesizing answers, and are increasingly deplo

SURGE: Approximation-free Training Free Particle Filter for Diffusion Surrogate

arXiv:2605.18745v1 Announce Type: cross Abstract: Diffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve samp

safetyarxiv-cs-lg

SurgLQA: Scalable Long-Horizon Surgical Video Question Answering

arXiv:2605.17915v1 Announce Type: new Abstract: Surgical Video Question Answering (VideoQA) provides a promising paradigm for dynamic intraoperative interpretation, enabling real-time decision support

Synthesis and Verification of Transformer Programs (Technical Report)

arXiv:2602.16473v2 Announce Type: replace Abstract: C-RASP is a simple programming language that was recently shown to capture concepts expressible by transformers. In this paper, we develop new algor

researcharxiv-cs-lg

TabH2O: A Unified Foundation Model for Tabular Prediction

arXiv:2605.18383v1 Announce Type: new Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning. Tab

Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms

arXiv:2605.17336v1 Announce Type: cross Abstract: Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models

arXiv:2605.17577v1 Announce Type: new Abstract: Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to impercepti

Task Abstention for Large Language Models in Code Generation

arXiv:2605.17029v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized automated code generation. One serious concern, however, is the so-called ``hallucination'', i.e., LL