Search: “models”

More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs

arXiv:2606.08471v1 Announce Type: cross Abstract: Recently, language models have made rapid progress across various domains and applications. However, their capability for self-improvement, i.e., whet

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Applications

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Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

DGX agent

arXiv:2606.09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applica

applicationsarxiv-cs-ai

9 Jun 2026

OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics

arXiv:2606.09826v1 Announce Type: cross Abstract: Vision-language model (VLM) agents are increasingly deployed in interactive game environments. Yet game benchmarks for VLM agents typically report a s

OmniTryOn: Video Try-On Anything at Once!

arXiv:2606.08514v1 Announce Type: new Abstract: Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restr

One Stone, Three Birds: Self-adaptive Optimal Transport for Multi-VLM Selection, Adaptation, and Ensembling

arXiv:2606.08126v1 Announce Type: new Abstract: Vision-language models (VLMs) enable visual recognition from semantic class descriptions, which makes them attractive when target annotations are scarce

researcharxiv-cs-cv

Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

arXiv:2606.08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this pa

PereStruct: Multimodal Semantic Assembly for Robust Historical Document Parsing

arXiv:2606.07661v1 Announce Type: new Abstract: Parsing historical documents with complex, non-standard layouts remains a fundamental bottleneck in large-scale archival digitization. Unlike modern typ

POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation

arXiv:2603.05500v2 Announce Type: replace-cross Abstract: Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this cha

PriFT: Prior-Support Guided Supervised Fine-Tuning

arXiv:2606.09396v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement le

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Programmable Silicon Retina on Pixel Processor Array

arXiv:2606.08370v1 Announce Type: cross Abstract: Standard dynamic vision sensors approximate retinal processing by detecting temporal contrast changes, offering high speed and high dynamic range. In

Quantum feature-map learning with reduced resource overhead

arXiv:2510.03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum f

Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive & Battery Manufacturing Extensions

arXiv:2606.07659v1 Announce Type: new Abstract: Automated surface defect detection is critical for ensuring rigorous quality control in high-speed manufacturing environments. While deep learning model

Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?

arXiv:2606.08063v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly un

Rosetta Memory: Adaptive Memory for Cross-LLM Agents

arXiv:2606.07711v1 Announce Type: cross Abstract: Memory is the key component for transforming a stateless LLM into a persistent, evolving agent through experience accumulation, long-horizon planning,

SafeRun: Enabling Determinism in LLM Planning for Running

arXiv:2606.09027v1 Announce Type: cross Abstract: Large Language Models enable flexible natural-language planning but remain unreliable in determinism-critical domains due to their probabilistic natur

SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests

arXiv:2606.08206v1 Announce Type: new Abstract: We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds. The model combines

Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation

arXiv:2606.08687v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA m

SOMA: From Surface Observations to Muscle Anatomy

arXiv:2606.09246v1 Announce Type: new Abstract: With the growing demand for realistic virtual humans, parametric body models have become a cornerstone of modern medicine, sports, and entertainment app

researcharxiv-cs-cv

SSAFE: Simple and Strong AI-Generated Image Detection via Frozen Vision Encoders

arXiv:2606.08634v1 Announce Type: new Abstract: The rapid advancement of generative models has blurred the boundary between synthetic and real imagery, creating an urgent need for reliable deepfake de

Stabilizing On-Policy Distillation for MLLM Reasoning with Global Normalization

arXiv:2606.09091v1 Announce Type: cross Abstract: On-policy distillation (OPD) has recently emerged as an important post-training paradigm. By using a stronger teacher model to provide dense, fine-gra

Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits

arXiv:2606.07615v1 Announce Type: cross Abstract: Deep neural networks often contain redundant hidden units. Removing individual weights can reduce parameter count, but unstructured sparsity is not al

Summarization is Not Dead Yet

arXiv:2606.08000v1 Announce Type: cross Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising

safetyarxiv-cs-ai

The Sample Complexity of Parameter-Free Stochastic Convex Optimization

arXiv:2506.11336v2 Announce Type: replace Abstract: We study the sample complexity of stochastic convex optimization when problem parameters such as the distance to optimality and the Lipschitz consta

Token Sample Complexity of Attention

arXiv:2512.10656v3 Announce Type: replace Abstract: As context windows in large language models continue to expand, it is essential to characterize how attention behaves at extreme sequence lengths. W

Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration

arXiv:2606.09474v1 Announce Type: new Abstract: Generalized Few-Shot Semantic Segmentation (GFSS) has traditionally been approached as a representation-learning problem, requiring task-specific adapta

Hardware

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution

arXiv:2606.09608v1 Announce Type: new Abstract: Diffusion-based generative models have achieved remarkable success in real-world image super-resolution (SR). With tiled diffusion techniques, these mod

hardwarearxiv-cs-cv

Tutorials

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

arXiv:2602.08222v2 Announce Type: replace Abstract: As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow hi

tutorialsarxiv-cs-ai

AdaGRPO: A Capability-Aware Adaptive Enhancement for Flow-based GRPO

arXiv:2606.06828v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has demonstrated remarkable success in aligning text-to-image (T2I) flow models with human preferences. Howeve

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Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning

arXiv:2603.14014v2 Announce Type: replace Abstract: We introduce Aumann-SHAP, an interaction-aware framework that decomposes counterfactual transitions by restricting the model to a local hypercube co

CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations

arXiv:2606.07488v1 Announce Type: new Abstract: Personalized virtual heart simulations face challenges in model personalization and computational cost. While neural surrogates offer state-of-the-art s

researcharxiv-cs-lg

Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws

arXiv:2606.06725v1 Announce Type: cross Abstract: Myocardial perfusion quantification using contrast-enhanced ultrasound offers a bedside non-ionizing alternative to nuclear imaging modalities. Howeve

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

arXiv:2506.11066v3 Announce Type: replace-cross Abstract: Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks pri

DaX: Learning General Pathology Representations Across Scales

arXiv:2606.06983v1 Announce Type: cross Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnificatio

Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows

arXiv:2606.06923v1 Announce Type: new Abstract: We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that de

From Vision to Text: A Compact Multimodal Approach for Robust, Cross-Domain Presentation Attack Detection on ID Cards

arXiv:2606.06966v1 Announce Type: new Abstract: Cross-domain shifts challenge Presentation Attack Detection (PAD) on ID Cards, given the restricted data available due to privacy concerns. This work pr

Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings

model-releasesarxiv-cs-cl

arXiv:2606.07123v1 Announce Type: new Abstract: Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP

LLM-Guided Evolution for Medical Decision Pipelines

model-releasesarxiv-cs-cl

arXiv:2606.07342v1 Announce Type: new Abstract: Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-gu

MacArena: Benchmarking Computer Use Agents on an Online macOS Environment

arXiv:2606.06560v1 Announce Type: cross Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidl

On the Geometry of On-Policy Distillation

arXiv:2606.07082v1 Announce Type: cross Abstract: On-policy distillation (OPD) is increasingly used to improve large language model reasoning, but its training dynamics remain poorly understood. We ch

Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese

model-releasesarxiv-cs-cl

arXiv:2606.07300v1 Announce Type: new Abstract: Language is a vehicle for thought, intricately tied to sounds, symbols, and meaning. However, most large language model (LLM) research focuses on meanin

RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning

arXiv:2606.07006v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations,

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Re-Centering Humans in LLM Personalization

arXiv:2606.06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data. It remains uncle

researcharxiv-cs-ai

ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios

arXiv:2602.16073v2 Announce Type: replace-cross Abstract: Developing autonomous driving systems for complex traffic environments requires balancing multiple objectives, such as avoiding collisions, ob

SEAM: Shortcut-Aware Real-Time Detection of Scripted vs. Spontaneous Speech for Interview Guardrails

arXiv:2606.06837v1 Announce Type: cross Abstract: Scripted vs spontaneous speech detection is appealing for interview guardrails, but benchmark performance can be inflated by shortcuts tied to corpus

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

arXiv:2606.07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to ca

Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry

arXiv:2603.26846v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) expand in capability and application scope, their trustworthiness becomes critical. A vital risk is intrinsic

safetyarxiv-cs-ai

Local Ai

The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs

arXiv:2606.07422v1 Announce Type: cross Abstract: Large language models are increasingly used to answer culturally grounded questions across languages, yet it remains unclear whether local cultural kn

local-aiarxiv-cs-ai

The Necessity of Setting Temperature in LLM-as-a-Judge

arXiv:2603.28304v2 Announce Type: replace Abstract: Using large language models (LLMs) as judges for evaluating model outputs has emerged as an important paradigm for automated evaluation. However, th

researcharxiv-cs-cl