Search: “models”

Converted, Not Equivalent: Benchmarking Codebase Conversion via Observational Equivalence

arXiv:2605.29054v1 Announce Type: cross Abstract: Coding agents increasingly act as codebase-scale collaborators that can assist with codebase conversion, but this progress has exposed a critical weak

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Model Releases

Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation

DGX agent

arXiv:2605.30000v1 Announce Type: new Abstract: Front-end web code has become a core product surface for every frontier LLM release, yet evaluating these interactive applications at development speed

model-releasesarxiv-cs-ai

29 May 2026

Local Ai

Demystifying Data Organization for Enhanced LLM Training

arXiv:2605.30334v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data

local-aiarxiv-cs-ai

Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking

arXiv:2605.30107v1 Announce Type: new Abstract: Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-para

Differentiable Belief-based Opponent Shaping

arXiv:2605.29042v1 Announce Type: new Abstract: Human coordination often relies on the ability to influence the beliefs of others through strategic action. In multi-agent reinforcement learning, oppon

DirectorBench: Diagnosing Long-Form Video Generation with Personalized Multi-Agent Evaluation

arXiv:2605.30090v1 Announce Type: new Abstract: Long-form video generation is rapidly moving from short, single-scene synthesis toward minute-long, multi-shot creation with narrative structure, cinema

Domain-Agnostic Feature Modulation for Semi-Supervised Domain Generalization

arXiv:2503.20897v2 Announce Type: replace Abstract: Semi-supervised domain generalization (SSDG) leverages a small fraction of labeled data alongside unlabeled data to enhance model generalization. Mo

researcharxiv-cs-cv

Hardware

EarlyTom: Early Token Compression Completes Fast Video Understanding

arXiv:2605.30010v1 Announce Type: new Abstract: Video large language models (Video-LLMs) have demonstrated strong capabilities in video understanding tasks. However, their practical deployment is stil

hardwarearxiv-cs-cv

Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching

arXiv:2605.30337v1 Announce Type: new Abstract: Test-time finetuning (TTFT) is a rapidly evolving paradigm that adapts a language model to each prompt by retrieving related sequences, updating the mod

researcharxiv-cs-lg

Entity-Collision: A Stratified Protocol for Attributing Retrieval Lift in Agent Memory

arXiv:2605.29630v1 Announce Type: cross Abstract: End-to-end agent-memory benchmarks report a single hit@k per retriever, confounding lexical leakage (uncontrolled query/gold/distractor entity overlap

Hardware

ESAM++: Efficient Online 3D Perception on the Edge

arXiv:2605.29505v1 Announce Type: new Abstract: Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computa

hardwarearxiv-cs-cv

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning

arXiv:2605.29002v1 Announce Type: new Abstract: Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories

Local Ai

FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation

arXiv:2605.29460v1 Announce Type: new Abstract: Federated fine-tuning of foundation models with Low-Rank Adaptation (LoRA) provides an efficient solution for reducing communication and computation cos

local-aiarxiv-cs-cv

FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning

arXiv:2605.29317v1 Announce Type: new Abstract: Parameter-efficient fine-tuning(PEFT) has largely focused on LoRA and its accuracy-oriented variants, leaving the original goal of reducing trainable pa

From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale

arXiv:2605.28826v1 Announce Type: new Abstract: In modern LLMs, linguistic features function not as stylistic artifacts but as probes of probability mass, allocated under training alignment objectives

safetyarxiv-cs-cl

From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs

arXiv:2605.30014v1 Announce Type: new Abstract: Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict acc

From Sublinear to Linear: Local Convergence in Finite-Width Networks via Locally Polyak-Lojasiewicz Regions

arXiv:2507.21429v3 Announce Type: replace-cross Abstract: We study local linear convergence of gradient descent for finite-width feedforward networks under the squared empirical loss. Prior work shows

From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks

arXiv:2605.29768v1 Announce Type: new Abstract: Existing traffic forecasting benchmarks assume a fixed sensor set, but real road-sensor networks grow continuously as the road network changes year by y

Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes

arXiv:2605.28965v1 Announce Type: new Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration

Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training

arXiv:2605.29494v1 Announce Type: new Abstract: Deep neural network training involves both forward propagation (from features through logits to loss) and backward propagation (from loss through gradie

HEART-Bench: Do LLM Agents Exhibit Human-like Psychology?

arXiv:2605.30058v1 Announce Type: new Abstract: While LLM agents have demonstrated remarkable task-oriented abilities such as planning, reasoning, and action, few works have treated them as complete h

How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions

arXiv:2605.29442v1 Announce Type: cross Abstract: AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectories that m

iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis

arXiv:2605.30179v1 Announce Type: cross Abstract: Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not

Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning

arXiv:2605.29776v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP demonstrate strong zero-shot generalization, but their performance significantly degrades in cross-domain sce

safetyarxiv-cs-cv

Information-Directed Offline-to-Online Reinforcement Learning

arXiv:2605.29405v1 Announce Type: new Abstract: Decision-making from offline datasets typically warm-starts a policy or score model from fixed offline data and then refines it with limited online inte

safetyarxiv-cs-lg

Joint Angle Estimation with Customized Wristband Based on Online Incremental Learning

arXiv:2605.29771v1 Announce Type: new Abstract: Intelligent wearable technology plays an increasingly important role in human-computer interaction, motion, and health monitoring. To ensure comfort and

researcharxiv-cs-ro

Kernel-based potential mean-field games with unbiased random Fourier U-statistics

arXiv:2605.29371v1 Announce Type: cross Abstract: We study the subclass of potential mean-field games in which the running interaction cost and the terminal target cost are both expressed through repr

Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime

arXiv:2605.29684v1 Announce Type: new Abstract: The scaling limit where both the size of the training set P and the width N of a deep neural network grow at the same rate, the so-called proportional-w

Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design

arXiv:2605.29421v1 Announce Type: new Abstract: Photonic crystal fiber (PCF) inverse design remains challenging because candidate geometries must satisfy coupled optical targets under expensive electr

Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment

arXiv:2605.28822v1 Announce Type: new Abstract: Defect grading of power transmission equipment (DGPTE) is crucial to the stability of electric energy transmission. Although existing machine learning m

researcharxiv-cs-cl

LogDx-CI: Benchmarking Log Reduction Tools for LLM Root-Cause Diagnosis

arXiv:2605.28876v1 Announce Type: cross Abstract: CI failure logs are large (median 5k lines, max 200k in this corpus) and noisy. Coding agents that try to debug them depend on an upstream tool to red

Looking around you: external information enhances representations for event sequences

arXiv:2502.10205v3 Announce Type: replace Abstract: Representation learning produces models in different domains, such as store purchases, client transactions, and general people's behavior. However,

applicationsarxiv-cs-lg

MAGA-Bench: Machine-Augment-Generated Text via Alignment Detection Benchmark

arXiv:2601.04633v2 Announce Type: replace Abstract: Machine-Generated Text (MGT) is becoming increasingly difficult to distinguish from Human-Written Text (HWT). This trend has exacerbated malicious a

Agents

MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs

arXiv:2605.29512v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended intera

agentsarxiv-cs-ai

Modality Alignment across Trees on Heterogeneous Hyperbolic Manifolds

arXiv:2510.27391v2 Announce Type: replace Abstract: Modality alignment is critical for vision-language models (VLMs) to effectively integrate information across modalities. However, existing methods e

safetyarxiv-cs-cv

Moment-KV: Momentum-Based Decode-Time KV Cache Compression for Long Generation

arXiv:2605.29873v1 Announce Type: new Abstract: Key-Value (KV) cache remains a major bottleneck for deploying Large Language Models (LLMs) in long-generation tasks. Prior work often applies uniform co

researcharxiv-cs-ai

Moment Matching Q-Learning

arXiv:2605.29033v1 Announce Type: new Abstract: Score-based and flow-based generative models exhibit remarkable expressive capacity in capturing complex distributions, and have been extensively deploy

researcharxiv-cs-lg

Local Ai

Multi-Scale Local Speculative Decoding for Image Generation

arXiv:2601.05149v2 Announce Type: replace Abstract: Autoregressive (AR) models have achieved remarkable success in image synthesis, yet their sequential nature imposes significant latency constraints.

local-aiarxiv-cs-cv

Tutorials

NeuROK: Generative 4D Neural Object Kinematics

arXiv:2605.30347v1 Announce Type: new Abstract: Data-driven approaches have revolutionized 3D vision, enabling transformers to effectively reconstruct and generate static 3D objects. However, generati

tutorialsarxiv-cs-cv

Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning

arXiv:2605.29592v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) with pre-trained models (PTMs) aims to sequentially adapt PTMs to new categories without forgetting old knowledge. Buil

researcharxiv-cs-cv

Obfuscation Rules for Detecting and Detoxifying Korean Toxicity

arXiv:2510.10961v3 Announce Type: replace-cross Abstract: As language models become increasingly deployed in online environments, toxicity detection and detoxification have received growing attention.

OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

arXiv:2605.29250v1 Announce Type: cross Abstract: Real-world information needs require access to structurally diverse knowledge sources, from unstructured text and relational tables to knowledge graph

Optimizing Latent Representations for Robust Building Damage Assessment Onboard Earth Observation Satellites

model-releasesarxiv-cs-cv

arXiv:2605.29575v1 Announce Type: new Abstract: Rapid identification of damaged buildings after natural disasters or on war areas is crucial to support emergency response and prioritize interventions.

P^2RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top-k Retrieval

arXiv:2603.14778v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy c

Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data

arXiv:2605.29058v1 Announce Type: new Abstract: Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support. Baymex is a re

applicationsarxiv-cs-lg

PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding

arXiv:2605.30126v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elasti

PhoneWorld: Scaling Phone-Use Agent Environments

arXiv:2605.29486v1 Announce Type: cross Abstract: A central bottleneck for phone-use agents is that controllable, reproducible environments covering real mobile behavior are hard to build at scale. Ex

Planning with the Views via Scene Self-Exploration

arXiv:2605.29563v1 Announce Type: new Abstract: Can VLMs predict how each camera move changes the view, and plan many such moves ahead? We call this capability view planning, requiring (1)understandin