Search: “models”

VISTA: A Benchmark for Real-Time Video Streaming under Network Impairments in Surgical Teleoperation

model-releasesarxiv-cs-ro

arXiv:2605.08886v1 Announce Type: cross Abstract: Real-time video streaming is crucial in surgical teleoperation, yet reproducible evaluation under realistic network impairments remains limited. This

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Tutorials

Voice Biomarkers for Depression and Anxiety

DGX agent

arXiv:2605.09908v1 Announce Type: cross Abstract: Current approaches to detecting depression and anxiety from speech primarily rely on machine learning techniques that utilize hand-engineered paraling

tutorialsarxiv-cs-ai

12 May 2026

Wavelet Policy: Imitation Learning in the Scale Domain with World Prior Memory

arXiv:2504.04991v4 Announce Type: replace Abstract: Conventional visuomotor imitation learning usually predicts future robot actions directly in the time domain. Such formulations often have limited p

safetyarxiv-cs-ro

Why is prompting hard? Understanding prompts on binary sequence predictors

arXiv:2502.10760v2 Announce Type: replace Abstract: Frontier models can be prompted or conditioned to do many tasks, but finding good prompts is not always easy, nor is understanding some performant p

researcharxiv-cs-cl

Agents

Workspace Optimization: How to Train Your Agent

arXiv:2605.09650v1 Announce Type: new Abstract: Modern agents built on frontier language models often cannot adapt their weights. What, then, remains trainable? We argue it is the agent's workspace, t

agentsarxiv-cs-ai

Your Simulation Runs but Solves the Wrong Physics: PDE-Grounded Intent Verification for LLM-Generated Multiphysics Simulation Code

arXiv:2605.09360v1 Announce Type: cross Abstract: Execution-based evaluation of LLM-generated code implicitly treats successful execution as a proxy for correctness. In scientific simulation, this pro

Z-Erase: Enabling Concept Erasure in Single-Stream Diffusion Transformers

arXiv:2603.25074v2 Announce Type: replace Abstract: Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Ne

safetyarxiv-cs-cv

Zero-Shot Chinese Character Recognition via Global-Local Dual-Branch Alignment and Hierarchical Inference

model-releasesarxiv-cs-cv

arXiv:2605.08814v1 Announce Type: new Abstract: Chinese character categories are extremely large, and unseen characters frequently arise in open-world scenarios, making zero-shot Chinese character rec

Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers

arXiv:2605.07892v1 Announce Type: new Abstract: Sparse training reduces the memory and computational costs of deep neural networks. However, sparse optimization methods, e.g., those adding an ell_1 pe

AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents

arXiv:2605.06607v2 Announce Type: replace-cross Abstract: Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemi

Amortized Molecular Optimization via Group Relative Policy Optimization

arXiv:2602.12162v3 Announce Type: replace Abstract: In structurally constrained molecular optimization, state-of-the-art methods restart an expensive oracle-driven search from scratch for every new in

Attention Transfer Is Not Universally Effective for Vision Transformers

model-releasesarxiv-cs-cv

arXiv:2605.07191v1 Announce Type: new Abstract: A recent work shows that Attention Transfer, which transfers only the attention patterns from a pre-trained teacher Vision Transformer (ViT) to a random

Attribution-Based Neuron Utility for Plasticity Restoration in Deep Networks

arXiv:2605.06834v1 Announce Type: new Abstract: Continual learning research attempts to conserve two fundamental capabilities: new knowledge acquisition and the preservation of previously acquired kno

Beyond 'I cannot fulfill this request': Alleviating Rigid Rejection in LLMs via Label Enhancement

arXiv:2605.07883v1 Announce Type: new Abstract: Large Language Models (LLMs) rely on safety alignment to obey safe requests while refusing harmful ones. However, traditional refusal mechanisms often l

safetyarxiv-cs-cl

BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation

arXiv:2605.07306v1 Announce Type: cross Abstract: Biological laboratory automation can reduce repetitive manual work and improve reproducibility, but reliable embodied execution in wet-lab environment

CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation

arXiv:2605.08057v1 Announce Type: cross Abstract: While recent advancements in inference-time learning have improved LLM reasoning on Text-to-SQL tasks, current solutions still struggle to perform wel

CASCADE: Context-Aware Relaxation for Speculative Image Decoding

arXiv:2605.07230v1 Announce Type: cross Abstract: Autoregressive generation is a powerful approach for high-fidelity image synthesis, but it remains computationally demanding and slow even on the most

researcharxiv-cs-ai

CommandSwarm: Safety-Aware Natural Language-to-Behavior-Tree Generation for Robotic Swarms

model-releasesarxiv-cs-ro

arXiv:2605.07764v1 Announce Type: new Abstract: Natural-language interfaces can make swarm robotics more accessible to non-expert operators, but they must translate ambiguous user intent into executab

Convergent Stochastic Training of Attention and Understanding LoRA

arXiv:2605.07959v1 Announce Type: new Abstract: Transformers have revolutionized machine learning and deploying attention layers in the model is increasingly standard across a myriad of applications.

researcharxiv-cs-lg

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences

arXiv:2605.07724v1 Announce Type: cross Abstract: Recursive retraining of generative models poses a critical representation challenge: when synthetic outputs are curated based on a fixed reward signal

safetyarxiv-cs-ai

Diffusion-APO: Trajectory-Aware Direct Preference Alignment for Video Diffusion Transformers

arXiv:2605.07503v1 Announce Type: new Abstract: Efficiently aligning large-scale video diffusion models with human intent requires a scalable and trajectory-aware pathway that bridges the inherent dis

safetyarxiv-cs-cv

Disagreement-Regularized Importance Sampling for Adversarial Label Corruption

arXiv:2605.07551v1 Announce Type: new Abstract: Standard Importance Sampling (IS) collapses under label corruption because high-norm examples, prioritized for variance reduction, are often adversarial

Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation

arXiv:2605.07323v1 Announce Type: new Abstract: Discovering governing differential equations from observational data is a fundamental challenge in scientific machine learning. Existing symbolic regres

DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain

arXiv:2605.07699v1 Announce Type: cross Abstract: LLM-based agents are increasingly deployed for routine but consequential tasks in real-world domains, where their behavior is governed by inherently a

Local Ai

EditRefiner: A Human-Aligned Agentic Framework for Image Editing Refinement

arXiv:2605.07457v1 Announce Type: new Abstract: Recent text-guided image editing (TIE) models have made remarkable progress, yet edited images still frequently suffer from fine-grained issues such as

local-aiarxiv-cs-cv

Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis

arXiv:2605.07888v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralised model training across distributed clients without requiring data centralisation. However, the generalisa

researcharxiv-cs-cv

EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation

arXiv:2605.07247v1 Announce Type: new Abstract: Scalable AI agents training relies on interactive environments that faithfully simulate the consequences of agent actions. Manually crafted environments

Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics

arXiv:2512.12602v4 Announce Type: replace Abstract: In this paper, we introduce Exact Flow Linear Attention~(EFLA), an exact-flow formulation of delta-rule linear attention. We show that the delta-rul

Exact Is Easier: Credit Assignment for Cooperative LLM Agents

arXiv:2603.06859v2 Announce Type: replace-cross Abstract: Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi-agent LLM systems this evaluation distorts t

ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression

arXiv:2605.07501v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance via extended chain-of-thought (CoT) reasoning, yet suffer from excessive token consumption an

researcharxiv-cs-cl

From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG

arXiv:2605.07273v1 Announce Type: cross Abstract: Multimodal RAG systems increasingly rely on vision-language retrievers to ground visual queries in external textual evidence. Existing adversarial stu

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction

arXiv:2605.08022v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networ

Goal-Conditioned Decision Transformer for Multi-Goal Offline Reinforcement Learning

arXiv:2410.06347v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) in robotics faces significant hurdles regarding sample efficiency and generalization across varying goals. While O

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction

arXiv:2605.07999v1 Announce Type: cross Abstract: Multiphoton photoreduction enables high-fidelity fabrication of complex 3D microstructures, yet reliable process-structure-property (PSP) prediction r

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations

arXiv:2605.07053v1 Announce Type: cross Abstract: Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed

Hierarchical Task Network Planning with LLM-Generated Heuristics

arXiv:2605.07707v1 Announce Type: new Abstract: HTN planning is a variation of classical planning where, instead of searching for a linear sequence of actions, an algorithm decomposes higher-level tas

Implicit Preference Alignment for Human Image Animation

arXiv:2605.07545v1 Announce Type: cross Abstract: Human image animation has witnessed significant advancements, yet generating high-fidelity hand motions remains a persistent challenge due to their hi

Inference-Time Attribute Distribution Alignment for Unconditional Diffusion

arXiv:2605.07456v1 Announce Type: new Abstract: Inference-time controllable generation is essential for real-world applications of unconditional diffusion models. However, most existing techniques foc

safetyarxiv-cs-lg

Local Ai

Instruction Tuning Changes How Upstream State Conditions Late Readout: A Cross-Patching Diagnostic

arXiv:2605.07284v1 Announce Type: new Abstract: Recent interpretability work has identified model-internal handles on post-trained behavior, including refusal directions, assistant/persona axes, and s

local-aiarxiv-cs-lg

Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation

model-releasesarxiv-cs-cl

arXiv:2605.07613v1 Announce Type: new Abstract: Conversational news recommendation requires grounding each suggestion in a rapidly evolving article corpus while addressing implicit user intents that l

InterLV-Search: Benchmarking Interleaved Multimodal Agentic Search

model-releasesarxiv-cs-cl

arXiv:2605.07510v1 Announce Type: cross Abstract: Existing benchmarks for multimodal agentic search evaluate multimodal search and visual browsing, but visual evidence is either confined to the input

Knowledge Transfer Scaling Laws for 3D Medical Imaging

arXiv:2605.06859v1 Announce Type: cross Abstract: Vision foundation models are increasingly moving beyond 2D to volumetric domains such as 3D medical imaging, where unified pretraining across differen

researcharxiv-cs-ai

Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents

arXiv:2605.06957v1 Announce Type: new Abstract: We present a dynamic policy-learning approach that combines generalized planning and hierarchical task decomposition for LLM-based agents. Our method, H

Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations

arXiv:2605.07460v1 Announce Type: new Abstract: Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observe

researcharxiv-cs-lg

Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis

arXiv:2511.09907v5 Announce Type: replace Abstract: Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-q

researcharxiv-cs-ai

Local Ai

LiteGUI: Distilling Compact GUI Agents with Reinforcement Learning

arXiv:2605.07505v1 Announce Type: new Abstract: Developing lightweight, on-device vision-language GUI agents is essential for efficient cross-platform automated interaction. However, current on-device

local-aiarxiv-cs-ai

MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge

arXiv:2507.21183v5 Announce Type: replace-cross Abstract: As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with

safetyarxiv-cs-ai

Mask2Cause: Causal Discovery via Adjacency Constrained Causal Attention

arXiv:2605.07280v1 Announce Type: cross Abstract: Leveraging deep learning for causal discovery in time series remains challenging because existing neural methods predominantly rely on component-wise