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Trading Human Curation for Synthetic Augmentation in RLVR

arXiv:2606.03800v1 Announce Type: cross Abstract: The supply of high-quality training tasks is a central bottleneck for reinforcement learning from verifiable rewards (RLVR) on agentic language models

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Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention

DGX agent

arXiv:2509.22854v2 Announce Type: replace Abstract: Implicit in-context learning (ICL) has newly emerged as a promising paradigm that simulates ICL behaviors in the representation space of large langu

safetyarxiv-cs-cl

3 Jun 2026

Training a Predictive Coding Network on ImageNet using Equilibrium Propagation

arXiv:2606.03584v1 Announce Type: new Abstract: Equilibrium Propagation (EP) is a physics-based training framework that has primarily been employed in energy-based models, including continuous Hopfiel

Applications

Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting

arXiv:2606.03792v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual

applicationsarxiv-cs-cv

Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection

arXiv:2606.02812v1 Announce Type: new Abstract: Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequ

agentsarxiv-cs-ai

Trajectory-Aware Node Contributions and the Limits of Static Controllability

arXiv:2606.03067v1 Announce Type: cross Abstract: A recurring data mining task in complex networks is to determine how individual nodes contribute to system behavior. Existing approaches rely on eithe

Trans GAN-WT: A Feature Extraction and Interactive Learning-Based Anomaly Detection Model for Wind Turbine Time Series Data

arXiv:2606.03112v1 Announce Type: cross Abstract: With the increasing scale and number of wind farms, wind turbines' daily operation and maintenance costs are increasing. To reduce operation and maint

Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing

arXiv:2509.03376v2 Announce Type: replace Abstract: Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundanc

tutorialsarxiv-cs-cv

Translating Classical Poetry into Modern Prose

arXiv:2606.02806v1 Announce Type: new Abstract: We introduce Padyam2Gadyam, a dataset for the task of poem-to-prose translation from 13th-17th Century Telugu Classical Poetry to contemporary Telugu an

researcharxiv-cs-cl

Applications

TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches

arXiv:2603.23117v2 Announce Type: cross Abstract: By integrating Chain-of-Thought (CoT) reasoning, Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, pa

applicationsarxiv-cs-ai

TreeFlash: Parallel AR-Approximation for Faster Speculative Decoding

arXiv:2606.03819v1 Announce Type: new Abstract: One-shot block drafters for speculative decoding generate the full draft in a single forward pass, achieving strong throughput by eliminating sequential

TriEval: A Resource-Efficient Pipeline for LLM Bias, Toxicity, and Truthfulness Assessment

arXiv:2606.03036v1 Announce Type: new Abstract: LLMs have evolved from basic chatbots to the backbone of the AI ecosystem, now widely used in healthcare, schools, and government services. The domain-w

TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning

arXiv:2606.03629v1 Announce Type: new Abstract: Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently,

TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models

arXiv:2606.03127v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models trained on large-scale data have made remarkable progress, but they remain vulnerable to distribution shifts at depl

safetyarxiv-cs-ro

TurtleAI: Benchmarking Multimodal Models for Visual Programming in Turtle Graphics

arXiv:2606.03626v1 Announce Type: cross Abstract: Vision-language models (VLMs) have been explored for visual programming, where they generate code to solve visual tasks. However, most prior work focu

Two-Action Apple Tasting with Switching Costs

arXiv:2606.03851v1 Announce Type: new Abstract: We study the two-action apple-tasting problem with switching costs against an oblivious adversary. In an equivalent normalized formulation, at each roun

Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models

arXiv:2303.15619v2 Announce Type: replace-cross Abstract: The choice of which tokens to mask is a central, under-examined design decision in masked language modeling (MLM). Standard pretraining masks

researcharxiv-cs-ai

Applications

Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

arXiv:2606.03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. The YOLO family has become widely deployed

applicationsarxiv-cs-ai

Uncertainty-Aware Clarification in LLM Agents with Information Gain

arXiv:2606.03135v1 Announce Type: new Abstract: Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous too

agentsarxiv-cs-ai

Unified Video-Action Joint Denoising for Dexterous Action and Data Generation

arXiv:2606.03868v1 Announce Type: new Abstract: Recent world action models leverage video foundation models by aligning broad visual-dynamics priors with executable robot actions. We revisit this alig

safetyarxiv-cs-cv

UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusion

arXiv:2606.03581v1 Announce Type: new Abstract: Unstructured scenes present unique challenges for autonomous driving, as irregular obstacles and sparse scene layouts undermine the effectiveness of tra

safetyarxiv-cs-cv

Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs

arXiv:2606.03719v1 Announce Type: new Abstract: The do-calculus defines a general system of inference for interventional queries, allowing causal quantities to be transformed through successive applic

researcharxiv-cs-ai

Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning

arXiv:2606.03962v1 Announce Type: cross Abstract: Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern applicati

safetyarxiv-cs-ai

Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift

arXiv:2606.03321v1 Announce Type: new Abstract: Artificial-intelligence surrogates can support second-by-second thermal-hydraulic forecasting, but models selected and frozen offline may become conditi

safetyarxiv-cs-lg

Value-Aware Stochastic KV Cache Eviction for Reasoning Models

arXiv:2606.03928v1 Announce Type: cross Abstract: Reasoning models improve accuracy through extended chains of thought, but their long outputs create a memory and compute bottleneck. KV cache eviction

researcharxiv-cs-cl

Video-Mirai: Autoregressive Video Diffusion Models Need Foresight

arXiv:2606.03971v1 Announce Type: new Abstract: Causal video generators must predict from the past, but they need not learn only from it. In streaming autoregressive video diffusion, each emitted segm

tutorialsarxiv-cs-cv

VidMsg: A Benchmark for Implicit Message Inference in Short Videos

arXiv:2606.03635v1 Announce Type: cross Abstract: Understanding short online videos involves more than identifying visible objects and actions; video makers often include an underlying message or purp

VistaHop: Benchmarking Multi-hop Visual Reasoning for Visual DeepSearch

arXiv:2606.03273v1 Announce Type: cross Abstract: Visual DeepSearch requires multimodal large reasoning model (MLRM) agents to answer complex visual queries by repeatedly inspecting image regions, gro

Visual Graph Scaffolds for Structural Reasoning in Large Language Models

arXiv:2606.02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at t

tutorialsarxiv-cs-ai

Local Ai

Visual Instruction Tuning Aligns Modalities through Abstraction

arXiv:2606.03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text. Yet, it remains unc

local-aiarxiv-cs-cl

VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models

model-releasesarxiv-cs-cv

arXiv:2512.22539v2 Announce Type: replace-cross Abstract: While Vision-Language-Action models (VLAs) are rapidly advancing towards generalist robot policies, it remains difficult to quantitatively und

VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring

model-releasesarxiv-cs-cv

arXiv:2606.03954v1 Announce Type: new Abstract: As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible conse

vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models

arXiv:2603.04444v3 Announce Type: replace-cross Abstract: As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- se

Local Ai

VulnAgent-R2: Evidence-Calibrated Multi-Agent Auditing for Repository-Level Vulnerability Detection

arXiv:2603.13384v2 Announce Type: replace-cross Abstract: Software vulnerabilities often depend on cross-file data flow, build options, framework conventions, and runtime guards, so isolated function

local-aiarxiv-cs-ai

WaterSIC: Information-Theoretically (Near) Optimal Linear Layer Quantization

model-releasesarxiv-cs-lg

arXiv:2603.04956v2 Announce Type: replace Abstract: This paper considers the problem of converting a given dense linear layer to low precision. The tradeoff between compressed length and output discre

Wavelet as Tokenizer: Preliminary Results on a Shared Wavelet Token Schema for Natural Signals

arXiv:2606.02631v1 Announce Type: cross Abstract: This paper studies whether audio, images, and video can share a common wavelet token schema rather than relying on separate modality-specific latent g

researcharxiv-cs-ai

Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning

arXiv:2509.19305v2 Announce Type: replace-cross Abstract: Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. Howev

Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance

arXiv:2601.22443v2 Announce Type: replace-cross Abstract: Can a diffusion model trained on bedrooms recover human faces? Diffusion models are widely used as priors for inverse problems, but standard a

researcharxiv-cs-cv

Local Ai

WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts

arXiv:2606.03220v1 Announce Type: cross Abstract: Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions

local-aiarxiv-cs-ai

What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents

arXiv:2606.02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceed

What Do Students Learn? A Feature-Level Analysis of Dark Knowledge

arXiv:2606.03052v1 Announce Type: new Abstract: Knowledge Distillation (KD) is a powerful tool for model compression, yet the precise mechanisms by which student models acquire feature representations

tutorialsarxiv-cs-lg

What Makes Interaction Trajectories Effective for Training Terminal Agents?

arXiv:2606.03461v1 Announce Type: new Abstract: Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumption remains poorly disentangled from task difficult

Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates

arXiv:2606.03265v1 Announce Type: new Abstract: Accurate and robust localization remains a fundamental challenge for autonomous ground vehicles. In this work, we propose a hybrid neural inertial navig

agentsarxiv-cs-ro

When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics

arXiv:2606.03569v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference. While vis

safetyarxiv-cs-ai

When Does Complexity Conditioning Help a Frozen Sentence Embedding? A Controlled Study of Per-Sentence and Pair-Level Difficulty Adaptation

arXiv:2606.03244v1 Announce Type: new Abstract: A common intuition is that sentence embeddings should adapt to the difficulty of the input. We test this intuition in a controlled, multi-seed setting:

researcharxiv-cs-cl

When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models

arXiv:2606.03712v1 Announce Type: new Abstract: Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph

safetyarxiv-cs-lg

When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning

arXiv:2606.02866v1 Announce Type: new Abstract: When does multi-agent debate help data cleaning, and when does it hurt? Across three benchmarks, four model families, and over 6,000 task-condition pair

agentsarxiv-cs-ai

When Model Merging Breaks Routing: Training-Free Calibration for MoE

arXiv:2606.03391v1 Announce Type: cross Abstract: Model merging has emerged as a cost-effective approach for consolidating the capabilities of multiple LLMs without retraining. However, existing mergi