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Hypothesis Testing with Conditional Queries: Learnability and the Value of Interaction

arXiv:2608.06262v1 Announce Type: new Abstract: Model evaluations may fix all tests before observing any responses or select later tests using earlier responses. We study this choice in a conditional-

safetyarxiv-cs-lg

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HyTBE: Hyperbolic Target-Background Expert Model for Cross-Domain Infrared Small Target Detection

DGX agent

arXiv:2608.05771v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) has achieved substantial progress under domain-consistent evaluation, yet detector performance often degrades

researcharxiv-cs-ai

7 Aug 2026

Agents

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

arXiv:2608.06161v1 Announce Type: new Abstract: Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptu

agentsarxiv-cs-ai

Hardware

IcFuzz: Fuzzing Isaac Sim with Semantic Stage Guidance and Multi-level Mutation

arXiv:2608.06088v1 Announce Type: new Abstract: Robotics simulators serve as a foundational infrastructure for embodied AI, facilitating safe and scalable robotic system development. NVIDIA Isaac Sim

hardwarearxiv-cs-ro

IDperturb: Enhancing Variation in Synthetic Face Generation via Angular Perturbation

arXiv:2602.18831v2 Announce Type: replace Abstract: Synthetic data has emerged as a practical alternative to authentic face datasets for training face recognition (FR) systems, especially as privacy a

applicationsarxiv-cs-cv

Tutorials

IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games

arXiv:2608.05422v1 Announce Type: new Abstract: While many algorithms blend reinforcement learning (RL) with counterfactual regret (CFR) methods to leverage tradeoffs in computational speed and perfor

tutorialsarxiv-cs-lg

IMMENSE: Inductive Multi-perspective User Classification in Social Networks

arXiv:2608.05259v1 Announce Type: cross Abstract: Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content. Young users, in particular, are

applicationsarxiv-cs-ai

Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks

arXiv:2608.05867v1 Announce Type: new Abstract: The use of ontologies and knowledge graphs is becoming increasingly widespread in the defence and national security domain. Numerous ontologies have bee

safetyarxiv-cs-ai

Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints

arXiv:2608.06265v1 Announce Type: new Abstract: Synthetic clinical benchmarks for enterprise AI agents can pass existing utility checks and still remain structurally unrealistic, especially in privacy

Local Ai

In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion

arXiv:2608.05237v1 Announce Type: cross Abstract: Current few-step autoregressive video diffusion models depend on previous fully denoised clean frames as context for all denoising steps of the curren

local-aiarxiv-cs-ai

In-Context VLA: Endowing Vision-Language-Action Models with Language via In-Context Post-Training and Agentic Tool Use

arXiv:2608.05738v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become the dominant recipe for generalist manipulation, yet they are almost universally trained by behavior clo

safetyarxiv-cs-ro

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation

arXiv:2608.05210v1 Announce Type: cross Abstract: Picture books and comics have long been used to disseminate hateful narratives because they are easily understood even by children, as exemplified by

Local Ai

Innovation-Residual Auditing of Autonomous Analysis Agents: Localization, Detection Limits, Error Control, and Identifiability

arXiv:2608.05490v1 Announce Type: new Abstract: Autonomous agents now carry out entire data analyses, selecting cohorts, joining tables, and fitting models with little step-by-step supervision. When s

local-aiarxiv-cs-ai

Inspecting Training Dynamics of Similarity Development in Supervised Vision Networks

arXiv:2505.21338v2 Announce Type: replace Abstract: For trustworthy and human-aware artificial intelligence, models should be evaluated beyond accuracy, among others through error predictability and s

safetyarxiv-cs-cv

Integrating Human Linguistic Insights into AI: Theory-Driven Representation for Multilingual Text-to-Speech

arXiv:2204.07228v2 Announce Type: replace Abstract: This paper explores the integration of human linguistic insights into multilingual text-to-speech (TTS) systems by evaluating the Featurally Undersp

researcharxiv-cs-cl

Tutorials

Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis

arXiv:2608.06037v1 Announce Type: new Abstract: Relational inductive biases are essential for capturing structural dependencies among data. This study investigates a dual-level relational framework fo

tutorialsarxiv-cs-ai

Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising

arXiv:2510.05589v3 Announce Type: replace-cross Abstract: Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices. However, the v

Investigating Artificial Intelligence Digital Sovereignty in Mobile Shopping Apps: A Case Study of Nigeria

arXiv:2608.06364v1 Announce Type: cross Abstract: The use of e-commerce mobile applications is expanding in Nigeria, creating both opportunities and risks, including fraud and reduced user control ove

applicationsarxiv-cs-ai

Invisible Shortcuts: Why Vision Encoders Know Your Camera

arXiv:2608.05424v1 Announce Type: new Abstract: Deep vision models exploit shortcuts, relying on cues that correlate with supervision signals. Prior work has focused on visible biases, such as object-

researcharxiv-cs-cv

IPV-Bench: Benchmarking Image Protection Methods under Diverse Image-to-Video Generation Scenarios

model-releasesarxiv-cs-cv

arXiv:2603.26154v2 Announce Type: replace Abstract: Image-to-video (I2V) generation models can be misused to animate a single image into a convincing fake video, motivating perturbation-based image pr

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

arXiv:2608.06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether simi

researcharxiv-cs-ai

Iterate or Widen? When Test-Time Refinement Helps LiDAR Scene Completion: A Controlled Study of Evidence Geometry, Training Coverage, and Compute

model-releasesarxiv-cs-cv

arXiv:2608.06014v1 Announce Type: new Abstract: Should a completion model spend extra test-time compute by iterating, or spend a similar parameter budget on a wider one-shot predictor? The answer is e

Local Ai

Iterative Hybrid Discrete-Continuous Viewpoint Planning for UAV Photogrammetry

arXiv:2608.05718v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) photogrammetry requires camera networks that provide sufficient surface coverage, image overlap, parallax, and resolution,

local-aiarxiv-cs-cv

JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment

model-releasesarxiv-cs-ro

arXiv:2608.05674v1 Announce Type: new Abstract: Robot data is scarce, so generalist policies need to learn from heterogeneous sources, including human egocentric video, simulation, and real robots, wh

JTA: Joint Testability Architecture for Scenario-Based Validation of Safety-Critical Software

arXiv:2608.05594v1 Announce Type: cross Abstract: Validation adequacy in safety-critical software depends on more than the system under test. Critical scenarios must be constructed under controlled co

safetyarxiv-cs-ro

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

model-releasesarxiv-cs-lg

arXiv:2608.06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Eq

KILVO: Kinematic-Inertial-LiDAR-Visual Odometry with Robust Multimodal Adaptation for Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2608.05647v1 Announce Type: new Abstract: This article presents a kinematic-inertial-LiDAR-visual odometry for humanoid robots, called KILVO. Tailored to the platform features, requirements, and

KV-Skill: Forging Expertise in the Model's Native Language

model-releasesarxiv-cs-lg

arXiv:2608.05475v1 Announce Type: new Abstract: Task knowledge is commonly stored either as text in the prompt or as an update to model weights. Text is modular but must be interpreted on every use, w

KVAE: Family of Tokenizers for Multimodal Generative Models

arXiv:2608.05798v1 Announce Type: new Abstract: Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation. This dependency positions t

researcharxiv-cs-cv

LangChoiceBench: Measuring and Explaining Programming-Language Choice in LLMs

model-releasesarxiv-cs-cl

arXiv:2608.06041v1 Announce Type: cross Abstract: Large language models (LLMs) have been shown to exhibit strong Python preferences when generating project-level code, but there is currently no system

Large Language Models Threaten Double-blind Review

arXiv:2608.05157v1 Announce Type: cross Abstract: Double blind peer review serves as the scientific community primary defense against status and affiliation bias. Its effectiveness rests on the assump

safetyarxiv-cs-ai

Latent Utility Q-Learning for Preference-Adaptive Dynamic Treatment Regimes

arXiv:2307.12022v3 Announce Type: replace-cross Abstract: Optimizing individualized treatment sequences for patients who weigh multiple, competing outcomes differently poses a challenge for dynamic tr

safetyarxiv-cs-lg

LAWM-3D: Learning 3D-Aware Latent Actions from Human Videos for Generalizable Robot World Models

arXiv:2608.05706v1 Announce Type: new Abstract: World models enable agents to perform forward rollout and planning without real-world interaction. However, their application in open-world embodied int

safetyarxiv-cs-cv

Layer-wise Positional Bias in Short-Context Language Modeling

arXiv:2601.04098v2 Announce Type: replace-cross Abstract: Transformer language models systematically prefer tokens at specific input positions regardless of semantic relevance---a phenomenon known as

LC-GRPO: Bridging Train-Inference Gap for Flow-Based GRPO with Langevin Correction

arXiv:2608.05600v1 Announce Type: cross Abstract: Flow-based generative models are typically sampled by solving a deterministic ordinary differential equation (ODE), whereas online reinforcement learn

safetyarxiv-cs-ai

LC-Implicit-QAOA: Active-Workspace-Capped Exact Objective-and-Gradient Evaluation for Training over Bounded QUBO Light Cones

arXiv:2608.05610v1 Announce Type: cross Abstract: QAOA training repeatedly queries an objective and all shared gradients, making exact evaluation a feasibility bottleneck even when QUBO terms have bou

researcharxiv-cs-lg

Agents

Learning Context-Free Grammars for Grammar-Constrained Decoding via Declarative Agentic Programming with Guarantees

arXiv:2608.05493v1 Announce Type: cross Abstract: Language models (LMs) are increasingly used to interact with external services via programs written in domain-specific languages (DSLs). Unfortunately

agentsarxiv-cs-ai

Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval

arXiv:2608.06060v1 Announce Type: new Abstract: Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-

researcharxiv-cs-cv

Agents

Learning Globally Reusable Skills for Coding Agents

arXiv:2608.06153v1 Announce Type: cross Abstract: Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches

agentsarxiv-cs-ai

Hardware

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

arXiv:2608.05819v1 Announce Type: new Abstract: Classical simulation remains essential for developing and validating quantum algorithms, but its cost grows rapidly with circuit size. Tensor-network co

hardwarearxiv-cs-lg

Learning visual representations for compositional analysis of artworks and photographs

arXiv:2608.06142v1 Announce Type: new Abstract: Composition, the deliberate arrangement of visual elements, is central to how meaning, emotion, and aesthetic quality are conveyed in artwork, yet it re

safetyarxiv-cs-cv

Learning When to Trust via Selective Context Preference Optimization

arXiv:2608.06377v1 Announce Type: cross Abstract: Language models increasingly condition their answers on external signals, and a single misleading one can turn a correct answer wrong. The obvious rem

LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation

arXiv:2601.05192v2 Announce Type: replace Abstract: Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construc

researcharxiv-cs-cl

LILAC: An Idempotent Neural Speech Codec

arXiv:2608.05727v1 Announce Type: cross Abstract: Neural Audio Codecs are widely adopted in speech generation and editing. However, existing neural audio codecs are not idempotent: across the paper's

researcharxiv-cs-lg

LiteKD-Net: Lightweight Knowledge-Distilled Network for Mobile Image Denoising

arXiv:2608.05739v1 Announce Type: new Abstract: Mobile image denoising requires both good restoration quality and low computational cost. In addition, it's annoying to collect large-scale LQ-GT clean

applicationsarxiv-cs-cv

LLM Inference Under Bursty Workload Distribution: Modifying the WAIT Algorithm

model-releasesarxiv-cs-lg

arXiv:2608.06135v1 Announce Type: new Abstract: Large Language Models (LLMs) such as ChatGPT and Claude are widely used for information retrieval and problem-solving. Recent work has focused on improv

LoDA: A Level of Detection Aware Method and a Multimodal Sensing Benchmark for Object Level Change Detection

model-releasesarxiv-cs-cv

arXiv:2608.05356v1 Announce Type: new Abstract: High-definition 3D LiDAR maps are important for autonomous driving and smart-city services, which require reliable detection of object-level changes in

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

arXiv:2604.01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visu