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ClosureBench: A Constructive Benchmark for Compositional Graph Reasoning

model-releasesarxiv-cs-lg

arXiv:2608.18242v1 Announce Type: new Abstract: We introduce ClosureBench, a constructive benchmark for compositional graph-relational reasoning with programmatically verified ground truth. Unlike fix

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Comment-level Topic Drift Analysis in the Reddit Corpus

DGX agent

arXiv:2608.19133v1 Announce Type: new Abstract: We present a novel application of embedding-based dynamic topic modeling techniques to detect and quantify topic drift at the comment level in a massive

researcharxiv-cs-cl

20 Aug 2026

Communications-Aware NMPC for Multi-Rotor Aerial Relay Networks Under Jamming Interference

arXiv:2603.28467v2 Announce Type: replace Abstract: Multi-Rotor Aerial Vehicles (MRAVs) are increasingly used in communication-dependent missions where connectivity loss directly compromises task exec

safetyarxiv-cs-ro

Competence, Not Accuracy: A Diagnostic for Reference-Free Judge Gates in Skill Optimization

arXiv:2608.18719v1 Announce Type: new Abstract: Text-space skill optimization adapts a frozen agent by evolving a natural-language skill document, accepting each candidate through a validation gate. E

Compiler-Guided Adaptive Proof Search with Cross-Model Synergy on Context-Dependent Theorem Proving

arXiv:2608.18084v1 Announce Type: new Abstract: Theorem proving in real-world Lean 4 projects is challenging because proofs often depend on project-specific context. While iterative refinement can use

applicationsarxiv-cs-cl

ComponentBench: Diagnosing Component-Level Failures in Computer-Use Agents

arXiv:2608.18307v1 Announce Type: new Abstract: Current evaluation of computer-use agents is split between long-horizon workflow benchmarks and atomic GUI-grounding tests. This leaves an under-instrum

Tutorials

Composed Historical Image Retrieval by Modeling Temporal Representations

arXiv:2608.18694v1 Announce Type: cross Abstract: While time evolves linearly, the geometry of neural embedding spaces is inherently multi-dimensional, often chaotic, and difficult to interpret. In pr

tutorialsarxiv-cs-ai

Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs

model-releasesarxiv-cs-cl

arXiv:2608.18578v1 Announce Type: new Abstract: Proactive interference (PI) is a documented failure mode in large language models in which retrieval of a repeatedly overwritten value degrades as prior

Computational Measurement of Team-Process Phase Dynamics in Collaborative Virtual Reality

model-releasesarxiv-cs-lg

arXiv:2608.18660v1 Announce Type: new Abstract: Collaborative virtual reality (VR) environments make team communication observable as it unfolds, but conventional transcript analyses often summarize e

Computational Orientalism: Measuring Structural Discourse Bias in Large Language Models Using the Middle East Cultural Sensitivity Score (MECSS)

arXiv:2608.18100v1 Announce Type: cross Abstract: AI systems now shape how hundreds of millions of people learn about cultures other than their own. When someone asks one of these systems about the Mi

CoMVS-GS: Collaborative Multi-View Stereo and 3D Gaussian Splatting for Surface Reconstruction

arXiv:2608.18413v1 Announce Type: new Abstract: 3D Gaussian Splatting enables efficient novel view synthesis, but accurate mesh reconstruction remains difficult in weakly observed and occluded regions

researcharxiv-cs-cv

ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety

arXiv:2508.20468v2 Announce Type: replace Abstract: Conspiracy theories erode public trust in science and institutions while resisting debunking by evolving and absorbing counter-evidence. As AI-gener

safetyarxiv-cs-cl

Continual Reasoning Gym: Diagnosing and Harnessing Shared Reasoning in Continual RLVR

arXiv:2608.18574v2 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) commonly post-trains reasoning models on multiple tasks, while rerunning multitask RLVR (MTRL) as

safetyarxiv-cs-lg

Continuous-Time Reinforcement Learning for Controlled Hawkes Jump-Diffusions

arXiv:2608.19151v1 Announce Type: new Abstract: We study stochastic control of multivariate Hawkes-driven stochastic differential equations with machine learning algorithms in a non-Markovian setting.

safetyarxiv-cs-lg

Contrasting Cost-Agnostic and Cost-Sensitive Losses under Limited Model Capacity via mathcal H-consistency

arXiv:2502.19522v2 Announce Type: replace Abstract: There is a prevalent debate in machine learning about whether practitioners should train models to optimize a task-agnostic objective (e.g., cross e

applicationsarxiv-cs-lg

Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline

arXiv:2608.18911v1 Announce Type: new Abstract: We introduce the CDSP (context-conditional deliberation signal pipeline), converting an investment committee's meeting transcripts into structured predi

Coordination on a Budget: Federated Active Learning with Few Labels

arXiv:2608.18634v1 Announce Type: new Abstract: Federated Active Learning (FAL) addresses the dual challenges of data privacy and label scarcity, where the absence of a global data view introduces add

Corrections of Zipf's and Heaps' Laws Derived from Hapax Rate Models

arXiv:2307.12896v5 Announce Type: replace Abstract: The article introduces corrections to Zipf's and Heaps' laws based on systematic models of the proportion of hapaxes, i.e., words that occur once. T

researcharxiv-cs-cl

COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts

arXiv:2608.18479v1 Announce Type: new Abstract: Semantic segmentation of aerial point cloud is trapped in a generalization crisis under distinct domain shifts. While test-time adaptation offers a priv

researcharxiv-cs-cv

Counterfactual Behavior Cloning: Offline Imitation Learning from Imperfect Human Demonstrations

arXiv:2505.10760v2 Announce Type: replace Abstract: Learning from humans is challenging because people are imperfect teachers. When everyday humans show the robot a new task they want it to perform, h

safetyarxiv-cs-ro

Counterfactual Contrastive Analysis

arXiv:2608.19032v1 Announce Type: cross Abstract: Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an input image tha

Local Ai

Coupled-cluster molecular properties across the main group that extrapolate beyond training size

arXiv:2608.18346v1 Announce Type: cross Abstract: Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whe

local-aiarxiv-cs-ai

Coverage-Driven RTL Assertion Generation with Formal Exploration and Neuro-Symbolic Refinement

arXiv:2608.18482v1 Announce Type: cross Abstract: Hardware functional verification relies on high-quality assertions to expose design bugs and establish confidence in Register Transfer Level (RTL) des

agentsarxiv-cs-ai

Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors

arXiv:2608.18515v1 Announce Type: new Abstract: Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing stu

researcharxiv-cs-cv

CTIFoundry: An Agent-Native Corpus Scaffold for Cyber Threat Intelligence

arXiv:2608.18613v1 Announce Type: new Abstract: Cyber threat intelligence (CTI) is increasingly consumed not by human analysts but by LLM agents that compose multi-step investigations at query time. T

DA-WAM: Decision-Aligned Future Latents for Driving World Models

arXiv:2608.19085v1 Announce Type: cross Abstract: Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full potential of world models for decision-making

DART-SD: Diamond-topology Aware Retrieval and Tuning for Self-Distillation of Multi-Turn Tool-Calling Agents

arXiv:2608.18524v1 Announce Type: cross Abstract: Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fun

Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements

arXiv:2608.18294v1 Announce Type: cross Abstract: An increasing number of scholars use AI to measure variables they subsequently include in downstream analyses. Although AI-measured variables are ofte

Decision-Metric Alignment in Latent World Models: Diagnostics and Action-Conditioned Objectives for MPC Planning

arXiv:2608.18746v1 Announce Type: cross Abstract: JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task var

safetyarxiv-cs-cv

Decomposing Wrong-Consensus Agreement in LLM Self-Consistency: A GPT-4.1 Case Study

arXiv:2608.18795v1 Announce Type: cross Abstract: Majority voting over multiple LLM samples is widely used to raise answer accuracy, yet its gain varies erratically: on hard questions it can even back

DeepTCM1.0: A Multi-Expert AI Agent for Deciphering Mechanisms of Chinese Herbal Formulae Based on General Large Language Models

arXiv:2608.18103v1 Announce Type: cross Abstract: Background: Mechanistic elucidation of traditional Chinese medicine (TCM) compound formulas remains a central challenge in the modernization of TCM. C

DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering

arXiv:2608.18988v1 Announce Type: cross Abstract: Retrieve-then-generate pipelines are commonly used to produce deep-research answers for open-ended questions, but retrieval alone is insufficient: LLM

DeGLIF for Label Noise Robust Node Classification using GNNs

arXiv:2506.00244v2 Announce Type: replace Abstract: Noisy labelled datasets are generally inexpensive compared to clean labelled datasets, and the same is true for graph data. In this paper, we propos

Denoising-Aware Inversion: Revealing Privacy Risks in Noise-Protected Text Embeddings

arXiv:2608.18610v1 Announce Type: cross Abstract: Dense text embeddings are widely used in data mining, retrieval, and downstream machine learning systems due to their compact and semantically rich re

DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning

arXiv:2608.18878v1 Announce Type: new Abstract: Oral diseases affect billions of people worldwide, underscoring a pressing need for accurate and reliable dental assessment that integrates heterogeneou

agentsarxiv-cs-ai

Hardware

Depth Anything V4: Dynamic 4D Scene Reconstruction via Riemannian Flow Matching on 4D Gaussian Splatting

arXiv:2608.18388v1 Announce Type: new Abstract: We present Depth Anything V4 (DAV4), a framework for dynamic 4D scene reconstruction from monocular video. Our key contribution is the application of Ri

hardwarearxiv-cs-cv

Designing Social Robots for Social-Cognition Training with Autistic Adults

arXiv:2608.18488v1 Announce Type: cross Abstract: Social robots have been widely explored as tools for autism intervention, yet this literature has focused predominantly on children and has rarely inv

applicationsarxiv-cs-ro

Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift

arXiv:2608.19088v1 Announce Type: cross Abstract: Object detection models deployed in safety-critical applications remain vulnerable to backdoor attacks that cause targeted misbehaviors when a hidden

DevGRU: Depth-guided Visual Navigation using a Collision-aware Recurrent Model

arXiv:2608.18470v1 Announce Type: new Abstract: Existing visual navigation models often aim to develop foundation models that can generalize robot navigation across diverse platforms. However, many of

researcharxiv-cs-ro

Different Facets of Verbalised Overconfidence: an Interpretability Study

arXiv:2608.18106v1 Announce Type: cross Abstract: Large language models tend to overconfidence, giving assertive answers when the evidence suggests hedging or abstention. Using controlled reasoning sc

Diffusion Models for High-Dimensional Clustered Data: Intrinsic-Dimension Adaptivity via Bayesian Classification

arXiv:2608.19067v1 Announce Type: cross Abstract: The empirical success of diffusion models in generative modelling has motivated theoretical work, including quantitative error bounds and qualitative

Discretizing Continuous Time Series for Imputation with Masked Diffusion Training

arXiv:2608.19119v1 Announce Type: cross Abstract: Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics and noise

applicationsarxiv-cs-ai

Do Large Language Models Hallucinate Electric Fata Morganas?

arXiv:2608.18816v1 Announce Type: cross Abstract: AI hallucinations - that is, outputs which are made up, cannot be verified, or contradict the source material - are generally regarded as an engineeri

DocClaw: A Unified Agentic System for Intelligent Document Processing

arXiv:2608.18685v1 Announce Type: new Abstract: Intelligent document processing (IDP) encompasses a broad range of tasks, including optical character recognition (OCR), document question answering (Do

agentsarxiv-cs-cv

Does Mapping Non-Maximal Probabilities to GMM Components Matter for S-JEPA Encoder Representations?

arXiv:2608.19084v1 Announce Type: new Abstract: S-JEPA uses soft Gaussian mixture model (GMM) posteriors instead of hard cluster labels to preserve uncertainty. It remains unclear whether the probabil

Dream2Reward: Transition-Alignment Reward Models from Positive Demonstrations for Robotic Manipulation

arXiv:2608.18787v1 Announce Type: new Abstract: Learning robotic policies requires dense rewards that remain informative when behavior departs from successful demonstrations. Progress-based rewards es

safetyarxiv-cs-ro

DyG^2T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer

arXiv:2608.18498v1 Announce Type: new Abstract: Modeling object dynamics from limited visual observations is a fundamental problem for enabling accurate motion trajectory prediction in embodied intera

applicationsarxiv-cs-cv

Dynamic SpectraFormer for Ultra-High-Definition Underwater Image Enhancement

arXiv:2608.18662v1 Announce Type: new Abstract: Underwater images suffer from color distortion, haze, and poor visibility due to light refraction and absorption in water. These challenges significantl

agentsarxiv-cs-cv