Search: “evaluation”

Rapidly Learning Soft Robot Control via Implicit Time-Stepping

arXiv:2511.06667v2 Announce Type: replace-cross Abstract: With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies

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Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents

DGX agent

arXiv:2607.07405v1 Announce Type: new Abstract: Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully. In policy-permissive

model-releasesarxiv-cs-ai

9 Jul 2026

SoccerNet 2026 Challenges Results

arXiv:2607.07320v1 Announce Type: new Abstract: The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision rese

applicationsarxiv-cs-cv

Synthetic Time Series Generation via Complex Networks

arXiv:2601.22879v2 Announce Type: replace Abstract: Time series data are essential for a wide range of applications, yet access to high-quality datasets is often constrained by privacy concerns, acqui

applicationsarxiv-cs-lg

Thinking Ahead: Foresight Intelligence in MLLMs and World Model

arXiv:2511.18735v3 Announce Type: replace-cross Abstract: In this work, we define Foresight Intelligence as the capability to anticipate and interpret future events-an ability essential for applicatio

Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning

arXiv:2607.07117v1 Announce Type: cross Abstract: In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data

arXiv:2607.07471v1 Announce Type: cross Abstract: Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP)

Abductive Corroboration of Probabilistic AI Models for Forensic Synthetic Media Detection

arXiv:2607.05434v1 Announce Type: cross Abstract: Artificial Intelligence (AI) models, at their core, apply general learnings from broad datasets to individual circumstances using probabilistic behavi

applicationsarxiv-cs-cv

Andha-Dhun: A First Look at Audio Descriptions in Hindi

arXiv:2607.06457v1 Announce Type: new Abstract: Audio Descriptions (ADs) narrate visual content for Blind and Low Vision (BLV) audiences during gaps in audiovisual media. There is growing momentum aro

researcharxiv-cs-cv

Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation

arXiv:2607.01499v2 Announce Type: replace Abstract: Recent advances in Image-to-Video generation allow a single image to be animated into a convincing video under text guidance, raising serious copyri

researcharxiv-cs-cv

Automated Recommendation of Programming Learning Content Using Pattern-based Knowledge Components

arXiv:2607.05409v1 Announce Type: cross Abstract: Introductory programming instruction relies on hands-on practice and short learning activities to support mastery of foundational concepts. Although m

safetyarxiv-cs-ai

Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving

arXiv:2607.05399v1 Announce Type: cross Abstract: Large language model serving is increasingly limited by KV-cache growth under long-context workloads, yet existing KV-cache compression techniques are

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective

model-releasesarxiv-cs-cv

arXiv:2607.05783v1 Announce Type: new Abstract: Environmental illusions (eg., shadows, reflections, and tire marks) are naturally existing yet overlooked phenomena in real-world driving environments.

Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic Judgment

arXiv:2606.14948v2 Announce Type: replace-cross Abstract: LLMs have substantially improved software engineering yet real-world development requires architectural understanding. Such understanding is p

Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents

arXiv:2510.19771v4 Announce Type: replace Abstract: LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and so

CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries

arXiv:2607.05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.e., the ability to infer and adapt to a user's i

applicationsarxiv-cs-ai

CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming

arXiv:2607.05571v1 Announce Type: new Abstract: Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on

Detoxify: A framework for abusive text transformation using LLMs

arXiv:2507.10177v2 Announce Type: replace-cross Abstract: Although Large Language Models (LLMs) have demonstrated significant advancements in natural language processing tasks, their effectiveness in

Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift

arXiv:2607.05908v1 Announce Type: new Abstract: Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine

applicationsarxiv-cs-lg

Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs

arXiv:2607.06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English. We p

researcharxiv-cs-ai

FIELDS: Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision

arXiv:2511.21245v3 Announce Type: replace Abstract: Monocular 3D face reconstruction estimates a 3D morphable model (3DMM) representation from a single image, providing geometry-aware expression codes

researcharxiv-cs-cv

From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications

arXiv:2308.16041v2 Announce Type: replace Abstract: Talking head generation has progressed rapidly from landmark- and GAN-based facial animation to diffusion models, neural rendering, 3D-aware avatars

safetyarxiv-cs-cv

Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction

arXiv:2607.05449v1 Announce Type: cross Abstract: Accurate work-zone geometry perception is critical for intelligent transportation systems, and ultra-wideband sensing offers a low-cost approach for i

applicationsarxiv-cs-ai

HoloCount: A Holistic Visual Counting Benchmark for MLLMs

model-releasesarxiv-cs-cv

arXiv:2607.06420v1 Announce Type: new Abstract: Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning. Wh

Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH

arXiv:2607.05956v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, r

applicationsarxiv-cs-ai

Is Domain Adaptation Always Helpful? A Frozen-Backbone Study of Cross-Domain Sentiment Transfer

arXiv:2607.05937v1 Announce Type: new Abstract: Sentiment analysis with frozen pre-trained language model (PLM) backbones has become a common paradigm, yet the practical benefit of explicit domain ada

safetyarxiv-cs-cl

Level-Crossing Density as a Mesh-Free High-Frequency Auxiliary Loss for Implicit Neural Representations

model-releasesarxiv-cs-lg

arXiv:2607.05815v1 Announce Type: new Abstract: The Minkowski functionals of a field's excursion sets -- area, boundary measure, and Euler characteristic -- describe its level-set morphology; the Eule

Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding

arXiv:2607.05936v1 Announce Type: cross Abstract: Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API invocation code remains challenging due to complex an

applicationsarxiv-cs-lg

Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization

arXiv:2607.05861v1 Announce Type: new Abstract: Large reasoning models (LRMs) improve language model capabilities by generating explicit thinking traces before final answers. In factuality-oriented qu

researcharxiv-cs-cl

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

arXiv:2607.06094v1 Announce Type: new Abstract: Faults on a cyber-physical system (CPS) are too rare and unrepresentative to characterise, or even to select a model on, so detection must instead model

Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators

arXiv:2607.05400v1 Announce Type: cross Abstract: Generative AI models, such as Large Language Models (LLMs) and diffusion models, have demonstrated impressive performance across a wide range of tasks

Reward as An Agent for Embodied World Models

arXiv:2606.19990v2 Announce Type: replace Abstract: While RL has become a promising tool for refining world models, existing methods largely rely on conservative rollouts near the training distributio

RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures

arXiv:2607.06018v1 Announce Type: new Abstract: Pretrained video generative models are promising backbones for visuomotor control, but their imagined futures often drift from task intent and are not r

safetyarxiv-cs-ro

RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications

arXiv:2607.06411v1 Announce Type: cross Abstract: Developers increasingly delegate real maintenance work to product-grade coding agents, and many state tasks in their native language, in the style of

SafeImpute: Reliable Clinical Data Imputation via Conformal Selection

model-releasesarxiv-cs-lg

arXiv:2607.05613v1 Announce Type: new Abstract: Clinical care often relies on key laboratory indicators, yet real-world patient visits are sparse and tests are ordered irregularly, leading to pervasiv

SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents

arXiv:2606.13757v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed in automated code-review systems, where their approvals can determine which code is mer

Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows

arXiv:2607.06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filterin

The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities

arXiv:2607.05743v1 Announce Type: cross Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies w

VASP Agent: An Agentic Framework for Autonomous First-principles Calculations

arXiv:2512.19458v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery. First-principles materials computation impose

VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2510.13808v2 Announce Type: replace Abstract: Large Vision Language Models (VLMs) excel at general visual reasoning but experience significant performance degradation when deployed in novel doma

A Fair Benchmarking of Deep Relational Database Learning Models

arXiv:2607.03659v1 Announce Type: cross Abstract: Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been eval

Hardware

A Multi-Task Deep Learning Framework for Real-Time Intelligent Video Surveillance with Temporal Event Validation

arXiv:2607.03131v1 Announce Type: cross Abstract: Modern video surveillance systems generate far more video streams than human operators can effectively monitor, making automated analysis essential fo

hardwarearxiv-cs-ai

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning

arXiv:2606.16933v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) systems often degrade when operating conditions differ from those previously encountered, reflecting distributiona

safetyarxiv-cs-ai

Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types

arXiv:2607.03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets. We extend Bir

ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion

arXiv:2607.03454v1 Announce Type: cross Abstract: In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control. Existing motion prior-based method

safetyarxiv-cs-lg

Agentic SABRE: An Uncertainty-Aware Neuro-Symbolic Multi-Agent Framework for Adaptive Ransomware Detection

arXiv:2607.04292v1 Announce Type: new Abstract: Ransomware has evolved into a complex, adaptive, and fast-moving adversary category in which static signatures and monolithic classifiers fail to genera

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

arXiv:2504.18433v3 Announce Type: replace Abstract: Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a g

An Exploration of Agentic Information Fusion for Test Maintenance Prediction

arXiv:2607.04786v1 Announce Type: cross Abstract: Test maintenance is a critical, yet costly, activity - particularly as codebases rapidly evolve. To assist, we present MAST, a multi-agent framework t