Search: “evaluation”

HMB-GAN: Hybrid Multi-Bezier GAN for Vector Shape Synthesis

arXiv:2609.21158v1 Announce Type: new Abstract: We explore the use of hybrid quantum-classical generative adversarial networks for synthesising CAD-ready vector geometries. Unlike prior work that oper

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Research

Image-Derived PM10 Estimation in Cattle Feedlot Using Machine Learning: Addressing Concentration Ranges Beyond Existing Digital Imaging Methods

DGX agent

arXiv:2609.20975v1 Announce Type: new Abstract: Affordable dust monitoring remains a pressing need for the cattle feedlot industry, yet camera-based PM estimation, despite its growing body of research

researcharxiv-cs-cv

21 Sept 2026

Learning Cardiac Features: ECG Biometrics Across Time and~Exercise

arXiv:2609.21962v1 Announce Type: new Abstract: Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authe

Learning End-to-End Control for Omnidirectional Aerial Motion on Overactuated Tilt-rotor Quadrotors

arXiv:2602.21583v2 Announce Type: replace Abstract: While reinforcement learning (RL) has been successfully applied to conventional quadrotors for agile and robust flight, whether actuator-level RL ca

safetyarxiv-cs-ro

LLMs as Feature Engineers for Text-and-Tabular Prediction

arXiv:2609.21894v1 Announce Type: new Abstract: We introduce an iterative framework that automates the extraction of interpretable, schema-bound categorical features from unstructured text for tabular

researcharxiv-cs-lg

Multi-Subject Pretraining Enables Short-Calibration Personalization for Closed-Corpus Surface EMG Speech Decoding

arXiv:2609.21288v1 Announce Type: new Abstract: Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data set

Not All Irregularity Is Equal: Causally Isolating a Rare Failure Mode in Japanese Morphological Inflection

arXiv:2609.21179v1 Announce Type: new Abstract: Neural morphological generation systems often achieve high aggregate accuracy on benchmark datasets, yet such performance can conceal systematic errors

On the Limitations of Large Language Models for Conceptual Database Modeling

arXiv:2605.11986v2 Announce Type: replace Abstract: This article analyzes the use of Large Language Models (LLMs) as support for the conceptual modeling of relational databases through the automatic g

OpenMAS-GCom. A Diagnostic Benchmark for Graph-enhanced Multi-Agent Systems

arXiv:2609.21527v1 Announce Type: new Abstract: Graph-enhanced multi-agent systems (G-MAS) coordinate large language model agents through communication graphs and role assignments, which determine how

OverThink: Slowdown Attacks on Reasoning LLMs

arXiv:2502.02542v5 Announce Type: replace Abstract: A reasoning language model (RLM) generates costly reasoning tokens, often hidden from the users, that help it excel at many tasks. Our Overthink att

safetyarxiv-cs-lg

Particle Competition and Cooperation for Robust Graph Convolutional Network Learning Under Label Noise

arXiv:2609.22053v1 Announce Type: new Abstract: Graph Convolutional Networks (GCNs) are highly sensitive to label noise, since corrupted supervision can propagate through the graph and degrade learned

PlaceReasoner-Beta: Reasoning-Driven Macro Placement and Benchmarking

arXiv:2609.21263v1 Announce Type: new Abstract: Automated macro placement remains a fundamental challenge in VLSI physical design. Despite decades of research, existing approaches predominantly optimi

Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation

arXiv:2609.21609v1 Announce Type: cross Abstract: Model-free reinforcement learning can acquire contact-rich robotic manipulation skills through trial-and-error interaction, but it often requires the

safetyarxiv-cs-ai

PRISM-BN: A Controlled Corpus and Benchmark for Text-to-Parameterized Bayesian Network Extraction

arXiv:2609.21673v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural s

PRIVET: PRoximIty leakage detection Via Extreme value Theory

arXiv:2510.24233v2 Announce Type: replace Abstract: Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or pr

researcharxiv-cs-lg

Reading Anxiety or Reading the Label? Comparing Fine-Tuned and Frontier Models for Anxiety Detection on Social Media

arXiv:2609.20847v1 Announce Type: new Abstract: Anxiety is among the most common mental health conditions, and people often write about it online well before seeking clinical help. Practitioners build

Rectify, Don't Regret: On-Policy Closed-Loop Training for Multimodal Trajectory Prediction

arXiv:2603.23393v2 Announce Type: replace Abstract: Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when

safetyarxiv-cs-ro

Reinforcement Learning under External Influence: Guarantees, Algorithms, and Sample Complexity

arXiv:2305.16056v5 Announce Type: replace-cross Abstract: In this paper, we study the problem of reinforcement learning under the influence of external events. For this, we consider Markov decision pr

safetyarxiv-cs-ai

Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies

arXiv:2609.21220v1 Announce Type: new Abstract: Generative robot policies can represent diverse, multimodal behaviors, but adapting pretrained policies to deployment-time constraints such as collision

safetyarxiv-cs-ro

SAGE: Schema-Guided LLMs for Grant Review

arXiv:2609.20829v1 Announce Type: new Abstract: Grant reviewers must apply detailed criteria to application forms, budgets, and supporting documents while producing assessments that colleagues can ins

researcharxiv-cs-cl

Scaling Vision-Language Reward Learning for Robot Manipulation in Parallel Simulation

model-releasesarxiv-cs-ro

arXiv:2609.21767v1 Announce Type: new Abstract: Vision-language models (VLMs) can replace human annotators in preference-based reward learning, but sequential API requests and single-environment data

The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models

arXiv:2609.21509v1 Announce Type: new Abstract: When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How mu

Towards Secure Cloud-Native Computing: Unveiling Kubernetes Misconfigurations with Large Language Models

arXiv:2609.20834v1 Announce Type: new Abstract: In the rapidly evolving landscape of cloud-native computing, Organizations are increasingly adopting infrastructure models that emphasize scalability, f

Tutorials

TRACE: Coverage Path Planning for Unknown Environments Using Hierarchical Coverage Tree

arXiv:2609.21777v1 Announce Type: new Abstract: This paper presents a novel online coverage path planning (CPP) algorithm, called TRACE, for real-time coverage of unknown environments. TRACE is built

tutorialsarxiv-cs-ro

TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimization

arXiv:2609.21859v1 Announce Type: new Abstract: Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on

Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judged

arXiv:2609.20989v1 Announce Type: cross Abstract: As generative AI is increasingly used as a source of personal financial guidance, understanding how people appraise such advice is important for suppo

safetyarxiv-cs-ai

Understanding Structural Representation in Foundation Models for Polymers

arXiv:2512.11881v2 Announce Type: replace-cross Abstract: From the relative scarcity of training data to the lack of standardized benchmarks, the creation of effective foundation models for polymers f

VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph

arXiv:2609.21804v1 Announce Type: new Abstract: Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visua

researcharxiv-cs-cv

VISPATH: Visual-Intent-Guided Path Reasoning for Multimodal Knowledge Graph Question Answering

arXiv:2609.20843v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) enables models to answer natural-language questions through structured graph reasoning and has achieved substa

ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation

arXiv:2609.21726v1 Announce Type: new Abstract: Reliable robotic grasping benefits from estimating the evolving physical interaction and selecting a grasp-dependent compression target. Tactile sensors

researcharxiv-cs-ro

Applications

A Functional Pilot for Certified Freshness-Aware Semantic--Spatial Range Retrieval

arXiv:2609.19855v1 Announce Type: cross Abstract: Geographic applications need every object inside a radius that satisfies a semantic threshold, yet embedding indexes return approximate top-ranked lis

applicationsarxiv-cs-ai

Agents

AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation

arXiv:2609.19527v1 Announce Type: cross Abstract: Air-ground transportation research increasingly relies on co-simulation, yet constructing scenarios remains labor-intensive and difficult to validate.

agentsarxiv-cs-ai

AVTrace: Diagnosing Audio-Visual Temporal Reasoning in Omni Models

arXiv:2609.19991v1 Announce Type: cross Abstract: Omni models can describe video content, but can they locate events in time, preserve event order, and judge audio-visual synchronization? We introduce

Applications

Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence

arXiv:2609.19965v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest s

applicationsarxiv-cs-cl

Benchmarking LLM Compliance with China AI Generated Content Regulations

arXiv:2609.19989v1 Announce Type: new Abstract: The widespread adoption of LLMs has led to escalating content compliance risks. Prior works have contributed to addressing these risks in the English co

Benchmarking MLLMs via Cognitive Expected Scene Graph for Safety-Critical Visual Negation Understanding

model-releasesarxiv-cs-cv

arXiv:2609.19767v1 Announce Type: new Abstract: True machine intelligence requires transcending passive pixel registration to master top-down functional reasoning over absent information via visual ne

Beyond Flattened Tokens: Structure-Preserving EEG Decoding with Reusable TriDim Blocks

arXiv:2609.19842v1 Announce Type: new Abstract: Effective EEG decoding requires representations that preserve organization among channels, local waveform dynamics, and long-range temporal context. Exi

Beyond Patch Removal: Persistent Adversarial Effects in Vision-Language-Action Policies

arXiv:2609.19669v1 Announce Type: new Abstract: Adversarial patches to Vision-Language-Action (VLA) policies can cause both immediate action corruption and persistent state effects that remain after t

researcharxiv-cs-cv

Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads

model-releasesarxiv-cs-ro

arXiv:2602.16870v2 Announce Type: replace Abstract: The Boreas Road Trip (Boreas-RT) dataset extends the multi-season Boreas dataset to new and diverse locations that pose challenges for modern autono

Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge

arXiv:2609.20147v1 Announce Type: cross Abstract: Cortical hypometabolism measured by Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a highly sensitive biomarker for dementia diagnosis.

CoMa: Contextual Massing Generation with Vision-Language Models

arXiv:2601.08464v2 Announce Type: replace-cross Abstract: Context-aware building massing is an important early-stage design task: given a site for buildings, a generated massing should not only fit th

Agents

Continual Enterprise World Model Discovery in Dynamic Systems

arXiv:2609.19551v1 Announce Type: new Abstract: In an enterprise system, updating one field can set another, create a record, or start an approval. These effects are produced by business rules that ar

agentsarxiv-cs-ai

Correct Now, Insufficient Later: Auditing Update Sufficiency in Context Compression

arXiv:2609.20045v1 Announce Type: cross Abstract: A memory can answer a current query correctly while discarding distinctions required by a later update. We investigate this failure with a paired-hist

DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation

arXiv:2609.20649v1 Announce Type: cross Abstract: Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots

safetyarxiv-cs-cv

Agents

Do AI Agents Understand Computer Architecture?

arXiv:2609.19387v1 Announce Type: new Abstract: Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot estab

agentsarxiv-cs-ai

Effective and Efficient Threat Hunting with Small Language Models

arXiv:2512.06660v3 Announce Type: replace-cross Abstract: Analysts in Security Operations Centers query massive telemetry streams using Kusto Query Language (KQL), but writing correct KQL demands spec

EliGSiR: Continual RGB-D Mapping with Gaussian Splatting under Bounded Compute

arXiv:2609.20348v1 Announce Type: new Abstract: Conventional 3D Gaussian Splatting assumes a closed set of observations and long optimization schedules. Continual RGB-D mapping in contrast poses the p

researcharxiv-cs-cv

Exploring Sparsity and Smoothness of Arbitrary Lp Norms in Adversarial Attacks

arXiv:2602.06578v2 Announce Type: replace-cross Abstract: Adversarial attacks against deep neural networks are commonly constructed under ell_p norm constraints, most often using p=1, p=2 or p=infty,