Search: “evaluation”

Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking

arXiv:2609.09577v1 Announce Type: cross Abstract: Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial m

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Research

NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfaces

DGX agent

arXiv:2609.09786v1 Announce Type: new Abstract: Electroencephalography (EEG)-based motor-imagery brain-computer interfaces (MI-BCIs) vary across subjects and sessions, complicating personalization fro

researcharxiv-cs-lg

10 Sept 2026

Noesis: Deterministic-First Retrieval with Two-Tier Context Hydration for Factuality-Critical Queries on Small Local Models

arXiv:2609.07663v1 Announce Type: cross Abstract: A wrong number is worse than no answer. Across factuality-critical domains -- audience metrics, scheduling and rights in media; dosages and lab values

On Scaling Coordinate-Based Neuroevolution: The Quadtree Bottleneck in ES-HyperNEAT

arXiv:2608.24480v1 Announce Type: cross Abstract: ES-HyperNEAT evolves substrate topology through adaptive quadtree subdivision; to our knowledge, no implementation with full population-level GPU para

OpenResearcher: a reproducible and scalable pipeline for training deep research agents

A deep research agent has to do more than answer a question. It plans, searches, opens documents, gathers evidence, reasons across sources, and keeps going across dozens or hundreds of tool calls befo

agentslambda-labs

PAGR: Proof-Carrying Algebraic-Geometric Retrieval: A Quiver-, Provenance-, and Sheaf-Theoretic Framework for Grounded LLM Retrieval

arXiv:2609.06127v1 Announce Type: cross Abstract: Retrieval-augmented generation is usually formulated as a statistical information-retrieval problem. Graph-based variants add relational structure, bu

Phi-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

model-releasesarxiv-cs-cl

arXiv:2609.10226v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in

Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation

arXiv:2510.26656v4 Announce Type: replace-cross Abstract: Likelihood-free inference (LFI) enables system identification in complex tasks via black-box modelling, abstracting nonlinearity and stochasti

ProMeta: Few-shot PROTAC-targeted degradation prediction across E3 ligases

arXiv:2609.09891v1 Announce Type: new Abstract: Proteolysis-targeting chimeras (PROTACs) have emerged as a transformative therapeutic strategy that selectively degrades historically ''undruggable'' ta

Qwen-Audio-3.0-ASR Technical Report

model-releasesarxiv-cs-cl

arXiv:2609.07549v2 Announce Type: replace Abstract: In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling,

Reducing Hallucinations in LLM-based Scientific Literature Analysis Using Peer Context Outlier Detection

arXiv:2604.01461v2 Announce Type: replace Abstract: Reducing hallucinations in Large Language Models (LLMs) is essential for accurate data extraction from large text corpora. Current methods, like pro

Reliability-Aware Hybrid-K Ensemble Selection for Cervical Cytology Classification: Integrating Discrimination, Calibration, and Selective Prediction

arXiv:2609.09189v1 Announce Type: cross Abstract: High classification accuracy alone is insufficient for clinical image analysis, where calibrated confidence and reliable uncertainty estimates are ess

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

arXiv:2609.10395v1 Announce Type: new Abstract: This paper describes the Rosetta system for Subtask 1 (Context-Aware English-to-Dialectal Arabic Dialogue Translation) of the AlexandriaX shared task, p

researcharxiv-cs-cl

Safe Harness Self-Evolution: A Theoretical Analysis of Feasibility and Limits

arXiv:2609.08175v1 Announce Type: new Abstract: Harness self-evolution is the process by which an agent modifies its prompts, tools, code, or orchestration in response to task feedback while keeping t

agentsarxiv-cs-ai

SCRIPTIOC-BENCH: A Benchmark for Recognizing Actionable Threat Intelligence from Script-Based Malware using LLMs

arXiv:2609.06149v1 Announce Type: cross Abstract: Script-based malware remains a prevalent attack technique. These scripts often contain indicators of compromise (IOCs) that provide actionable threat

SE-GoS: Self-Evolving Graph-of-Skills for Skill Library at Scale

arXiv:2609.08228v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on reusable skills, yet as skill libraries scale to thousands of entries, effective retrieval becomes a bottleneck.

Applications

Solving versus Verifying: Catching Contradictions in Tax Reasoning Systems

arXiv:2609.05928v1 Announce Type: cross Abstract: Large language models now compute correct tax liabilities on over 90% of well-formed cases in statutory benchmarks, which makes them candidates for th

applicationsarxiv-cs-ai

Sparks of In Silico Cognitive Science: Theories from Simulated Data Can Generalize to Humans

arXiv:2609.08003v1 Announce Type: new Abstract: Behavioral foundation models have been proposed as stand-ins for human participants across settings, but it is unclear whether theories discovered on th

SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction

arXiv:2609.06229v1 Announce Type: cross Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical s

SynThermFace: Amplifying Limited Paired Data for Visible-Thermal Face Recognition via Synthetic Data Generation

arXiv:2609.10303v1 Announce Type: new Abstract: Face recognition (FR) is a widely used modality for biometric authentication, but conventional models rely on visible-spectrum imagery and degrade when

The Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societies

arXiv:2609.05514v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear wheth

The Internal Anatomy of Strategic Choice in Large Language Models

arXiv:2609.07478v1 Announce Type: new Abstract: Large language models act as strategic agents and models of human choice, yet choosing like a strategic agent does not mean computing like one. We recor

agentsarxiv-cs-ai

The Surprising Effectiveness of Approximate Value Iteration in Self-Play

arXiv:2609.09094v1 Announce Type: new Abstract: Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever.

Translation Invariance of Neural Operators for the FitzHugh-Nagumo Model

arXiv:2603.17523v2 Announce Type: replace Abstract: Neural operators (NOs) are powerful deep learning frameworks designed to learn solution operators of partial differential equations. This study eval

Typed Federated Artifacts for the Agentic Web:Sharing Tool-Routing Knowledge Across Frozen,Heterogeneous LLM Agents

arXiv:2609.06815v1 Announce Type: cross Abstract: An open, networked web will allow agents to run frozen models from multiple vendors, keep their history private, and teach each other which tool to ca

Unsound Search with Policy and Value Networks in Legends of Code and Magic

arXiv:2609.06816v1 Announce Type: new Abstract: Decision-time search in perfect and imperfect information games with enumerable belief states are effective methods for game AI. Collectible card games

WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic

arXiv:2609.06840v1 Announce Type: cross Abstract: Web Application Firewalls (WAFs) mainly rely on signatures to detect known attacks, which can leave gaps against modified or previously unseen payload

When Auditors Fabricate: Batch-Size Degradation and Confident Hallucination in LLM Detection of Planted Document Contamination

model-releasesarxiv-cs-cl

arXiv:2609.09696v1 Announce Type: new Abstract: Large language models are increasingly proposed as automated auditors of document quality, yet their reliability as detectors of planted errors is poorl

When Do Large Language Models Exhibit Unsolicited Deception?

arXiv:2504.00285v2 Announce Type: replace Abstract: Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are al

agentsarxiv-cs-cl

When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems

arXiv:2609.07741v1 Announce Type: new Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be truly

Who Are They to Each Other? Multi-Agent Reasoning for Speaker Relationship Inference

arXiv:2609.09628v1 Announce Type: cross Abstract: Inferring speaker relationships from spoken conversations is an important step towards socially aware speech understanding. However, this task remains

agentsarxiv-cs-cl

X2Streaming-ASR: wait when uncertain, emit when ready for streaming ASR

arXiv:2609.08672v1 Announce Type: cross Abstract: Streaming automatic speech recognition (ASR) for real-time voice agents and full-duplex dialogue must provide accurate partial transcripts with low co

A Black-Box Adversarial Attack on Human Pose Estimation and Keypoint-Based Action Recognition Models

arXiv:2609.08013v1 Announce Type: new Abstract: Human pose estimation and keypoint-based action recognition models are increasingly deployed as components of video understanding pipelines, yet their v

A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution

arXiv:2609.05985v1 Announce Type: new Abstract: Robots that follow open-ended language instructions need to connect semantic intent to visual scene understanding, geometric feasibility, object states,

researcharxiv-cs-ro

A Shared-Backbone Approach for Multi-Task MedMNIST Classification

arXiv:2609.06838v1 Announce Type: new Abstract: Multi-task biomedical classification requires models to generalize across disparate modalities and class distributions. We study 11 heterogeneous MedMNI

AnomalyCraft-700K: Component-Level Controllable and Verifiable Synthetic Anomalies for Fine-Grained Video Anomaly Understanding

arXiv:2609.06978v1 Announce Type: new Abstract: Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and

safetyarxiv-cs-cv

Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

arXiv:2609.06656v1 Announce Type: new Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasti

Tutorials

BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards

arXiv:2510.09596v2 Announce Type: replace Abstract: Today's generative models thrive with large amounts of supervised data and informative reward functions characterizing the quality of the generation

tutorialsarxiv-cs-lg

Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation

arXiv:2609.07126v1 Announce Type: cross Abstract: In world model planning, sensing inputs pass through an encoder and predictor before affecting planner decisions, so final task success alone cannot r

researcharxiv-cs-lg

Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers

arXiv:2504.15928v3 Announce Type: replace Abstract: Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied acro

CoSA: Correlation-Guided Change A ttention with Learnable Residual Gating for Remote Sensing Change Detection

model-releasesarxiv-cs-cv

arXiv:2609.08914v1 Announce Type: new Abstract: Pixel-level annotation of fixed traffic-camera imagery is expensive, while crosswalk models trained from street-level imagery face a substantial viewpoi

Counterfactual Tests for Measuring Chain-of-Thought Faithfulness in Visual Language Models

model-releasesarxiv-cs-cv

arXiv:2609.06704v1 Announce Type: new Abstract: Chain-of-thought (CoT) may often look plausible, yet it may not faithfully reflect the model's decision-making process. While methods for measuring the

CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning

arXiv:2609.05708v1 Announce Type: cross Abstract: Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the

agentsarxiv-cs-lg

D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems

arXiv:2609.07350v1 Announce Type: cross Abstract: Climate change is increasing the severity and unpredictability of natural disasters. In time-critical crises such as wildfires, traditional monitoring

agentsarxiv-cs-cv

DCLP++: Learning to Navigate with Footprint Clearance and Relative Motion

arXiv:2609.08711v1 Announce Type: new Abstract: We present DCLP++, a local navigation frameworkthat uses footprint clearance as the geometric basis for studying relative motion features in dynamic env

safetyarxiv-cs-ro

Diffusion models for eye-gaze trajectory generation using position and velocity representations

arXiv:2609.05522v1 Announce Type: new Abstract: Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of priva

DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning

model-releasesarxiv-cs-cv

arXiv:2411.15876v3 Announce Type: replace-cross Abstract: Overfitting remains a significant challenge in deep learning, often arising from data outliers, noise, and limited training data. To address t