Search: “evaluation”

FCA-Guided Counterfactual Explanations for Multi-Modal Breast Cancer Diagnosis: A Framework Achieving Perfect Validity with Emergent Sparsity

arXiv:2609.20067v1 Announce Type: new Abstract: Deep learning models for multi-modal breast cancer diagnosis achieve high predictive accuracy but remain clinically unacceptable without actionable, cou

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Local Ai

FORGE: Forensic Reasoning with Grounded Evidence

DGX agent

arXiv:2503.15867v4 Announce Type: replace-cross Abstract: Forensic deepfake analysis demands more than binary classification: investigators need region-grounded natural language explanations they can

local-aiarxiv-cs-ai

18 Sept 2026

From Intent to Action: Benchmarking LLM Safety in Vehicle Voice Command Authorization

arXiv:2609.19630v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into vehicle voice assistants. But linking natural-language requests to vehicle functions creat

Local Ai

GeLaCo: An Evolutionary Approach to Layer Compression

arXiv:2507.10059v2 Announce Type: replace Abstract: Large Language Models have achieved remarkable performance across a large number of tasks, but face critical deployment and usage barriers due to su

local-aiarxiv-cs-cl

Agents

GPT-6-Astra in a Navigation Workflow: Behavioral Analysis in Zero-Shot Vision-and-Language Navigation in Continuous Environments

arXiv:2609.20116v1 Announce Type: new Abstract: We study GPT-6-Astra in a zero-shot Vision-and-Language Navigation in Continuous Environments (VLN-CE) system, where it interprets instructions, assesse

agentsarxiv-cs-ro

GR2PO: Group Relative Return Policy Optimization for Continuous Robot Control

arXiv:2609.19850v1 Announce Type: new Abstract: Actor-critic architecture has been widely used in continuous robot control. However, they rely on learning a value network, introducing additional compu

safetyarxiv-cs-ro

HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classification

model-releasesarxiv-cs-cv

arXiv:2609.19755v1 Announce Type: cross Abstract: Accurate classification of brain disorders from neuroimaging data remains challenging because of substantial inter-subject heterogeneity and the compl

Imagine-TAMP: Imagination-Guided Task and Motion Planning in Partial Observability

arXiv:2609.20396v1 Announce Type: new Abstract: Robots operating in cluttered environments must often manipulate objects whose locations are only partially observable. A central challenge is deciding

researcharxiv-cs-ro

Instance Segmentation and Fine-grained Classification for Urban Buildings with Adaptive Region Dividing and Spatially-Supervised Contrastive Learning

arXiv:2609.19631v1 Announce Type: new Abstract: Accurate instance-level and functional understanding of urban buildings in large-scale point clouds is essential for digital city modeling and urban ana

applicationsarxiv-cs-cv

Is It Still Worth Training a Classical Model in the Era of LLMs? A Crossover Benchmark on Tabular Data

arXiv:2609.20218v1 Announce Type: cross Abstract: Large language models can label a tabular row from a plain-English description with no training - a capability now shipping in mainstream spreadsheet

Ischemic Stroke Segmentation and Net Water Uptake Quantification on Multicenter Non-Contrast CT Using Supervised Target-Domain Adaptation

arXiv:2609.20151v1 Announce Type: new Abstract: Objectives: Quantitative assessment of infarct hypodensity on non-contrast computed tomography (NCCT), including net water uptake (NWU), requires manual

LLM-as-an-Improver: Turning Verification into Better Candidates

arXiv:2609.19515v1 Announce Type: new Abstract: Verifier-based selection improves LLM performance by generating multiple candidate solutions and using a verifier to select the most promising one. Howe

researcharxiv-cs-ai

MAGS: Multi-agent Auto-formalization Guarantees Safety for Agentic Outputs

arXiv:2609.19391v1 Announce Type: new Abstract: LLM coding agents now generate complex programs at a scale that makes thorough human review increasingly difficult, raising the risk of safety and secur

safetyarxiv-cs-ai

Mammography Foundation Models for Opportunistic Prediction of Major Adverse Cardiovascular Events

arXiv:2609.19385v1 Announce Type: cross Abstract: Cardiovascular disease (CVD) remains the leading cause of death among women, yet cardiovascular risk assessment often relies on clinical variables tha

Music Hallucination in Audio-Language Models: A Hierarchical Formulation and Empirical Study

arXiv:2609.20195v1 Announce Type: cross Abstract: Audio-language models increasingly generate confident music descriptions that are unsupported by the input audio. We present, to our knowledge, the fi

safetyarxiv-cs-ai

Performance of Machine Learning Classification in Sonomammogram Images using BI-RADS

arXiv:2311.08493v2 Announce Type: replace-cross Abstract: This research aims to investigate the classification accuracy of various state-of-the-art image classification models across different categor

PetriBench: Benchmarking LLM Reasoning over Dynamic State Spaces

arXiv:2609.19883v1 Announce Type: cross Abstract: Characterizing LLM reasoning remains an open challenge, as many existing benchmarks isolate specific reasoning skills, rely on external knowledge, or

Placement Is Free, Composition Is Not: The Latin Square as a Provably-Balanced Construction for Heterogeneous Sequence-Mixer Stacks

model-releasesarxiv-cs-lg

arXiv:2609.20269v1 Announce Type: new Abstract: Since GPT, most Transformers have repeated the same attention mechanism at every layer. Yet this design is largely a convention rather than a tested con

Predict Before You Deploy: Offline Prediction of Quantization-Induced Task Degradation for World Action Models

arXiv:2609.19441v1 Announce Type: cross Abstract: World action models (WAMs) rely on video-generation backbones, requiring substantial memory and compute for deployment. Post-training quantization red

PROVIA: Procedure State Tracking for Online Mistake Detection in Egocentric Videos

arXiv:2609.20638v1 Announce Type: new Abstract: An assistant watching egocentric video should notice a mistake from past frames alone, before the next step begins, and keep working once the person rec

QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning

model-releasesarxiv-cs-cv

arXiv:2609.19990v1 Announce Type: new Abstract: The high visual-token load in multimodal large language models (MLLMs) motivates training-free pruning to reduce later-layer computation, but under a fi

Agents

Quantifying Overclaiming Propensity in Frontier LLM Agents

arXiv:2609.20812v1 Announce Type: cross Abstract: Frontier coding agents are increasingly trusted to work autonomously for long periods, yet an agent's final response is often the only account of that

agentsarxiv-cs-ai

RAUL: Reference-Assisted Ureteroscopy Localization for Skill Assessment

arXiv:2609.19236v1 Announce Type: new Abstract: Objective: Incomplete navigation of anatomy during ureteroscopic kidney stone surgeries can contribute to repeat interventions. While skilled surgeons h

Reach or Solve? Attributing Agentic RL Gains with Checkpoint Handoffs

arXiv:2609.19636v1 Announce Type: new Abstract: Reinforcement learning now trains language-model agents that act over dozens of steps in live environments. The gains are large, and they are read as be

Red-Teaming Auto Mode: Improving Blocking Classifiers Against Malign Coding Agents

arXiv:2609.19587v1 Announce Type: cross Abstract: To keep coding agents from going off the rails, production systems now review each proposed action with a blocking monitor that can reject it before i

RISC-V and machine learning: a survey

arXiv:2609.20677v1 Announce Type: new Abstract: The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware.

applicationsarxiv-cs-lg

SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessment

model-releasesarxiv-cs-cl

arXiv:2609.20584v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for safety-critical engineering, yet their reliability in regulated functional-safety workflows

Seismic Site Response Prediction from Sparse Observations Using Finite-Element-Pretrained Latent Dynamics

arXiv:2609.20451v1 Announce Type: new Abstract: Numerical site-response predictions often deviate from observations, yet correcting these discrepancies is difficult because records are limited in both

researcharxiv-cs-lg

Semantic Layer Induction from Raw Telemetry via Hierarchical LLM and RAG Abstraction

arXiv:2609.19615v1 Announce Type: cross Abstract: Modern applications generate massive volumes of raw telemetry data, but translating those noisy, heterogeneous event streams into actionable business

applicationsarxiv-cs-ai

Should This Case Be Adapted? Prediction Fragmentation Controls Test-Time Adaptation

model-releasesarxiv-cs-cv

arXiv:2609.20700v1 Announce Type: new Abstract: Episodic test-time adaptation resets a frozen segmenter to source weights M_0 on each case and adapts for a fixed step count. A fixed horizon conflates

SIMLIFE: Pattern Understanding for Long-Horizon Human-Agent Partnership

arXiv:2609.19610v1 Announce Type: new Abstract: Understanding humans over long horizons requires agents to infer not only what people need in the moment, but also how routines form, why they repeat, a

Structured Four-Stage Legal Translation: From Natural-Language Traffic Rules to PROLOG

arXiv:2609.20334v1 Announce Type: new Abstract: Traffic regulations are written for human interpretation and therefore rely on shared background knowledge and flexible phrasing, which inherently intro

Summarization Bias: The Directional Collapse of Objective Projection into Told-Mode Labels in Large Language Models --- A Conceptual Framework and Registered Test Protocol

arXiv:2609.20712v1 Announce Type: new Abstract: This paper introduces and operationalizes summarization bias: a proposed systematic tendency of large language models (LLMs) to represent narrative mean

safetyarxiv-cs-cl

Tele-Traversability: Rethinking Traversability for Teleoperated Ground Robots in Terrain Navigation

arXiv:2609.19577v1 Announce Type: new Abstract: Teleoperation, a human-in-the-loop control scheme, allows a human operator to remotely command and guide a mobile robot to navigate in off-road environm

safetyarxiv-cs-ro

The AR Fairness Metamodel: A Structured Framework for Fairness Measures

arXiv:2609.19234v1 Announce Type: cross Abstract: This paper presents the AR fairness metamodel, a framework designed to represent, analyze, and compare different fairness scenarios. The metamodel con

safetyarxiv-cs-ai

Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment

arXiv:2609.20199v1 Announce Type: new Abstract: Cognitive workload reflects the mental effort required during task performance and is central to the design of adaptive human-machine systems. The use o

researcharxiv-cs-lg

Towards High-DoF Dexterous Manipulation through VLA Post-Training

arXiv:2609.19666v1 Announce Type: new Abstract: Imitation-learned vision--language--action (VLA) foundation models acquire broad manipulation capabilities by scaling robot data across tasks and embodi

safetyarxiv-cs-ro

Towards Scaling Marine Perception with Synthetic Data

arXiv:2609.20680v1 Announce Type: cross Abstract: Scalable machine learning in challenging underwater environments is strongly limited by the lack of labeled real-world training data. This data is oft

applicationsarxiv-cs-cv

Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding

model-releasesarxiv-cs-lg

arXiv:2609.20214v1 Announce Type: cross Abstract: Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while

TripScore: Aligning LLMs for Real-World Travel Planning via Expert-Calibrated Reward

arXiv:2510.09011v4 Announce Type: replace Abstract: In our deployed travel-planning service, most users give minimal inputs or free-form requests rather than the structured constraint checklists assum

Local Ai

VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control

arXiv:2609.19554v1 Announce Type: cross Abstract: Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence,

local-aiarxiv-cs-cv

What Does Privileged Information Add to On-Policy Self-Distillation?

model-releasesarxiv-cs-cl

arXiv:2609.20612v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a worked solution. Giving the teacher

WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution

arXiv:2609.20593v1 Announce Type: new Abstract: Word-in-Context (WiC) remains challenging for language models, despite recent progress on lexical-semantic tasks. We hypothesise that this difficulty ar

researcharxiv-cs-cl

Agents

WZPlanner: Safe End-to-End Path Planning for Autonomous Driving in Work Zones

arXiv:2609.19393v1 Announce Type: new Abstract: Work zones alter lane geometry through temporary traffic controls and closures that may be absent from on-board maps, challenging autonomous vehicle (AV

agentsarxiv-cs-cv

A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning

arXiv:2609.17804v1 Announce Type: new Abstract: Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it

researcharxiv-cs-ai

A Systematic Review of NLP for Ghanaian Languages: Datasets, Models, and a Research Roadmap

arXiv:2405.06818v2 Announce Type: replace Abstract: Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward a

safetyarxiv-cs-cl

An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows

arXiv:2511.18060v3 Announce Type: replace-cross Abstract: Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasse

researcharxiv-cs-lg

BadQubits: An LLM-Based Framework for Static Pre-Execution Detection of Structurally Harmful Quantum Circuits

arXiv:2609.18965v1 Announce Type: cross Abstract: This paper presents BadQubits, an LLM-based framework for static pre-execution detection of structurally harmful OpenQASM 2.0 circuits. The framework