Search: “evaluation”

When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide

arXiv:2604.19206v1 Announce Type: new Abstract: The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely ha

safetyarxiv-cs-cv

22 Apr 2026

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Research

When Does Verification Pay Off? A Closer Look at LLMs as Solution Verifiers

DGX agent

arXiv:2512.02304v2 Announce Type: replace Abstract: Large language models (LLMs) can act as both problem solvers and solution verifiers, where the latter select high-quality answers from a pool of sol

researcharxiv-cs-cl

22 Apr 2026

Agents

Whispers in the Machine: Confidentiality in Agentic Systems

arXiv:2402.06922v5 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents combine LLMs with external tools to automate tasks such as scheduling meetings, managing documents, or

agentsarxiv-cs-lg

22 Apr 2026

Local Ai

Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments

arXiv:2509.12516v2 Announce Type: replace Abstract: Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes,

local-aiarxiv-cs-ro

22 Apr 2026

A Computational Method for Measuring 'Open Codes' in Qualitative Analysis

arXiv:2411.12142v4 Announce Type: replace Abstract: Qualitative analysis is critical to understanding human datasets in many social science disciplines. A central method in this process is inductive c

A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection

arXiv:2604.01798v2 Announce Type: replace Abstract: Breast cancer is a highly heterogeneous disease with diverse molecular profiles. The PAM50 gene signature is widely recognized as a standard for cla

Local Ai

A Lightweight Transformer for Pain Recognition from Brain Activity

arXiv:2604.16491v1 Announce Type: new Abstract: Pain is a multifaceted and widespread phenomenon with substantial clinical and societal burden, making reliable automated assessment a critical objectiv

local-aiarxiv-cs-cv

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations

arXiv:2604.17494v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) are essential for interpreting black-box models, yet they often become invalid when models are slightly changed. Exis

A proposal for PU classification under Non-SCAR using clustering and logistic model

arXiv:2604.17130v1 Announce Type: cross Abstract: The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification wh

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era

arXiv:2604.16586v1 Announce Type: new Abstract: Molecular property prediction integrates quantum chemistry, cheminformatics, and deep learning to connect molecular structure with physicochemical and b

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling

arXiv:2506.07969v2 Announce Type: replace Abstract: We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in whic

A Unification of Discrete, Gaussian, and Simplicial Diffusion

arXiv:2512.15923v2 Announce Type: replace Abstract: To model discrete sequences such as DNA, proteins, and language using diffusion, practitioners must choose between three major methods: diffusion in

Agents

Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration

arXiv:2508.18025v4 Announce Type: replace-cross Abstract: Autonomous planetary exploration demands real-time, high-fidelity environmental perception. Standard deep learning models require massive comp

agentsarxiv-cs-cv

Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark

arXiv:2604.16984v1 Announce Type: new Abstract: This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted

AeroRAG: Structured Multimodal Retrieval-Augmented LLM for Fine-Grained Aerial Visual Reasoning

arXiv:2604.17889v1 Announce Type: new Abstract: Despite recent progress in multimodal large language models (MLLMs), reliable visual question answering in aerial scenes remains challenging. In such sc

AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis

arXiv:2604.17846v1 Announce Type: new Abstract: MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by the

Aligning Backchannel and Dialogue Context Representations via Contrastive LLM Fine-Tuning

arXiv:2604.16622v1 Announce Type: new Abstract: Backchannels (e.g., `yeah', `mhm', and `right') are short, non-interruptive feedback signals whose lexical form and prosody jointly convey pragmatic mea

safetyarxiv-cs-cl

Aligning Language Models for Lyric-to-Melody Generation with Rule-Based Musical Constraints

arXiv:2604.18489v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise in lyric-to-melody generation, but models trained with Supervised Fine-Tuning (SFT) often produce musically

safetyarxiv-cs-cl

Alignment Data Map for Efficient Preference Data Selection and Diagnosis

arXiv:2505.23114v3 Announce Type: replace Abstract: Human preference data is essential for aligning large language models (LLMs) with human values, but collecting such data is often costly and ineffic

safetyarxiv-cs-cl

An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal

arXiv:2604.18293v1 Announce Type: new Abstract: Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word

An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules

arXiv:2604.16550v1 Announce Type: new Abstract: Despite the high accuracy of 'black box' deep learning models, drug discovery still relies on protein-ligand interaction principles and heuristics. To i

Applications

An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs

arXiv:2603.04545v2 Announce Type: replace Abstract: Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference qu

applicationsarxiv-cs-lg

An Uncertainty-Aware Loss Function Incorporating Fuzzy Logic: Application to MRI Brain Image Segmentation

arXiv:2604.16490v1 Announce Type: new Abstract: Accurate brain image segmentation, particularly for distinguishing various tissues from magnetic resonance imaging (MRI) images, plays a pivotal role in

Applications of deep generative models to DNA reaction kinetics and to cryogenic electron microscopy

arXiv:2604.16851v1 Announce Type: cross Abstract: This dissertation explores how deep generative models can advance the analysis of challenging biological problems by integrating domain knowledge with

Hardware

AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization

arXiv:2604.18137v1 Announce Type: cross Abstract: Processing-in-Memory (PIM) architectures offer a promising solution to the memory bottlenecks in data-intensive machine learning, yet often overlook t

hardwarearxiv-cs-lg

ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation

arXiv:2604.18362v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) remains unreliable in long-form settings, where retrieved evidence is noisy or contradictory, making it difficult f

ARCS: Autoregressive Circuit Synthesis with Topology-Aware Graph Attention and Spec Conditioning

arXiv:2603.29068v3 Announce Type: replace Abstract: This paper presents ARCS (Autoregressive Circuit Synthesis), a system for amortized analog circuit generation. ARCS produces complete, SPICE-simulat

safetyarxiv-cs-lg

ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs

arXiv:2511.02356v2 Announce Type: replace-cross Abstract: Despite extensive safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. However, existing methods generally l

safetyarxiv-cs-lg

Attention-ResUNet for Automated Fetal Head Segmentation

arXiv:2604.18148v1 Announce Type: new Abstract: Automated fetal head segmentation in ultrasound images is critical for accurate biometric measurements in prenatal care. While existing deep learning ap

Authenticated Contradictions from Desynchronized Provenance and Watermarking

arXiv:2603.02378v2 Announce Type: replace-cross Abstract: Cryptographic provenance standards such as C2PA and invisible watermarking are positioned as complementary defenses for content authentication

Automated Classification of Plasma Regions at Mars Using Machine Learning

arXiv:2604.17131v1 Announce Type: cross Abstract: The plasma environment around Mars is highly variable because it is strongly influenced by the solar wind. Accurate identification of plasma regions a

Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond

arXiv:2408.11338v2 Announce Type: replace-cross Abstract: Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning

Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions

arXiv:2604.17894v1 Announce Type: new Abstract: Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains labor-intensive. Exist

Applications

Automatic Speech Recognition for Documenting Endangered Languages: Case Study of Ikema Miyakoan

arXiv:2603.26248v2 Announce Type: replace Abstract: Language endangerment poses a major challenge to linguistic diversity worldwide, and technological advances have opened new avenues for documentatio

applicationsarxiv-cs-cl

Agents

Autonomous Unmanned Aircraft Systems for Enhanced Search and Rescue of Drowning Swimmers: Image-Based Localization and Mission Simulation

arXiv:2604.18088v1 Announce Type: new Abstract: Drowning is an omnipresent risk associated with any activity on or in the water, and rescuing a drowning person is particularly challenging because of t

agentsarxiv-cs-cv

Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning

arXiv:2604.16702v1 Announce Type: new Abstract: Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestria

safetyarxiv-cs-ro

AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning

arXiv:2604.18445v1 Announce Type: new Abstract: Performance, power, and area (PPA) optimization is a fundamental task in RTL design, requiring a precise understanding of circuit functionality and the

AutoRubric: Rubric-Based Generative Rewards for Faithful Multimodal Reasoning

arXiv:2510.14738v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have rapidly advanced from perception tasks to complex multi-step reasoning, yet reinforcement learning wit

AVRT: Audio-Visual Reasoning Transfer through Single-Modality Teachers

arXiv:2604.16617v1 Announce Type: new Abstract: Recent advances in reasoning models have shown remarkable progress in text-based domains, but transferring those capabilities to multimodal settings, e.

BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation

arXiv:2604.16514v1 Announce Type: new Abstract: Autoregressive vision-language models (VLMs) deliver strong multimodal capability, but their token-by-token decoding imposes a fundamental inference bot

BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes

arXiv:2509.15974v2 Announce Type: replace Abstract: Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competi

BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks

arXiv:2602.06221v2 Announce Type: replace Abstract: Multiple-choice question answering (MCQA) is standard in NLP, but benchmarks lack rigorous quality control. We present BenchMarker, an education-ins

Beyond Feature Fusion: Contextual Bayesian PEFT for Multimodal Uncertainty Estimation

arXiv:2604.16615v1 Announce Type: new Abstract: We introduce CoCo-LoRA, a multimodal, uncertainty-aware parameter-efficient fine-tuning method for text prediction tasks accompanied by audio context. E

Local Ai

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling

arXiv:2508.16745v2 Announce Type: replace Abstract: Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. We study this questio

local-aiarxiv-cs-lg

Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detection

arXiv:2604.18248v1 Announce Type: cross Abstract: Current open-source prompt-injection detectors converge on two architectural choices: regular-expression pattern matching and fine-tuned transformer c

Bias-constrained multimodal intelligence for equitable and reliable clinical AI

arXiv:2604.16884v1 Announce Type: new Abstract: The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. Howeve

safetyarxiv-cs-cv

Bielik Guard: Efficient Polish Language Safety Classifiers for LLM Content Moderation

arXiv:2602.07954v4 Announce Type: replace Abstract: As Large Language Models (LLMs) become increasingly deployed in Polish language applications, the need for efficient and accurate content safety cla

Bounded Ratio Reinforcement Learning

arXiv:2604.18578v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) has become the predominant algorithm for on-policy reinforcement learning due to its scalability and empirical robust

safetyarxiv-cs-lg