Search: “evaluation”

Althea: Human-AI Collaboration for Fact-Checking and Critical Reasoning

arXiv:2602.11161v2 Announce Type: replace-cross Abstract: The web's information ecosystem demands fact-checking systems that are both scalable and epistemically trustworthy. Automated approaches offer

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Local Ai

An Edge-Host-Cloud Architecture for Robot-Agnostic, Caregiver-in-the-Loop Personalized Cognitive Exercise: Multi-Site Deployment in Dementia Care

DGX agent

arXiv:2604.16408v1 Announce Type: new Abstract: We present Speaking Memories, a distributed, stakeholder-in-the-loop robotic interaction platform for personalized cognitive exercise support. Rather th

local-aiarxiv-cs-ro

21 Apr 2026

An `Inverse' Experimental Framework to Estimate Market Efficiency

arXiv:2604.18130v1 Announce Type: new Abstract: Digital marketplaces processing billions of dollars annually represent critical infrastructure in sociotechnical ecosystems, yet their performance optim

Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records

arXiv:2507.20993v3 Announce Type: replace Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These poli

ArgBench: Benchmarking LLMs on Computational Argumentation Tasks

arXiv:2604.17366v1 Announce Type: new Abstract: Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection

Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation

arXiv:2604.18468v1 Announce Type: new Abstract: Closed-loop simulation is a core component of autonomous vehicle (AV) development, enabling scalable testing, training, and safety validation before rea

AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data

arXiv:2604.16793v1 Announce Type: cross Abstract: In astronomical imaging, the low photon count of exposures necessitates extensive post-processing steps, including contamination removal and denoising

Agents

AutoVQA-G: Self-Improving Agentic Framework for Automated Visual Question Answering and Grounding Annotation

arXiv:2604.17488v1 Announce Type: new Abstract: Manual annotation of high-quality visual question answering with grounding (VQA-G) datasets, which pair visual questions with evidential grounding, is c

agentsarxiv-cs-cv

Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale

arXiv:2604.18572v1 Announce Type: new Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.g., text and images) align and eventually conver

BengaliMoralBench: A Benchmark for Auditing Moral Reasoning in Large Language Models within Bengali Language and Culture

arXiv:2511.03180v2 Announce Type: replace Abstract: As multilingual Large Language Models (LLMs) gain traction across South Asia, their alignment with local ethical norms, particularly for Bengali, sp

BOOKAGENT: Orchestrating Safety-Aware Visual Narratives via Multi-Agent Cognitive Calibration

arXiv:2604.16541v1 Announce Type: new Abstract: Recent advancements in Large Generative Models (LGMs) have revolutionized multi-modal generation. However, generating illustrated storybooks remains an

Brain-CLIPLM: Decoding Compressed Semantic Representations in EEG for Language Reconstruction

arXiv:2604.16370v1 Announce Type: new Abstract: Decoding natural language from non-invasive electroencephalography (EEG) remains fundamentally limited by low signal-to-noise ratio and restricted infor

Bridging Coarse and Fine Recognition: A Hybrid Approach for Open-Ended Multi-Granularity Object Recognition in Interactive Educational Games

arXiv:2604.16785v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled open-ended object recognition, yet they struggle with fine-grained tasks. In co

Agents

Bridging the Ex-Vivo to In-Vivo Gap: Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments

arXiv:2512.23786v2 Announce Type: replace Abstract: Accurate Monocular Depth Estimation (MDE) is critical for autonomous robotic surgery. However, existing self-supervised methods often exhibit a seve

agentsarxiv-cs-cv

CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China

arXiv:2510.08986v2 Announce Type: replace Abstract: We introduce CAPC-CG, the Chinese Adaptive Policy Communication (Central Government) Corpus, the first open dataset of Chinese policy directives ann

safetyarxiv-cs-cl

CaTS-Bench: Can Language Models Describe Time Series?

arXiv:2509.20823v5 Announce Type: replace-cross Abstract: Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation,

CCAR: Intrinsic Robustness as an Emergent Geometric Property

arXiv:2604.16861v1 Announce Type: cross Abstract: Standard supervised learning optimizes for predictive accuracy but remains agnostic to the internal geometry of learned features, often yielding repre

Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation

arXiv:2604.17178v1 Announce Type: new Abstract: Emotional Support Conversation (ESC) plays a critical role in mental health assistance by providing accessible psychological support in real-world appli

safetyarxiv-cs-cl

Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessment

arXiv:2604.17074v1 Announce Type: new Abstract: The rapid advancement of generative models has led to a growing volume of AI-generated videos, making the automatic quality assessment of such videos in

ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship

arXiv:2604.18356v1 Announce Type: new Abstract: Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While r

Compositional Steering of Large Language Models with Steering Tokens

arXiv:2601.05062v2 Announce Type: replace Abstract: Deploying LLMs in real-world applications requires controllable output that satisfies multiple desiderata at the same time. While existing work exte

CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction

arXiv:2604.16742v1 Announce Type: cross Abstract: Scientists have long sought to accurately predict outcomes of real-world events before they happen. Can AI systems do so more reliably? We study this

DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation

arXiv:2604.17833v1 Announce Type: new Abstract: What appears effortless to a human waiter remains a major challenge for robots. Manipulating objects nonprehensilely on a tray is inherently difficult,

researcharxiv-cs-ro

Debate as Reward: A Multi-Agent Reward System for Scientific Ideation via RL Post-Training

arXiv:2604.16723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated potential in automating scientific ideation, yet current approaches relying on iterative prompting or c

Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness

arXiv:2603.04881v2 Announce Type: replace Abstract: Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade perf

DocQAC: Adaptive Trie-Guided Decoding for Effective In-Document Query Auto-Completion

arXiv:2604.18257v1 Announce Type: cross Abstract: Query auto-completion (QAC) has been widely studied in the context of web search, yet remains underexplored for in-document search, which we term DocQ

Does Welsh media need a review? Detecting bias in Nation.Cymru's political reporting

arXiv:2604.17628v1 Announce Type: new Abstract: Wales' political landscape has been marked by growing accusations of bias in Welsh media. This paper takes the first computational step toward testing t

safetyarxiv-cs-cl

Dual-End Consistency Model

arXiv:2602.10764v2 Announce Type: replace Abstract: The slow iterative sampling nature remains a major bottleneck for the practical deployment of diffusion and flow-based generative models. While cons

EasyVideoR1: Easier RL for Video Understanding

arXiv:2604.16893v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large languag

Edit Fidelity Field: Semantics-Aware Region Isolation for Training-Free Scene Text Editing

arXiv:2604.17500v1 Announce Type: new Abstract: Scene text editing (STE) has achieved remarkable progress in accurately rendering target text through diffusion-based methods. However, we identify a cr

applicationsarxiv-cs-cv

Tutorials

EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

arXiv:2508.17008v2 Announce Type: replace Abstract: Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall exper

tutorialsarxiv-cs-cl

Enhancing Anomaly-Based Intrusion Detection Systems with Process Mining

arXiv:2604.18066v1 Announce Type: cross Abstract: Anomaly-based Intrusion Detection Systems (IDSs) ensure protection against malicious attacks on networked systems. While deep learning-based IDSs achi

researcharxiv-cs-lg

Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms

arXiv:2604.16757v1 Announce Type: new Abstract: The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and

Tutorials

FlowC2S: Flowing from Current to Succeeding Frames for Fast and Memory-Efficient Video Continuation

arXiv:2604.17625v1 Announce Type: new Abstract: This paper introduces a novel methodology for generating fast and memory-efficient video continuations. Our method, dubbed FlowC2S, fine-tunes a pre-tra

tutorialsarxiv-cs-cv

Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning

model-releasesarxiv-cs-lg

arXiv:2604.18379v1 Announce Type: new Abstract: Most data-driven ionospheric forecasting models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-base

From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning

arXiv:2601.04278v2 Announce Type: replace Abstract: Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfull

From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?

arXiv:2604.17968v1 Announce Type: cross Abstract: Although large language models (LLMs) are increasingly used as annotators at scale, they are typically treated as a pragmatic fallback rather than a f

From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs

arXiv:2601.03682v2 Announce Type: replace Abstract: Recent studies reveal that large language models (LLMs) exhibit limited logical reasoning abilities in mathematical problem-solving, instead often r

From Inheritance to Saturation: Disentangling the Evolution of Visual Redundancy for Architecture-Aware MLLM Inference Acceleration

arXiv:2604.16462v1 Announce Type: new Abstract: High-resolution Multimodal Large Language Models (MLLMs) face prohibitive computational costs during inference due to the explosion of visual tokens. Ex

FUSE: Ensembling Verifiers with Zero Labeled Data

arXiv:2604.18547v1 Announce Type: cross Abstract: Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In p

Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease

arXiv:2601.00921v2 Announce Type: replace Abstract: Quantum methods are increasingly proposed for healthcare, but translational biomarker studies demand transparent benchmarking and robust small-datas

applicationsarxiv-cs-lg

Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT

arXiv:2511.16698v2 Announce Type: replace Abstract: SNOMED CT is a biomedical ontology with a hierarchical representation, modelling terminological concepts at a large scale. Knowledge retrieval in SN

Local Ai

How Creative Are Large Language Models in Generating Molecules?

arXiv:2604.18031v1 Announce Type: new Abstract: Molecule generation requires satisfying multiple chemical and biological constraints while searching a large and structured chemical space. This makes i

local-aiarxiv-cs-cl

How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function

arXiv:2604.17581v1 Announce Type: new Abstract: How much data is enough to make a scientific discovery? As biomedical datasets scale to millions of samples and AI models grow in capacity, progress inc

researcharxiv-cs-lg

Identifying Ethical Biases in Action Recognition Models

arXiv:2604.17971v1 Announce Type: new Abstract: Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has n

Improving LLM Code Reasoning via Semantic Equivalence Self-Play with Formal Verification

arXiv:2604.17010v1 Announce Type: new Abstract: We introduce a self-play framework for semantic equivalence in Haskell, utilizing formal verification to guide adversarial training between a generator

In Search of Lost DNA Sequence Pretraining

arXiv:2604.16570v1 Announce Type: new Abstract: DNA sequence encoding is fundamental to gene function prediction, protein synthesis, and diverse downstream biological tasks. Despite the substantial pr

researcharxiv-cs-lg

Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling

arXiv:2604.17001v1 Announce Type: new Abstract: The recently established Convolution Nuclear Norm Minimization (CNNM) addresses the problem of extit{tensor completion with arbitrary sampling} (TCAS),