Search: “evaluation”

An Experimental Modular Instrument With a Haptic Feedback Framework for Robotic Surgery Training

arXiv:2604.27385v1 Announce Type: new Abstract: Robotic-assisted surgery offers significant clinical advantages but largely eliminates direct haptic feedback, increasing the risk of excessive tool-tis

researcharxiv-cs-ro

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

ANCORA: Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning

DGX agent

arXiv:2604.27644v1 Announce Type: cross Abstract: We propose a paradigm shift from learning to answer to learning to question: can a language model generate verifiable problems, solve them, and turn t

safetyarxiv-cs-ai

1 May 2026

Automated Detection of Mutual Gaze and Joint Attention in Dual-Camera Settings via Dual-Stream Transformers

arXiv:2604.27105v1 Announce Type: new Abstract: Analyzing mutual gaze (MG) and joint attention (JA) is critical in developmental psychology but traditionally relies on labor-intensive manual coding. A

researcharxiv-cs-cv

Automatic Causal Fairness Analysis with LLM-Generated Reporting

arXiv:2604.27011v1 Announce Type: cross Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation. Most Au

AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data

arXiv:2604.27266v1 Announce Type: new Abstract: This paper introduces AutoREC, an open-source Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circ

agentsarxiv-cs-lg

Autoregressive Synthesis of Sparse and Semi-Structured Mixed-Type Data

model-releasesarxiv-cs-lg

arXiv:2603.01444v2 Announce Type: replace Abstract: Synthetic data generation is an important capability for privacy-preserving data sharing, system benchmarking and test data provisioning. For mixed-

AutoSP: Unlocking Long-Context LLM Training Via Compiler-Based Sequence Parallelism

model-releasesarxiv-cs-lg

arXiv:2604.27089v1 Announce Type: new Abstract: Large-language-models (LLMs) demonstrate enormous utility in long-context tasks which require processing prompts that consist of tens to hundreds of tho

AutoSurfer -- Teaching Web Agents through Comprehensive Surfing, Learning, and Modeling

arXiv:2604.27253v1 Announce Type: new Abstract: Recent advances in multimodal large language models (LLMs) have revolutionized web agents that can automate complex tasks on websites. However, their ac

Hardware

Benchmarking Deep Learning Models for Object Detection on Edge Computing Devices

arXiv:2409.16808v1 Announce Type: cross Abstract: Modern applications, such as autonomous vehicles, require deploying deep learning algorithms on resource-constrained edge devices for real-time image

hardwarearxiv-cs-lg

Beyond Pixel Fidelity: Minimizing Perceptual Distortion and Color Bias in Night Photography Rendering

arXiv:2604.28136v1 Announce Type: new Abstract: Night Photography Rendering (NPR) poses a significant challenge due to the extreme contrast between dark and illuminated areas in scenes, stemming from

safetyarxiv-cs-cv

Calibrating Attribution Proxies for Reward Allocation in Participatory Weather Sensing

arXiv:2604.27944v1 Announce Type: new Abstract: Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contribut

Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Study

arXiv:2602.10140v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can now synthesize non-trivial executable code from textual descriptions, raising an important question: can LLMs

CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting

arXiv:2604.27840v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have shown great promise in time series forecasting. However, most existing LLM-based forecasting methods still

agentsarxiv-cs-ai

Characterizing the Consistency of the Emergent Misalignment Persona

arXiv:2604.28082v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignme

CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model

arXiv:2506.09110v3 Announce Type: replace Abstract: Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation

COHERENCE: Benchmarking Fine-Grained Image-Text Alignment in Interleaved Multimodal Contexts

arXiv:2604.27389v1 Announce Type: cross Abstract: In recent years, Multimodal Large Language Models (MLLMs) have achieved remarkable progress on a wide range of multimodal benchmarks. Despite these ad

Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models

arXiv:2604.27251v1 Announce Type: cross Abstract: Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further eli

researcharxiv-cs-ai

Connected Dependability Cage: Run-Time Function and Anomaly Monitoring for the Development and Operation of Safe Automated Vehicles

arXiv:2604.27728v1 Announce Type: new Abstract: The advancement of automated vehicles introduces complex safety challenges, particularly in dynamic and unpredictable environments where AI-enabled perc

safetyarxiv-cs-ro

Consumer Attitudes Towards AI in Digital Health: A Mixed-Methods Survey in Australia

arXiv:2604.27744v1 Announce Type: new Abstract: AI applications are increasingly being introduced into digital health. While technical performance has advanced rapidly, successful deployment mainly de

Context as Prior: Bayesian-Inspired Intent Inference for Non-Speaking Agents with a Household Cat Testbed

arXiv:2604.27445v1 Announce Type: new Abstract: Many agents in real-world environments cannot reliably communicate their goals through language, including household pets, pre-verbal infants, and other

applicationsarxiv-cs-cv

Cross-Lingual Sentiment Misalignment: Auditing Multilingual Language Models for Inversion Risk, Dialectal Representation, and Affective Stability

arXiv:2602.17469v2 Announce Type: replace Abstract: Recent advances in multilingual representation learning aim to bridge the performance gap between high- and low-resource languages, yet their abilit

safetyarxiv-cs-cl

Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

arXiv:2604.27033v1 Announce Type: new Abstract: Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and

safetyarxiv-cs-lg

Tutorials

Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining

arXiv:2509.05291v2 Announce Type: replace-cross Abstract: Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, bec

tutorialsarxiv-cs-ai

Culture-inspired Multi-modal Color Palette Generation and Colorization: A Chinese Youth Subculture Case

arXiv:2102.05231v1 Announce Type: cross Abstract: Color is an essential component of graphic design, acting not only as a visual factor but also carrying cultural implications. However, existing resea

researcharxiv-cs-ai

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

arXiv:2604.27775v1 Announce Type: cross Abstract: Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hard

Debiasing Reward Models via Causally Motivated Inference-Time Intervention

arXiv:2604.27495v1 Announce Type: cross Abstract: Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often sensitive to spurious

Deep Learning-Based Segmentation of Peritoneal Cancer Index Regions from CT Imaging

arXiv:2604.27697v1 Announce Type: cross Abstract: Peritoneal metastases are currently assessed using diagnostic laparoscopy to determine Sugarbaker's Peritoneal Cancer Index (sPCI), which works by div

researcharxiv-cs-ai

DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures

arXiv:2604.28118v1 Announce Type: cross Abstract: Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal componen

Tutorials

Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders

arXiv:2604.28176v1 Announce Type: cross Abstract: Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as b

tutorialsarxiv-cs-lg

Designing Ethical Learning for Agentic AI: Toegye Yi Hwang's Ethical Emotion Regulation Framework

arXiv:2604.26958v1 Announce Type: cross Abstract: Agentic AI systems capable of autonomous goal setting and proactive intervention introduce new challenges for regulating moral-emotional processes in

Diagnosing Capability Gaps in Fine-Tuning Data

model-releasesarxiv-cs-lg

arXiv:2604.27547v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) for domain-specific tasks requires training datasets that comprehensively cover the target capabilities a pract

Diffusion-OAMP for Joint Image Compression and Wireless Transmission

arXiv:2604.27952v1 Announce Type: cross Abstract: Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in pra

Do World Action Models Generalize Better than VLAs? A Robustness Study

model-releasesarxiv-cs-ro

arXiv:2603.22078v3 Announce Type: replace Abstract: Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predictin

Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA

arXiv:2604.27994v1 Announce Type: new Abstract: Learned driving agents often degrade when deployed in unseen environments. This paper studies a deliberately bounded instance of that problem in the CAR

safetyarxiv-cs-ro

Local Ai

Effective Prompt Pool Learning for Continual Category Discovery

arXiv:2407.19001v3 Announce Type: replace Abstract: This paper studies effective prompt pool learning for Continual Category Discovery (CCD), a challenging open-world setting where a model must discov

local-aiarxiv-cs-cv

Hardware

Efficient Training on Multiple Consumer GPUs with RoundPipe

arXiv:2604.27085v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe intercon

hardwarearxiv-cs-ai

Local Ai

Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents

arXiv:2604.27143v1 Announce Type: cross Abstract: Recent research has demonstrated the potential of Large Language Models (LLMs) for autonomous penetration testing, particularly when using cloud-based

local-aiarxiv-cs-ai

EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory

arXiv:2604.27695v1 Announce Type: cross Abstract: Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi

researcharxiv-cs-cl

Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models

arXiv:2604.28149v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in en

Exploration Hacking: Can LLMs Learn to Resist RL Training?

arXiv:2604.28182v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignmen

safetyarxiv-cs-cl

Exploring Applications of Transfer-State Large Language Models: Cognitive Profiling and Socratic AI Tutoring

arXiv:2604.27454v1 Announce Type: new Abstract: Large language models (LLMs) sometimes exhibit qualitative shifts in response style under sustained self-referential dialogue conditions (Berg et al., 2

safetyarxiv-cs-cl

Exploring Interaction Paradigms for LLM Agents in Scientific Visualization

arXiv:2604.27996v1 Announce Type: new Abstract: This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate vi

Exploring Vision Neural Network Pruning via Screening Methodology

arXiv:2502.07189v2 Announce Type: replace Abstract: The remarkable performance of modern deep neural networks (DNNs) is largely driven by their massive scale, often comprising tens to hundreds of mill

applicationsarxiv-cs-lg

FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning

arXiv:2604.28024v1 Announce Type: new Abstract: Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learnin

applicationsarxiv-cs-lg

Flying by Inference: Active Inference World Models for Adaptive UAV Swarms

arXiv:2604.27935v1 Announce Type: new Abstract: This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-

agentsarxiv-cs-ro

Foreclassing: A new machine learning perspective on human decision making with temporal data

arXiv:2503.04956v2 Announce Type: replace-cross Abstract: Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and u

applicationsarxiv-cs-lg

From Context to Skills: Can Language Models Learn from Context Skillfully?

arXiv:2604.27660v1 Announce Type: new Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learn

agentsarxiv-cs-ai

From LLM-Driven Trading Card Generation to Procedural Relatedness: A Pokemon Case Study

arXiv:2604.27972v1 Announce Type: new Abstract: Since the dawn of Trading Card Games, the genre has grown into a multi-billion-dollar industry engaging millions of analog and digital players worldwide

applicationsarxiv-cs-ai