Search: “evaluation”

Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models

arXiv:2503.00794v2 Announce Type: replace Abstract: Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where pre

safetyarxiv-cs-ro

Agents

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Detecting Privilege Escalation in Polyglot Microservices via Agentic Program Analysis

DGX agent

arXiv:2605.15569v1 Announce Type: cross Abstract: Microservices are widely adopted in modern cloud systems due to their scalability and fault tolerance. However, microservice architectures introduce s

agentsarxiv-cs-ai

18 May 2026

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

model-releasesarxiv-cs-cl

arXiv:2605.15518v1 Announce Type: new Abstract: The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misus

Deterministic Event-Graph Substrates as World Models for Counterfactual Reasoning

arXiv:2605.15967v1 Announce Type: new Abstract: We study event-graph substrates: a class of world models that represent agent state as an append-only log of typed RDF triples and answer counterfactual

Differentially Private Motif-Preserving Multi-modal Hashing

arXiv:2605.15460v1 Announce Type: cross Abstract: Cross-modal hashing enables efficient retrieval by encoding images and text into compact binary codes. State-of-the-art methods rely on semantic simil

Tutorials

DiscussLLM: Teaching Large Language Models When to Speak

arXiv:2508.18167v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, yet they largely operate as

tutorialsarxiv-cs-cl

DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding

arXiv:2605.15542v1 Announce Type: new Abstract: GUI agents powered by Multimodal Large Language Models (MLLMs) have demonstrated impressive capability in understanding and executing user instructions.

Agents

DrugSAGE:Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery

arXiv:2605.15461v1 Announce Type: cross Abstract: Building state-of-the-art (SOTA) predictive models for drug discovery requires expensive search over tools, architectures, and training strategies. Cu

agentsarxiv-cs-ai

Echo-Forcing: A Scene Memory Framework for Interactive Long Video Generation

arXiv:2605.16003v1 Announce Type: new Abstract: Autoregressive video diffusion models enable open-ended generation through local attention and KV caching. However, existing training-free long-video op

EduVQA: Towards Concept-Aware Assessment of Educational AI-Generated Videos

arXiv:2603.03066v2 Announce Type: replace Abstract: Existing AI-generated video quality assessment (AIGVQA) methods mainly focus on global perceptual realism and coarse text-video alignment, while ove

Efficiently Solving Mixed-Hierarchy Games with Quasi-Policy Approximations

arXiv:2602.01568v2 Announce Type: replace-cross Abstract: Multi-robot coordination often exhibits hierarchical structure, with some robots' decisions depending on the planned behaviors of others. Whil

safetyarxiv-cs-ro

End-to-end plaque counting and virus titration from laboratory plate images with deep learning

arXiv:2605.16008v1 Announce Type: new Abstract: Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-op

Ensemble Monitoring for AI Control: Diverse Signals Outweigh More Compute

arXiv:2605.15377v1 Announce Type: new Abstract: As AI systems are increasingly deployed in autonomous agentic settings at scale, it is important to ensure the actions they take are safe and aligned wi

Entity-Centric World Models: Interaction-Aware Masking for Causal Video Prediction

arXiv:2605.15466v1 Announce Type: new Abstract: Learning predictive world models from unlabelled video is a foundational challenge in artificial intelligence. While Joint Embedding Predictive Architec

Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrodinger Samplers

arXiv:2605.16126v1 Announce Type: cross Abstract: For a fixed flow-based generative model under a small inference budget, sample quality can depend strongly on where the sampler spends its few functio

Entropy-Based Characterisation of the Polarised Regime in Latent Variable Models

arXiv:2605.15965v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) often exhibit a polarised regime in which latent variables separate into active, passive, and mixed subsets. Existing cr

researcharxiv-cs-lg

Eskwai for Students: Generative AI Assistant for Legal Education in Ghana

arXiv:2605.15380v1 Announce Type: new Abstract: Recent advances in generative AI have shown their potential to be leveraged for legal education. Yet, work on the development and deployment of such sys

safetyarxiv-cs-cl

Applications

ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations

arXiv:2601.23068v2 Announce Type: replace-cross Abstract: Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely us

applicationsarxiv-cs-ai

f-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data

arXiv:2605.15417v1 Announce Type: cross Abstract: In GFlowNets and variational inference, it has been shown that the mean square error between target and model log probabilities is an effective, low v

Fair outputs, Biased Internals: Causal Potency and Asymmetry of Latent Bias in LLMs for High-Stakes Decisions

arXiv:2605.15217v1 Announce Type: new Abstract: Instruction-tuned language models exhibit behavioural fairness in high-stakes decisions while retaining biased associations in their internal representa

FAR: Function-preserving Attention Replacement for IMC-friendly Inference

arXiv:2505.21535v4 Announce Type: replace-cross Abstract: While transformers dominate modern vision and language models, their attention mechanism remains poorly suited for in-memory computing (IMC) d

local-aiarxiv-cs-ai

Fast Expanding Safe Circular Regions for Efficient Local Path Planning

arXiv:2605.16009v1 Announce Type: new Abstract: Local navigation is one of the fundamental problems in robot navigation, and numerous approaches have been proposed over the years, including methods su

local-aiarxiv-cs-ro

Fault tolerance estimation in digital circuits with visualised generative networks

arXiv:2605.15212v1 Announce Type: cross Abstract: We propose a new numerical method to estimate the fault tolerance of failure modes in digital circuit structures with a generative network sampling te

Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions

model-releasesarxiv-cs-lg

arXiv:2605.15355v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, makin

Applications

FedOptima: Optimizing Resource Utilization in Federated Learning

arXiv:2504.13850v2 Announce Type: replace-cross Abstract: Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low res

applicationsarxiv-cs-lg

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosures

arXiv:2604.05966v2 Announce Type: replace Abstract: Financial reporting systems increasingly leverage Large Language Models (LLMs) to extract and summarize corporate disclosures. However, most existin

safetyarxiv-cs-cl

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control

arXiv:2604.04539v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a core approach for robot control when expert demonstrations are unavailable. On-policy methods such as Proximal Poli

safetyarxiv-cs-lg

Flowette: Flow Matching with Graphette Priors for Graph Generation

arXiv:2602.23566v2 Announce Type: replace-cross Abstract: We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs

Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems

arXiv:2605.16198v1 Announce Type: new Abstract: We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle,

ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation

model-releasesarxiv-cs-cl

arXiv:2605.15794v1 Announce Type: new Abstract: We present ForMaT (Format-Preserving Multilingual Translation), a parallel corpus of 3,956 PDFs across 15 language pairs that preserves original layout

Hardware

frax: Fast Robot Kinematics and Dynamics in JAX

arXiv:2604.04310v2 Announce Type: replace Abstract: In robot control, planning, and learning, there is a need for rigid-body dynamics libraries that are highly performant, easy to use, and compatible

hardwarearxiv-cs-ro

From Pairs to Sequences: Track-Aware Policy Gradients for Keypoint Detection

arXiv:2602.20630v4 Announce Type: replace Abstract: Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning

safetyarxiv-cs-cv

GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks

arXiv:2605.15836v1 Announce Type: cross Abstract: Learning visuomotor policies from scarce expert demonstrations remains a core challenge in robotic manipulation. A primary hurdle lies in distilling h

GenShield: Unified Detection and Artifact Correction for AI-Generated Images

arXiv:2605.16122v1 Announce Type: cross Abstract: Diffusion-based image synthesis has made AI-generated images (AIGI) increasingly photorealistic, raising urgent concerns about authenticity in applica

GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions

arXiv:2605.15764v1 Announce Type: cross Abstract: Understanding social interactions requires reasoning over subtle non-verbal cues, yet current multimodal large language models (MLLMs) often fail to i

Grounded Reinforcement Learning for Visual Reasoning

arXiv:2505.23678v3 Announce Type: replace Abstract: While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual

local-aiarxiv-cs-cv

Hallucinations are inevitable but can be made statistically negligible

arXiv:2502.12187v3 Announce Type: replace Abstract: Hallucinations, a phenomenon where a language model (LM) generates nonfactual content, pose a significant challenge to the practical deployment of L

researcharxiv-cs-cl

Hybrid LLM-based Intelligent Framework for Robot Task Scheduling

arXiv:2605.15486v1 Announce Type: cross Abstract: This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed

Agents

ICRL: Learning to Internalize Self-Critique with Reinforcement Learning

arXiv:2605.15224v1 Announce Type: new Abstract: Large language model-based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed,

agentsarxiv-cs-ai

Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

model-releasesarxiv-cs-lg

arXiv:2605.05652v2 Announce Type: replace Abstract: Simulation-based inference (SBI) provides amortized Bayesian parameter inference from simulator-generated data without requiring explicit likelihood

Tutorials

Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling

arXiv:2504.09544v3 Announce Type: replace-cross Abstract: Recent advances in self-supervised deep learning have improved our ability to quantify cellular morphological changes in high-throughput micro

tutorialsarxiv-cs-cv

Interaction-Aware Influence Functions for Group Attribution

arXiv:2605.15675v1 Announce Type: cross Abstract: Influence functions approximate how removing a training example changes a quantity of interest, called the target function, such as a held-out loss. T

Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench

arXiv:2605.15226v1 Announce Type: cross Abstract: We ask whether agentic AI systems built for software engineering transfer to realistic hardware engineering. Existing hardware LLM benchmarks isolate

local-aiarxiv-cs-ai

Applications

ITGPT: Generative Pretraining on Irregular Timeseries

arXiv:2605.16069v1 Announce Type: new Abstract: Timeseries regression models often struggle to leverage large volumes of labeled multimodal data, particularly when the data are irregularly sampled or

applicationsarxiv-cs-lg

KaRMA: A Kinematic Metric for Fine Manipulation Ability in Robotic Hands

arXiv:2605.15548v1 Announce Type: new Abstract: Traditional robotic hand metrics focus on static properties such as workspace, manipulability, and grasp stability. However, these metrics do not direct

researcharxiv-cs-ro

LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification

arXiv:2603.10881v2 Announce Type: replace Abstract: Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low si

researcharxiv-cs-lg

Layer Equivalence Is Not a Property of Layers Alone: How You Test Redundancy Changes What You Find

arXiv:2605.16234v1 Announce Type: cross Abstract: When researchers ask whether two transformer layers are 'equivalent' for compression, they often conflate distinct tests. Replacement asks whether one

Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI

arXiv:2605.15895v1 Announce Type: cross Abstract: Clinical application of high-resolution diffusion MRI is hindered by hardware limitations and prohibitive scan times, motivating computational super-r

researcharxiv-cs-cv