All entries

EvoRubrics: Dynamic Rubrics as Rewards via Adversarial Co-Evolution for LLM Reinforcement Learning

arXiv:2606.23038v1 Announce Type: new Abstract: Rubric-based rewards offer interpretable and fine-grained optimization signals for reinforcement learning in open-ended tasks where verifiable answers a

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Exact and Approximate Range Queries for Efficient Ball Mapper Construction

DGX agent

arXiv:2601.01405v2 Announce Type: replace-cross Abstract: Ball Mapper is a tool in topological data analysis that summarizes a finite metric dataset by covering it with metric balls and encoding their

researcharxiv-cs-lg

23 Jun 2026

Expected Free Energy-based Planning as Variational Inference

arXiv:2606.20658v1 Announce Type: cross Abstract: Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expecte

Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation

arXiv:2606.23131v1 Announce Type: new Abstract: Background: Laparoscopic camera navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are tim

safetyarxiv-cs-cv

Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling

arXiv:2606.22780v1 Announce Type: new Abstract: Mental illnesses among drug users are an increasing international issue, particularly in regions where early detection cannot be easily undertaken. The

Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance

arXiv:2503.21321v2 Announce Type: replace-cross Abstract: With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent tr

Explanations for Automatic Speech Recognition

arXiv:2302.14062v2 Announce Type: replace-cross Abstract: We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ul

Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep

arXiv:2408.07822v2 Announce Type: replace-cross Abstract: We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention

Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm

arXiv:2509.23946v3 Announce Type: replace Abstract: Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or

Applications

Exposing the Illusion of Erasure in Knowledge Editing for LLMs

arXiv:2606.23276v1 Announce Type: new Abstract: Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mecha

applicationsarxiv-cs-lg

Expressivity Saturation: Reduced Affine Region Usage Under Increasing Task Complexity

arXiv:2606.21687v1 Announce Type: new Abstract: Piecewise-affine neural networks (e.g., with ReLU or LeakyReLU activations) implement continuous piecewise-affine maps, and the number of affine regions

Extraction and Analysis of Multimodal Concepts in Vision Language Models through Sparse Autoencoders

arXiv:2606.21197v1 Announce Type: new Abstract: Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioni

safetyarxiv-cs-cv

Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization

model-releasesarxiv-cs-ro

arXiv:2606.21100v1 Announce Type: new Abstract: The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggl

Factored Gossip DiLoCo: Reducing Blocking Communication in DiLoCo

arXiv:2606.22768v1 Announce Type: new Abstract: To make large-scale distributed training practical outside high-bandwidth datacenters, we must reduce blocking, high-volume synchronization. While DiLoC

Failure Analysis in Transition: An Industry Survey of Challenges, Priorities, and Standardization Needs in Advanced Packaging and Heterogeneous Integration

arXiv:2606.22149v1 Announce Type: cross Abstract: Failure analysis is being reshaped by heterogeneous integration, chiplet-based architectures, hybrid bonding, backside technologies, & increasingly bu

Fair Transit Stop Placement: A Clustering Perspective and Beyond

arXiv:2602.06776v2 Announce Type: replace-cross Abstract: We study the transit stop placement (TrSP) problem in general metric spaces, where agents travel between source-destination pairs and may eith

FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data

arXiv:2606.23515v1 Announce Type: cross Abstract: Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

arXiv:2602.23611v2 Announce Type: replace-cross Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such

FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimization

arXiv:2503.22934v2 Announce Type: replace Abstract: Image classification models trained on clean data often degrade sharply when exposed to corrupted test or deployment data, such as images with impul

FAIRVAR: Fair Federated Learning via Variance Regularization

arXiv:2508.12042v2 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data. However, heteroge

Faithful Grounded Visual Reasoning via Learned Proxy-Tokens

arXiv:2606.23354v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in Visual Question Answering (VQA), yet their 'black-box' nature hinders deplo

Fara-1.5: Scalable Learning Environments for Computer Use Agents

arXiv:2606.20785v1 Announce Type: cross Abstract: Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires tw

FARM: Find Anything using Relational Spatial Memory

arXiv:2606.15476v2 Announce Type: replace Abstract: Robots operating in homes, warehouses, and other object-rich environments need memory systems that can find specific object instances on demand. Obj

researcharxiv-cs-ro

FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving

arXiv:2606.21587v1 Announce Type: new Abstract: Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency. Standard

Fast Nonparametric Conditional Independence Testing via Two-Stage Regression

arXiv:2606.18011v2 Announce Type: replace-cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration,

Fast-TurboQuant: A Multiplier-Free Online Vector Quantization Approach

arXiv:2606.21448v1 Announce Type: new Abstract: As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-

hardwarearxiv-cs-lg

FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction

arXiv:2508.18031v2 Announce Type: replace Abstract: Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from

Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation

arXiv:2606.22510v1 Announce Type: new Abstract: While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observa

Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models

arXiv:2606.22618v1 Announce Type: new Abstract: Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sus

Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm

arXiv:2606.22466v1 Announce Type: new Abstract: Federated learning (FL) is an emerging distributed machine learning paradigm that enables local devices to jointly train a global model while keeping da

Federated Temporal Attention Intelligence for Cyber-Resilient IoMT: Lightweight Digital Twins and PPO-Driven Honeypot Deception

arXiv:2606.21422v1 Announce Type: new Abstract: The rapid proliferation of Internet of Medical Things (IoMT) devices introduces critical cybersecurity vulnerabilities in healthcare environments where

FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMs

arXiv:2606.22875v1 Announce Type: new Abstract: Training Latent Diffusion Models (LDMs) within Federated Learning (FL) has attracted increasing attention due to its ability to combine the powerful gen

FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting

arXiv:2507.18219v3 Announce Type: replace Abstract: Federated Graph Learning (FGL) is a distributed learning paradigm that enables collaborative training over large-scale subgraphs located on multiple

Feed-forward Motion In-betweening for Any 4D

arXiv:2606.22131v1 Announce Type: new Abstract: 4D dynamics (3D geometry evolving over time) is a fundamental representation of the physical world and plays a crucial role in world modeling (e.g., ani

FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism

arXiv:2606.22180v1 Announce Type: cross Abstract: Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retriev

hardwarearxiv-cs-lg

FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection

arXiv:2606.22487v1 Announce Type: new Abstract: Automated frame selection for fetal biometry remains under addressed, with most prior work targeting generic quality assessment or downstream measuremen

Few-Shot Hyperspectral Aphid Detection via FastGAN Synthetic Data Generation, Transformer-Based Classification and Explainable AI

arXiv:2606.21267v1 Announce Type: new Abstract: Early detection of aphid infestation in crops is essential for preventing yield loss and reducing unnecessary pesticide use. Hyperspectral imaging combi

FGGS-LiDAR: Ultra-Fast, GPU-Accelerated Simulation from General 3DGS Models to LiDAR

arXiv:2509.17390v2 Announce Type: replace Abstract: While 3D Gaussian Splatting (3DGS) has emerged as a strong representation for photorealistic rendering, its vast ecosystem of assets remains difficu

hardwarearxiv-cs-ro

Fidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution

arXiv:2606.21910v1 Announce Type: new Abstract: Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs over a continuous range of upscalin

FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks

arXiv:2509.17053v2 Announce Type: replace Abstract: Many contact-rich manipulation tasks require precise force regulation. However, most imitation learning (IL) policies remain position-centric and la

safetyarxiv-cs-ro

FiLM-Coordinated Dual-Branch Transformer for Global-Local Dependency Modeling in Language Modeling

arXiv:2606.21075v1 Announce Type: cross Abstract: Standard Transformers use a single self-attention pathway to model both global dependencies and local patterns, creating tension between long-range st

Fine-grained Human Motion Understanding with Language Models

arXiv:2606.20888v1 Announce Type: new Abstract: In this work, we propose methodname, an LLM-based model for fine-grained human motion understanding that represents motion as a sequence of skeletal pos

Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study

arXiv:2602.17431v2 Announce Type: replace-cross Abstract: Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are larg

Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think

model-releasesarxiv-cs-ro

arXiv:2606.20246v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion pa

Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design

arXiv:2606.21683v1 Announce Type: cross Abstract: We consider the parameter estimation problem in logistic regression with Gaussian design: the estimation of a fixed unknown parameter heta^*in R^d (|h

FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes

arXiv:2606.20769v1 Announce Type: cross Abstract: AI systems for peer review fail on three fronts: they train on Computer Science and Machine Learning venues alone, ignore the iterative dialogue that

Applications

Flatness Preserves Instruction Following in Vision-Language-Action Models

arXiv:2606.23641v1 Announce Type: new Abstract: Vision-language-action (VLA) models have the potential for open-world generalization by leveraging pretrained vision-language representations, yet downs

applicationsarxiv-cs-ro

FleetAgent: Teleoperation Assistant for Autonomous Fleets via Vectorized V2N Messages

arXiv:2606.21222v1 Announce Type: new Abstract: Large-scale autonomous fleets rely on teleoperation to resolve rare failures, yet streaming raw sensor data from many vehicles is costly, and remote ope

hardwarearxiv-cs-ro