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Evaluating Chinese Ambiguity Understanding in Large Language Models

arXiv:2605.15635v1 Announce Type: new Abstract: Linguistic ambiguity is critical to the robustness of Large Language Models (LLMs), yet existing research focuses mostly on English, with limited attent

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Human DGX agent

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Evaluating Design Video Generation: Metrics for Compositional Fidelity

DGX agent

arXiv:2605.16223v1 Announce Type: cross Abstract: Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain. Unlike natur

researcharxiv-cs-ai

18 May 2026

Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation

arXiv:2605.15707v1 Announce Type: cross Abstract: Whole-heart multi-compartment CT segmentation is clinically important, but standard CNNs do not explicitly enforce anatomical plausibility. Based on s

researcharxiv-cs-cv

Explainable AI Isn't Enough! Rethinking Algorithmic Contestability

arXiv:2605.16041v1 Announce Type: cross Abstract: Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising

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

Extrapolation Guarantees for Perturbation Modeling Under the Additive Latent Shift Assumption

arXiv:2504.18522v3 Announce Type: replace-cross Abstract: We consider the problem of modeling the effects of perturbations like gene knockouts on measurements such as single-cell RNA counts. Given dat

researcharxiv-cs-lg

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

Local Ai

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

Hardware

FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization

arXiv:2605.15824v1 Announce Type: new Abstract: Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot suppor

hardwarearxiv-cs-cv

Local 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 Imputation under Heterogeneous Feature Spaces

arXiv:2605.16099v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative training across decentralized clients, but most methods assume aligned feature schemas, an assumption th

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

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

Feedback World Model Enables Precise Guidance of Diffusion Policy

arXiv:2605.15705v1 Announce Type: cross Abstract: World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become un

Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries

arXiv:2605.15680v1 Announce Type: new Abstract: Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate lev

Few-Step Diffusion Language Models via Trajectory Self-Distillation

arXiv:2602.12262v3 Announce Type: replace Abstract: Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decodin

researcharxiv-cs-cl

FFAvatar: Few-Shot, Feed-Forward, and Generalizable Avatar Reconstruction

model-releasesarxiv-cs-cv

arXiv:2605.15320v1 Announce Type: cross Abstract: Avatar reconstruction has traditionally relied on per-subject optimization that requires hours of computation or on expensive preprocessing that limit

FINESSE-Bench: A Hierarchical Benchmark Suite for Financial Domain Knowledge and Technical Analysis in Large Language Models

arXiv:2605.15482v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being applied to financial analysis, reporting, investment decision support, risk management, compliance,

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

FLASH: Efficient Visuomotor Policy via Sparse Sampling

arXiv:2605.15492v1 Announce Type: cross Abstract: Generative models such as diffusion and flow matching have become dominant paradigms for visuomotor policy learning, yet their reliance on iterative d

safetyarxiv-cs-cv

Flash-GRPO: Efficient Alignment for Video Diffusion via One-Step Policy Optimization

model-releasesarxiv-cs-cv

arXiv:2605.15980v1 Announce Type: new Abstract: Group Relative Policy Optimization has emerged as essential for aligning video diffusion models with human preferences, but faces a critical computation

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

Flatness-based trajectory planning for 3D overhead cranes with friction compensation and collision avoidance

arXiv:2510.24457v2 Announce Type: replace Abstract: This paper presents an optimal trajectory generation method for 3D overhead cranes by leveraging differential flatness. This framework enables the d

researcharxiv-cs-ro

FlipAttack: Jailbreak LLMs via Flipping

arXiv:2410.02832v2 Announce Type: replace-cross Abstract: This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we

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

Fluency and Faithfulness in Human and Machine Literary Translation

arXiv:2605.15282v1 Announce Type: new Abstract: Literary translation requires balancing target-language fluency with faithfulness to the source. Recent large language models (LLMs) often produce fluen

researcharxiv-cs-cl

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision

arXiv:2312.05975v3 Announce Type: replace-cross Abstract: Explainability is a vital aspect of modern AI for real-world impact and usability. The main objective of this paper is to emphasise the need t

applicationsarxiv-cs-ai

FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy

arXiv:2605.15944v1 Announce Type: cross Abstract: Visuomotor policies aim to learn complex manipulation tasks from expert demonstrations. However, generating smooth and coherent trajectories remains c

FORGE: Self-Evolving Agent Memory With No Weight Updates via Population Broadcast

arXiv:2605.16233v1 Announce Type: new Abstract: Can LLM agents improve decision-making through self-generated memory without gradient updates? We propose FORGE (Failure-Optimized Reflective Graduation

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

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

FormulaCode: Evaluating Agentic Optimization on Large Codebases

arXiv:2603.16011v2 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to op

Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning

arXiv:2605.15299v1 Announce Type: cross Abstract: In search and recommendation systems, predictive models often suffer from temporal instability when certain input features introduce volatility in out

applicationsarxiv-cs-ai

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

Frequency-domain Event-based Imaging for Selective Surveillance

arXiv:2605.15392v1 Announce Type: cross Abstract: Event-based cameras (EBCs) are an attractive sensing modality for surveillance due to their reporting of pixel-level radiance changes with microsecond

researcharxiv-cs-cv

From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding

arXiv:2605.15951v1 Announce Type: new Abstract: Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grou

researcharxiv-cs-cv

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery

arXiv:2605.15412v1 Announce Type: cross Abstract: Modern quantitative trading increasingly relies on systematic models to extract predictive signals from large-scale financial data, where alpha factor

From Flat Language Labels to Typological Priors: Structured Language Conditioning for Multilingual Speech-to-Speech Translation

arXiv:2605.16026v1 Announce Type: cross Abstract: Compositional speech-to-speech translation (S2ST) systems built upon speech large language models (SpeechLLMs) have recently shown promising performan

From Full and Partial Intraoral Scans to Crown Proposal: A Classification-Guided Restoration Assistance Pipeline

arXiv:2605.15241v1 Announce Type: cross Abstract: Single-unit crown restoration is among the most common procedures in clinical dentistry, with CAD/CAM workflows now designing crowns directly from int

safetyarxiv-cs-cv

Agents

From Gridworlds to Warehouses: Adapting Lightweight One-shot Multi-Agent Pathfinding for AGVs

arXiv:2605.15799v1 Announce Type: cross Abstract: Multi-agent pathfinding (MAPF) under one-shot planning is a core component of warehouse automation, yet classical formulations typically assume four-c

agentsarxiv-cs-ro

From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs

arXiv:2508.20810v3 Announce Type: replace Abstract: Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static

From I/O to Code with Discovery Agent

arXiv:2605.15334v1 Announce Type: cross Abstract: The automatic synthesis of a program from any form of specification is regarded as a holy grail of computer science. Fueled by LLMs, NL2Code has achie

From Layers to Networks: Comparing Neural Representations via Diffusion Geometry

model-releasesarxiv-cs-lg

arXiv:2605.15901v1 Announce Type: new Abstract: Diffusion geometry is a manifold learning framework that uses random walks defined by Markov transition matrices to characterize the geometry of a datas

From LLM-Generated Conjectures to Lean Formalizations: Automated Polynomial Inequality Proving via Sum-of-Squares Certificates

arXiv:2605.15445v1 Announce Type: new Abstract: Automated proving of polynomial inequalities is a fundamental challenge in automated mathematical reasoning, where rich algebraic structure and a rapidl

From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI

arXiv:2506.22440v2 Announce Type: replace-cross Abstract: This paper introduces the Generality-Accuracy-Simplicity (GAS) framework to analyze how large language models (LLMs) are reshaping organizatio

From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint

arXiv:2605.15862v1 Announce Type: new Abstract: Adaptive biomechanical systems may show similar observable gait performance while differing in latent organization and longitudinal behavior. This study

researcharxiv-cs-lg