Search: “papers”

OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents

arXiv:2606.26350v1 Announce Type: new Abstract: Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tas

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PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology

DGX agent

arXiv:2512.17621v2 Announce Type: replace Abstract: While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity

safetyarxiv-cs-cv

26 Jun 2026

ProvenAI: Provenance-Native Traces of Evidence in Generated Answers

arXiv:2606.26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source mea

Local Ai

Quantization in Federated Learning: Methods, Challenges and Future Directions

arXiv:2606.26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally co

local-aiarxiv-cs-lg

Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints

arXiv:2606.26106v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress. Whil

Reproducibility Study of 'AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models'

arXiv:2606.26783v1 Announce Type: cross Abstract: Fang et al. (2025) introduced a null-space constrained projection, named AlphaEdit, for locate-then-edit knowledge editing methods, theoretically guar

Revisiting Action Factorization for Complex Action Spaces

arXiv:2606.26574v1 Announce Type: new Abstract: Many real-world control problems involve hybrid discrete-continuous action spaces. For example, steering and signaling in autonomous driving, and aiming

agentsarxiv-cs-lg

Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy

arXiv:2606.26527v1 Announce Type: new Abstract: Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient auton

safetyarxiv-cs-lg

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP

arXiv:2606.27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become fo

Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling

arXiv:2606.26617v1 Announce Type: new Abstract: Scaling laws describe how learning performance varies with model size, data size, and compute. While recent theoretical work has established scaling law

safetyarxiv-cs-lg

SpaceRipple: Lightweight Semantic Delivery for Mission-Oriented LEO Earth Observation Satellite Networks

arXiv:2606.26559v1 Announce Type: cross Abstract: Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limite

TaskTok: Delving into Task Tokens for Task-driven Image Restoration

arXiv:2606.26615v1 Announce Type: new Abstract: While traditional image restoration focuses on perceptual quality, Task-Driven Image Restoration (TDIR) aims to maximize the performance of downstream h

Tutorials

Theory of the Frequency Principle for General Deep Neural Networks

arXiv:1906.09235v3 Announce Type: replace Abstract: Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal

tutorialsarxiv-cs-lg

Theory-Scale Auto-Formalization of Logics for Computer Science

model-releasesarxiv-cs-lg

arXiv:2606.26525v1 Announce Type: new Abstract: Auto-formalization is critical for scalable formal verification, but existing progress largely focuses on isolated statements, while theory-scale auto-f

TMP: Tree-structured Mixed-policy Pruning for Large-scale Image Generation and Editing

model-releasesarxiv-cs-cv

arXiv:2606.27089v1 Announce Type: new Abstract: Modern image generation model rapidly grows their sizes to meet high-fidelity image synthesis. However, they gradually become unaffordable for their eno

TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference

arXiv:2606.27161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of

UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation

arXiv:2603.01461v2 Announce Type: replace Abstract: Echocardiography is critical for diagnosing cardiovascular diseases, yet the shortage of skilled sonographers hinders timely patient care, due to hi

Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching

arXiv:2606.27342v1 Announce Type: cross Abstract: Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they

UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehicles

arXiv:2511.18254v3 Announce Type: replace Abstract: LiDAR scene flow is the task of estimating per-point 3D motion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on

agentsarxiv-cs-cv

Unsupervised Memory-Enhanced Video Transformers: Obstacle Detection for Autonomous Agricultural Rover

arXiv:2606.26151v1 Announce Type: cross Abstract: While autonomous rovers have become indispensable to precision farming, achieving consistent operational safety remains a critical challenge. Conventi

Vulnerability of Natural Language Classifiers to Evolutionary Generated Adversarial Text

arXiv:2606.27215v1 Announce Type: new Abstract: Deep learning models have achieved impressive performance across various fields but remain vulnerable to adversarial inputs, particularly in NLP, where

When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework

arXiv:2606.26400v1 Announce Type: new Abstract: Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and aut

When are likely answers right? On Sequence Probability and Correctness in LLMs

arXiv:2606.27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, ei

researcharxiv-cs-lg

XMSE-Aware Adaptive Empirical Bayes Estimation

arXiv:2606.26975v1 Announce Type: cross Abstract: Empirical Bayes (EB) estimators can match the first-order asymptotic risk of maximum likelihood (ML) while behaving very differently at second order:

A Bregman Perspective on Classification and Regression Trees

arXiv:2606.13984v2 Announce Type: replace-cross Abstract: Classification and Regression Trees (CART) constitute one of the most influential paradigms in statistical learning. Although a variety of imp

researcharxiv-cs-lg

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation

arXiv:2606.25476v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance across natural language processing tasks, yet their deployment in high-stakes appl

A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks

arXiv:2606.26059v1 Announce Type: new Abstract: The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. A

Adaptive-Horizon Conflict-Based Search for Closed-Loop Multi-Agent Path Finding

arXiv:2602.12024v3 Announce Type: replace Abstract: Multi-Agent Path Finding (MAPF) is a core coordination problem for large robot fleets in automated warehouses and logistics. Existing approaches are

safetyarxiv-cs-ro

Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction

arXiv:2606.25003v1 Announce Type: new Abstract: Federated split learning (FSL) enables collaborative training across bandwidth-constrained IoT devices, but repeated activation and gradient exchange cr

safetyarxiv-cs-lg

Adaptive Oscillatory Inductive Bias for Modeling Sharp Prosodic Dynamics in Diffusion-Based TTS

arXiv:2606.25424v1 Announce Type: cross Abstract: Diffusion-based text-to-speech (TTS) models have achieved significant improvements in speech quality. However, modeling sharp prosodic transitions and

safetyarxiv-cs-cl

AeroCast: Probabilistic 3D Trajectory Prediction for Non-Cooperative Aerial Obstacles via Transformer-MDN Architecture

arXiv:2606.25122v1 Announce Type: cross Abstract: Autonomous aerial vehicles operating in shared airspace must predict the future positions of non-cooperative obstacles to plan evasive maneuvers befor

agentsarxiv-cs-lg

AI Coaching for Accelerating Human Skill Development with Reinforcement Learning

arXiv:2606.25337v1 Announce Type: new Abstract: AI copilots can substantially boost human performance through shared control, but excessive assistance can induce over-reliance and skill atrophy. This

agentsarxiv-cs-ro

An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages

arXiv:2604.02596v3 Announce Type: replace Abstract: In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepr

researcharxiv-cs-cl

An Improved Variational Method for Image Denoising

arXiv:2410.02587v2 Announce Type: replace Abstract: The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which meas

Applications

Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints

arXiv:2603.11606v2 Announce Type: replace Abstract: Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view c

applicationsarxiv-cs-cv

ARTOO-DARTU: Studying AR-HRC With AR Obstruction Mitigation During a Warehouse Task

arXiv:2606.25202v1 Announce Type: cross Abstract: Human-robot collaboration (HRC) often requires robot intentions and internal states to be conveyed to users for task efficiency and safety. Recently,

safetyarxiv-cs-ro

Attractive and Repulsive Pattern Control in Sequence Generation

arXiv:2606.24911v1 Announce Type: cross Abstract: Variable-order Markov models preserve local symbolic syntax by adapting context length, but long continuations can enter recurring high-order 'tunnels

BitNet Text Embeddings

arXiv:2606.25674v1 Announce Type: new Abstract: LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone

researcharxiv-cs-cl

Causality-Based Parametric Control Barrier Function for Safe Multi-Vehicle Interaction

arXiv:2606.25134v1 Announce Type: new Abstract: Safe control has been widely studied in various safety-critical applications, for instance, autonomous driving. In order to ensure the autonomous vehicl

safetyarxiv-cs-ro

CKM-Driven Communication-Aware UAV Intelligent Trajectory Optimization for Urban Inspection

arXiv:2606.24979v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) are increasingly employed in urban inspection tasks, where reliable communication is critical but challenging due to the

researcharxiv-cs-lg

CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks

arXiv:2604.03314v2 Announce Type: replace-cross Abstract: Foundation models have revolutionized AI, but adapting them efficiently for multimodal tasks, particularly in dual-stream architectures compos

Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks

arXiv:2606.25858v1 Announce Type: cross Abstract: Federated learning is vulnerable to backdoor attacks in which malicious clients inject poisoned updates while preserving benign-task performance. In t

Tutorials

Compositional Behavioral Semantics for State Abstraction in Reinforcement Learning

arXiv:2606.25357v1 Announce Type: new Abstract: State abstraction plays a key role in scaling reinforcement learning to complex but structured systems. In studying such systems, a wide range of behavi

tutorialsarxiv-cs-lg

Constituency Structure over Eojeol in Korean Treebanks

arXiv:2512.22487v2 Announce Type: replace Abstract: The design of Korean constituency treebanks raises a central representational question concerning the choice of terminal units. Although Korean word

safetyarxiv-cs-cl

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints

arXiv:2606.25605v1 Announce Type: new Abstract: Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains in

CrossAccent-TTS: Cross-Lingual Accent-Intensity Controllable Text-to-Speech via Disentangled Speaker and Accent Representations

arXiv:2606.25403v1 Announce Type: cross Abstract: Accent conversion and controllability remain fundamental challenges in cross-lingual text-to-speech (TTS), particularly for low-resource and phonetica

Data-Driven Evolution of Library and Information Science Research Methods (1990-2022): A Perspective Based on Fine-grained Method Entities

arXiv:2606.25320v1 Announce Type: cross Abstract: Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Inform

researcharxiv-cs-cl

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

model-releasesarxiv-cs-lg

arXiv:2510.04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiv