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CADDesigner: Conceptual CAD Model Generation with a General-Purpose Agent

arXiv:2508.01031v5 Announce Type: replace Abstract: Computer-Aided Design (CAD) is widely used for conceptual design and parametric 3D modeling, but typically requires a high level of expertise from d

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Calibration-Free Gas Source Localization with Mobile Robots: Source Term Estimation Based on Concentration Measurement Ranking

DGX agent

arXiv:2605.13208v1 Announce Type: new Abstract: Efficient Gas Source Localization (GSL) in real-world settings is crucial, especially in emergency scenarios. Mobile robots equipped with low-cost, in-s

applicationsarxiv-cs-ro

14 May 2026

Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis

arXiv:2605.12507v1 Announce Type: cross Abstract: While Large Language Model (LLM) multi-agent systems (MAS) offer a transformative approach to simulating human behavior in complex systems, it remains

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution

arXiv:2605.13295v1 Announce Type: cross Abstract: LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling,

local-aiarxiv-cs-ai

Causal Fine-Tuning under Latent Confounded Shift

arXiv:2410.14375v3 Announce Type: replace Abstract: Adapting to latent confounded shift remains a core challenge in modern AI. This setting is driven by hidden variables that induce spurious correlati

Causal Learning with the Invariance Principle

arXiv:2605.13589v1 Announce Type: cross Abstract: Causal discovery, the problem of inferring the direction of causality, is generally ill-posed. We use the language of structural causal models (SCM) t

Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling

arXiv:2605.13646v1 Announce Type: cross Abstract: End-to-end autonomous driving, which bypasses traditional modular pipelines by directly predicting future trajectories from sensor inputs, has recentl

safetyarxiv-cs-ai

CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks

arXiv:2605.12580v1 Announce Type: new Abstract: Randomized neural networks (RdNNs) enable efficient, backpropagation-free training by freezing randomly initialized input-to-hidden weights, which permi

Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies

arXiv:2508.01049v2 Announce Type: replace Abstract: Independent on-policy policy gradient algorithms are widely used for multi-agent reinforcement learning (MARL) in cooperative and no-conflict games,

Certified Robustness under Heterogeneous Perturbations via Hybrid Randomized Smoothing

arXiv:2605.12876v1 Announce Type: new Abstract: Randomized smoothing provides strong, model-agnostic robustness certificates, but existing guarantees are limited to single modalities, treating continu

CHAL: Council of Hierarchical Agentic Language

arXiv:2605.12718v1 Announce Type: new Abstract: Multi-agent debate has emerged as a promising approach for improving LLM reasoning on ground-truth tasks, yet current methodologies face certain structu

ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture

arXiv:2605.12553v1 Announce Type: cross Abstract: Accurate channel state information (CSI) prediction is essential for improving the reliability and spectral efficiency of massive MIMO-OFDM systems in

local-aiarxiv-cs-ai

Tutorials

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting

arXiv:2509.23597v5 Announce Type: replace-cross Abstract: Time series forecasting remains a critical challenge across numerous domains, yet the effectiveness of complex models often varies unpredictab

tutorialsarxiv-cs-ai

Characterizing Universal Object Representations Across Vision Models

arXiv:2605.13675v1 Announce Type: new Abstract: Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. Ho

safetyarxiv-cs-cv

ChatSR: Multimodal Large Language Models for Scientific Formula Discovery

arXiv:2406.05410v3 Announce Type: replace Abstract: Current multimodal large language models (MLLMs) are mainly focused on the understanding and processing of perceptual modalities such as images and

safetyarxiv-cs-ai

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction

arXiv:2605.13262v1 Announce Type: new Abstract: Modern SMILES-based chemical language models obtain strong MoleculeNet performance by treating SMILES as generic text and compensating with multi-millio

Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

arXiv:2605.13709v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely applied in educational practices, such as for generating children's stories. However, the generated stories ar

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation

arXiv:2605.12857v1 Announce Type: cross Abstract: Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is av

CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence

model-releasesarxiv-cs-cv

arXiv:2605.12882v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have significantly advanced document understanding, yet current Doc-VQA evaluations score only the final answ

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

arXiv:2605.13178v1 Announce Type: cross Abstract: In large vision-language models, visual tokens typically constitute the majority of input tokens, leading to substantial computational overhead. To ad

Clustering in pure-attention hardmax transformers and its role in sentiment analysis

arXiv:2407.01602v2 Announce Type: replace-cross Abstract: Transformers are extremely successful machine learning models whose mathematical properties remain poorly understood. Here, we rigorously char

CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem

arXiv:2605.13638v1 Announce Type: cross Abstract: A quantum compiler is a critical piece in the quantum computing pipeline since it allows an abstract quantum circuit to be run on a physical quantum c

Code-Centric Detection of Vulnerability-Fixing Commits: A Unified Benchmark and Empirical Study

arXiv:2605.13138v1 Announce Type: cross Abstract: Automated detection of vulnerability-fixing commits (VFCs) is critical for timely security patch deployment, as advisory databases lag patch releases

CodeClash: Benchmarking Goal-Oriented Software Engineering

arXiv:2511.00839v2 Announce Type: replace-cross Abstract: Current benchmarks for coding evaluate language models (LMs) on concrete, well-specified tasks such as fixing specific bugs or writing targete

CoGE: Sim-to-Real Online Geometric Estimation for Monocular Colonoscopy

arXiv:2605.13038v1 Announce Type: cross Abstract: Geometric estimation including depth estimation and scene reconstruction is a crucial technique for colonoscopy which can provide surgeons with 3D spa

local-aiarxiv-cs-ai

Cognifold: Always-On Proactive Memory via Cognitive Folding

arXiv:2605.13438v1 Announce Type: new Abstract: Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cogni

Collaborating in Multi-Armed Bandits with Strategic Agents

arXiv:2605.13145v1 Announce Type: new Abstract: We study collaborative learning in multi-agent Bayesian bandit problems, where strategic agents collectively solve the same bandit instance. While multi

agentsarxiv-cs-lg

Collaborative Parameter Learning: Mitigating Forgetting via Parameter-Level Gradient Analysis

arXiv:2601.21577v2 Announce Type: replace Abstract: Catastrophic forgetting during knowledge injection impairs the ability of large language models to acquire new knowledge without overwriting previou

Color Constancy in Hyperspectral Imaging via Reduced Spectral Spaces

arXiv:2605.13306v1 Announce Type: new Abstract: Illuminant estimation aims to infer scene illumination from image measurements despite intrinsic ambiguities between surface reflectance and lighting. M

researcharxiv-cs-cv

Compact 3D Gaussian Splatting For Dense Visual SLAM

model-releasesarxiv-cs-cv

arXiv:2403.11247v3 Announce Type: replace Abstract: Recent work has shown that 3D Gaussian-based SLAM enables high-quality reconstruction, accurate pose estimation, and real-time rendering of scenes.

Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis

arXiv:2605.13248v1 Announce Type: cross Abstract: The analysis of physiological time series, such as electrocardiograms (ECG) and photoplethysmograms (PPG), is persistently hindered by modality and fr

Applications

Conditional Compatibility Learning for Context-Dependent Anomaly Detection

arXiv:2601.22868v3 Announce Type: replace Abstract: Anomaly detection usually assumes that abnormality is an intrinsic property of an observation. A defect is a defect, and a rare object is rare, rega

applicationsarxiv-cs-cv

Tutorials

Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'

arXiv:2605.13642v1 Announce Type: cross Abstract: Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without cl

tutorialsarxiv-cs-lg

Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models

arXiv:2511.15743v2 Announce Type: replace Abstract: Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial

ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis

arXiv:2605.12736v1 Announce Type: new Abstract: Template based single step retrosynthesis predicts reactants by selecting and applying an explicit reaction template, making each prediction traceable t

Constitutional Governance in Metric Spaces

arXiv:2605.13362v1 Announce Type: cross Abstract: Computational social choice and algorithmic decision theory offer rich aggregation theory but no end-to-end, polynomial-time process for egalitarian s

Applications

Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling

arXiv:2605.12754v1 Announce Type: new Abstract: Deep generative models provide state-of-the-art performance across a wide array of applications, with recent studies showing increasing applicability fo

applicationsarxiv-cs-lg

Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search

arXiv:2510.08992v3 Announce Type: replace Abstract: While researchers have made significant progress in enabling large language models (LLMs) to perform multi-step planning, LLMs struggle to ensure th

Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profiling

arXiv:2605.13337v1 Announce Type: cross Abstract: Security Information and Event Management (SIEM) systems aggregate log data from heterogeneous sources to detect coordinated attacks. Traditional rule

Context Matters: Auditing Gender Bias in T2I Generation through Risk-Tiered Use-Case Profiles

arXiv:2605.13113v1 Announce Type: cross Abstract: Text-to-image (T2I) generative models are increasingly used to produce content for education, media, and public-facing communication, and are starting

safetyarxiv-cs-ai

Context Training with Active Information Seeking

arXiv:2605.13050v1 Announce Type: cross Abstract: Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or nich

Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning

arXiv:2605.13346v1 Announce Type: new Abstract: Contextual bandits (CB) are online sequential decision-making problems under partial feedback that underpin many adaptive services. There is a growing d

local-aiarxiv-cs-lg

Continual Fine-Tuning of Large Language Models via Program Memory

arXiv:2605.13162v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT), particularly Low-Rank Adaptation (LoRA), has become a standard approach for adapting Large Language Models (LLMs

Continual Learning with Multilingual Foundation Model

arXiv:2605.13415v1 Announce Type: cross Abstract: This paper presents a multi-stage framework for detecting reclaimed slurs in multilingual social media discourse. It addresses the challenge of identi

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs

arXiv:2605.12713v1 Announce Type: cross Abstract: In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed. From these models, we ide

Controlling Logical Collapse in LLMs via Algebraic Ontology Projection over F2

arXiv:2605.12968v1 Announce Type: cross Abstract: Do large language models internally encode ontological relations in a formally verifiable algebraic structure? We introduce Algebraic Ontology Project

Convergent Differential Privacy Analysis for General Federated Learning

arXiv:2408.15621v3 Announce Type: replace Abstract: The powerful cooperation of federated learning (FL) and differential privacy~(DP) provides a promising paradigm for the large-scale private clients.

Coordinating Multiple Conditions for Trajectory-Controlled Human Motion Generation

arXiv:2605.13729v1 Announce Type: cross Abstract: Trajectory-controlled human motion generation aims to synthesize realistic human motions conditioned on both textual descriptions and spatial trajecto