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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

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

DGX agent

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

model-releasesarxiv-cs-lg

14 May 2026

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

safetyarxiv-cs-lg

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

Local Ai

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

CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions

arXiv:2605.12980v1 Announce Type: cross Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) remains challenging, particularly for de novo generation beyond database coverage. A

Coreset-Induced Conditional Velocity Flow Matching

arXiv:2605.12951v1 Announce Type: cross Abstract: We propose Coreset-Induced Conditional Velocity Flow Matching (CCVFM), a generative model that augments hierarchical rectified flow with a data-inform

Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models

arXiv:2605.12519v1 Announce Type: cross Abstract: Training language models to produce both correct answers and sound reasoning remains an open challenge. Reinforcement learning with verifiable rewards

Tutorials

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces

arXiv:2605.12809v1 Announce Type: cross Abstract: A critical step for reliable large language models (LLMs) use in healthcare is to attribute predictions to their training data, akin to a medical case

tutorialsarxiv-cs-ai

COSMIC: Concurrent Optimization of Structure, Material, and Integrated Control for robotic systems

arXiv:2605.12654v1 Announce Type: new Abstract: Replicating and surpassing the autonomy of natural organisms remains a long-standing goal in robotics. Yet most robotic systems have their structure, ma

safetyarxiv-cs-ro

CoT-Guard: Small Models for Strong Monitoring

arXiv:2605.12746v1 Announce Type: cross Abstract: Monitoring the chain-of-thought (CoT) of reasoning models is a promising approach for detecting covert misbehavior (i.e., hidden objectives) in code g

Counterfactual Reasoning for Causal Responsibility Attribution in Probabilistic Multi-Agent Systems

arXiv:2605.13077v1 Announce Type: cross Abstract: Responsibility allocation -- determining the extent to which agents are accountable for outcomes -- is a fundamental challenge in the design and analy

safetyarxiv-cs-ai

Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems

arXiv:2605.13174v1 Announce Type: cross Abstract: A likelihood-free transport filtering method is proposed based on the couplings between state and observation variables. By exploiting a block-triangu

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure

arXiv:2509.18993v3 Announce Type: replace Abstract: Low-rank architectures have become increasingly important for efficient large language model (LLM) pre-training, providing substantial reductions in

CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesis

arXiv:2605.12650v1 Announce Type: new Abstract: Foundation diffusion models can generate photorealistic natural images, but adapting them to medical imaging remains challenging. In medical adaptation,

safetyarxiv-cs-cv

CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation

arXiv:2605.12938v1 Announce Type: cross Abstract: Camera-conditioned video generation requires positional encoding that remains reliable under changes in camera motion, lens configuration, and scene s

CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference

arXiv:2605.12545v1 Announce Type: cross Abstract: Aesthetic image cropping aims to enhance the aesthetic quality of an image by improving its composition through spatial cropping. Previous methods oft

safetyarxiv-cs-ai

Cross Modality Image Translation In Medical Imaging Using Generative Frameworks

arXiv:2605.13686v1 Announce Type: cross Abstract: Medical image-to-image (I2I) translation enables virtual scanning, i.e. the synthesis of a target imaging modality from a source one without additiona

CUBic: Coordinated Unified Bimanual Perception and Control Framework

arXiv:2605.13452v1 Announce Type: cross Abstract: Recent advances in visuomotor policy learning have enabled robots to perform control directly from visual inputs. Yet, extending such end-to-end learn

Cycle-resolved Cephalopod-Inspired Pulsed-Jet Robot With High-Volume Expulsion and Drag-Reduced Gliding

arXiv:2605.05875v2 Announce Type: replace Abstract: Cephalopod pulsed-jet locomotion is not a single isolated expulsion event, but a coordinated cycle involving jet expulsion, passive gliding, and man

researcharxiv-cs-ro

D-VLA: A High-Concurrency Distributed Asynchronous Reinforcement Learning Framework for Vision-Language-Action Models

arXiv:2605.13276v1 Announce Type: new Abstract: The rapid evolution of Embodied AI has enabled Vision-Language-Action (VLA) models to excel in multimodal perception and task execution. However, applyi

Data Agent: Learning to Select Data via End-to-End Dynamic Optimization

arXiv:2603.07433v2 Announce Type: replace-cross Abstract: Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typic

safetyarxiv-cs-cv

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning

arXiv:2605.12906v1 Announce Type: cross Abstract: Data selection during supervised fine-tuning (SFT) can critically change the behavior of large language models (LLMs). Although existing work has stud

Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning

arXiv:2603.10305v3 Announce Type: replace Abstract: Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information acro

DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions

arXiv:2509.19538v2 Announce Type: replace-cross Abstract: Diffusion-based world models have demonstrated strong capabilities in synthesizing realistic long-horizon trajectories for offline reinforceme

Debunking Grad-ECLIP: A Comprehensive Study on Its Incorrectness and Fundamental Principles for Model Interpretation

arXiv:2605.12952v1 Announce Type: new Abstract: Grad-ECLIP is published at ICML 2024 and represents a new Transformer interpretation technical route (intermediate features-based). First, this paper de

researcharxiv-cs-cv

Agents

Decentralized Ranking Aggregation via Gossip: Convergence and Robustness

arXiv:2602.22847v2 Announce Type: replace-cross Abstract: The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often

agentsarxiv-cs-ai

Decision Support for Marketplace Policies under Incomplete Evidence: From Replay to Launch Readiness

arXiv:2605.12840v1 Announce Type: cross Abstract: Marketplace platforms routinely evaluate pricing and allocation policies using logged observational data, yet strong offline performance does not impl

safetyarxiv-cs-lg

Decision Tree Learning on Product Spaces

arXiv:2605.12983v1 Announce Type: new Abstract: Decision tree learning has long been a central topic in theoretical computer science, driven by its practical importance. A fundamental and widely used

Applications

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models

arXiv:2605.13540v1 Announce Type: cross Abstract: Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learnin

applicationsarxiv-cs-ai

Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration

arXiv:2603.22273v4 Announce Type: replace Abstract: The process of discovery requires active exploration -- the act of collecting new and informative data. However, efficient autonomous exploration re

safetyarxiv-cs-lg