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Conceptors for Semantic Steering

model-releasesarxiv-cs-cl

arXiv:2605.04980v1 Announce Type: cross Abstract: Activation-based steering provides control of LLM behavior at inference time, but the dominant paradigm reduces each concept to a single direction who

Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Models

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DGX agent

arXiv:2605.04830v1 Announce Type: new Abstract: Diffusion models undergo a phase transition in a critical time window during generation dynamics, with two complementary diagnoses of criticality. The s

local-aiarxiv-cs-lg

7 May 2026

Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation

arXiv:2605.04366v1 Announce Type: cross Abstract: Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation off

Conditional outlier detection for clinical alerting

arXiv:2605.05124v1 Announce Type: new Abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electr

Conflict-Aware Fusion: Mitigating Logic Inertia in Large Language Models via Structured Cognitive Priors

model-releasesarxiv-cs-cl

arXiv:2512.06393v5 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve high accuracy on many reasoning benchmarks but remain brittle under structural perturbations of rule-base

Confronting Label Indeterminacy in Automated Bail Decisions

arXiv:2605.04073v1 Announce Type: new Abstract: Bail decisions present a fundamental challenge for data-driven decision support systems. When bail is denied, the counterfactual outcome of whether the

Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers

arXiv:2605.04080v1 Announce Type: new Abstract: This research investigated how online criminal activities can be better understood and connected using data-driven machine learning methods. Many illega

safetyarxiv-cs-cl

Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR

arXiv:2504.11101v4 Announce Type: replace Abstract: Optical Character Recognition (OCR) is fundamental to Vision-Language Models (VLMs) and high-quality data generation for LLM training. Yet, despite

ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2605.05126v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models primarily focus on mapping 2D observations to actions, but exhibit notable limitations in spatiotemporal per

Constrained Extreme Gradient Boosting for Adapting Reduced-Order Models

model-releasesarxiv-cs-lg

arXiv:2605.04130v1 Announce Type: new Abstract: High-fidelity simulations, such as computational fluid dynamics and finite element analysis, are essential for modeling complex engineering systems but

Tutorials

Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloads

arXiv:2605.04052v1 Announce Type: cross Abstract: Low Earth orbit (LEO) satellites increasingly carry compute hardware capable of on-board processing, yet each satellite generates roughly two orders o

tutorialsarxiv-cs-cv

Constraint-Enhanced Reinforcement Learning Based on Dynamic Decoupled Spherical Radial Squashing

model-releasesarxiv-cs-lg

arXiv:2605.04185v1 Announce Type: new Abstract: When deploying reinforcement learning policies to physical robots, actuator rate constraints -- hard limits on how fast each joint can move per control

Contact Matrix: Enhancing Dance Motion Synthesis with Precise Interaction Modeling

arXiv:2605.04662v1 Announce Type: new Abstract: Generating realistic reactive motions, in which one person reacts to the fixed motions of others, is challenging due to strict interaction constraints a

Agents

ContextPilot: Fast Long-Context Inference via Context Reuse

arXiv:2511.03475v4 Announce Type: replace Abstract: AI applications increasingly depend on long-context inference, where LLMs consume substantial context to support stronger reasoning. Common examples

agentsarxiv-cs-lg

Contextual Memory-Enhanced Source Coding for Low-SNR Communications

arXiv:2605.04400v1 Announce Type: cross Abstract: While Separate Source-Channel Coding (SSCC) retains the practical benefits of modular system design, its effectiveness in noisy text transmission is f

Contextual Multi-Objective Optimization: Rethinking Objectives in Frontier AI Systems

arXiv:2605.03900v1 Announce Type: new Abstract: Frontier AI systems perform best in settings with clear, stable, and verifiable objectives, such as code generation, mathematical reasoning, games, and

local-aiarxiv-cs-ai

Continual Distillation of Teachers from Different Domains

arXiv:2605.04059v1 Announce Type: cross Abstract: Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual

Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics

arXiv:2605.05097v1 Announce Type: cross Abstract: LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicit

researcharxiv-cs-cl

Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live

model-releasesarxiv-cs-ai

arXiv:2511.02230v4 Announce Type: replace-cross Abstract: KV cache management is essential for efficient LLM inference. To maximize utilization, existing inference engines evict finished requests' KV

Copula-Based Endogeneity Correction for Doubly Robust Estimation of Treatment Effect

arXiv:2605.03278v2 Announce Type: cross Abstract: Doubly Robust (DR) estimation of treatment effect relies on an untestable assumption that is the absence of unobserved confounding. This assumption is

safetyarxiv-cs-ai

Hardware

Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs

arXiv:2605.04357v1 Announce Type: cross Abstract: The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide

hardwarearxiv-cs-cl

Agents

cotomi Act: Learning to Automate Work by Watching You

arXiv:2605.03231v1 Announce Type: new Abstract: What if a browser agent could learn your work simply by watching you do it? We present cotomi Act, a browser-based computer-using agent that combines re

agentsarxiv-cs-ai

Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models

arXiv:2605.04555v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) offers a promising approach for data-efficient energy management in buildings, combining the strengths of pred

Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models

arXiv:2605.04413v1 Announce Type: new Abstract: Structural causal models provide a unified semantics for interventions and counterfactuals, but most identifiability results rely on restrictive assumpt

local-aiarxiv-cs-lg

Covariance-Aware Goodness for Scalable Forward-Forward Learning

arXiv:2605.04346v1 Announce Type: cross Abstract: The Forward-Forward algorithm eliminates global gradient flow and full network activations storage. However, in convolutional settings, existing BP-fr

local-aiarxiv-cs-cv

Tutorials

CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization

arXiv:2605.05136v1 Announce Type: new Abstract: Domain Generalization (DG) aims to learn representations that remain robust under out-of-distribution (OOD) shifts and generalize effectively to unseen

tutorialsarxiv-cs-cv

CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies

arXiv:2605.04470v1 Announce Type: new Abstract: Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induce

Critical Windows of Complexity Control: When Transformers Decide to Reason or Memorize

arXiv:2605.04396v1 Announce Type: new Abstract: Recent work has shown that Transformers' compositional generalization is governed by complexity control, initialization scale and weight decay, which st

Cross-Dataset Linkage of Brain MRI using Image Similarity Measures

arXiv:2602.10043v2 Announce Type: replace Abstract: Head magnetic resonance imaging (MRI) data are routinely collected and shared for research under strict regulatory frameworks that require the remov

Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features

model-releasesarxiv-cs-lg

arXiv:2605.04905v1 Announce Type: new Abstract: Electrospinning is a highly sensitive fabrication process in which small variations in operating parameters can significantly influence fiber morphology

Cross-Tokenizer Likelihood Scoring Algorithms for Language Model Distillation

arXiv:2512.14954v2 Announce Type: replace Abstract: Computing next-token likelihood ratios between two language models (LMs) is a standard task in training paradigms such as knowledge distillation. Si

researcharxiv-cs-cl

Applications

Cryptographic Registry Provenance: Structural Defense Against Dependency Confusion in AI Package Ecosystems

arXiv:2605.03309v1 Announce Type: cross Abstract: Dependency confusion attacks exploit a structural gap in software distribution: once a package is installed, there is no cryptographic proof of which

applicationsarxiv-cs-ai

Hardware

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

arXiv:2605.05023v1 Announce Type: new Abstract: Efficient CUDA implementations of attention mechanisms are critical to modern deep learning systems, yet supporting diverse and evolving attention varia

hardwarearxiv-cs-lg

D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models

arXiv:2605.05204v1 Announce Type: new Abstract: The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterpa

safetyarxiv-cs-cv

DALight-3D: A Lightweight 3D U-Net for Brain Tumor Segmentation from Multi-Modal MRI

arXiv:2605.04518v1 Announce Type: new Abstract: Automatic brain tumor segmentation from multi-modal MRI remains challenging because volumetric models often incur substantial computational cost. This p

DART: A Vision-Language Foundation Model for Comprehensive Rope Condition Monitoring

arXiv:2605.04943v1 Announce Type: new Abstract: The condition monitoring (CM) of synthetic fibre ropes (SFRs) used in offshore, maritime, and industrial settings demands more than a classifier: inspec

Tutorials

Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise

arXiv:2408.09929v2 Announce Type: replace-cross Abstract: Inspired by the idea of Positive-incentive Noise (Pi-Noise or pi-Noise) that aims at learning the reliable noise beneficial to tasks, we scien

tutorialsarxiv-cs-cv

Data-dependent Exploration for Online Reinforcement Learning from Human Feedback

arXiv:2605.04477v1 Announce Type: new Abstract: Online reinforcement learning from human feedback (RLHF) has emerged as a promising paradigm for aligning large language models (LLMs) by continuously c

Dataset-Driven Channel Masks in Transformers for Multivariate Time Series

arXiv:2410.23222v3 Announce Type: replace Abstract: Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale

Deco: Extending Personal Physical Objects into Pervasive AI Companion through a Dual-Embodiment Framework

arXiv:2605.03882v1 Announce Type: cross Abstract: Individuals frequently form deep attachments to physical objects (e.g., plush toys) that usually cannot sense or respond to their emotions. While AI c

researcharxiv-cs-ai

Decompose to Understand, Fuse to Detect: Frequency-Decoupled Anomaly Detection for Encrypted Network Traffic

arXiv:2605.02970v1 Announce Type: cross Abstract: Network traffic anomaly detection represents a critical cybersecurity task, yet widespread encryption makes this task increasingly challenging. In res

safetyarxiv-cs-ai

Deep Reprogramming Distillation for Medical Foundation Models

arXiv:2605.04447v1 Announce Type: new Abstract: Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation model

Hardware

Deep Wave Network for Modeling Multi-Scale Physical Dynamics

arXiv:2605.04198v1 Announce Type: new Abstract: Performance of deep learning models is strongly governed by architectural capacity, with width and depth as primary controls. However, in physical-scien

hardwarearxiv-cs-lg

Deepfake Audio Detection Using Self-supervised Fusion Representations

arXiv:2605.03420v1 Announce Type: cross Abstract: This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level

researcharxiv-cs-ai

DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation

arXiv:2605.04324v1 Announce Type: new Abstract: Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target d

Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs

arXiv:2605.04903v1 Announce Type: cross Abstract: Large language models (LLMs) show strong potential for neural architecture generation, yet existing approaches produce complete model implementations

Applications

Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series

arXiv:2605.04957v1 Announce Type: new Abstract: Point forecasting for graph-structured multivariate time series is a fundamental problem, but rigorous uncertainty quantification for such predictions i

applicationsarxiv-cs-lg

Demystifying Manifold Constraints in LLM Pre-training

arXiv:2605.04418v1 Announce Type: new Abstract: The empirical success of large language model (LLM) pre-training relies heavily on heuristic stabilization techniques, such as explicit normalization la