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Considerations for an Integrated Detector Design at FCC-ee: A Human-AI Exploration

arXiv:2606.07564v1 Announce Type: cross Abstract: This report explores detector design considerations for the Future Circular Collider in its electron-positron mode (FCC-ee) through an extended dialog

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning

DGX agent

arXiv:2606.08088v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has recently become a key paradigm for improving the reasoning abilities of Large Language Models

safetyarxiv-cs-lg

9 Jun 2026

Constrained Paraphrase Consistency for LLM Hallucination Detection

arXiv:2606.08158v1 Announce Type: cross Abstract: Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely

Constrained user-item allocation for e-commerce marketing campaigns

arXiv:2606.09623v1 Announce Type: new Abstract: When running marketing campaigns, retailers must decide which products to promote and which users to target. These decisions are inherently coupled: eff

safetyarxiv-cs-lg

Constraint-Aware Optimization for Robust Protein Stability Prediction

arXiv:2606.08100v1 Announce Type: new Abstract: Multimodal DeltaDelta G predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on t

safetyarxiv-cs-lg

Contemporary AI lacks the imagination to diverge or negate in science

arXiv:2606.08251v1 Announce Type: cross Abstract: Bold projections that artificial intelligence will accelerate scientific discovery have raced ahead of evidence from working scientists, and the field

Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM

arXiv:2606.09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, o

Context-Fractured Decomposition Attacks on Tool-Using LLM Agents: Exploiting Artifact Provenance Gaps

arXiv:2606.09084v1 Announce Type: cross Abstract: Tool-using LLM agents interact with the world through actions that persist state in artifacts (e.g., workspace files or logs). Consequently, jailbreak

Context Over Compute Human-in-the-Loop Outperforms Iterative Chain-of-Thought Prompting in Interview Answer Quality

arXiv:2603.09995v2 Announce Type: replace-cross Abstract: Behavioral interview evaluation using large language models presents unique challenges that require structured assessment, realistic interview

Context Rot in AI-Assisted Software Development: Repurposing Documentation Consistency for AI Configuration Artifacts

arXiv:2606.09090v1 Announce Type: cross Abstract: Developers increasingly provide AI coding assistants with persistent context through configuration files such as CLAUDE.md, AGENTS.md, and .cursorrule

ContextShift: A Controlled Benchmark for Context Dependence in Object Detection

model-releasesarxiv-cs-cv

arXiv:2606.09495v1 Announce Type: new Abstract: Modern object detectors achieve strong performance on standard benchmarks, yet their robustness to contextual variation remains insufficiently understoo

Continual Quadruped Robots Coordination via Semantic Skill Discovery

arXiv:2606.08102v1 Announce Type: cross Abstract: Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptabil

Local Ai

Continuous Language Diffusion as a Decoder-Interface Problem

arXiv:2606.08810v1 Announce Type: cross Abstract: Gaussian-corrupted sentence embeddings have no direct linguistic interpretation, yet continuous diffusion language models can generate fluent text fro

local-aiarxiv-cs-lg

Contour Field based Elliptical Shape Prior for the Segment Anything Model

arXiv:2504.12556v2 Announce Type: replace Abstract: The elliptical shape prior information plays a vital role in improving the accuracy of image segmentation for specific tasks in medical and natural

researcharxiv-cs-cv

Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents

arXiv:2606.07904v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly rely on external APIs, but standard tool schemas describe how to call a tool, not when the tool

Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning

arXiv:2606.07770v1 Announce Type: new Abstract: Self-supervised methods that learn representations and predict dynamics fully in the latent space, such as JEPA, have been shown to confuse slowly varyi

safetyarxiv-cs-lg

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers

arXiv:2606.07604v1 Announce Type: cross Abstract: Analyzing attention weights has become a standard approach for interpreting the information flow of Large Language Models (LLMs). However, this approa

Convergence Bound and Critical Batch Size of Muon Optimizer

arXiv:2507.01598v5 Announce Type: replace Abstract: Muon, a recently proposed optimizer that leverages the inherent matrix structure of neural network parameters, has demonstrated strong empirical per

Convolutional Sparse Coding via the Locally Competitive Algorithm on Loihi 2

arXiv:2606.08584v1 Announce Type: new Abstract: Sparse coding provides a principled framework for signal representation by expressing an input as a linear combination of only a small number of basis f

Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication

arXiv:2505.03528v2 Announce Type: replace Abstract: Cooperative perception, leveraging shared information from multiple vehicles via vehicle-to-vehicle (V2V) communication, plays a vital role in auton

agentsarxiv-cs-cv

Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning

arXiv:2606.08064v1 Announce Type: new Abstract: Humans exhibit remarkable motor agility, enabling a wide range of dynamic skills such as running and jumping, which highlights the great potential of hu

safetyarxiv-cs-ro

Correct Looks Better: Pairwise Comparisons Reveal Accuracy Rankings

arXiv:2606.09409v1 Announce Type: new Abstract: Pairwise comparisons combined with aggregation methods like Elo have become central to evaluating generative models, yet concerns remain that they rewar

Correcting Mean Bias in Text Embeddings: A Refined Renormalization with Training-Free Improvements on MMTEB

arXiv:2511.11041v2 Announce Type: replace-cross Abstract: We find that current sentence-embedding models produce outputs with a consistent bias: every embedding e decomposes as ilde e + mu, where the

Correlation Is Not Enough: Embedding Human Metadata for Individual Causal Discovery

arXiv:2606.09672v1 Announce Type: new Abstract: Ask a pretrained biomedical language model whether 'cortisol 28 ug/dL' and 'stock-market volatility' are related, and it returns a cosine similarity of

CoSeP: Complementary Separability Pruning via Class-Separability Clustering

arXiv:2505.13225v2 Announce Type: replace Abstract: Neural network pruning aims to compress models for efficient deployment, yet two fundamental challenges remain. First, many methods rely on per-comp

researcharxiv-cs-cv

Cosmo3DFlow: Wavelet Flow Matching for Spatial-to-Spectral Compression in Reconstructing the Early Universe

arXiv:2602.10172v2 Announce Type: replace-cross Abstract: Reconstructing the early universe from the evolved present-day universe is a challenging and computationally demanding problem in modern astro

Cost-Aware Speculative Execution for LLM-Agent Workflows: An Integrated Five-Dimension Method

arXiv:2606.07846v1 Announce Type: cross Abstract: LLM-agent workflows chain model calls and tool invocations, and spend most of their wall-clock time waiting on upstream operations before downstream o

Local Ai

Counterfactual Reasoning for Fine-Grained Evidence Disentanglement in VideoQA

arXiv:2606.09181v1 Announce Type: new Abstract: Recent advances in video multimodal models have significantly improved VideoQA performance. However, these systems often rely on spurious statistical co

local-aiarxiv-cs-cv

Counterfactual Transport Flows for Offline Conservative Trajectory Refinement

arXiv:2606.09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as

CoVEBench: Can Video Editing Models Handle Complex Instructions?

arXiv:2606.08415v1 Announce Type: cross Abstract: While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly

Applications

CP4D: Compositional Physics-aware 4D Scene Generation

arXiv:2606.09187v1 Announce Type: new Abstract: 4D generation (extit{i.e.}, dynamic 3D generation) has recently emerged as a rapidly growing research frontier due to its powerful spatiotemporal modeli

applicationsarxiv-cs-cv

CRAG: Can 3D Generative Models Help 3D Assembly?

arXiv:2602.22629v2 Announce Type: replace Abstract: Most existing 3D assembly methods treat the problem as pure pose estimation, rearranging observed parts via rigid transformations. In contrast, huma

researcharxiv-cs-cv

CRANE: Knowledge Editing for Reasoning MLLMs

model-releasesarxiv-cs-cv

arXiv:2606.09033v1 Announce Type: new Abstract: The emergence of reasoning multimodal large language models (MLLMs), which generate explicit chain-of-thought (CoT) reasoning before producing answers,

Cranio-Diff: Diffusion-based Cross-domain Craniofacial Reconstruction with 2D X-ray Skull Guidance and Structural Identity Constraints

arXiv:2606.09699v1 Announce Type: new Abstract: The state-of-the-art generative models, such as CycleGAN, Pix2Pix, and diffusion models have demonstrated remarkable performance in the face generation

safetyarxiv-cs-cv

Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing

arXiv:2606.07636v1 Announce Type: new Abstract: Editing a long-form video from heterogeneous footage requires more than selecting clips: an agent must preserve narrative intent across material prepara

safetyarxiv-cs-cv

Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation

arXiv:2606.09160v1 Announce Type: cross Abstract: This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a

Cross-LLM Consistency in Inference: Evidence from Shared Interactions

arXiv:2606.08129v1 Announce Type: new Abstract: Large language models (LLMs) differ in architecture, training data, and optimization procedures, yet they may still develop similar internal inference p

Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization

arXiv:2606.07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone video

Cryptographic Backdoor for Neural Networks: Boon and Bane

arXiv:2509.20714v2 Announce Type: replace-cross Abstract: In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the att

researcharxiv-cs-lg

Applications

CSFlow: Aligning Flow Matching with Human Contrast Sensitivity

arXiv:2606.08833v1 Announce Type: new Abstract: We introduce Contrast Sensitive Flow (CSFlow), a weighting scheme that connects the human eye's Contrast Sensitivity Function (CSF) to the iterative den

applicationsarxiv-cs-cv

Local Ai

CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control

arXiv:2606.09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather tha

local-aiarxiv-cs-ai

CTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis

arXiv:2602.19330v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock

Culturally-Adapted Red-Teaming Across East and Southeast Asian Contexts: A Methodological and Comparative Analysis

arXiv:2606.09178v1 Announce Type: cross Abstract: Multilingual safety evaluation of large language models (LLMs) has predominantly relied on direct translation (DT) of English benchmarks into target l

Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning

arXiv:2606.08311v1 Announce Type: new Abstract: Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. High

CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation

arXiv:2601.15408v2 Announce Type: replace-cross Abstract: Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consis

Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space

arXiv:2603.29824v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning methods such as LoRA enable efficient adaptation of large pretrained models, but often lag behind full fine-tuning i

Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles

arXiv:2606.07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where

Applications

Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity

arXiv:2606.07726v1 Announce Type: new Abstract: Large Language Models are typically benchmarked by evaluating every model on every test query. For practitioners seeking the best model to deploy, this

applicationsarxiv-cs-lg