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Chaos-Enhanced Prototypical Networks for Few-Shot Medical Image Classification

arXiv:2604.17300v1 Announce Type: cross Abstract: The scarcity of labeled clinical data in oncology makes Few-Shot Learning (FSL) a critical framework for Computer Aided Diagnostics, but we observed t

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Characterizing Model-Native Skills

DGX agent

arXiv:2604.17614v1 Announce Type: cross Abstract: Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on

safetyarxiv-cs-cl

21 Apr 2026

Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation

arXiv:2602.19577v2 Announce Type: replace Abstract: Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, an

safetyarxiv-cs-ro

Agents

Chatting about Conditional Trajectory Prediction

arXiv:2604.18126v1 Announce Type: cross Abstract: Human behavior has the nature of mutual dependencies, which requires human-robot interactive systems to predict surrounding agents trajectories by mod

agentsarxiv-cs-cv

Chatting about Upper-Body Expressive Human Pose and Shape Estimation

model-releasesarxiv-cs-cv

arXiv:2604.17959v1 Announce Type: new Abstract: Expressive Human Pose and Shape Estimation (EHPS) plays a crucial role in various AR/VR applications and has witnessed significant progress in recent ye

CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation

arXiv:2505.20779v5 Announce Type: replace Abstract: A hallmark of human innovation is recombination -- the creation of novel ideas by integrating elements from existing concepts and mechanisms. In thi

Chronax: A Jax Library for Univariate Statistical Forecasting and Conformal Inference

arXiv:2604.16719v1 Announce Type: new Abstract: Time-series forecasting is central to many scientific and industrial domains, such as energy systems, climate modeling, finance, and retail. While forec

applicationsarxiv-cs-lg

Agents

CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents

arXiv:2603.15421v2 Announce Type: replace Abstract: Large language model agents heavily rely on external memory to support knowledge reuse and complex reasoning tasks. Yet most memory systems store ex

agentsarxiv-cs-cl

Local Ai

CLASP: Training-Free LLM-Assisted Source Code Watermarking via Semantic-Preserving Transformations

arXiv:2510.11251v2 Announce Type: replace-cross Abstract: The proliferation of open-source code and large language models (LLMs) for code generation has amplified the risks of unauthorized reuse and i

local-aiarxiv-cs-lg

Class-specific diffusion models improve military object detection in a low-data domain

arXiv:2604.18076v1 Announce Type: new Abstract: Diffusion-based image synthesis has emerged as a promising source of synthetic training data for AI-based object detection and classification. In this w

Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer

arXiv:2604.16563v1 Announce Type: new Abstract: Systolic murmurs are extra heart sounds that occur during the contraction phase of the cardiac cycle, often indicating heart abnormalities caused by tur

ClawEnvKit: Automatic Environment Generation for Claw-Like Agents

arXiv:2604.18543v1 Announce Type: cross Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale. We argue that wh

Clinical Note Bloat Reduction for Efficient LLM Use

arXiv:2604.16364v1 Announce Type: cross Abstract: Health systems are rapidly deploying large language models (LLMs) that use clinical notes for clinical decision support applications. However, modern

Closing the Modality Reasoning Gap for Speech Large Language Models

arXiv:2601.05543v2 Announce Type: replace Abstract: Although Speech Large Language Models have achieved notable progress, a substantial modality reasoning gap remains: their reasoning performance on s

safetyarxiv-cs-cl

Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering

arXiv:2604.18024v1 Announce Type: new Abstract: In multi-view clustering, the quality of different views may vary substantially, and low-quality or degraded views can impair overall clustering perform

applicationsarxiv-cs-lg

Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion

arXiv:2604.16552v1 Announce Type: new Abstract: Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a

CoAct: Co-Active LLM Preference Learning with Human-AI Synergy

arXiv:2604.17501v1 Announce Type: new Abstract: Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated p

CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora

arXiv:2604.18027v1 Announce Type: cross Abstract: Transpilation, or code translation, aims to convert source code from one programming language (PL) to another. It is beneficial for many downstream ap

CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment

arXiv:2506.02264v3 Announce Type: replace Abstract: Building Task-Oriented Dialogue (TOD) systems that generalize across different tasks remains a challenging problem. Data-driven approaches often str

Coevolving Representations in Joint Image-Feature Diffusion

arXiv:2604.17492v1 Announce Type: new Abstract: Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents

COFFAIL: A Dataset of Successful and Anomalous Robot Skill Executions in the Context of Coffee Preparation

arXiv:2604.18236v1 Announce Type: new Abstract: In the context of robot learning for manipulation, curated datasets are an important resource for advancing the state of the art; however, available dat

safetyarxiv-cs-ro

Agents

CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving

arXiv:2509.00789v2 Announce Type: replace Abstract: The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs):

agentsarxiv-cs-cv

Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situations

arXiv:2507.20409v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting helps models think step by step. But naive CoT breaks down in visually grounded social tasks, where models must per

Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation

arXiv:2604.17178v1 Announce Type: new Abstract: Emotional Support Conversation (ESC) plays a critical role in mental health assistance by providing accessible psychological support in real-world appli

safetyarxiv-cs-cl

Tutorials

CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering

arXiv:2604.16930v1 Announce Type: new Abstract: Visual Question Answering (VQA) requires models to identify the correct answer options based on both visual and textual evidence. Recent Mixture-of-Expe

tutorialsarxiv-cs-cv

CoLLM: A Unified Framework for Co-execution of LLMs Federated Fine-tuning and Inference

arXiv:2604.16400v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly adopted in edge intelligence to power domain-specific applications and personalized services, the qua

Combined Hyperbolic and Euclidean Soft Triple Loss Beyond the Single Space Deep Metric Learning

model-releasesarxiv-cs-cv

arXiv:2510.05643v2 Announce Type: replace Abstract: Deep metric learning (DML) aims to learn a neural network mapping data to an embedding space, which can represent semantic similarity between data p

Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training

arXiv:2506.01732v2 Announce Type: replace Abstract: Large Language Models (LLMs) are pre-trained on large data from different sources and domains. These datasets often contain trillions of tokens, inc

applicationsarxiv-cs-cl

Comparing Human and Large Language Model Interpretation of Implicit Information

arXiv:2604.17085v1 Announce Type: new Abstract: The interpretation of implicit meanings is an integral aspect of human communication. However, this framework may not transfer to interactions with Larg

Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessment

arXiv:2604.17074v1 Announce Type: new Abstract: The rapid advancement of generative models has led to a growing volume of AI-generated videos, making the automatic quality assessment of such videos in

ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship

arXiv:2604.18356v1 Announce Type: new Abstract: Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While r

Compiling OpenSCENARIO 2.1 for Scenario-Based Testing in CARLA

arXiv:2604.16452v1 Announce Type: new Abstract: While the ASAM OpenSCENARIO 2.1 Domain-Specific Language (DSL) enables declarative, intent-driven authoring for Scenario-Based Testing (SBT), its integr

researcharxiv-cs-ro

Complex normalizing flows can be information Kahler-Ricci flows

arXiv:2604.17954v1 Announce Type: cross Abstract: We develop interconnections between the complex normalizing flow for data drawn from Borel probability measures on the twofold realification of the co

Composed Vision-Language Retrieval for Skin Cancer Case Search via Joint Alignment of Global and Local Representations

arXiv:2603.09108v2 Announce Type: replace Abstract: Medical image retrieval aims to identify clinically relevant lesion cases to support diagnostic decision making, education, and quality control. In

safetyarxiv-cs-cv

Compositional Steering of Large Language Models with Steering Tokens

arXiv:2601.05062v2 Announce Type: replace Abstract: Deploying LLMs in real-world applications requires controllable output that satisfies multiple desiderata at the same time. While existing work exte

applicationsarxiv-cs-cl

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding

arXiv:2511.08480v3 Announce Type: replace Abstract: Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enh

Concurrent Criterion Validation of a Validity Screen for LLM Confidence Signals via Selective Prediction

arXiv:2604.17716v1 Announce Type: new Abstract: The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid. We test whether these classifications

Hardware

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning

arXiv:2412.00069v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer

hardwarearxiv-cs-cl

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

arXiv:2604.17616v1 Announce Type: new Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation m

Conditional Evidence Reconstruction and Decomposition for Interpretable Multimodal Diagnosis

arXiv:2604.17030v1 Announce Type: new Abstract: Neurobiological and neurodegenerative diseases are inherently multifactorial, arising from coupled influences spanning genetic susceptibility, brain alt

applicationsarxiv-cs-cv

Conformal Prediction-Based MPC for Stochastic Linear Systems

arXiv:2512.10738v2 Announce Type: replace-cross Abstract: We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown

researcharxiv-cs-ro

Conformal Risk Control under Non-Monotone Losses: Theory and Finite-Sample Guarantees

arXiv:2604.01502v2 Announce Type: replace-cross Abstract: Conformal risk control (CRC) provides distribution-free guarantees for controlling the expected loss at a user-specified level. Existing theor

ConforNets: Latents-Based Conformational Control in OpenFold3

arXiv:2604.18559v1 Announce Type: cross Abstract: Models from the AlphaFold (AF) family reliably predict one dominant conformation for most well-ordered proteins but struggle to capture biologically r

researcharxiv-cs-lg

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models

arXiv:2511.02757v2 Announce Type: replace Abstract: Zeroth-order or derivative-free optimization (MeZO) is an attractive strategy for finetuning large language models (LLMs) because it eliminates the

Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping

model-releasesarxiv-cs-cv

arXiv:2510.09741v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) often miss small details and spatial relations in cluttered scenes, leading to errors in fine-grained perce

Contact-Rich Robotic Assembly in Construction via Diffusion Policy Learning

arXiv:2511.17774v3 Announce Type: replace Abstract: Fabrication uncertainty arising from tolerance accumulation, material imperfection, and positioning errors remains a critical barrier to automated r

safetyarxiv-cs-ro

Context Matters: Peer-Aware Student Behavioral Engagement Measurement via VLM Action Parsing and LLM Sequence Classification

arXiv:2601.06394v3 Announce Type: replace Abstract: Understanding student behavior in the classroom is essential to improve both pedagogical quality and student engagement. Existing methods for predic

Continual Safety Alignment via Gradient-Based Sample Selection

arXiv:2604.17215v1 Announce Type: new Abstract: Large language models require continuous adaptation to new tasks while preserving safety alignment. However, fine-tuning on even benign data often compr

safetyarxiv-cs-lg