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Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples

arXiv:2604.20824v1 Announce Type: new Abstract: The central problem in biomedical imaging are batch effects: systematic technical variations unrelated to the biological signal of interest. These batch

applicationsarxiv-cs-lg

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Human DGX agent

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Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation

DGX agent

arXiv:2604.19826v1 Announce Type: cross Abstract: AI coding assistants increasingly generate code alongside tests. How developers structure test code, whether inline with the implementation or in sepa

researcharxiv-cs-ai

23 Apr 2026

CoAuthorAI: A Human in the Loop System For Scientific Book Writing

arXiv:2604.19772v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in scientific writing but struggle with book-length tasks, often producing inconsistent structure a

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

arXiv:2510.18471v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between the

Coding with Eyes: Visual Feedback Unlocks Reliable GUI Code Generating and Debugging

arXiv:2604.19750v1 Announce Type: cross Abstract: Recent advances in Large Language Model (LLM)-based agents have shown remarkable progress in code generation. However, current agent methods mainly re

Cognis: Context-Aware Memory for Conversational AI Agents

arXiv:2604.19771v1 Announce Type: cross Abstract: LLM agents lack persistent memory, causing conversations to reset each session and preventing personalization over time. We present Lyzr Cognis, a uni

applicationsarxiv-cs-ai

Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers

arXiv:2604.20027v1 Announce Type: cross Abstract: For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially

Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training

arXiv:2508.00414v3 Announce Type: replace Abstract: General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reason

Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models

arXiv:2604.20370v1 Announce Type: new Abstract: Forecasting the life-cycle trajectory of a newly launched product is important for launch planning, resource allocation, and early risk assessment. This

Local Ai

Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models

arXiv:2602.10386v2 Announce Type: replace Abstract: Graph problems are fundamentally challenging for large language models (LLMs). While LLMs excel at processing unstructured text, graph tasks require

local-aiarxiv-cs-lg

Tutorials

Combo-Gait: Unified Transformer Framework for Multi-Modal Gait Recognition and Attribute Analysis

arXiv:2510.10417v2 Announce Type: replace-cross Abstract: Gait recognition is an important biometric for human identification at a distance, particularly under low-resolution or unconstrained environm

tutorialsarxiv-cs-ai

Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding

arXiv:2604.19921v1 Announce Type: new Abstract: Negation is a common and important semantic feature in natural language, yet Large Language Models (LLMs) struggle when negation is involved in natural

researcharxiv-cs-cl

COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling

arXiv:2604.20720v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit performance disparities across languages, with naive multilingual fine-tuning frequently degrading performa

Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models

arXiv:2602.12036v2 Announce Type: replace Abstract: Large-scale verifiable prompts underpin the success of Reinforcement Learning with Verifiable Rewards (RLVR), but they contain many uninformative ex

researcharxiv-cs-cl

Computing the Reachability Value of Posterior-Deterministic POMDPs

arXiv:2602.07473v2 Announce Type: replace Abstract: Partially observable Markov decision processes (POMDPs) are a fundamental model for sequential decision-making under uncertainty. However, many veri

Concept Graph Convolutions: Message Passing in the Concept Space

arXiv:2604.20082v1 Announce Type: new Abstract: The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks v

ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2604.20358v1 Announce Type: new Abstract: The Composed Image Retrieval (CIR) task provides a flexible retrieval paradigm via a reference image and modification text, but it heavily relies on exp

Confidence-Based Mesh Extraction from 3D Gaussians

arXiv:2603.24725v2 Announce Type: replace Abstract: Recently, 3D Gaussian Splatting (3DGS) greatly accelerated mesh extraction from posed images due to its explicit representation and fast software ra

Construction of a Battery Research Knowledge Graph using a Global Open Catalog

arXiv:2604.20241v1 Announce Type: new Abstract: Battery research is a rapidly growing and highly interdisciplinary field, making it increasingly difficult to track relevant expertise and identify pote

researcharxiv-cs-cl

Context Attribution with Multi-Armed Bandit Optimization

arXiv:2506.19977v2 Announce Type: replace Abstract: Understanding which parts of the retrieved context contribute to a large language model's generated answer is essential for building interpretable a

Continuous Semantic Caching for Low-Cost LLM Serving

arXiv:2604.20021v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar queries h

applicationsarxiv-cs-cl

Control Consistency Losses for Diffusion Bridges

arXiv:2512.05070v2 Announce Type: replace-cross Abstract: Simulating the conditioned dynamics of diffusion processes, given their initial and terminal states, is an important but challenging problem i

Tutorials

Convergent Evolution: How Different Language Models Learn Similar Number Representations

arXiv:2604.20817v1 Announce Type: cross Abstract: Language models trained on natural text learn to represent numbers using periodic features with dominant periods at T=2, 5, 10. In this paper, we iden

tutorialsarxiv-cs-ai

Cooperative Profiles Predict Multi-Agent LLM Team Performance in AI for Science Workflows

model-releasesarxiv-cs-cl

arXiv:2604.20658v1 Announce Type: new Abstract: Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-solvin

CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

arXiv:2603.23694v2 Announce Type: replace Abstract: Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time point

safetyarxiv-cs-cv

Cortex 2.0: Grounding World Models in Real-World Industrial Deployment

arXiv:2604.20246v1 Announce Type: cross Abstract: Industrial robotic manipulation demands reliable long-horizon execution across embodiments, tasks, and changing object distributions. While Vision-Lan

applicationsarxiv-cs-ai

Cover meets Robbins while Betting on Bounded Data: ln n Regret and Almost Sure lnln n Regret

arXiv:2604.20172v1 Announce Type: new Abstract: Consider betting against a sequence of data in [0,1], where one is allowed to make any bet that is fair if the data have a conditional mean m_0 in (0,1)

Coverage, Not Averages: Semantic Stratification for Trustworthy Retrieval Evaluation

arXiv:2604.20763v1 Announce Type: cross Abstract: Retrieval quality is the primary bottleneck for accuracy and robustness in retrieval-augmented generation (RAG). Current evaluation relies on heuristi

CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation

arXiv:2604.19941v1 Announce Type: new Abstract: Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challe

CRAFT: Training-Free Cascaded Retrieval for Tabular QA

model-releasesarxiv-cs-cl

arXiv:2505.14984v2 Announce Type: replace Abstract: Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries. Traditional d

Agents

CreativeGame:Toward Mechanic-Aware Creative Game Generation

arXiv:2604.19926v1 Announce Type: new Abstract: Large language models can generate plausible game code, but turning this capability into iterative creative improvement remains difficult. In practice,

agentsarxiv-cs-ai

Cross-Modal Taxonomic Generalization in (Vision-) Language Models

arXiv:2603.07474v2 Announce Type: replace-cross Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grou

applicationsarxiv-cs-ai

CubeDAgger: Interactive Imitation Learning for Dynamic Systems with Efficient yet Low-risk Interaction

arXiv:2505.04897v2 Announce Type: replace-cross Abstract: Interactive imitation learning makes an agent's control policy robust by stepwise supervisions from an expert. The recent algorithms mostly em

safetyarxiv-cs-lg

CXR-LanIC: Language-Grounded Interpretable Classifier for Chest X-Ray Diagnosis

arXiv:2510.21464v2 Announce Type: replace Abstract: Deep learning models have achieved remarkable accuracy in chest X-ray diagnosis, yet their widespread clinical adoption remains limited by the black

CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge

arXiv:2604.20389v1 Announce Type: cross Abstract: The rapid evolution and use of Large Language Models (LLMs) in professional workflows require an evaluation of their domain-specific knowledge against

DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles

arXiv:2604.20771v1 Announce Type: cross Abstract: The Internet of Vehicles (IoV) is advancing modern transportation by improving safety, efficiency, and intelligence. However, the reliance on the Cont

Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms

arXiv:2604.20492v1 Announce Type: cross Abstract: In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local dataset

safetyarxiv-cs-lg

Agents

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints

arXiv:2604.20031v1 Announce Type: cross Abstract: We consider what we refer to as {Decision-Focused Federated Learning (DFFL)} framework, i.e., a predict-then-optimize approach employed by a collectio

agentsarxiv-cs-lg

Local Ai

Decoding Text Spans for Efficient and Accurate Named-Entity Recognition

arXiv:2604.20447v1 Announce Type: new Abstract: Named Entity Recognition (NER) is a key component in industrial information extraction pipelines, where systems must satisfy strict latency and throughp

local-aiarxiv-cs-cl

Deconstructing Superintelligence: Identity, Self-Modification and Differance

arXiv:2604.19845v1 Announce Type: new Abstract: Self-modification is often taken as constitutive of artificial superintelligence (SI), yet modification is a relative action requiring a supplement outs

Degrees, Levels, and Profiles of Contextuality

arXiv:2603.26692v3 Announce Type: replace-cross Abstract: We introduce a new notion, that of a contextuality profile of a system of random variables. Rather than characterizing a system's contextualit

Depression Risk Assessment in Social Media via Large Language Models

arXiv:2604.19887v1 Announce Type: cross Abstract: Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The prolifera

Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa

model-releasesarxiv-cs-cl

arXiv:2604.19776v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health

Agents

DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation

arXiv:2604.20841v1 Announce Type: new Abstract: Recent advances in video generative models enable the synthesis of realistic human-object interaction videos across a wide range of scenarios and object

agentsarxiv-cs-cv

Local Ai

Device-Native Autonomous Agents for Privacy-Preserving Negotiations

arXiv:2601.00911v3 Announce Type: replace-cross Abstract: Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-of

local-aiarxiv-cs-ai

Diagnosing CFG Interpretation in LLMs

arXiv:2604.20811v1 Announce Type: new Abstract: As LLMs are increasingly integrated into agentic systems, they must adhere to dynamically defined, machine-interpretable interfaces. We evaluate LLMs as

Diagnosing Urban Street Vitality via a Visual-Semantic and Spatiotemporal Framework for Street-Level Economics

arXiv:2604.19798v1 Announce Type: cross Abstract: Micro-scale street-level economic assessment is fundamental for precision spatial resource allocation. While Street View Imagery (SVI) advances urban

DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories

arXiv:2604.20443v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been shown to possess Theory of Mind (ToM) abilities. However, it remains unclear whether this stems from robust rea