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arXiv:2604.19925v1 Announce Type: cross Abstract: AI agents powered by large language models are increasingly acting on behalf of humans in social and economic environments. Prior research has focused

agentsarxiv-cs-ai

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Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations

DGX agent

arXiv:2509.25844v3 Announce Type: replace Abstract: When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM

researcharxiv-cs-cl

23 Apr 2026

Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData

model-releasesarxiv-cs-cv

arXiv:2511.02849v2 Announce Type: replace-cross Abstract: Individualized therapy is driven forward by medical data analysis, which provides insight into the patient's context. In particular, for Type

Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization

arXiv:2604.20365v1 Announce Type: cross Abstract: While Central Pattern Generators (CPGs) and Multi-Layer Perceptrons (MLP) are widely used paradigms in robot control, few systematic studies have been

Best Policy Learning from Trajectory Preference Feedback

arXiv:2501.18873v4 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful approach for aligning generative models, but its reliance on learned rew

safetyarxiv-cs-lg

Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning

arXiv:2512.15146v3 Announce Type: replace Abstract: Test-time reinforcement learning mitigates the reliance on annotated data by using majority voting results as pseudo-labels, emerging as a complemen

Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models

arXiv:2604.16902v2 Announce Type: replace Abstract: Native Omni-modal Large Language Models (OLLMs) have shifted from pipeline architectures to unified representation spaces. However, this native inte

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

arXiv:2510.11423v3 Announce Type: replace-cross Abstract: Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), allows users to flag misleading posts, attach con

Beyond ZOH: Advanced Discretization Strategies for Vision Mamba

arXiv:2604.20606v1 Announce Type: cross Abstract: Vision Mamba, as a state space model (SSM), employs a zero-order hold (ZOH) discretization, which assumes that input signals remain constant between s

Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring

arXiv:2604.19984v1 Announce Type: cross Abstract: Research has documented LLMs' name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate c

Bimanual Robot Manipulation via Multi-Agent In-Context Learning

arXiv:2604.20348v1 Announce Type: cross Abstract: Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf

Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods

arXiv:2604.20243v1 Announce Type: new Abstract: Color constancy is a fundamental ability of many biological visual systems and a crucial step in computer imaging systems. Bio-inspired modeling offers

researcharxiv-cs-cv

Bootstrapping Post-training Signals for Open-ended Tasks via Rubric-based Self-play on Pre-training Text

arXiv:2604.20051v1 Announce Type: new Abstract: Self-play has recently emerged as a promising paradigm to train Large Language Models (LLMs). In self-play, the target LLM creates the task input (e.g.,

Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation

arXiv:2601.03396v2 Announce Type: replace Abstract: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underex

safetyarxiv-cs-cl

Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control

model-releasesarxiv-cs-lg

arXiv:2601.02896v2 Announce Type: replace Abstract: Controlling emergent behavioral personas (e.g., sycophancy, hallucination) in Large Language Models (LLMs) is critical for AI safety, yet remains a

Calibrating conditional risk

arXiv:2604.20409v1 Announce Type: new Abstract: We introduce and study the problem of calibrating conditional risk, which involves estimating the expected loss of a prediction model conditional on inp

researcharxiv-cs-lg

Tutorials

Camera Control for Text-to-Image Generation via Learning Viewpoint Tokens

arXiv:2604.19954v1 Announce Type: new Abstract: Current text-to-image models struggle to provide precise camera control using natural language alone. In this work, we present a framework for precise c

tutorialsarxiv-cs-cv

Can 'AI' Be a Doctor? A Study of Empathy, Readability, and Alignment in Clinical LLMs

arXiv:2604.20791v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in healthcare, yet their communicative alignment with clinical standards remains insufficiently

Agents

Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History?

arXiv:2604.19785v1 Announce Type: cross Abstract: Sensitive information, such as knowledge about an individual's personality, can be can be misused to influence behavior (e.g., via personalized messag

agentsarxiv-cs-ai

Can We Locate and Prevent Stereotypes in LLMs?

arXiv:2604.19764v1 Announce Type: cross Abstract: Stereotypes in large language models (LLMs) can perpetuate harmful societal biases. Despite the widespread use of models, little is known about where

CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence

arXiv:2603.28032v2 Announce Type: replace-cross Abstract: The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation inf

CAST: Achieving Stable LLM-based Text Analysis for Data Analytics

arXiv:2602.15861v2 Announce Type: replace-cross Abstract: Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labelin

Catalyzing Informed Residential Energy Retrofit Decisions via Domain-Specific LLM

arXiv:2602.20181v2 Announce Type: replace-cross Abstract: Residential energy retrofit initiation is often stalled by an expertise gap, where homeowners lack the technical literacy required for structu

Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?

arXiv:2502.07963v4 Announce Type: replace-cross Abstract: Medical research faces well-documented challenges in translating novel treatments into clinical practice. Publishing incentives encourage rese

Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury

arXiv:2604.20259v1 Announce Type: new Abstract: Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle wit

researcharxiv-cs-lg

CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs

model-releasesarxiv-cs-cv

arXiv:2604.20460v1 Announce Type: new Abstract: Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausib

Local Ai

CEDAR: Context Engineering for Agentic Data Science

arXiv:2601.06606v2 Announce Type: replace-cross Abstract: We demonstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underex

local-aiarxiv-cs-ai

Centering Ecological Goals in Automated Identification of Individual Animals

arXiv:2604.20626v1 Announce Type: cross Abstract: Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement,

Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows

arXiv:2604.20200v1 Announce Type: new Abstract: Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namel

Applications

CHASM: Unveiling Covert Advertisements on Chinese Social Media

arXiv:2604.20511v1 Announce Type: cross Abstract: Current benchmarks for evaluating large language models (LLMs) in social media moderation completely overlook a serious threat: covert advertisements,

applicationsarxiv-cs-ai

ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration

arXiv:2604.19856v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot gene

Agents

CHORUS: An Agentic Framework for Generating Realistic Deliberation Data

arXiv:2604.20651v1 Announce Type: new Abstract: Understanding the intricate dynamics of online discourse depends on large-scale deliberation data, a resource that remains scarce across interactive web

agentsarxiv-cs-ai

CLIP-RD: Relative Distillation for Efficient CLIP Knowledge Distillation

arXiv:2603.25383v3 Announce Type: replace Abstract: CLIP aligns image and text embeddings via contrastive learning and demonstrates strong zero-shot generalization. Its large-scale architecture requir

safetyarxiv-cs-cv

CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values

arXiv:2509.03740v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) like CLIP have shown impressive zero-shot and few-shot learning capabilities across diverse applications. Howeve

Applications

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

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

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

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

Applications

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

researcharxiv-cs-lg

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