Search: “framework”

Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking

arXiv:2604.22760v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly operate as autonomous agents that reason over external APIs to perform complex tasks. However, their reliabi

safetyarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings

DGX agent

arXiv:2604.24597v1 Announce Type: cross Abstract: We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using qua

researcharxiv-cs-ai

28 Apr 2026

Quasi-Equivariant Metanetworks

model-releasesarxiv-cs-lg

arXiv:2604.23720v1 Announce Type: new Abstract: Metanetworks are neural architectures designed to operate directly on pretrained weights to perform downstream tasks. However, the parameter space serve

Query2Diagram: Answering Developer Queries with UML Diagrams

arXiv:2604.23816v1 Announce Type: cross Abstract: Software documentation frequently becomes outdated or fails to exist entirely, yet developers need focused views of their codebase to understand compl

QuietWalk: Physics-Informed Reinforcement Learning for Ground Reaction Force-Aware Humanoid Locomotion Under Diverse Footwear

arXiv:2604.23702v1 Announce Type: new Abstract: Humanoid robots operating in human-centered environments (e.g., homes, hospitals, and offices) must mitigate foot--ground impact transients, as impact-i

safetyarxiv-cs-ro

RACANet: Reliability-Aware Crowd Anchor Network for RGB-T Crowd Counting

arXiv:2604.24543v1 Announce Type: new Abstract: RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation

Radial Load--Reserve Certificates for Wasserstein Propagation in Isotropic Diffusion Samplers

arXiv:2603.19670v3 Announce Type: replace Abstract: Nonasymptotic diffusion analyses often decompose sampling error into score estimation, continuous reverse-time propagation, discretization, and term

Rank, Head-Channel Non-Identifiability, and Symmetry Breaking: A Precise Analysis of Representational Collapse in Transformers

model-releasesarxiv-cs-cl

arXiv:2604.23681v1 Announce Type: cross Abstract: A widely cited result by Dong et al. (2021) showed that Transformers built from self-attention alone, without skip connections or feed-forward layers,

RAS: a Reliability Oriented Metric for Automatic Speech Recognition

arXiv:2604.24278v1 Announce Type: cross Abstract: Automatic speech recognition systems often produce confident yet incorrect transcriptions under noisy or ambiguous conditions, which can be misleading

RAT: RunAnyThing via Fully Automated Environment Configuration

arXiv:2604.23190v1 Announce Type: cross Abstract: Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of config

Agents

Reconstructive Authority Model: Runtime Execution Validity Under Partial Observability

arXiv:2604.22898v1 Announce Type: cross Abstract: Autonomous systems increasingly operate under partial observability where execution-relevant state is never fully accessible. Existing governance mech

agentsarxiv-cs-ai

Applications

RedParrot: Accelerating NL-to-DSL for Business Analytics via Query Semantic Caching

arXiv:2604.22758v1 Announce Type: cross Abstract: Recently, at Xiaohongshu, the rapid expansion of e-commerce and advertising demands real-time business analytics with high accuracy and low latency. T

applicationsarxiv-cs-ai

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

arXiv:2604.24334v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) chunking methods often create excessive redundancy, increasing storage costs and slowing retrieval. This s

researcharxiv-cs-cl

RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generation

arXiv:2604.24218v1 Announce Type: cross Abstract: As the complexity of System-on-Chip (SoC) designs grows, the shift-left paradigm necessitates the rapid development of high-fidelity reference models

Reflective Flow Sampling Enhancement

arXiv:2603.06165v2 Announce Type: replace-cross Abstract: The growing demand for text-to-image generation has led to rapid advances in generative modeling. Recently, text-to-image diffusion models tra

safetyarxiv-cs-ai

Reinforcement Learning with Backtracking Feedback

arXiv:2602.08377v2 Announce Type: replace-cross Abstract: Addressing the critical need for robust safety in Large Language Models (LLMs), particularly against adversarial attacks and in-distribution e

Agents

Representation Homogeneity and Systemic Instability in AI-Dominated Financial Markets: A Structural Approach

arXiv:2604.22818v1 Announce Type: cross Abstract: This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can gene

agentsarxiv-cs-ai

ResAF-Net: An Anchor-Free Attention-Based Network for Tree Detection and Agricultural Mapping in Palestine

arXiv:2604.23653v1 Announce Type: cross Abstract: Reliable agricultural data is essential for food security, land-use planning, and economic resilience, yet in Palestine, such data remains difficult t

Right-to-Act: A Pre-Execution Non-Compensatory Decision Protocol for AI Systems

arXiv:2604.24153v1 Announce Type: new Abstract: Current AI systems increasingly operate in contexts where their outputs directly trigger real-world actions. Most existing approaches to AI safety, risk

safetyarxiv-cs-ai

RL-ASL: A Dynamic Listening Optimization for TSCH Networks Using Reinforcement Learning

arXiv:2604.07533v2 Announce Type: replace-cross Abstract: Time Slotted Channel Hopping (TSCH) is a widely adopted Media Access Control (MAC) protocol within the IEEE 802.15.4e standard, designed to pr

Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss

model-releasesarxiv-cs-cl

arXiv:2604.23323v1 Announce Type: new Abstract: Audio-text retrieval enables semantic alignment between audio content and natural language queries, supporting applications in multimedia search, access

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA

arXiv:2604.23783v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterativ

Local Ai

S^2IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction

arXiv:2604.23296v1 Announce Type: cross Abstract: Aspect Sentiment Quad Prediction (ASQP) has seen significant advancements, largely driven by the powerful semantic understanding and generative capabi

local-aiarxiv-cs-ai

Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics

arXiv:2603.13502v2 Announce Type: replace Abstract: Wirelessly-connected robotic systems empower robots with real-time intelligence by leveraging remote computing resources for decision-making. Howeve

safetyarxiv-cs-ro

SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation

arXiv:2509.25358v4 Announce Type: replace Abstract: Large-scale robot learning has made progress on complex manipulation tasks, yet long horizon, contact rich problems, especially those involving defo

safetyarxiv-cs-ro

Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins

arXiv:2512.15930v2 Announce Type: replace-cross Abstract: Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80% o

Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection

arXiv:2604.23437v1 Announce Type: cross Abstract: The global financial ecosystem confronts a critical asymmetry: while fraud syndicates operate as borderless, distributed networks, banking institution

Hardware

Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models

arXiv:2604.24708v1 Announce Type: cross Abstract: Training large neural networks with data-parallel stochastic gradient descent allocates N GPU replicas to compute effectively identical updates -- a p

hardwarearxiv-cs-ai

Scheming Ability in LLM-to-LLM Strategic Interactions

arXiv:2510.12826v2 Announce Type: replace-cross Abstract: As large language model (LLM) agents are deployed autonomously in diverse contexts, evaluating their capacity for strategic deception becomes

Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces

arXiv:2604.22948v1 Announce Type: new Abstract: History-dependent sampling can reduce long-run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history th

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

arXiv:2512.23213v3 Announce Type: replace-cross Abstract: We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for

Seeing Is No Longer Believing: Frontier Image Generation Models, Synthetic Visual Evidence, and Real-World Risk

arXiv:2604.24197v1 Announce Type: cross Abstract: Frontier image generation has moved from artistic synthesis toward synthetic visual evidence. Systems such as GPT Image 2, Nano Banana Pro, Nano Banan

Seer: Language Instructed Video Prediction with Latent Diffusion Models

arXiv:2303.14897v4 Announce Type: replace Abstract: Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video p

safetyarxiv-cs-cv

Self-Abstraction Learning for Effective and Stable Training of Deep Neural Networks

arXiv:2604.24313v1 Announce Type: cross Abstract: Training large-scale deep neural networks effectively and stably is essential for applying deep learning across various fields. However, conventional

Hardware

Self-Rewarding Vision-Language Model via Reasoning Decomposition

arXiv:2508.19652v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) often suffer from visual hallucinations: generating things that are not consistent with visual inputs and language sho

hardwarearxiv-cs-cv

SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation

arXiv:2604.23274v1 Announce Type: new Abstract: Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabe

SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning

arXiv:2604.23747v1 Announce Type: cross Abstract: Recent mixed-policy optimization methods for LLM reasoning that interleave or blend supervised and reinforcement learning signals report improvements

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation

arXiv:2604.22825v1 Announce Type: cross Abstract: Large segmentation foundation models such as the Segment Anything Model (SAM) have reshaped promptable segmentation in natural images, and recent effo

Shape of Memory: a Geometric Analysis of Machine Unlearning in Second-Order Optimizers

arXiv:2604.23046v1 Announce Type: new Abstract: We argue that current definitions of machine unlearning are underspecified for second-order optimizers. We compare first-order and second-order learners

ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation

arXiv:2506.18493v2 Announce Type: replace Abstract: Customizing image generation remains a core challenge in controllable image synthesis. For single-concept generation, maintaining both identity pres

safetyarxiv-cs-cv

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning

arXiv:2506.05425v2 Announce Type: replace-cross Abstract: Understanding social interaction, which encompasses perceiving numerous and subtle multimodal cues, inferring unobservable mental states and r

SketchVLM: Vision language models can annotate images to explain thoughts and guide users

arXiv:2604.22875v1 Announce Type: cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models

SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing

arXiv:2604.23392v1 Announce Type: new Abstract: Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widel

SPAGS: Sparse-View Articulated Object Reconstruction from Single State via Planar Gaussian Splatting

arXiv:2511.17092v4 Announce Type: replace Abstract: Articulated objects are ubiquitous in daily environments, and their 3D reconstruction holds great significance across various fields. However, exist

Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction

arXiv:2604.23551v1 Announce Type: new Abstract: Reconstructing realistic underwater scenes from underwater video remains a meaningful yet challenging task in the multimedia domain. The inherent spatio

Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization

arXiv:2502.12672v4 Announce Type: replace-cross Abstract: Fine-tuning speech representation models can enhance performance on specific tasks but often compromises their cross-task generalization abili

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation

arXiv:2505.16637v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated remarkable capabilities in machine translation (MT). However, most advanced MT-specifi

StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery

arXiv:2604.22892v1 Announce Type: new Abstract: Feature selection in high-dimensional genomic data (d gg n) demands methods that are simultaneously accurate, sparse, and stable. Existing approaches ei

safetyarxiv-cs-lg