Search: “models”

A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging

arXiv:2605.29753v1 Announce Type: cross Abstract: Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware c

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

AG-REPA: Causal Layer Selection for Representation Alignment in Audio Flow Matching

DGX agent

arXiv:2603.01006v2 Announce Type: replace-cross Abstract: REPresentation Alignment (REPA) improves the training of generative flow models by aligning intermediate hidden states with pretrained teacher

safetyarxiv-cs-ai

29 May 2026

Industry

AI Disruptors: How the Next Generation of Business is Being Built

This DigitalOcean blog post examines how emerging AI technologies are transforming business models and enabling startups to challenge traditional industries. It likely covers practical examples of AI-

industrydigitalocean

Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception

arXiv:2605.29064v1 Announce Type: new Abstract: We study how persona prompting shapes language generated by multimodal large language models in an urban perception setting. Using 59,808 annotations fr

researcharxiv-cs-cl

Anytime-Valid Federated Conformal RAG for LLM Swarms

arXiv:2605.29139v1 Announce Type: cross Abstract: Federated Conformal RAG (FC-RAG) provides distribution-free coverage for a bandwidth-limited swarm of weak language models, but only at a fixed horizo

safetyarxiv-cs-lg

Aryabhata 2: Scaling Reinforcement Learning for Advanced STEM Reasoning

arXiv:2605.28829v1 Announce Type: cross Abstract: Competitive STEM examinations such as JEE and NEET require multi-step symbolic reasoning, precise numerical computation, and deep conceptual understan

applicationsarxiv-cs-ai

b9393

local-aillama-cpp-releases

B9393 is a build release of llama.cpp, the C/C++ inference framework for running large language models locally. llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a

b9401

local-aillama-cpp-releases

b9401 is a release build of llama.cpp, an LLM inference framework in C/C++ that provides tools for running large language models locally. As an intermediate build in the llama.cpp project, it includes

b9402

local-aillama-cpp-releases

B9402 is a release of llama.cpp, an open-source library that performs inference on large language models such as Llama and was developed in pure C/C++ with no dependencies. The release includes comman

Hardware

Bastion: Budget-Aware Speculative Decoding with Tree-structured Block Diffusion Drafting

arXiv:2605.29727v1 Announce Type: new Abstract: Block-diffusion drafters have recently emerged as a powerful alternative for speculative decoding by predicting multiple future-token distributions in a

hardwarearxiv-cs-lg

Beyond Attack Success Rate: Temporal Logit Observability for LLM Safety Failures

arXiv:2605.29629v1 Announce Type: new Abstract: Attack Success Rate (ASR) evaluates each jailbreak with a single yes/no label at the end of generation, telling us whether a failure happened but not ho

Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priors

arXiv:2605.30065v1 Announce Type: new Abstract: In this work, we focus on zero-shot 3D style transfer that can generate multi-view consistent stylized views of the 3D scene given an arbitrary style im

researcharxiv-cs-cv

Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm

arXiv:2603.26668v2 Announce Type: replace-cross Abstract: As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the

Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations

arXiv:2601.08064v2 Announce Type: replace Abstract: Confidence estimation (CE) indicates how reliable the answers of large language models are and impacts user trust and decision-making. Existing eval

safetyarxiv-cs-cl

Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent

arXiv:2603.01311v2 Announce Type: replace Abstract: The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for thi

agentsarxiv-cs-cl

CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning

arXiv:2605.29602v1 Announce Type: new Abstract: Multi-modal Retrieval-Augmented Generation (MMRAG) has emerged as a powerful paradigm for enhancing Multimodal Large Language Models in knowledge-intens

researcharxiv-cs-cv

COMET: Concept Space Dissection of the Modality Gap in Audio-Text Multimodal Contrastive Embeddings

arXiv:2605.29628v1 Announce Type: cross Abstract: Contrastive Language-Audio Pretraining (CLAP) models are widely used for audio understanding and support modality-agnostic condition swapping in many

CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems

arXiv:2605.29612v1 Announce Type: cross Abstract: Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over s

agentsarxiv-cs-cl

Conformal Certification of Reasoning Trace Prefixes

arXiv:2605.30085v1 Announce Type: new Abstract: Language model reasoning traces are rarely all-or-nothing; they frequently contain valid intermediate steps before a critical error occurs. Existing unc

Context Distillation as Latent Memory Management

arXiv:2605.28889v1 Announce Type: cross Abstract: Context distillation compresses contextual information into model parameters, yet existing methods often ignore how multiple distilled latent memories

Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties

arXiv:2505.02743v2 Announce Type: replace Abstract: Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data g

tutorialsarxiv-cs-lg

CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution

arXiv:2605.30133v1 Announce Type: new Abstract: We introduce CorPipe 26, our winning submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution. The fifth edition of this shared ta

researcharxiv-cs-cl

Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations

arXiv:2410.07287v2 Announce Type: replace-cross Abstract: Climate change poses an existential threat, necessitating effective climate policies to enact impactful change. Decisions in this domain are i

Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning

arXiv:2605.29464v1 Announce Type: cross Abstract: In randomized trials involving multiple treatments, bivariate survival outcomes present significant analytical challenges for making decisions. This p

researcharxiv-cs-lg

Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning

arXiv:2605.30039v1 Announce Type: new Abstract: Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains throu

tutorialsarxiv-cs-ai

Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale

arXiv:2605.30200v1 Announce Type: new Abstract: The double-edged sword of integrating Large Language Models (LLMs) requires an effective triadic collaboration mechanism among LLMs, teachers and studen

applicationsarxiv-cs-ai

Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding

arXiv:2605.29336v1 Announce Type: new Abstract: Improving the quality of model-generated summaries, especially factuality, the accuracy of a summary with respect to its source content, remains a chall

researcharxiv-cs-cl

EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance

arXiv:2505.21876v2 Announce Type: replace-cross Abstract: Recent approaches for video generation with camera control often create anchor videos (i.e., rendered videos that approximate desired camera m

EvA: An Evidence-First Audio Understanding Paradigm for LALMs

arXiv:2603.27667v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) still struggle in complex acoustic scenes because they often fail to preserve task-relevant acoustic evide

Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection

arXiv:2605.30099v1 Announce Type: new Abstract: Valuable decisions and highly prioritized analysis now depend on applications such as facial biometrics, social media photo tagging, and human robots in

applicationsarxiv-cs-cv

Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach

arXiv:2605.29161v1 Announce Type: cross Abstract: Generating realistic graph-structured data is challenging due to discrete connectivity, varying graph sizes, and class-specific structural patterns. R

Feedback-to-Rubrics: Can We Learn Expert Criteria from Inline Comments?

arXiv:2605.29857v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for writing and review support, but their usefulness depends on context-dependent criteria, such as e

tutorialsarxiv-cs-lg

Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control

arXiv:2605.29937v1 Announce Type: cross Abstract: Diffusion models are effective for waypoint prediction in visual navigation, but standard sampling and test time guidance can produce unreliable or in

safetyarxiv-cs-lg

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation

arXiv:2605.29461v1 Announce Type: new Abstract: LLM-conditioned segmentation has recently advanced rapidly by coupling large language models with iterative mask generation frameworks. However, we iden

safetyarxiv-cs-cv

Tools

Function invocations now billed per unit

Vercel updated its billing model to charge for serverless function invocations on a per-unit basis rather than previous pricing structures. This change affects how customers are charged for executing

toolsvercel-blog

GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering

arXiv:2605.29584v1 Announce Type: new Abstract: Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe

safetyarxiv-cs-cl

GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation

arXiv:2602.17200v2 Announce Type: replace Abstract: Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. In th

safetyarxiv-cs-cv

Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation

arXiv:2605.30282v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, la

safetyarxiv-cs-ro

Generative Spatiotemporal Intent Sequence Recommendation via Implicit Reasoning in Amap

arXiv:2605.28888v1 Announce Type: cross Abstract: Real-world user behavior rarely consists of isolated actions; instead, it often forms intent flows governed by spatiotemporal dependencies. To provide

applicationsarxiv-cs-lg

Genetically Aligned Patient Representations Improve Hematological Diagnosis

arXiv:2605.29980v1 Announce Type: cross Abstract: Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream

GrepSeek: Training Search Agents for Direct Corpus Interaction

arXiv:2605.29307v1 Announce Type: cross Abstract: Large Language Model (LLM) search agents have shown strong promise for knowledge-intensive language tasks through multiple rounds of reasoning and inf

Hijacking Agent Memory: Stealthy Trojan Attacks Through Conversational Interaction

arXiv:2605.29960v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly leverage long term memory to support persistent and autonomous task execution. However, this capability

agentsarxiv-cs-ai

How Consistent Are LLM Agents? Measuring Behavioral Reproducibility in Multi-Step Tool-Calling Pipelines

arXiv:2605.28840v1 Announce Type: cross Abstract: Large language model (LLM) agents with tool-calling capabilities are increasingly deployed in production systems, yet a fundamental reliability questi

agentsarxiv-cs-ai

Hardware

How Together AI built the world’s fastest speech-to-text stack

Together AI developed an optimized speech-to-text system focused on achieving the fastest processing speeds through technical innovations in their inference stack and model optimization. The approach

hardwaretogether-ai-blog

Inferring Code Correctness from Specification

arXiv:2605.29822v1 Announce Type: cross Abstract: Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale. However, validating the

IP-Adapter Is All You Need: Towards Fine-Tuning-Free Diffusion-Based Talking Face Generation

arXiv:2605.30230v1 Announce Type: new Abstract: With the rapid advancement of diffusion models, talking face generation has made remarkable progress. However, existing diffusion-based methods still re

researcharxiv-cs-cv

KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks

arXiv:2411.00278v4 Announce Type: replace Abstract: Time series anomaly detection (TSAD) underpins real-time monitoring in cloud services and web systems, allowing rapid identification of anomalies to

local-aiarxiv-cs-lg

Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoning

arXiv:2605.30245v1 Announce Type: new Abstract: Current plan-based reasoning methods improve large language models (LLMs) by inserting a planning stage before execution, giving rise to the question ri

tutorialsarxiv-cs-cl