Search: “models”

The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology

arXiv:2605.26346v1 Announce Type: new Abstract: Objective: To describe the design and early clinical evaluation of The Daily Dose (TDD), an LLM-driven, automated clinical summarization and clinical-tr

researcharxiv-cs-cl

27 May 2026

Model Releases

VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions

DGX agent

arXiv:2605.27141v1 Announce Type: new Abstract: Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such setti

model-releasesarxiv-cs-ai

27 May 2026

A Comprehensive Dataset for Human vs. AI Generated Text Detection

arXiv:2510.22874v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authentic

A Tabular Schedule Abstraction for Communication-Aware Evaluation of Pipeline-Parallel LLM Training

arXiv:2605.24006v1 Announce Type: cross Abstract: Pipeline parallelism is a key technique for distributed training of large language models because it reduces per-device parameter and activation memor

Advancing Graph Few-Shot Learning via In-Context Learning

arXiv:2605.24410v1 Announce Type: new Abstract: Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning

Asking LLMs to Verify First is Almost Free Lunch

arXiv:2511.21734v2 Announce Type: replace-cross Abstract: To enhance the reasoning capabilities of Large Language Models (LLMs) without high costs of training, nor extensive test-time sampling, we int

CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation

arXiv:2605.25378v1 Announce Type: cross Abstract: Customized image editing aims to equip pre-trained diffusion models with specific visual effects using limited paired data, typically via Low-Rank Ada

ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions

arXiv:2605.24279v1 Announce Type: new Abstract: A frontier language model's acknowledged 'helpful programming assistant' persona does not survive long agentic-coding sessions in the deployment regime

D^2-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing

arXiv:2605.25893v1 Announce Type: new Abstract: Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring

Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction

arXiv:2605.24212v1 Announce Type: cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outc

Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation

arXiv:2605.25781v1 Announce Type: new Abstract: Evaluating structured-information extraction from historical documents at scale requires high-precision ground-truth annotations, yet traditional manual

FairJudge: Abstention-Aware Multimodal Judges for Fairness and Alignment Evaluation in Text-to-Image Models

arXiv:2510.22827v3 Announce Type: replace-cross Abstract: Evaluating text-to-image (T2I) systems requires judging not only whether an image matches a prompt, but also whether socially salient attribut

safetyarxiv-cs-lg

HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space

arXiv:2509.22299v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures in large language models (LLMs) deliver exceptional performance and reduced inference costs compared to

HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing

arXiv:2605.24687v1 Announce Type: cross Abstract: Text-to-Image (T2I) models have made significant strides in visual realism and semantic consistency, yet they often perpetuate and amplify societal bi

KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI

arXiv:2510.02327v2 Announce Type: replace-cross Abstract: Real-time speech-to-speech (S2S) models excel at generating natural, low-latency conversational responses but often lack deep knowledge and se

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology

arXiv:2605.24608v1 Announce Type: new Abstract: We develop a rigorous algebraic framework for deep convolutional architectures, CNNs, ResNets, and encoder--decoder networks such as UNet, grounded in l

researcharxiv-cs-ai

LLMs Show No Signs Of Individuated Metacognition

arXiv:2605.24299v1 Announce Type: new Abstract: Confidence-weighted routing, selective abstention, and ensemble weighting all assume that a model's stated confidence is informative about its capabilit

Local Ai

Local MAP Sampling for Diffusion Models

arXiv:2510.07343v3 Announce Type: replace-cross Abstract: Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from p(x_0 mid y). While posterior

local-aiarxiv-cs-ai

MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization

arXiv:2605.24019v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) achieve outstanding performance, yet their huge model size severely hinders deployment on edge devices with limited reso

Neural Router: Semantic Content Matching for Agentic AI

arXiv:2605.25701v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI across the edge-clou

QUIET: A Multi-Blank Cascaded Story Cloze Benchmark for LLM Creative Generation Capability

arXiv:2605.25955v1 Announce Type: cross Abstract: Large language models (LLMs) face a dual challenge in creative capability evaluation: existing benchmarks (e.g., Story Cloze Test, HellaSwag) measure

SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks

arXiv:2605.25492v1 Announce Type: new Abstract: Pairwise model comparisons drawn from foundation-model benchmarks ('A is safer than B') are read as quantitative verdicts but hinge on harness choices b

SODE: Analyzing Social Dynamics in LLM Agents

arXiv:2605.23949v1 Announce Type: cross Abstract: As Large Language Models (LLMs) evolve into interactive agents, understanding their behavioral alignment within human social dynamics becomes essentia

Stop Comparing LLM Agents Without Disclosing the Harness

arXiv:2605.23950v1 Announce Type: new Abstract: This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely

safetyarxiv-cs-ai

System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineer…

System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineering leverage actually sits. The labs own the model. You own

model-releasesdair-ai--x

Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation

arXiv:2605.24211v1 Announce Type: cross Abstract: Analogies help learners understand unfamiliar concepts by relating them to known concepts. Despite recent advances, large language models (LLMs) conti

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts

arXiv:2605.24846v1 Announce Type: cross Abstract: Large language models (LLMs) display strong comprehensive abilities, yet the internal mechanisms that support these behaviors remain insufficiently un

Towards end-to-end LLM-based censoring-aware survival analysis

arXiv:2605.25399v1 Announce Type: new Abstract: Objective: Survival analysis is central to medical prediction, yet large language models (LLMs) are rarely used as end-to-end survival models because ce

researcharxiv-cs-ai

Triplet-Block Diffusion RWKV

arXiv:2605.25969v1 Announce Type: new Abstract: Causal Transformer language models suffer from strictly sequential decoding and a quadratic per-step attention cost. While linear-time causal models and

researcharxiv-cs-cl

Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

arXiv:2605.23733v1 Announce Type: cross Abstract: Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Traini

Tutorials

Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework

arXiv:2512.15767v2 Announce Type: replace-cross Abstract: Simulating complex unsteady physical phenomena relies on detailed mathematical models, simulated for instance by using the Finite Element Meth

tutorialsarxiv-cs-ai

ComfyUI-Angelo now supports Qwen Edit

model-releasesr-stablediffusion

ComfyUI-Angelo now supports Qwen-Image-Edit, an advanced image editing model that provides text editing features and the ability to edit both semantics and appearance of images. The model applies Qwen

DeepSeek V4 Flash IS BACK on Nous Portal for FREE for use in Hermes Agent! Check it out at https://portal.nousresearch.com/manage-subscripti…

model-releasesnous-research--x

DeepSeek V4 Flash model has been made available again on the Nous Research portal at no cost for use with Hermes Agent applications. Users can access and utilize this model through the Nous portal's s

Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics

arXiv:2605.23089v1 Announce Type: cross Abstract: Model-based reinforcement learning improves sample efficiency by learning a world model. However, existing latent world models such as DreamerV3 do no

safetyarxiv-cs-ai

Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches

arXiv:2512.12677v2 Announce Type: replace-cross Abstract: We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource const

FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning

arXiv:2605.22898v1 Announce Type: new Abstract: Federated learning protocols face a structural trilemma: canonical server-based aggregation~ite{mcmahan2017} creates a single point of failure and gradi

safetyarxiv-cs-lg

ImProver 2: Iteratively Self-Improving LMs for Neurosymbolic Proof Optimization

arXiv:2605.22885v1 Announce Type: new Abstract: Formal mathematics libraries are rapidly expanding, creating a growing need to refactor verified proofs for maintainability and to improve training data

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

arXiv:2605.23082v1 Announce Type: cross Abstract: Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as

OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents

arXiv:2605.23657v1 Announce Type: new Abstract: Skills, i.e., structured workflow instructions distilled for large language models (LLMs), are becoming an increasingly important mechanism for improvin

PGT: Procedurally Generated Tasks for improving visual grounding in MLLMs

arXiv:2605.23883v1 Announce Type: cross Abstract: Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this wo

PoisonForge: Task-Level Targeted Poisoning Benchmark for Instruction-Tuned LLMs

arXiv:2605.23168v1 Announce Type: cross Abstract: When practitioners fine-tune LLMs on unvetted datasets, an adversary can exploit the data supply chain through task-level poisoning: inserting a small

Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models

arXiv:2605.23522v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators.

safetyarxiv-cs-ai

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation

arXiv:2412.19098v4 Announce Type: replace Abstract: Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding ta

Integrable Elasticity via Neural Demand Potentials

arXiv:2605.22820v1 Announce Type: new Abstract: We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-dema

On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs

arXiv:2602.12506v3 Announce Type: replace Abstract: Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivatin

Towards Solving the Gilbert-Pollak Conjecture via Large Language Models

arXiv:2601.22365v2 Announce Type: replace-cross Abstract: The Gilbert-Pollak Conjecture itep{gilbert1968steiner}, also known as the Steiner Ratio Conjecture, states that for any finite point set in th

Hardware

WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving

arXiv:2512.09472v2 Announce Type: replace-cross Abstract: Deploying multiple models within shared GPU clusters is a key strategy to improve resource efficiency in large language model (LLM) serving. E

hardwarearxiv-cs-lg

Access Paths for Efficient Ordering with Large Language Models