Search: “models”

Periodic Topological Deep Learning for Polymer Design and Discovery

arXiv:2605.26833v1 Announce Type: cross Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging.

Persistent AI Agents in Academic Research: A Single-Investigator Implementation Case Study

DGX agent

arXiv:2605.26870v1 Announce Type: cross Abstract: Background: Large language models are typically evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens wh

local-aiarxiv-cs-ai

27 May 2026

Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability

arXiv:2605.26790v1 Announce Type: new Abstract: Low-thrust trajectory design relies heavily on repeated evaluations of fuel consumption and transfer feasibility, which require expensive optimal contro

Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics

arXiv:2605.27296v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in diverse cultural contexts, yet their ability to master aesthetic stylistics, i.e., the strateg

Agents

QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents

arXiv:2605.27068v1 Announce Type: cross Abstract: Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM)

agentsarxiv-cs-ai

Scaling, Benchmarking, and Reasoning of Vision-Language Agents for Mobile GUI Navigation

arXiv:2605.27134v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown rapid progress in mobile GUI navigation. This paper presents a systematic study of data scaling, benchmarking,

Hardware

SIA: Self Improving AI with Harness & Weight Updates

arXiv:2605.27276v1 Announce Type: new Abstract: Humans are the bottleneck in building and improving AI. Both the models and the agents that wrap them are written, tuned, and corrected by people. The l

hardwarearxiv-cs-ai

SWE-Adept: An LLM-Based Agentic Framework for Deep Codebase Analysis and Structured Issue Resolution

arXiv:2603.01327v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strong performance on self-contained programming tasks. However, they still struggle with repository-leve

Unique Lives, Shared World: Learning from Single-Life Videos

arXiv:2512.04085v2 Announce Type: replace Abstract: We introduce the 'single-life' learning paradigm, where we train a distinct vision model exclusively on egocentric videos captured by one individual

safetyarxiv-cs-cv

Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization

arXiv:2605.26433v1 Announce Type: new Abstract: Large language model (LLM) summarization systems may pass compact vector representations of private inputs to downstream retrieval, monitoring, audit, o

Warp’s big bet on building open source with GPT-5.5

Warp is making a significant investment in developing open source tools and integrations built on GPT-5.5, OpenAI's advanced language model. The initiative aims to leverage GPT-5.5's capabilities to c

model-releasesopenai

Why Prompt Optimization Works, and Why It Sometimes Doesn't: A Causal-Inspired Edit-Level Analysis

arXiv:2605.26655v1 Announce Type: new Abstract: Automated prompt optimization methods (e.g., DSpy, TextGrad) can substantially improve the performance of large language model (LLM), however, their gen

A Multi-Probe Audit of Clinical-Interview Depression Detection Benchmarks

arXiv:2605.23977v1 Announce Type: new Abstract: This paper audits benchmark evaluation in clinical-interview depression detection through four complementary probes across DAIC/E-DAIC, CMDC, ANDROIDS,

AI Content Moderation in Therapy Conversations

arXiv:2605.25454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly being used for emotional support. They are also being developed for formal therapy purposes. However, LL

AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications

arXiv:2602.22769v3 Announce Type: replace Abstract: Large Language Models (LLMs) are deployed as autonomous agents in increasingly complex applications, where enabling long-horizon memory is critical

AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity prediction

arXiv:2605.24520v1 Announce Type: cross Abstract: Missense variant interpretation remains challenging because pathogenicity depends on heterogeneous evidence from population frequency, evolutionary co

Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC

arXiv:2605.25626v1 Announce Type: new Abstract: Social media platforms enable large-scale cross-lingual communication, but translating user-generated content (UGC) remains challenging due to its infor

Can LLMs Time Travel? Enhancing Temporal Consistency in Legal Agentic Search through Reinforcement Learning

arXiv:2605.25920v1 Announce Type: cross Abstract: While large language models (LLMs) augmented with agentic search capabilities show promise for legal reasoning, they overlook a fundamental constraint

Causal methods for LLM development and evaluation

arXiv:2605.25998v1 Announce Type: new Abstract: Large language model (LLM) development is currently driven by large-scale empirical iteration over data mixtures, reward models, routing strategies, and

safetyarxiv-cs-lg

Code2UML: Agentic LLMs with context engineering for scalable software visualization

arXiv:2605.24453v1 Announce Type: cross Abstract: Large Language Model (LLM)-based code analysis tools are adopted to automate software documentation tasks. However, the scalability of these approache

Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning

arXiv:2605.24810v1 Announce Type: cross Abstract: Off-dynamics offline reinforcement learning seeks to learn a target-domain policy from a large source dataset and a limited target dataset under misma

Decompose-and-Refine: Structured Legal Question Answering with Parametric Retrieval

arXiv:2605.24454v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance in the legal domain, demonstrating notable potential in Legal Question Answering (LQA). Howev

Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations

arXiv:2605.24748v1 Announce Type: cross Abstract: Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space e

researcharxiv-cs-lg

DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking

arXiv:2605.26087v1 Announce Type: cross Abstract: Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of establis

Does Continued Pretraining on a Learner Corpus Improve Automated Essay Scoring on English Proficiency Tests? Evidence from EFCAMDAT

arXiv:2605.25924v1 Announce Type: new Abstract: Recent automated essay scoring (AES) studies increasingly use pretrained transformer models, but these models are usually pretrained on general-domain E

researcharxiv-cs-cl

EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs

arXiv:2605.23954v1 Announce Type: cross Abstract: Audio Large Language Models (ALLMs) are highly vulnerable to real-world noise, which often induces severe semantic drift and hallucinations. Existing

EchoPilot: Training-Free Ultrasound Video Segmentation via Scale-Space Semantic Prompting and Reliability-Gated Memory

arXiv:2605.25944v1 Announce Type: cross Abstract: Ultrasound video segmentation is clinically valuable yet difficult due to speckle noise, weak boundaries, and rapid anatomical deformation. Recent pro

Enhancing Reliability in LLM-Based Secure Code Generation

arXiv:2605.24300v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for code generation, but their security reliability remains inconsistent across languages and prompting s

Local Ai

Evolving Causal Regulatory Networks (ECR-Net)

arXiv:2605.25211v1 Announce Type: new Abstract: Modern machine learning models excel at pattern recognition but remain brittle, often failing to generalize out of distribution (OOD) because they captu

local-aiarxiv-cs-lg

Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection

arXiv:2508.09801v3 Announce Type: replace-cross Abstract: Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive tech

researcharxiv-cs-ai

FoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis

arXiv:2605.24503v1 Announce Type: cross Abstract: As AI-powered compliance monitoring becomes increasingly important in public governance and industrial safety, the ability to provide verifiable evide

Fourier Feature Pyramids for Physics-Informed Neural Networks

arXiv:2605.24278v1 Announce Type: new Abstract: We present an improved neural field architecture for solving partial differential equations (PDEs). Current physics-informed neural networks (PINNs) pro

From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer

arXiv:2510.23008v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising performance in generating diagnostic conclusions from imaging findings, thereby supporting

Generalizable Vision-Language Few-Shot Adaptation with Predictive Prompts and Negative Learning

arXiv:2505.11758v2 Announce Type: replace-cross Abstract: Few-shot adaptation of vision-language models remains fundamentally limited by how negative class signals are handled at inference. Existing m

GIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer

arXiv:2605.24243v1 Announce Type: cross Abstract: In 3D scene understanding, deep learning models rely on large models and extensive training to capture basic geometric structures that are present in

safetyarxiv-cs-ai

Grouter: Decoupling Routing from Representation for Accelerated MoE Training

arXiv:2603.06626v2 Announce Type: replace-cross Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneou

safetyarxiv-cs-ai

HiMed: Incentivizing Hindi Reasoning in Medical LLMs

arXiv:2605.24635v1 Announce Type: new Abstract: Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in

How we evolved Google’s global and data center networks for the AI era

model-releasesgoogle-cloud-ai

Over the last 25 years of building Google’s global network, we’ve navigated major architectural eras — from the Internet, to streaming, and the cloud. Today, we are squarely in the midst of a fourth:

INDUCTION: Finite-Structure Concept Synthesis in First-Order Logic

arXiv:2602.18956v3 Announce Type: replace Abstract: We introduce INDUCTION, a benchmark for finite structure concept synthesis in first order logic. Given small finite relational worlds with extension

Insuring Every Action: An Authority Frontier Framework for Runtime Actuarial Control of Autonomous AI Agents

arXiv:2605.25632v1 Announce Type: new Abstract: Autonomous AI agents increasingly issue side-effect-bearing actions: database mutations, refunds, payments, external commitments. We propose the Actuari

JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates

arXiv:2605.25469v1 Announce Type: new Abstract: Quantization-aware training (QAT) is widely deployed but typically relies on the Straight-Through Estimator (STE), which passes gradients through non-di

Learning dynamical systems with biochemically informed neural ordinary differential equations

arXiv:2605.24170v1 Announce Type: cross Abstract: Ordinary differential equation models of biochemical reactions are often formulated as stoichiometric systems in which the dynamics arise from a colle

researcharxiv-cs-lg

Learning Sparse Compositional Functions with Norm-Constrained Neural Networks

arXiv:2605.25608v1 Announce Type: cross Abstract: The ability of deep neural networks to learn hierarchical features is widely regarded as a key mechanism underlying their success in high-dimensional

MDIA: A Multi-Agent Diagnostic Intelligence Pipeline on HealthBench Professional

arXiv:2605.24699v1 Announce Type: new Abstract: Most reported gains on agentic-LLM clinical benchmarks are often attributed to prompt engineering, yet our results suggest that larger improvements can

Meta-Soft: Leveraging Composable Meta-Tokens for Context-Preserving KV Cache Compression

arXiv:2605.22337v2 Announce Type: replace Abstract: The KV cache used in large language models has linearly growing time complexity, so LLMs face memory blow-up and reduced decoding efficiency when th

MR-LiDAR: A Multi-Resolution Roadside LiDAR Benchmark for Perception Diagnostics and Deployment Guidance

model-releasesarxiv-cs-ro

arXiv:2605.24777v1 Announce Type: new Abstract: LiDAR model selection is a critical issue in roadside sensing systems, as it directly determines both perception capability and deployment cost. However

MuCRASP: Multimodal Chain-of-thought Reasoning aware Structured Pruning

arXiv:2605.25842v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex multimodal tasks, but their large parameter sizes m

Agents

Multi-Agent Specification-based Metamorphic Testing of FMU-Based Simulations

arXiv:2605.25101v1 Announce Type: cross Abstract: In many industrial domains, the Functional Mock-up Interface (FMI) is used to exchange simulation models as Functional Mock-up Units (FMUs) across dif

agentsarxiv-cs-ai