Search: “software”

PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization

model-releasesarxiv-cs-cl

arXiv:2605.15222v1 Announce Type: cross Abstract: Large language models (LLMs) can often generate functionally correct code, but their ability to produce efficient implementations for performance-crit

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Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI

DGX agent

arXiv:2605.15567v1 Announce Type: new Abstract: This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI. The metacognitive solut

applicationsarxiv-cs-ai

18 May 2026

Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation

arXiv:2605.15714v1 Announce Type: cross Abstract: This position paper argues that the machine learning community should prioritize early-stage quality assurance in annotation pipelines over the prevai

Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search

arXiv:2605.16238v1 Announce Type: new Abstract: Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling tea

SaaS-Bench: Can Computer-Use Agents Leverage Real-World SaaS to Solve Professional Workflows?

arXiv:2605.15777v1 Announce Type: new Abstract: Computer-Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text-based reasoning toward action execution in more complex envi

A Security Analysis of the OpenClaw AI Agent Framework

arXiv:2603.27517v3 Announce Type: replace-cross Abstract: AI agent frameworks connecting large language model (LLM) reasoning to host execution surfaces -- shell, filesystem, containers, and messaging

safetyarxiv-cs-ai

Articraft: An Agentic System for Scalable Articulated 3D Asset Generation

arXiv:2605.15187v1 Announce Type: new Abstract: A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large lan

agentsarxiv-cs-cv

ClickRemoval: An Interactive Open-Source Tool for Object Removal in Diffusion Models

arXiv:2605.14461v1 Announce Type: new Abstract: Existing object removal tools often rely on manual masks or text prompts, making precise removal difficult for non-expert users in complex scenes and of

researcharxiv-cs-cv

Comparing Developer and LLM Biases in Code Evaluation

arXiv:2603.24586v2 Announce Type: replace-cross Abstract: As LLMs are increasingly used as judges in code applications, they should be evaluated in realistic interactive settings that capture partial

safetyarxiv-cs-cl

Computational Imaging Priors for Wireless Capsule Endoscopy: Monte Carlo-Guided Hemoglobin Mapping for Rare-Anomaly Detection

arXiv:2605.15062v1 Announce Type: new Abstract: Background. RGB-trained capsule-endoscopy classifiers underperform on small-vessel vascular findings by conflating hemoglobin contrast with bile and ill

researcharxiv-cs-cv

Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs

arXiv:2605.15077v1 Announce Type: cross Abstract: Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. U

Correctness-Aware Repository Filtering Under Maximum Effective Context Window Constraints

arXiv:2605.14362v1 Announce Type: cross Abstract: Context window efficiency is a practical constraint in large language model (LLM)-based developer tools. Paulsen [12] shows that all tested models deg

Applications

Enhanced and Efficient Reasoning in Large Learning Models

arXiv:2605.14036v1 Announce Type: new Abstract: In current Large Language Models we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, ther

applicationsarxiv-cs-ai

SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples

model-releasesarxiv-cs-cv

arXiv:2507.07776v3 Announce Type: replace Abstract: Unrestricted adversarial attacks aim to fool computer vision models without being constrained by ell_p-norm bounds to remain imperceptible to humans

What Do AI Agents Talk About? Discourse and Architectural Constraints in the First AI-Only Social Network

model-releasesarxiv-cs-cl

arXiv:2603.07880v5 Announce Type: replace Abstract: Moltbook is the first large-scale social network built for autonomous AI agent-to-agent interaction. Early studies on Moltbook have interpreted its

BiPneu: Design and Control of a Bipolar-Pressure Pneumatic System for Soft Robots

arXiv:2605.12804v1 Announce Type: new Abstract: Positive-negative pressure regulation is critical to soft robotic actuators, enabling large motion ranges and versatile actuation modes. However, achiev

researcharxiv-cs-ro

Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack

arXiv:2605.12673v1 Announce Type: new Abstract: Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hackin

Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion

arXiv:2605.13293v1 Announce Type: new Abstract: Boundary Representation (BRep) is the standard format for Computer-Aided Design (CAD), yet reconstructing high-quality BReps from single-view images rem

researcharxiv-cs-cv

Applications

It's not the Language Model, it's the Tool: Deterministic Mediation for Scientific Workflows

arXiv:2605.13245v1 Announce Type: new Abstract: Language models can produce convincing scientific analyses, but repeated generations on the same data do not guarantee the same result. A researcher may

applicationsarxiv-cs-ai

MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows

model-releasesarxiv-cs-cv

arXiv:2603.24649v2 Announce Type: replace Abstract: Medical imaging benchmarks often evaluate VLMs on pre-selected 2D images, slices, crops, or patches, making evaluation closer to visual recognition.

Revisiting DAgger in the Era of LLM-Agents

arXiv:2605.12913v1 Announce Type: new Abstract: Long-horizon LM agents learn from multi-turn interaction, where a single early mistake can alter the subsequent state distribution and derail the whole

safetyarxiv-cs-lg

Local Ai

A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory

arXiv:2605.11847v1 Announce Type: cross Abstract: Analog content-addressable memories (aCAMs) based on memristors provide a promising pathway toward energy-efficient large-scale associative computing

local-aiarxiv-cs-lg

Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications

arXiv:2605.11855v1 Announce Type: new Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term de

researcharxiv-cs-lg

Hardware

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces

arXiv:2605.11333v1 Announce Type: cross Abstract: The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed ma

hardwarearxiv-cs-lg

OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness

arXiv:2605.11525v1 Announce Type: new Abstract: Missing values are routinely treated as defects to be eliminated through deletion or imputation prior to machine learning. In many applied domains, howe

safetyarxiv-cs-lg

A Controlled Diagnostic Study of Hardware-Induced Distortions in Hardware-Aware Training

arXiv:2605.09416v1 Announce Type: new Abstract: Hardware-aware training (HAT) is widely used to improve the robustness of neural networks on non-ideal AI accelerators, such as analog in-memory computi

researcharxiv-cs-lg

AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators

arXiv:2605.08647v1 Announce Type: cross Abstract: Multi-agent systems achieve state-of-the-art outcomes through peer collaboration. However, when an agent in the pipeline silently drops a constraint,

Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49%

arXiv:2605.08112v1 Announce Type: cross Abstract: AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product dec

Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation

arXiv:2605.09222v1 Announce Type: cross Abstract: Logs are ubiquitous in modern systems. Unfortunately, their unstructured nature in flat sequences limits understanding of execution behaviors, hinderi

Local Ai

End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor

arXiv:2605.09570v1 Announce Type: new Abstract: With the rapid growth of mobile robotics and embedded intelligence, there is an increasing demand for efficient on-device data processing on edge platfo

local-aiarxiv-cs-lg

Engineering Robustness into Personal Agents with the AI Workflow Store

arXiv:2605.10907v1 Announce Type: cross Abstract: The dominant paradigm for AI agents is an 'on-the-fly' loop in which agents synthesize plans and execute actions within seconds or minutes in response

General Agent Evaluation

arXiv:2602.22953v2 Announce Type: replace Abstract: General-purpose agents perform tasks in unfamiliar environments without domain-specific manual customization. Yet no study has systematically measur

Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning

model-releasesarxiv-cs-lg

arXiv:2605.08109v1 Announce Type: new Abstract: Inertial microfluidic devices (IMDs) offer low-cost, high-throughput alternative techniques for many traditional particle- (or cell-) manipulation tasks

Governing AI-Assisted Security Operations: A Design Science Framework for Operational Decision Support

arXiv:2605.09534v1 Announce Type: cross Abstract: Engineering managers increasingly must decide how to introduce generative artificial intelligence (AI), retrieval-augmented generation, and coding age

safetyarxiv-cs-ai

HiDrive: A Closed-Loop Benchmark for High-Level Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2605.09972v1 Announce Type: cross Abstract: End-to-end autonomous driving has witnessed rapid progress, yet existing benchmarks are increasingly saturated, with state-of-the-art models achieving

Locking Pretrained Weights via Deep Low-Rank Residual Distillation

model-releasesarxiv-cs-lg

arXiv:2605.10777v1 Announce Type: new Abstract: The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling the

MDGYM: Benchmarking AI Agents on Molecular Simulations

arXiv:2605.08941v1 Announce Type: new Abstract: The promise of AI-driven scientific discovery hinges on whether AI agents can autonomously design and execute the computational workflows that underpin

Position: AI Security Policy Should Target Systems, Not Models

arXiv:2605.09504v1 Announce Type: cross Abstract: We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory, paral

Pretraining large language models with MXFP4

arXiv:2605.09825v1 Announce Type: cross Abstract: Why does full-pipeline FP4 training of large language models often diverge, even when forward activations and activation gradients remain stable? We a

Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding

arXiv:2507.11198v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) enable new possibilities for qualitative research at scale, including annotation and qualitative coding of educat

VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation

arXiv:2605.08553v1 Announce Type: cross Abstract: Large language models can generate useful code from natural language, but their outputs come without correctness guarantees. Verifiable code generatio

VISOR: A Vision-Language Model-based Test Oracle for Testing Robot

model-releasesarxiv-cs-ro

arXiv:2605.10408v1 Announce Type: cross Abstract: Testing robots requires assessing whether they perform their intended tasks correctly, dependably, and with high quality, a challenge known as the tes

Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility

model-releasesarxiv-cs-cl

arXiv:2605.06856v1 Announce Type: cross Abstract: Generative AI systems achieve impressive performance on standard benchmarks yet fail to deliver real-world utility, a disconnect we identify across 28

Hardware

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control

arXiv:2510.07625v2 Announce Type: replace Abstract: While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear traject

hardwarearxiv-cs-ro

PerfCoder: Large Language Models for Interpretable Code Performance Optimization

arXiv:2512.14018v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance cod

Text-to-CAD Evaluation with CADTests

arXiv:2605.07807v1 Announce Type: cross Abstract: Text-to-CAD has recently emerged as an important task with the potential to substantially accelerate design workflows. Despite its significance, there

Applications

User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums

arXiv:2509.11777v2 Announce Type: replace Abstract: Customer feedback in industrial forums offers rich but underexplored insights into real-world product experience. Yet systematic analysis remains ch

applicationsarxiv-cs-cl

AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures