Search: “software”

DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization

arXiv:2605.11015v2 Announce Type: replace-cross Abstract: Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining wh

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Agents

Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System

DGX agent

arXiv:2610.11899v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded as components in software systems, marketed under labels such as chatbot, copilot, retrieval-au

agentsarxiv-cs-ai

9 Oct 2026

Interpretable Memory Models for Spaced Repetition

arXiv:2610.10548v1 Announce Type: cross Abstract: Spaced repetition software schedules reviews with a memory model fit to review logs. Accuracy on a test set is not sufficient evidence of quality sinc

researcharxiv-cs-lg

MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning

arXiv:2610.11727v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become a cornerstone in software engineering for enhancing Large Language Models (LLMs) with domain-specific

researcharxiv-cs-ai

MemTrace: State-Consistent Memory for Long-Horizon Coding Agents

arXiv:2610.04838v2 Announce Type: replace Abstract: As coding agents take on long-horizon software evolution tasks spanning multiple files and stages, longer execution trajectories introduce two coupl

On the estimation and validity of AI time horizons---a statistical look at the METR plot

arXiv:2610.12466v1 Announce Type: new Abstract: METR's 50% time horizon measures the human completion time of software tasks that an AI solves with 50% probability, allowing AI capabilities to be expr

researcharxiv-cs-ai

Runnable Commit Untangling for Coding Agents

arXiv:2610.11593v1 Announce Type: cross Abstract: Coding agents produce large, tangled patches that mix multiple development purposes, making the code hard to review and maintain. Commit untangling of

TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution

arXiv:2610.10619v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly reshaping software engineering, accompanied by an explosion of new code benchmarks. Yet nearly all exist

A Framework for the Systematic Review of ML Assets in AI Registries

arXiv:2610.09551v1 Announce Type: new Abstract: Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, eva

safetyarxiv-cs-lg

8 Oct 2026

Hardware

Accelerating Floating-Point Satisfiability Solving via Gradient Normalization

arXiv:2610.08808v1 Announce Type: cross Abstract: Satisfiability Modulo Theories (SMT) solvers are foundational to software verification, program analysis, and compiler testing, particularly over the

hardwarearxiv-cs-lg

8 Oct 2026

Coding-Agent Benchmarks Should Match Their Users' Task Flows

model-releasesarxiv-cs-cl

arXiv:2610.09633v1 Announce Type: cross Abstract: The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engin

8 Oct 2026

Applications

Beyond Marginals: A Multi-Dimensional Evaluation Framework for Multi-Table Synthetic Data Generation

arXiv:2610.06854v1 Announce Type: cross Abstract: Synthetic data generation is critical for privacy compliance, machine learning augmentation, and software testing. While single-table evaluation is we

applicationsarxiv-cs-lg

Beyond the Leaderboard: Multi-Dimensional Evaluation of Dense and Mixture-of-Experts Models for Automated Program Repair

arXiv:2610.08173v1 Announce Type: cross Abstract: Automated Program Repair (APR) with language models is usually evaluated by whether a generated patch passes the test suite, which can hide difference

Applications

In With the Old: Enhancing 'Classical' Document Automation with Generative AI

arXiv:2610.07480v1 Announce Type: new Abstract: Software-based legal assistance systems have leveraged many different forms of knowledge representation and reasoning. This article explores how documen

applicationsarxiv-cs-ai

Tutorials

Learning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis

arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. E

tutorialsarxiv-cs-ai

RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway

arXiv:2610.06923v1 Announce Type: new Abstract: Artificial intelligence has advanced individual radiotherapy tasks, yet these capabilities remain separated across clinical stages, software environment

SIGMA: Self-Improving Alignment Generalization from a Model Spec

arXiv:2610.07935v1 Announce Type: new Abstract: LLM agents are increasingly capable of executing complex tasks and of recursively improving themselves on easy-to-verify objectives such as software eng

safetyarxiv-cs-ai

TeleTune: Evolving Agent Skills From Offline Telemetry

arXiv:2610.05437v2 Announce Type: replace Abstract: Computer-use agents need to capture procedural knowledge of how people use software. User telemetry offers a scalable source of this knowledge. Howe

Autoware in Construction: Gap Analysis and LiDAR Perception Toward Off-Road Autonomous Driving

arXiv:2610.04187v1 Announce Type: new Abstract: Autoware is an open-source autonomous driving software platform widely adopted by researchers and industry developers. Originally developed primarily fo

agentsarxiv-cs-ro

Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning

arXiv:2610.06366v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; pra

EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild

arXiv:2604.01554v2 Announce Type: replace-cross Abstract: Binary Function Similarity Detection (BFSD) is a core problem in software security, supporting tasks such as vulnerability analysis, malware c

Mind the Gaps: From Failure Attribution to Closed-Form Repair of Code Language Models

arXiv:2610.05277v1 Announce Type: cross Abstract: Code language models must be maintained like the software around them: when a library evolves, a model keeps writing the interface that it saw during

researcharxiv-cs-lg

Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents

arXiv:2610.03014v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly connect model-generated decisions to security-sensitive software capabilities such as command exe

GTDD: Generative Test-Driven Development for AI Coding Agents with Adversarial Testing

arXiv:2610.02952v1 Announce Type: cross Abstract: Test-driven development gives AI coding agents executable requirements for implementing software. Because these agents can adapt their implementations

safetyarxiv-cs-lg

Harness-Aware Distillation for Small Language Model Agents

arXiv:2610.02858v1 Announce Type: new Abstract: Language model agents are deployed with a harness, the software around the model that manages its context, tools, and feedback. When such an agent is di

safetyarxiv-cs-ai

Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting

arXiv:2610.02571v1 Announce Type: cross Abstract: As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideratio

Local Ai

Law And Order: Tax Law Autoformalization

arXiv:2610.02792v1 Announce Type: new Abstract: Legal systems are increasingly implemented through software, yet scalable methods for translating legal texts into accurate symbolic representations rem

local-aiarxiv-cs-ai

Are AI Coders Snitches? An Empirical Study of Pretraining Data Detection on Code Large Language Models

arXiv:2507.17389v2 Announce Type: replace-cross Abstract: Recent advances in code large language models (CodeLLMs) have made them indispensable tools in modern software engineering. However, these mod

Authorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback

arXiv:2610.00347v1 Announce Type: cross Abstract: Self-modifying AI agents can replace, fork, and roll back identity-bearing software while descendants remain executable. Per-successor authorization d

Benchmarking System One decision models against trained classifiers and language models for automated decision gates

arXiv:2610.00346v1 Announce Type: new Abstract: Software that hands branching decisions to a model needs a declared option and a probability it can threshold. Typed decision models, also called System

researcharxiv-cs-lg

The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching

arXiv:2610.01768v1 Announce Type: cross Abstract: With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, s

EngramBench: A Capability-Grounded Benchmark for Skill-Evolution Harnesses

arXiv:2609.39284v1 Announce Type: cross Abstract: While large language models have achieved remarkable success in isolated code generation, authentic software engineering requires sustained reasoning,

1 Oct 2026

CineSubBench: Evaluating LLMs on Long-Form Narrative and Cultural Understanding from Multilingual Movie Subtitles

arXiv:2609.36218v1 Announce Type: cross Abstract: Large language models are increasingly evaluated in specialized domains such as law, medicine, software engineering, and cybersecurity, yet film remai

30 Sept 2026

Complexity-Aware Evaluation of LLM Comprehension

arXiv:2609.37405v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for software engineering tasks that require understanding existing source code, including behavior

30 Sept 2026

Local Ai

Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection

arXiv:2609.34142v1 Announce Type: new Abstract: Continuous video analytics requires accurate localization at low latency within embedded power budgets. This paper presents a hardware-software design m

local-aiarxiv-cs-cv

29 Sept 2026

LogicEnvGen: Task-Logic Driven Generation of Diverse Simulated Environments for Embodied AI

model-releasesarxiv-cs-ro

arXiv:2601.13556v2 Announce Type: replace Abstract: Simulated environments play an essential role in embodied AI, functionally analogous to test cases in software engineering. However, existing enviro

29 Sept 2026

What Stops Recursive Self-Improvement in Robotics? Lessons from 123 Rounds of Agentic Skill Discovery

arXiv:2609.31760v1 Announce Type: cross Abstract: Can a robot improve itself the way coding agents now improve software? We built an agentic system to find out. It watches a robot fail, works out whic

agentsarxiv-cs-lg

29 Sept 2026

A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Code

arXiv:2609.30642v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become integrated into software development workflows, concerns regarding unintentional biases in AI-generated code. A

BabelCoder: Agentic Code Translation with Specification Alignment

arXiv:2512.06902v2 Announce Type: replace-cross Abstract: As software systems evolve, developers increasingly work across multiple programming languages and often face the need to migrate code from on

Beyond Approved Actions: Runtime Validation of Persistent Outcomes in Agent Workflows

arXiv:2609.31301v1 Announce Type: cross Abstract: Large language model agents increasingly act on software systems, no longer merely generating text but also changing databases and online services. Ho

Compact Documentation for Coding Agents: A Benchmark, an Optimizer, and Why It Does Not Transfer

arXiv:2609.31587v1 Announce Type: cross Abstract: We investigate whether natural-language documentation helps coding agents resolve software issues, and we build the tools to construct and evaluate it

Rethinking Data Quality for AI-Driven Systems: Evidence from Practitioner Interviews

arXiv:2609.31191v1 Announce Type: cross Abstract: Data quality research has usually treated data as an input that is stored, processed, and validated. In AI-driven software-intensive systems, data als

Applications

Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning

arXiv:2603.17174v2 Announce Type: replace-cross Abstract: Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown tha

applicationsarxiv-cs-ai

Driving Epidemic Models with AI Agents: the Epydemix Agent Framework

arXiv:2609.28692v1 Announce Type: new Abstract: Artificial Intelligence agents based on large language models provide convenient natural language interfaces to scientific software, but reliability is

From Text Decisions to Pixels: An Study of Jev-Style Visual Choice Model

arXiv:2609.29283v1 Announce Type: new Abstract: Visual software often needs a decision over supplied alternatives rather than a generated explanation. We present PixelJev, a native-image decision inte

Low-Cost Assays for Measuring Model Behavior Across Vendors and Releases

arXiv:2609.30012v1 Announce Type: cross Abstract: Language models advise people, keep them company, and write software while they sleep. Measuring what they do is hard: behavior has to be sampled repe

RECLAIM: Can Agents Reproduce the Claims of Machine Learning Papers?

arXiv:2609.28850v1 Announce Type: new Abstract: Reproducing a machine learning paper involves most research steps, from installing software and debugging to running experiments, work that AI agents in

SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories

arXiv:2609.29465v1 Announce Type: new Abstract: Large language model based coding agents have made substantial progress on repository-level software engineering tasks. Existing repository benchmarks,