arXiv AI
The Compiler May Read It, the Agent May Not: Keeping Part of a Research Code Away from a Coding Agent
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 16
Language-Based Agent Control
The paper introduces Language-Based Agent Control (LBAC), a programming model that extends static typing and runtime enforcement to agentic applications, ensuring that agent-generated programs are well‑typed before execution. LBAC allows agents to perform arbitrary side‑effect‑free computation and recursively invoke subagents while maintaining uniform policy enforcement across both agent and developer code. The authors demonstrate LBAC through case studies on I/O sandboxing, data provenance, and information‑flow control.
arXiv AI
Sep 2
Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents
The paper introduces Scientific Agent Skills, an open library of 163 procedural knowledge modules across 16 scientific domains such as genomics, cheminformatics, medical imaging, study design, and scientific communication. Each skill is organized as a directory containing a versioned, human‑readable instruction file, reference material, and runnable scripts, allowing a language‑model agent to load the appropriate procedure only when needed. The library is publicly licensed and hosted on GitHub, but the authors report no task‑level evaluation or host selection rate.
arXiv AI
Jun 18
Code-Augur: Agentic Vulnerability Detection via Specification Inference
arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.
arXiv AI
Jul 24
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
arXiv:2607. 20759v1 Announce Type: cross Abstract: AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools.
arXiv AI
Aug 17
Agent-Orchestration in Autonomous Chip Design
arXiv:2608. 14035v1 Announce Type: new Abstract: Recent developments in large language models (LLMs) and tool-using agents encourage people to explore the potential of using agents in chip design.
arXiv AI
Aug 26
Rebuild Dossier: Mechanically-Enforced Specs for Agentic App Rebuilds, and What Model-Tier Failures Reveal
The paper introduces rebuild‑dossier, an open‑source tool that locks an application’s real interface before code is written and enforces one‑test‑at‑a‑time building through automated checks. In experiments, a compliant agent failed a held‑back test while a rule‑breaking agent passed, showing that a passing test suite can be gamed. The study also demonstrates that the automated check mechanism, rather than interface‑locking alone, is crucial for reliable rebuilds, and that multi‑level verification catches errors that single‑level checks miss.