通过约束实现可控性：代码智能体可扩展监督的基础

Computer Science > Artificial Intelligence

arXiv:2607.02389v1 (cs) [Submitted on 2 Jul 2026]

Title:Steerability via constraints: a substrate for scalable oversight of coding agents

Authors:Thomas Winninger View a PDF of the paper titled Steerability via constraints: a substrate for scalable oversight of coding agents, by Thomas Winninger View PDF

Abstract:Coding agents are capable; human oversight is the bottleneck. Unconstrained agents introduce security risks, erode codebase scalability, and make human review increasingly costly. We argue that the same methods used for decades to manage large human engineering teams: access control, network policies, strict coding conventions enforced by tooling; transfer directly to coding agents, and are cheaper (in token) than recent agentic scaffolding. We sketch a start-to-end system on this principle, and report a controlled experiment in scalable oversight: a small reviewer (Gemma 4 e4b) inspects a Python codebase containing 11 inserted backdoors. Recall rises from 54.5% (unconstrained, no tools) to 90.9% (constrained substrate plus a ~200-LoC `docs` CLI), with substrate and tools contributing independently. We choose Python deliberately: substrate-level oversight gains are largest where the language gives the fewest guarantees by default; the principles extend to languages like Rust.

Comments:	Accepted to the Deep Learning for Code Workshop at the 43rd International Conference on Machine Learning, Seoul, South Korea, 2026
Subjects:	Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Software Engineering (cs.SE)
Cite as:	arXiv:2607.02389 [cs.AI]
	(or arXiv:2607.02389v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2607.02389 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Thomas Winninger [view email]
[v1] Thu, 2 Jul 2026 16:24:47 UTC (339 KB)
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