Bayesian control for coding agents
Modern coding agents pair LLM generators with various tools, including cheap diagnostics and expensive verifiers. The tool-use decisions are typically governed by orchestrators that often use fixed rules and ignore uncertainty. We formulate orchestration as cost-sensitive sequential hypothesis testing: a Bayesian controller maintains a belief over candidate correctness and dynamically decides whether to gather more evidence, refine the candidate, verify it, or stop. Across six generators and nin
Record details
Published: 23 June 2026
Source: arXiv
Category: Research
Topics: Healthcare · Agents & autonomy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
arXiv · 22 June 2026
Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems
arXiv · 24 June 2026
How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
arXiv · 24 June 2026
Clinical Harness for Governable Medical AI Skill Ecosystems
arXiv · 25 June 2026
Diagnosing Task Insensitivity in Language Agents
arXiv · 25 June 2026
Learned Coordination Conventions in Cooperative MARL: Measuring the Translation Gap Between Theory-Informed Roles and Learned Routing
arXiv · 28 June 2026
How to cite this record
ethics.ai (23 June 2026), “Bayesian control for coding agents,” evidence record 655, https://ethics.ai/record/655 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.