mlsanityData to 14 Oct 2026

The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development

arXiv 2606.27045 ↗First posted 25 Jun 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

AI coding agents dramatically accelerate implementation speed but introduce two structural failure modes that existing spec-driven approaches do not fully solve: (1) context explosion -- the agent must reason over an entire repository at once, degrading output quality as the context window fills; and (2) silent spec-code drift -- code evolves, the specification does not, and the divergence becomes invisible until it is costly to repair. We present the Spec Growth Engine, a lightweight framework that addresses both failure modes through a machine-readable spec graph whose nodes carry explicit contract/design separation, a Spine context assembler that scopes agent context to an ownership path, a vertical-slice growth protocol that enforces hardest-first ordering, and a drift gate that makes spec-code divergence a blocking merge condition. The design synthesises well-established software engineering principles (Parnas information hiding, C4, ADRs, Walking Skeleton, Reflexion Models, Fitness Functions) into a lean, code-coupled, machine-enforced whole -- without the overhead of heavy-weight frameworks such as RUP or MDA.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 2 of its 32 references that are in the map. First posted on arXiv 25 Jun 2026.

Builds on

2 of 32 references.

References · 2

  1. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  2. 2025LongCodeBench: Evaluating Coding LLMs at 1M Context Windowscited by 37

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  3. 2026Grounding AI Agents in Contracts: An Empirical Evaluation of Spec-Driven Test Generation
  4. 2026Verus-SpecGym: An Agentic Environment for Evaluating Specification Autoformalization
  5. 2025NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
  6. 2026RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents
  7. 2026Understanding the Architecture of Coding Agents: An Exploratory Study Using a Research Prototype
  8. 2026SWE-Duel: Towards a Self-Scaling Arena for Coding Agents