Intermediate Action Checks

Repository agents need a structural code locator for requests that contain no useful names. LogicLoc is a concrete pattern for that layer: extract repository facts, let the model write Datalog over those facts, then gate the query with parser checks and a synthesize-check-refine loop before execution in Soufflé. The practical use case is issue triage and repo navigation when the request sounds like a property of the codebase, not a search string. The paper’s example query asks for non-__init__ functions with more than 15 parameters and returns two exact matches in Astropy, which is the kind of answer a lexical retriever often misses. A cheap product test is simple: collect internal engineering questions that mention behavior, structure, or constraints but omit file and symbol names, then compare exact hit rate against your current repo search or agent retrieval stack.

Coding assistants can add a requirement-check step before code generation and another one after the first draft. REA-Coder gives a direct template: turn the prompt into a checklist of requirement questions, compare the model’s answers with reference answers, rewrite the requirement when gaps appear, generate code, then inspect failed outputs by masking key semantic spans in the requirement and asking the model to recover them from the code. This fits teams that already have tests but still see code that solves the wrong task. The reported gains are large enough to justify a narrow rollout on difficult prompt classes: average improvement over the best baseline reaches 30.25% on CodeContests-raw and 26.75% on CodeContests, and even the pre-generation alignment step alone improves first-pass code over zero-shot by 210.44% on APPS and 344.67% on xCodeEval. A practical first deployment is a pre-submit gate for coding agents working on tickets with long natural-language acceptance criteria.

Teams using AGENTS.md or similar rule files can move rule-following out of prompt memory and into an execution gate tied to plan steps. Zoro shows what that support layer looks like: attach rules to specific steps after planning, require proof for each applied rule during execution, and demand unit tests before advancing on testable rules. That addresses a common failure in long coding sessions, where the agent drifts away from architectural, workflow, or UI constraints and the developer has to keep repeating them. The reported result is a 57% increase in rule following across 36 sessions, and the system is designed to work with existing agents through a shared instruction file and evidence directory. The immediate build is a thin wrapper around your current coding agent that records which rule blocked progress, what proof was submitted, and which rules users keep editing after failures.