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arxiv: 2606.13174 · v1 · pith:VBXCPJ5Jnew · submitted 2026-06-11 · 💻 cs.LG · cs.CL

Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents

Yujun Zhou , Kehan Guo , Haomin Zhuang , Xiangqi Wang , Yue Huang , Zhenwen Liang , Pin-Yu Chen , Tian Gao
show 3 more authors
Nuno Moniz Nitesh V. Chawla Xiangliang Zhang
This is my paper

Pith reviewed 2026-06-27 07:03 UTC · model grok-4.3

classification 💻 cs.LG cs.CL
keywords LLM agentsruntime enforcementuser correctionspreference compliancecoding agentsTRACEmemory baselines
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The pith

TRACE mines user corrections and compiles them into runtime checks that coding agents must satisfy before finishing tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Interactive LLM coding agents often violate the same user preferences across sessions even after explicit corrections. The paper introduces TRACE, a pipeline that extracts corrections from chat, rewrites them as atomic rules, and inserts them as mandatory runtime checks. On ClawArena tasks this drops held-out preference violations from 100% to 37.6% in-distribution and to 2% out-of-distribution. On MemoryArena-derived tasks it reduces in-distribution violations to 60.5% while matching or beating memory baselines on task success. The approach therefore supplies a mechanism for agents to accumulate and enforce user-specific preferences without repeated restatement.

Core claim

TRACE acquires rules from user chat corrections at test time, rewrites them as atomic enforceable statements, and compiles them into runtime checks that must pass before an agent completes future coding tasks, producing large measured reductions in preference violations on both in- and out-of-distribution benchmarks.

What carries the argument

The TRACE pipeline that mines corrections, rewrites them as atomic rules, and compiles them into pre-completion runtime enforcement checks.

If this is right

  • Agents can enforce user preferences via compiled checks rather than memory retrieval alone.
  • Out-of-distribution violation rates can fall from 100% to 2% on ClawArena-style tasks.
  • In-distribution violation rates can fall from 100% to 60.5% on memory-intensive tasks while preserving task-pass performance.
  • Users can avoid restating the same correction in every new session.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mining-and-compilation pattern could extend to non-coding agents if corrections can be expressed as checkable conditions.
  • A growing per-user rule set might eventually require conflict detection when new corrections contradict earlier ones.
  • If rule extraction accuracy is high, the method could reduce the total number of user interventions needed over long interaction histories.

Load-bearing premise

User corrections can be automatically mined and rewritten as atomic rules that stay faithful to original intent and generalize to future tasks without false positives or missed violations.

What would settle it

A held-out test set in which mined rules are compiled and applied yet preference-violation rates remain near 100% on both in- and out-of-distribution tasks.

read the original abstract

Interactive LLM agents are becoming part of daily work, but they do not reliably become easier to work with over time: a correction remembered in one session may still be violated in the next. We study this gap between preference access and preference compliance. In tasks derived from anonymized real-user friction cases, Mem0 memory still leaves 57.5% of applicable preference checks violated. We introduce Test-time Rule Acquisition and Compiled Enforcement (TRACE), a drop-in skill-layer pipeline for coding-agent runtimes that mines user corrections, rewrites them as atomic rules, and compiles them into runtime checks that must pass before an agent completes future tasks. Unlike runtime checks written ahead of time by developers, TRACE skills come from the user's own chat corrections. We evaluate TRACE with simulated user-in-the-loop experiments on ClawArena coding-agent tasks and MemoryArena-derived memory-intensive tasks. On ClawArena, TRACE reduces held-out preference violation from 100.0% to 37.6% on in-distribution tasks and from 100.0% to 2.0% on out-of-distribution tasks. On MemoryArena-derived tasks, TRACE reduces in-distribution violation from 100.0% to 60.5% while matching or exceeding the strongest memory baseline on task pass. These results suggest that compiling corrections into runtime enforcement can address a repeated-friction failure mode that memory alone does not reliably solve, reducing the need for users to restate the same correction across future sessions. Experiment code is available at https://github.com/YujunZhou/TRACE_exp, and the deployable skill is available at https://github.com/YujunZhou/tellonce.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces TRACE, a pipeline that mines user corrections from coding-agent interactions, rewrites them as atomic rules, and compiles them into runtime enforcement checks. It claims this addresses repeated preference violations that memory systems like Mem0 fail to prevent, with simulated user-in-the-loop experiments on ClawArena showing violation reductions from 100% to 37.6% (in-distribution) and 100% to 2.0% (out-of-distribution), and similar gains on MemoryArena-derived tasks while matching baselines on task success.

Significance. If the automatic mining and compilation process proves reliable, the approach could meaningfully reduce repeated user friction in interactive LLM agents by converting one-off corrections into persistent, enforceable checks. The public experiment code and deployable skill links are a clear strength for reproducibility.

major comments (2)
  1. [Evaluation] Evaluation (simulated experiments on ClawArena and MemoryArena): the reported violation reductions (e.g., 100.0% o 2.0% OOD) are presented without any metrics on rule-extraction accuracy, fidelity of the mined rules to the original user corrections, or false-positive rates on held-out checks. This is load-bearing for the central claim that automatic compilation, rather than simulation choices or oracle rules, drives the gains.
  2. [§4] §4 and abstract: no human validation or comparison to manually authored rules is reported, leaving open whether the automatic rewriting step preserves intent or introduces blocking false positives that would undermine real deployment.
minor comments (1)
  1. [Methods] Clarify in the methods how 'preference checks' and 'violations' are operationalized on held-out data, including any inter-annotator or automated judgment protocol.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our evaluation and validation approach. We address each point below and will revise the manuscript accordingly where feasible.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation (simulated experiments on ClawArena and MemoryArena): the reported violation reductions (e.g., 100.0% to 2.0% OOD) are presented without any metrics on rule-extraction accuracy, fidelity of the mined rules to the original user corrections, or false-positive rates on held-out checks. This is load-bearing for the central claim that automatic compilation, rather than simulation choices or oracle rules, drives the gains.

    Authors: We agree that explicit metrics on rule-extraction accuracy and fidelity would strengthen the central claim. The current evaluation measures end-to-end violation reduction on held-out tasks after mining and compilation, with the simulation designed to isolate the effect of the compiled rules. In revision we will add a quantitative fidelity analysis (e.g., semantic similarity scores between mined rules and original corrections on a sampled subset) and report false-positive rates by measuring unnecessary blocks on tasks where no applicable correction exists. These additions will clarify that gains derive from the automatic pipeline rather than simulation artifacts. revision: yes

  2. Referee: [§4] §4 and abstract: no human validation or comparison to manually authored rules is reported, leaving open whether the automatic rewriting step preserves intent or introduces blocking false positives that would undermine real deployment.

    Authors: The experiments use simulated user-in-the-loop interactions on tasks derived from anonymized real-user friction cases, as stated in the paper. We acknowledge that direct human validation and side-by-side comparison to manually authored rules would better support deployment claims. In the revision we will add a discussion of potential false positives in the rewriting step together with a small-scale manual inspection of mined-rule fidelity on a subset of examples. Full human-in-the-loop studies remain future work. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical results on held-out tasks with no equations or self-referential reductions

full rationale

The paper describes an empirical pipeline (TRACE) for mining corrections into rules and evaluates it via simulated user-in-the-loop experiments on ClawArena and MemoryArena-derived tasks. Reported metrics (e.g., violation reductions from 100% to 37.6%/2.0%) are direct measurements on held-out preference checks, not quantities derived from the same data by construction. No equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The central claim rests on external benchmarks rather than reducing to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that corrections yield reliable atomic rules; no explicit free parameters or new physical entities are described in the abstract.

axioms (1)
  • domain assumption User corrections contain extractable atomic preferences that can be formalized as runtime checks without loss of intent
    This premise enables the mining and compilation steps of TRACE.
invented entities (1)
  • TRACE skills no independent evidence
    purpose: Compiled runtime enforcement mechanisms derived from user corrections
    New construct introduced to provide the enforcement layer on top of existing agent runtimes.

pith-pipeline@v0.9.1-grok · 5873 in / 1278 out tokens · 32086 ms · 2026-06-27T07:03:17.832934+00:00 · methodology

discussion (0)

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Reference graph

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