PaperlayerView exact revision on arXiv

Source-linked AI summary

What Else Needs Fixing? Exploring Cost-Effective Test-Time Compute for Revision Propagation in Artifacts Generated Through Conversation

Daisuke Kikuta

arXiv:2609.03254v1cs.CLcs.LG

TL;DR

Local revisions in conversationally generated artifacts can require LLMs to infer dependencies and propagate changes across related elements. The paper introduces a human-annotated benchmark and evaluates nine revision methods with multiple LLMs. Baselines achieve 68.3–93% accuracy, while selecting among three parallel samples improves accuracy by 2.2–9.7% at the most favorable reported cost-effectiveness.

  • Problem

    Users often specify only local changes, leaving LLMs to infer conversationally embedded dependencies and propagate revisions to preserve artifact consistency.

  • Method

    The paper introduces RevPropBench and evaluates nine revision methods with six LLMs on conversationally generated JSON artifacts.

  • Results

    68.3–93% baseline accuracy is achieved across models, while selecting from three parallel samples with LLM-based or medoid selection improves accuracy by 2.2–9.7%.

  • Takeaways & Limitations

    Test-time compute can improve revision reliability while balancing performance gains and inference cost in this conversational artifact setting.

  • Takeaways & Limitations

    The findings do not generalize to repository-level code, knowledge-editing, or document-editing settings where dependency information is largely available in advance.

Abstract

from arXiv · show

Large Language Models (LLMs) often help users generate artifacts through iterative cycles of generation and revision in conversation. A challenge here is that, when users specify only a local change during revision, LLMs must instead identify the relevant dependencies and propagate the revision to all affected parts of the artifact. This paper studies this ability of LLMs on conversationally generated artifacts, where the artifact context and its dependencies may be embedded in the conversation history. Toward practical use, we also explore cost-effective test-time compute for this new setting. Specifically, we introduce a new benchmark for this setting, and evaluate nine revision methods, including sequential reflection and parallel sampling variants, using gpt-oss-20b/120b, gpt-5.4-mini, and qwen3.5-9b/27b/122b on the benchmark. The results show that baselines achieve accuracies of 68.3--93%, and the most cost-effective method is selecting from three parallel samples using either LLM-based or medoid selection, which improves accuracy by 2.2--9.7%. Our code and dataset are available at https://github.com/ntt-dkiku/llm-revision-propagation.

1 Introduction

The paper studies revision propagation when local changes require LLMs to infer dependencies embedded in conversational context. It introduces RevPropBench and evaluates cost-effective test-time methods for this setting.

  • Motivation: Conversational artifacts require LLMs to identify implicit dependencies and revise affected elements beyond the explicitly mentioned component.Users often cannot specify every affected part, so revisions must preserve artifact-wide consistency.
  • Research gap: Revision propagation in conversationally generated artifacts remains unexplored because dependencies may be established outside the artifact through conversation.Prior settings largely rely on explicit or statically analyzable dependency structures.
  • Contributions: RevPropBench evaluates revision propagation across conversationally generated JSON artifacts using human-annotated samples.The benchmark includes planning, recordkeeping, and configuration domains, with artifact sizes of 10, 50, and 100 JSON elements.
  • Contributions: The study evaluates nine revision methods with six LLMs to provide practical guidance for cost-effective method selection.Methods include sequential reflection and parallel sampling variants.
  • Results: 68.3–93% baseline accuracy is achieved across models, while selecting from three parallel samples improves accuracy by 2.2–9.7%.Both LLM-based and medoid-based selection are reported as the most cost-effective methods.
  • Contributions: The authors release the benchmark instance, data sampling process, and annotation tool for reproducibility and future extension.These resources support continued benchmark development.

2 RevPropBench

RevPropBench models artifact generation followed by local revision requests, evaluating whether generated JSON patches exactly match gold revisions. It combines synthetic sampling, human annotation, and a diverse 150-sample benchmark.

  • Task definition: RevPropBench has generation and revision phases, where an LLM builds a JSON artifact conversationally before applying a local revision request through JSON patches.The task tests revision propagation from conversation history and artifact context.
  • Benchmark construction: Samples are synthetically generated through LLM-assisted scenario design and then receive tentative gold patches followed by human review and correction.Scenario criteria include domain diversity and diverse revision-propagation patterns.
  • Annotation: JSON patches are ordered RFC 6902 operations represented by op, path, and value, with keyword matchers and optional elements supporting contextual variation.Optional elements count as correct when edited or left unchanged, but incorrect edits remain errors.
  • Metrics: Completion rate measures the proportion of samples whose patched artifact exactly matches the gold-patched artifact.Failure analysis separates missed necessary edits, unnecessary edits, and wrong values.
  • Benchmark construction: 150 samples cover 50 scenarios across nine practical domains, with small, medium, and large artifacts containing 10, 50, and 100 elements.Each scenario contributes one sample at each artifact size.
  • Benchmark statistics: As artifact size grows, the number of propagated revisions increases, while scenario patterns demonstrate diverse propagation cases.These trends are summarized in RevPropBench statistics.

3 Evaluation Settings

The evaluation compares nine single-pass and test-time compute revision methods across six LLMs, using scenario-level development and test splits. Methods include reflection, rule-based selection, medoid selection, and LLM-based selection.

  • Data split: The 150 samples are split into 30 development samples and 120 test samples at the scenario level.All three artifact-size variants of each scenario remain in the same split, and reported metrics use the test split.
  • Revision methods: Nine methods include single-pass baselines, sequential reflection, rule-based parallel selection, medoid selection, and LLM-based selection.Baselines use the artifact, conversation history, or both as auxiliary context.
  • Parallel selection: Rule-based selectors OR, AND, and MAJ update leaf elements under any, unanimous, or strict-majority candidate agreement, respectively.MED selects the candidate with the smallest mean leaf-level disagreement from the others.
  • Evaluated models: Six LLMs span gpt-oss-20b/120b, gpt-5.4-mini, and qwen3.5-9b/27b/122b-a10b across two model families and three parameter scales.Reasoning is enabled for all models, with medium effort for GPT models.
  • Evaluation protocol: Each sample is evaluated five times using seeds 0, 42, 84, 126, and 168.The maximum output context length is 32K tokens and temperature is generally 0.6 when supported.
  • Compute settings: FLECT uses four reflection iterations and five total LLM calls, while parallel sampling uses five outputs with consecutive seeds.SELECT additionally uses an LLM-based selection call, with caching preserving identical same-seed outputs.

4 Evaluation Results

Across six models, performance depends on context, model scale, and revision method. SELECT and MED offer the most consistent gains, while cost analyses favor a small number of calls.

  • Context and model comparison: 68.3–93.0% completion rates span models with the strongest baseline, J+H.Performance generally increases with model scale within both model families.
  • Method comparison: SELECT improves over J+H by +3.3–12.5%, achieving the highest completion rate on four of six models and the second-highest on two.MED is the second-most consistent method, with +1.8–7.7% improvement and top-two performance on four models.
  • Method comparison: AND falls −13.2–21.3% below J+H on every model, whereas OR and MAJ are competitive on some models but inconsistent overall.AND discards edits without unanimous agreement; OR accepts any proposed edit and therefore introduces more over edits.
  • Failure analysis: MED reduces outlier over-edits, while SELECT targets the most complete candidate and reduces misses most strongly.Their different failure profiles reflect a trade-off between typicality and completeness when returning one whole candidate.
  • Cost analysis: Performance largely saturates around four to five LLM calls, with highest average cost-effectiveness from SELECT at three or four calls or MED at three calls.The authors recommend SELECT with four calls; MED with three calls is an alternative when latency matters.

5 Related Work

Prior revision-propagation work targets code, knowledge, or documents with relatively explicit dependency sources. This paper studies conversationally generated artifacts, where dependencies may be implicit in the surrounding dialogue, and evaluates cost-effective test-time compute.

  • Existing revision-propagation settings: Repository-level coding propagates edits through dependencies such as call graphs, imports, variable references, and extracted repository graphs.Knowledge editing instead evaluates ripple effects across related facts, while document editing tracks dependent claims, references, and descriptions.
  • Positioning: Conversationally generated artifacts often lack pre-existing dependency information, because relationships may be established implicitly during multi-turn conversation outside the artifact.This distinguishes the paper’s setting from repository graphs, knowledge graphs, document structure, and explicit references.
  • Positioning: The paper adopts JSON for practical utility and automated evaluation, while differing from prior JSON-editing work by studying the conversational generation process and cost-effective test-time compute.Its objective is revision propagation rather than merely modifying the JSON artifact itself.
  • Test-time compute: Test-time compute improves outputs through inference-time reasoning, sampling, or search, including parallel sampling and sequential reflection approaches.The paper applies these ideas to revision propagation in conversationally generated artifacts.

6 Conclusion

The paper introduces a benchmark for revision propagation across conversationally generated JSON artifacts and studies test-time compute for improving it. Selecting among three parallel samples improves accuracy while balancing performance and inference cost.

  • Conclusion: The benchmark evaluates whether LLMs propagate revisions across JSON artifacts generated through conversation.It addresses a setting where dependencies may be embedded in conversational context rather than explicitly available in the artifact.
  • Conclusion: 2.2–9.7% accuracy improvement comes from selecting one of three parallel samples using LLM-based or medoid-based selection, compared to a single inference.The authors present this as the most cost-effective test-time compute strategy in the studied setting.

Limitations

The paper’s scope is limited to conversationally generated artifacts whose dependencies are unavailable in advance and may be established outside the artifact. Its synthetic, controlled benchmark also omits ambiguous real-world cases, may not cover all patterns, and could become less discriminative as models improve.

  • Scope: The work does not generalize to repository-level code, knowledge, or document editing, where dependency information is largely available in advance.It instead targets settings where dependencies may be established by conversation outside the artifact.
  • Real-world conversations: The benchmark uses synthetically generated conversations rather than actual human–LLM interaction data.Dependencies are controlled deterministically, excluding ambiguous cases where people may disagree about jointly revising elements.
  • Coverage and bias: The 150-sample benchmark covers nine domains, six propagation patterns, and three artifact sizes but does not exhaustively cover all possible patterns.Because conversations were generated using GPT-5.5, samples may also be easier for GPT models, although task overlap was mitigated during construction.
  • Performance saturation: 93% baseline completion by gpt-5.4-mini raises concerns that future model improvements could saturate benchmark comparisons.The authors provide a construction tool so the benchmark can be continuously updated to increase difficulty.

Ethical Considerations

The benchmark is synthetic and was reviewed for privacy and ethically problematic content. The authors report no direct privacy concerns and no ethically problematic samples in their review.

  • Privacy: The synthetic benchmark does not raise direct privacy concerns related to personal information.This follows from the benchmark being synthetic rather than based on personal interaction data.
  • Ethical content: The authors reviewed scenarios and generated samples for ethically problematic content and found none.Review occurred during scenario construction and after data sampling.

A Latency Analysis

Latency depends on how revision methods use inference calls and on the deployment environment. Medoid selection with three parallel samples is the most latency-efficient method on average, while Qwen models incur larger multipliers than GPT models for SELECT.

  • Parallel sampling: MED executes samples in parallel, so latency is determined by the slowest sample and remains below 1.5× J+H as sample count increases.Its latency is only slightly higher than J+H because parallel execution avoids summing sample latencies.
  • Efficiency: MED with k = 3 is the most latency-efficient method on average across all models when performance gain is compared with latency increase.The comparison uses gain latency/latencyJ+H−1.
  • Model differences: SELECT latency remains around 2–3× for GPT models but rises to 3.3–6× for Qwen models.The higher Qwen multiplier is attributed to the increased amount of reasoning.
  • Deployment: The practical effect of latency multipliers on responsiveness depends heavily on the deployment environment.Absolute latency for J+H also depends heavily on deployment conditions.
  • Cost calculation: API cost is calculated as input price times input tokens plus output price times output tokens.The listed per-token prices are used for the cost analysis.

C Details of Benchmark Construction

RevPropBench constructs scenarios around conversationally established dependencies and evaluates revision propagation across six patterns. The benchmark includes representative scenarios spanning nine domains, with examples showing how local changes propagate while independent details remain unchanged.

  • Scenario coverage: RevPropBench’s 50 scenarios cover six propagation patterns, with a representative scenario for each and every pattern appearing across multiple domains.Table 3 summarizes the patterns and representative scenarios.
  • C.1 Scenarios: In the trip-scheduling example, shifting the anchored start date requires all dates derived from it to shift by the same delta.Independent traveler details that do not derive from the anchor remain unchanged.
  • C.1 Scenarios: The trip scenario centers the artifact on one anchor date and derives other dates from fixed offsets stated in the conversation.The anchor therefore uniquely determines the dates that must propagate during revision.
  • C.1 Scenarios: Figure 7 presents a scenario and generated sample containing a conversation, artifact, and local revision request.The example simulates planning a trip schedule whose activities are recorded relative to a trip date.
  • Annotation: Figure 8 shows the graphical user interface of the annotation tool.

C.2 Annotation

The annotation process creates gold JSON patches that capture direct and dependency-driven cascade revisions, with optionality and value-matching rules. It also describes candidate-artifact comparison and merge procedures used by revision methods, alongside safeguards for high-stakes applications.

  • Gold patch annotation: Annotators create gold patches using replace, add, or remove operations, each specifying a path, expected value, and direct or cascade kind.Direct targets are explicitly requested; cascade targets require revision because of dependencies.
  • Gold patch annotation: Optional operations mark changes that are contextually valid both when applied and when left unchanged.Value comparisons may use exact, regular-expression, any-value, or reference-equality matching.
  • Annotation process: 23 of 150 samples were revised during annotation, including one revision request and 22 patch revisions.One annotator spent more than 32 hours over 10 days on annotation and double-checking with Claude Code.
  • Candidate merging: OR, AND, and MAJ merge sampled candidate artifacts element-wise, treating leaf-level JSON key–value pairs as independent merge units.Array elements are separate units only when current and candidate arrays have the same length; otherwise the key–array pair remains indivisible.
  • Candidate merging: MED selects one whole candidate by minimizing its average disagreement with the other candidates across flattened merge units.A missing unit counts as a mismatch.
  • Practical safeguards: Because a single over-edit or missed propagated change can cause significant errors in financial and invoice processing, the paper recommends human review before applying changes.One proposed safeguard presents all propagated edits and applies only those explicitly approved by the user.
Loading 2609.03254v1…