Structured Meta-Objective Reframing via Multi-Agent Difference Processing: A Five-Dimensional Diff Analysis of Goal Reorganization in LLM Systems

Abstract

Large language model (LLM) agents are typically optimized to achieve given objectives, but real-world objectives are often incomplete, ambiguous, or misaligned with underlying needs. This paper proposes a structured architecture for meta-objective reframing—the process of critically examining and reorganizing objectives themselves rather than simply pursuing them. Building on the RDAC framework (Relational Density, Diversity, Stability, and Closure; Ishikawa, 2026), we introduce a four-role pipeline consisting of a Goal Expander, Goal Critic, Goal Synthesizer, and Goal Diff. We also propose a five-dimensional descriptive schema—Object, Metric, Scope, Premise, and Relation—for characterizing structural changes in objectives. In an exploratory study across four executed objective conditions, conducted through the ChatGPT web interface in Japanese, the model-generated dimensional labels were found to be substantially saturated across conditions. Metric, Scope, and Premise were marked as changed in all 27 retained Diff outputs, Relation in 26/27, and Object in 21/27. These results do not support the condition sensitivity of the Relation dimension previously inferred in Version 1. A minimum-change constraint on the Synthesizer produced visibly graded revised objectives, but did not materially alter the near-saturated dimensional-label pattern. In a controlled primary-dimension probe using four human-authored test sentences, the intended primary dimension was identified in all four cases, while secondary activations—particularly in Premise—revealed cross-dimensional sensitivity. Qualitatively, the compound-objective outputs included several forms of relational restructuring, including conditionalization, staging, and parallelization. Similar structures were also observed informally in single-objective conditions, so these patterns are reported descriptively rather than as condition-specific effects. The study highlights both the usefulness and the limitations of the proposed schema. The five dimensions provide a structured vocabulary for describing objective changes, but the model-generated boolean labels were not sufficiently discriminative across the generated reframings and were not validated by independent human coding. Meta-objective reframing is therefore positioned here as a structural cognitive scaffold for human decision-makers rather than as an autonomous goal-setting mechanism or a validated measurement system. Version 2 (September 2026) — corrected and reassessed Version 2 corrects and reassesses Version 1 on the basis of a re-examination of the retained output files and the experimental record. No new experiments were conducted. The main corrections are as follows: RDAC terminology and mapping were corrected. RDAC is aligned with the canonical framework—Relational Density, Diversity, Stability, and Closure—and the mapping between the pipeline and RDAC dimensions was revised. In particular, the present single-pass, feed-forward pipeline does not instantiate Closure in the strict RDAC sense. All 27 retained Diff outputs were re-tallied across all executed conditions. Relation was marked as changed in 26/27 outputs, including nearly all single-objective outputs. The previous claim that Relation was condition-sensitive is therefore withdrawn. The ablation interpretation was narrowed. The minimum-change constraint affected the qualitative distribution of Synthesizer outputs, producing more visibly graded revisions, but did not produce more discriminative dimensional-label patterns. The evaluation procedure was corrected. The dimensional labels were generated by the model itself. No systematic independent human coding, blinded classification, or inter-rater reliability analysis was performed. Interpretation was conducted post hoc by the author with AI assistance. The execution environment was clarified. Runs were performed through the ChatGPT web interface using the default model available at the time; the exact model and model version were not recorded, and sampling parameters were not controlled. All prompts, goal statements, and outputs were in Japanese. Retained file timestamps place the runs on 27–28 March 2026. The experimental-condition record was clarified. Seven goal conditions were designed and four were executed (G1, G4, G6, and G7). Strict repeated runs exist for G1-T2/G1-T3 and G7-T1/G7-T2. Related Work and claim calibration were substantially revised. External references were added, and claims throughout the paper were adjusted to reflect the exploratory scale and limitations of the evidence. The supplementary materials include all prompt versions, the retained output files (verbatim copies of the author's originals), the four primary-dimension probe outputs, and a run/prompt-version manifest.