DesDD: A Design-Enabled Framework with Dual-Layer Debugging for LLM-based Iterative API Orchestrating
Bibliographic record
Abstract
In contemporary Software Engineering (SE), coordinated API calls are necessary to perform complex data retrieval operations as well as tasks.Large Language Models (LLMs) offer highly potential capabilities for natural language parsing and automation of tasks, which sparked research into integrating APIs orchestration with LLMs.Nonetheless, while existing LLM-based frameworks have developed considerably, yet they experience challenges in tackling complex tasks which tend to involve iterative, step-by-step problemsolving.Current frameworks lack structured guidance, relying on LLMs' own capabilities, resulting in blind iterations, inefficient error correction, and inefficient token utilization.This work introduces DesDD (Design-enabled framework with Dual-layer Debugging), a structured framework for LLM-driven iterative API orchestration.By applying software engineering design principles, we organize API orchestration workflows into distinct design and coding phases.Our dual-layer debugging mechanism detects and corrects errors in * Z.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".