arXiv Computation and Language By Xing Zhang, Guanghui Wang, Yanwei Cui, Mengdie Flora Wang, Peiyang He

UnitBoost: Managing Compound LLM Systems with a Merge Operator, Not a Model

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UnitBoost proposes a non‑generative merge operator to manage compound LLM systems, replacing the opaque higher‑level LLM that traditionally coordinates worker outputs. The operator maps worker outputs to slot‑value proposals, uses a constrained argmax to assemble the final answer, and explicitly tracks unfilled slots as residuals for subsequent rounds, thereby achieving order‑free processing and unit provenance. Across three held‑out benchmarks, UnitBoost outperforms both gold‑label‑selected candidates and input‑matched generative managers, improving compound‑system performance by up to 0.182 points and raising FanOutQA cell F1 from 0.4778 to 0.5524.

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arXiv AI
Aug 25

Right-Sizing LLM-Agent Decomposition in VAT Determination: A Pilot Controlled Sweep

The study evaluates how to best split tasks among large‑language‑model agents for cross‑border VAT determination, comparing one broad agent to configurations ranging from one to five narrow agents. Across 4,400 runs—including token‑matched and failure‑injection scenarios—the intermediate configurations achieved the highest accuracy but did not surpass the fine‑endpoint benchmark, leaving the optimal decomposition hypothesis unconfirmed. The pilot provides a preregistered heuristic for right‑sizing decomposition, along with an oracle, dataset, and analysis pipeline.

By Pedro Santos