The Decomposition Tax: LLM Pipelines Lose Up to 40 Accuracy Points at Their Own Interfaces

arXiv:2609.32825 · cs.AI, cs.CL, cs.SE · Submitted 2026-09-26 · Read on arXiv

cs.AI, cs.CL, cs.SE

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Grounding — 38× in this paper · explained in 3 episodes
• Accuracy — 26× in this paper · explained in 1 episode
• Benchmark — 16× in this paper · explained in 2 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Multi-Agent System — 4× in this paper · explained in 6 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Admissibility — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
• Question Decomposition Improves the Faithfulness of Model-Generated Reasoning

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