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The Missing Complement: State-Conditioned Minimal Sufficient Evidence for Coding Agents

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TL;DR - This paper reframes retrieval for coding agents as finding a compact, state-conditioned set of evidence that supplies every fact missing from the agent’s current context. Its MSS-Complement method improves evidence completeness and downstream repair localization while using substantially smaller prompts.

  • SERBench evaluates retrieval on 500 held-out agent states from 45 repositories, requiring retrieved sets to cover every annotated fact needed for the next decision.
  • MSS-Complement constructs jointly sufficient evidence sets through three semantic calls and returns 4–8 intact source units within 6,144 tokens.
  • It achieves 73.0% complete-set recovery with five items and 80.6% with eight, versus 61.4% and 72.4% for Qwen3 embeddings with reranking.
  • On AMA-Bench, it reduces answer-prompt size by 76.2% while improving accuracy by 2.08 points over the benchmark’s memory agent.

Sources (1)

The Missing Complement: State-Conditioned Minimal Sufficient Evidence for Coding Agents

arXiv cs.IR Zhexi Feng, Ruiyi Zhang, Yongbo Yang, Pengtao Xie 2026-09-17 arXiv:2609.20050
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-24 14:18:05.621932 UTC

TL;DR - This paper reframes retrieval for coding agents as finding a compact, state-conditioned set of evidence that supplies every fact missing from the agent’s current context. Its MSS-Complement method improves evidence completeness and downstream repair localization while using substantially smaller prompts.

  • SERBench evaluates retrieval on 500 held-out agent states from 45 repositories, requiring retrieved sets to cover every annotated fact needed for the next decision.
  • MSS-Complement constructs jointly sufficient evidence sets through three semantic calls and returns 4–8 intact source units within 6,144 tokens.
  • It achieves 73.0% complete-set recovery with five items and 80.6% with eight, versus 61.4% and 72.4% for Qwen3 embeddings with reranking.
  • On AMA-Bench, it reduces answer-prompt size by 76.2% while improving accuracy by 2.08 points over the benchmark’s memory agent.
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