[Submitted on 29 Jun 2026]
Title:Contrastive Reflection for Iterative Prompt Optimization
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Abstract:LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR evaluation. Improving the prompts that control these agents is an optimization problem, but in applied IR settings it often looks less like blind search and more like debugging. Engineers need to know which behavior failed, which nearby behavior still worked, what distinguishes the two, and whether a prompt edit improves held-out quality without introducing regressions.
We present Contrastive Reflection, an iterative prompt-optimization framework for agentic IR workflows. The framework starts from a task-centric quality definition: QA agents expose retrieval or reasoning traces, and grading agents expose dimension-level scores and rationales. These structured traces are used to identify error-anchored behavioral slices, add nearby successful examples from the same region, and ask a Teacher LLM to propose a targeted prompt edit. Candidate edits are accepted only when validation performance improves, optionally subject to regression checks. We instantiate the framework with a tree-based slice selector, but the contribution is the contrastive reflection loop rather than the tree itself.
On a public HotpotQA retrieval-augmented QA setup, one tree-selected contrastive repair improves held-out exact-match accuracy from 51.4% to 60.4%. Failure-only and random-evidence variants improve less and break more previously correct examples. A light instruction-only comparison places the method near modern prompt optimizers: MIPROv2 reaches 59.4% and GEPA 57.0%. The result is an interpretable optimization loop for IR agents, aimed at making prompt repair more inspectable and validation-driven.
Comments: 6 pages, 1 figure. To appear at Agent4IR @ KDD 2026 (KDD 2026 Workshop on AI Agents for Information Retrieval)
Subjects:
Artificial Intelligence (cs.AI)
ACM classes: I.2.7; H.3.3; I.2.6
Cite as: arXiv:2606.30840 [cs.AI]
(or arXiv:2606.30840v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.30840
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Derek Koh [view email] [v1] Mon, 29 Jun 2026 19:16:07 UTC (58 KB)
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