Jun 2026· arXiv.org· Vol abs/2606.30840· 1 citation· 18 references
Computer Science
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.
RLMOpt is introduced, a prompt optimizer that makes the search policy itself language-model-driven through a recursive language model (RLM), which operates over a tool-based environment, inspecting task information, analyzing failures, generating candidates, allocating evaluation budget, and deciding when to stop.
ReASearch is presented, a unified framework for reasoning-driven optimization in which the agent autonomously decides what to evaluate, how to diagnose failures, which edits to make, and when to verify or restart.
Prompt engineering is now a significant aspect of large language models (LLMs) to make them as effective in applications like conversational agents, educational assistants, automated code-generating systems, and content generation systems. Nonetheless, urgent design is frequently done in informal and solitary fashion devoid of systematic processes of collective enhancement, quality evaluation, or evolutionary monitoring. This paper suggests NeuroPrompt, an open-source prompt engineering system, which combines version tracking with evolution, directed acyclic graph (DAG) based lineage modeling, and community-based quality assessment. Users can create prompts, fork better prompts, and assess the performance of prompts with a multi-rater consensus system to assess the prompts based on clarity, creativity, and usefulness. The forking is directed by a utility-based decision model that ensures that unnecessary prompt duplication is avoided, and the effectiveness of optimization is enhanced. Experimental analysis reveals that collaborative prompt evolution is more effective in prompt quality and the overall accuracy of prompt generation in providing relevant and useful responses to tasks is 96.3% in comparison against baseline prompt design methods which has a score of about 88-91. There are also the results of the improved consensus reliability and lesser variance of ratings among the community evaluators. The suggested system will change timely engineering into a transparent and organized evolutionary process allowing sharing of knowledge systematically and optimizing prompt through collaborative optimization to large-scale AI initiatives.
D. Ragunath, Vaishak C J· International Conference Com...· 0 citations
This work introduces MemeMind, which uses an offline reference answer to recover missing experience in Anime, Comic, and Game meme interpretation and shows that constructing successful tool use for failed groups provides the largest component gain and produces more effective evidence acquisition at inference time.
Run Yang, Weihang Wang, Boheng Sheng et al.· 1 citation
This work introduces CAPO (Constraint-Aware Prompt Optimization), a primal-dual method that combines pool-based rewrites with adaptive constraint weighting to optimize system prompts under explicit operational constraints.
Victor Ye Dong, Reid Pryzant, Yi Liu et al.· 0 citations
This work introduces PromptPack, a scalable, high-throughput LLM annotation agent that cuts LLM costs by 89% and accelerates throughput by 2.5x while fully preserving AUC.
Sebastian Koralewski, Merwan Barlier, Yulia Stolin et al.· 0 citations