Agentic Prompt Optimization for E-Commerce Catalog Enrichment at Scale

The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows

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.

From Tacit Knowledge to Structured Documents: A Framework for Knowledge Elicitation

The Assistant-Scribe-Knowledge Checker (ASK) framework for generating structured specification documents through guided interviews is evaluated in a consulting-firm setting where consultants are required to produce project “return-of-experience” documents to capture reusable knowledge.

Beyond Prompt or Skill? Attribution-Guided Optimization of Modular LLM Programs

Large language models can solve increasingly diverse reasoning tasks, yet their performance remains highly sensitive to task prompts, intermediate instructions, and the way reusable problem-solving knowledge is incorporated. Existing optimization methods usually focus on only one part of this design space: they either...

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

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.

From Weak Task Specifications to Scientific Extraction Agents: Optimizing Task Construction

Most methods that optimize LLM prompts and agent workflows assume that task-specific output schemas, extraction instructions, and evaluation criteria are predefined. For scientific extraction agents, however, a short task goal may not fully determine these components, while specifying them manually is costly. We study...

Prompt Template-Driven Large Model SQL Generation