Human Feedback Types
provisional (inferred)None explicit
No explicit feedback protocol extracted.
"Context engineering has emerged as a primary lever for improving AI systems without parameter updates."
HFEPX · Eval paper review
SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration
Ziyi Zhu, Luka Smyth, Saki Shinoda, Jinghong Chen
Published
Jun 17, 2026
Citations
0
Trust level
Provisional
Usefulness score
Unavailable
Extraction confidence
0% (Provisional)
Derived from abstract and metadata only.
Signals refreshed
Jun 17, 2026
Should you rely on this paper?
Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.
This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.
Abstract-only analysis, low confidence
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
- This page is still relying on abstract and metadata signals, not a fuller protocol read.
Best use
Background context only
Use if you need
A provisional background reference while structured extraction finishes.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This page is still relying on abstract and metadata signals, not a fuller protocol read.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
Unavailable
Provisional (processing)
Eval-fit score is unavailable until extraction completes.
If you are doing eval pipeline work, start here
Abstract
Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients do not function as real gradients motivates treating automatic prompt optimization (APO) as black-box search. We introduce SPO (Stochastic Prompt Optimization), a framework for stochastic search over prompt space, and compare three strategies of increasing sophistication: error-informed random search, a genetic algorithm with evolutionary operators, and SAGE (SPO via Agent-Guided Exploration), a multi-agent pipeline with diagnostic code execution. Across three benchmarks, no single strategy dominates; effectiveness depends on the interaction of landscape structure with error type. We further deploy SAGE on a mental-health chatbot under a continuous optimization paradigm, where it compounds eight cycles of individually-noisy A/B tests into a statistically robust gain in next-day retention. We argue that coupling qualitative diagnosis with quantitative validation is what makes agentic optimization effective for open-ended task-oriented dialogue.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
provisional (inferred)None explicit
Validate eval design from full paper text.
"Context engineering has emerged as a primary lever for improving AI systems without parameter updates."
Quality Controls
provisional (inferred)Not reported
No explicit QC controls found.
"Context engineering has emerged as a primary lever for improving AI systems without parameter updates."
Benchmarks / Datasets
provisional (inferred)Not extracted
No benchmark anchors detected.
"Context engineering has emerged as a primary lever for improving AI systems without parameter updates."
Reported Metrics
provisional (inferred)Not extracted
No metric anchors detected.
"Context engineering has emerged as a primary lever for improving AI systems without parameter updates."
Rater Population
provisional (inferred)Unknown
Rater source not explicitly reported.
"Context engineering has emerged as a primary lever for improving AI systems without parameter updates."
Human feedback details
This page is using abstract-level cues only right now. Treat the signals below as provisional.
- Potential human-data signal: No explicit human-data keywords detected.
- Potential benchmark anchors: No benchmark names detected in abstract.
- Abstract highlights: 3 key sentence(s) extracted below.
Evaluation details
Evaluation fields are inferred from the abstract only.
- Potential evaluation modes: No explicit eval keywords detected.
- Potential metric signals: No metric keywords detected.
- Confidence: Provisional (metadata-only fallback).
Research brief
Metadata summary
Context engineering has emerged as a primary lever for improving AI systems without parameter updates.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Context engineering has emerged as a primary lever for improving AI systems without parameter updates.
- Recent work showing that textual gradients do not function as real gradients motivates treating automatic prompt optimization (APO) as black-box search.
- We introduce SPO (Stochastic Prompt Optimization), a framework for stochastic search over prompt space, and compare three strategies of increasing sophistication: error-informed random search, a genetic algorithm with evolutionary operators, and SAGE (SPO via Agent-Guided Exploration), a multi-agent pipeline with diagnostic code execution.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.