# SCOPE: Prompt Evolution for Enhancing Agent Effectiveness

arXiv: https://arxiv.org/abs/2512.15374 · HTML: https://mlsanity.com/paper/2512.15374/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 16 of 16)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://mlsanity.com/paper/2507.19457/) (2025)
- [Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models](https://mlsanity.com/paper/2510.04618/) (2025)
- [ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory](https://mlsanity.com/paper/2509.25140/) (2025)
- [Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://mlsanity.com/paper/2504.07952/) (2025)
- [Scaling Long-Horizon LLM Agent via Context-Folding](https://mlsanity.com/paper/2510.11967/) (2025)
- [xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations](https://mlsanity.com/paper/2506.13651/) (2025)

## Built on by (most cited first, 18 of 18)

- [From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws](https://mlsanity.com/paper/2606.06324/) (2026)
- [ReCreate: Reasoning and Creating Domain Agents Driven by Experience](https://mlsanity.com/paper/2601.11100/) (2026)
- [Position: Agentic Evolution is the Path to Evolving LLMs](https://mlsanity.com/paper/2602.00359/) (2026)
- [Adaptive Information Control for Search-Augmented LLM Reasoning](https://mlsanity.com/paper/2602.01672/) (2026)
- [TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking](https://mlsanity.com/paper/2602.03224/) (2026)
- [DLLM Agent: See Farther, Run Faster](https://mlsanity.com/paper/2602.07451/) (2026)
- [RewardHarness: Self-Evolving Agentic Post-Training](https://mlsanity.com/paper/2605.08703/) (2026)
- [ReThinker: Scientific Reasoning by Rethinking with Guided Reflection and Confidence Control](https://mlsanity.com/paper/2602.04496/) (2026)
- [Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts](https://mlsanity.com/paper/2604.00901/) (2026)
- [Scoped Verification for Reliable Long-Horizon Agentic Context Evolution under Distribution Shift](https://mlsanity.com/paper/2607.09175/) (2026)
- [C-MOP: Integrating Momentum and Boundary-Aware Clustering for Enhanced Prompt Evolution](https://mlsanity.com/paper/2602.10874/) (2026)
- [MemDLM: Memory-Enhanced DLM Training](https://mlsanity.com/paper/2603.22241/) (2026)
- [FocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning](https://mlsanity.com/paper/2605.09932/) (2026)
- [Verilog-Evolve: Feedback-Driven and Skill-Evolving Verilog Generation](https://mlsanity.com/paper/2605.26498/) (2026)
- [Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference](https://mlsanity.com/paper/2607.02882/) (2026)
- [MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents](https://mlsanity.com/paper/2608.12428/) (2026)
- [Causal Improvement Graph for Agentic Harness Optimization](https://mlsanity.com/paper/2610.05039/) (2026)
- [Curriculum Brain: Constructing Curriculum Knowledge Graphs as a Substrate for Cognitive Diagnosis](https://mlsanity.com/paper/2610.05860/) (2026)

## Abstract (arXiv)

Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts. However, a critical bottleneck remains: while agents have access to this context, their static prompts lack the mechanisms to manage it effectively, leading to recurring Corrective and Enhancement failures. To address this capability gap, we introduce Self-evolving Context Optimization via Prompt Evolution (SCOPE). SCOPE frames context management as an textitonline optimization problem, synthesizing guidelines from execution traces to automatically evolve the agent's prompt. We propose a Dual-Stream mechanism that routes guidelines between tactical memory (immediate error correction) and strategic memory, which is continuously refined through conflict resolution, subsumption pruning, and consolidation. To maximize strategy coverage, Perspective-Driven Exploration evolves multiple parallel prompts guided by distinct optimization perspectives. Experiments on the HLE benchmark show that SCOPE improves task success rates from 14.23% to 38.64% without human intervention. We make our code publicly available at https://github.com/JarvisPei/SCOPE.