SCOPE: Prompt Evolution for Enhancing Agent Effectiveness

• URL: http://arxiv.org/abs/2512.15374v1
• Date: Wed, 17 Dec 2025 12:25:05 GMT
• Title: SCOPE: Prompt Evolution for Enhancing Agent Effectiveness
• Authors: Zehua Pei, Hui-Ling Zhen, Shixiong Kai, Sinno Jialin Pan, Yunhe Wang, Mingxuan Yuan, Bei Yu,
• Abstract summary: Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts.<n>While agents have access to this context, their static prompts lack the mechanisms to manage it effectively.<n>We introduce textbfSCOPE (Self-evolving Context Optimization via Prompt Evolution)<n>We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles)
• Score: 53.75986399936395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textbf{SCOPE} (Self-evolving Context Optimization via Prompt Evolution). SCOPE frames context management as an \textit{online optimization} problem, synthesizing guidelines from execution traces to automatically evolve the agent's prompt. We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles). Furthermore, we introduce Perspective-Driven Exploration to maximize strategy coverage, increasing the likelihood that the agent has the correct strategy for any given task. 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.

Related papers

• DLLM Agent: See Farther, Run Faster [94.74432470237817]
  Diffusion large language models (DLLMs) have emerged as an alternative to autoregressive (AR) decoding with appealing efficiency and modeling properties.<n>We study this in a controlled setting by instantiatingDLLM and AR backbones within the same agent workflow.<n>We find thatDLLM Agents are on average over 30% faster end to end than AR agents, with some cases exceeding 8x speedup.
  arXiv  Detail & Related papers  (2026-02-07T09:01:18Z)
• Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios [0.9069311779417014]
  This paper introduces an agent framework grounded in real-world practical experience.<n>An end-to-end framework named Jenius-Agent has been integrated with three key optimizations.<n>Experiments show a 20 percent improvement in task accuracy, along with a reduced token cost, response latency, and invocation failures.
  arXiv  Detail & Related papers  (2026-01-05T07:35:12Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• AgentProg: Empowering Long-Horizon GUI Agents with Program-Guided Context Management [24.465443389008055]
  AgentProg is a program-guided approach for agent context management.<n>It reframes the interaction history as a program with variables and control flow.<n> Experiments on AndroidWorld and our extended long-horizon task suite demonstrate that AgentProg has achieved the state-of-the-art success rates.
  arXiv  Detail & Related papers  (2025-12-11T07:37:38Z)
• Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models [18.829572148850563]
  We introduce ACE (Agentic Context Engineering), a framework that treats contexts as evolving playbooks.<n>Across agent and domain-specific benchmarks, ACE consistently outperforms strong baselines.<n> ACE could adapt effectively without labeled supervision and instead by leveraging natural execution feedback.
  arXiv  Detail & Related papers  (2025-10-06T09:30:18Z)
• Stochastic Self-Organization in Multi-Agent Systems [28.70691568233268]
  Multi-agent systems (MAS) based on Large Language Models (LLMs) have the potential to solve tasks that are beyond the reach of any single LLM.<n>We introduce a response-conditioned framework that adapts communication on-the-fly.
  arXiv  Detail & Related papers  (2025-10-01T09:08:04Z)
• HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution [13.440964262446558]
  Hierarchical Variable Agent (HiVA) is a novel framework modeling agentic as self-organized graphs with the Semantic-Topological Evolution (STEV) algorithm.<n> Experiments on dialogue, coding, Longcontext Q&A, mathematical, and agentic benchmarks demonstrate improvements of 5-10% in task accuracy and enhanced resource efficiency.
  arXiv  Detail & Related papers  (2025-08-29T18:51:18Z)
• SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents [32.76299758137446]
  Large Language Model (LLM)-based agents have recently shown impressive capabilities in complex reasoning and tool use via multi-step interactions with their environments.<n>These trajectories contain rich feedback that can navigate agents toward the right directions for solving problems correctly.<n>Although prevailing approaches, such as Monte Carlo Tree Search (MCTS), can effectively balance exploration and exploitation, they ignore the interdependence among various trajectories.<n>We propose SE-Agent, a Self-Evolution framework that enables Agents to optimize their reasoning processes iteratively.
  arXiv  Detail & Related papers  (2025-08-04T05:51:55Z)
• DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal [55.13854171147104]
  Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development.<n>We present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents.<n>We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.
  arXiv  Detail & Related papers  (2025-03-18T14:02:59Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [112.04307762405669]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine.<n>G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents [7.4159044558995335]
  We introduce MOSS (llM-oriented Operating System Simulation), a novel framework that integrates code generation with a dynamic context management system. At its core, the framework employs an Inversion of Control container in conjunction with decorators to enforce the least knowledge principle. We show how this framework can enhance the efficiency and capabilities of agent development and highlight its advantages in moving towards Turing-complete agents.
  arXiv  Detail & Related papers  (2024-09-24T14:30:21Z)
• Optimistic Multi-Agent Policy Gradient [37.08607405659866]
  Relative overgeneralization (RO) occurs when agents converge towards a suboptimal joint policy.<n>No methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods.<n>We propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO problem.
  arXiv  Detail & Related papers  (2023-11-03T14:47:54Z)
• Distributed Adaptive Learning Under Communication Constraints [54.22472738551687]
  This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data.
  arXiv  Detail & Related papers  (2021-12-03T19:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.