Challenge: Existing agentic systems cannot search the whole design space due to the restriction of human-designed components.
Approach: They propose a Gödel Agent framework that allows agents to recursively improve themselves without relying on fixed algorithms or fixed algorithms.
Outcome: The proposed framework surpasses manual crafted agents in performance, efficiency, and generalizability.

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ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems (2024.emnlp-demo)

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Challenge: Recent studies show that large language models can be used to construct complex multi-agent systems.
Approach: They propose a toolkit for recursive multi-agent systems that supports custom tool-use, delegation schemes, event-based logging, and interactive replay.
Outcome: The proposed tool achieves significant performance gains on agentic benchmarks and identify potential areas of improvement through visualization and debugging tools.
WebEvolver: Enhancing Web Agent Self-Improvement with Co-evolving World Model (2025.emnlp-main)

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Challenge: Agent self-improvement, where agents train their underlying Large Language Model (LLM) on self-sampled trajectories, shows promising results but often stagnates in web environments due to limited exploration and under-utilization of pretrained web knowledge.
Approach: They propose a co-evolving Large Language Model (LLM) that predicts the next observation based on current observation and action within the web environment.
Outcome: The proposed framework shows that agents can perform better in real-world web environments without using any distillation from more powerful close-sourced models.
Learn Like Humans: Use Meta-cognitive Reflection for Efficient Self-Improvement (2026.acl-long)

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Challenge: Existing self-improving frameworks rely on inefficient, multi-turn recursive loops that incur high computational costs.
Approach: They propose a framework that achieves efficient self-evolution within a single recurrence cycle.
Outcome: The proposed framework outperforms state-of-the-art self-evolving systems while significantly reducing computational overhead.
POLARIS: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair (2026.findings-acl)

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Challenge: Gödel agent Polaris makes policy level changes with small, auditable patches that persist in the policy and are reused on unseen instances within each benchmark.
Approach: They propose a Gödel agent that performs policy repair via experience abstraction . Polaris makes policy level changes with small, auditable patches that persist in the policy .
Outcome: The proposed agent improves on MGSM, DROP, GPQA, and LitBench models over the base policy and competitive baselines.
LLM Agents Making Agent Tools (2025.acl-long)

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Challenge: Large language models (LLMs) can perform multi-step tasks by dynamically utilising external software components.
Approach: They propose an agentic framework that autonomously transforms papers with code into LLM-compatible tools.
Outcome: The proposed framework outperforms current state-of-the-art software engineering agents in 80% of tasks and is openly available on GitHub.
SELFGOAL: Your Language Agents Already Know How to Achieve High-level Goals (2025.naacl-long)

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Challenge: Existing approaches to improve the performance of language agents without training are not available.
Approach: They propose an automatic approach to break down high-level goals into tree structure of more practical subgoals during interaction with environments while identifying the most useful subgoal.
Outcome: The proposed approach significantly improves the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments.
ReCreate: Reasoning and Creating Domain Agents Driven by Experience (2026.acl-long)

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Challenge: Large Language Model (LLM) agents are reshaping the industrial landscape, but tasks differ widely, making them labor-intensive to build.
Approach: They propose an experience-driven framework for the automatic creation of domain agents . they leverage agent interaction histories to provide rich concrete signals on success or failure .
Outcome: The proposed framework outperforms human-designed agents and existing methods in experiments across diverse domains.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)

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Challenge: Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience.
Approach: They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications .
Outcome: a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance .
Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)

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Challenge: Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs.
Approach: They propose to use large language models to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal.
Outcome: The proposed use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, and the need for new metrics and benchmarks.
EvolveSearch: An Iterative Self-Evolving Search Agent (2025.emnlp-main)

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Challenge: Existing approaches to enabling LLM web search proficiency struggle with data production in open-search domains, while supervised fine-tuning struggles with data utilization efficiency.
Approach: They propose an iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without external human-annotated reasoning data.
Outcome: EvolveSearch achieves 4.7% improvement over current state-of-the-art in seven benchmarks . supervised fine-tuning struggles with data production in open-search domains compared with RL .

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