myWiki/raw/papers/zhou-agent-symbolic-learning-2024.md

title, created, type, arxiv, authors, venue, affiliation, tags

title	created	type	arxiv	authors	venue	affiliation	tags
Symbolic Learning Enables Self-Evolving Agents	2026-05-29	paper-raw	2406.18532	Wangchunshu Zhou Yixin Ou Shengwei Ding Long Li Jialong Wu Tiannan Wang Jiamin Chen Shuai Wang Xiaohua Xu Ningyu Zhang Huajun Chen Yuchen Eleanor Jiang	arXiv preprint (cs.CL), June 2024	AIWaves Inc.	agent symbolic-learning self-evolving optimization

Symbolic Learning Enables Self-Evolving Agents

Authors: Zhou et al. (AIWaves, 2024) arXiv: 2406.18532 Code: https://github.com/aiwaves-cn/agents

Abstract

The AI community has been exploring a pathway to AGI by developing "language agents". A fundamental limitation is that current agent research is model-centric/engineering-centric — progress requires substantial manual engineering. Agent symbolic learning introduces a systematic framework that enables language agents to optimize themselves in a data-centric way using symbolic optimizers. Agents are considered as symbolic networks where learnable weights are defined by prompts, tools, and pipeline structure.

Key Contributions

1. Agent as Symbolic Network: Pipeline = computation graph, Nodes = layers, Prompts/Tools = weights
2. Symbolic Back-Propagation: Language Loss propagated backward through the pipeline → Language Gradients for each node
3. Holistic Joint Optimization: All symbolic components optimized together, avoiding local optimum
4. Self-Evolving: Language Loss doesn't need ground-truth, enabling learning after deployment