Textual Equilibrium Propagation for Deep Compound AI Systems

Wed, 28 Jan 2026 00:00:00 +0000

Authors: Minghui Chen, Wenlong Deng, James Zou, Han Yu, Xiaoxiao Li

Published in: Accepted to The Fourteenth International Conference on Learning Representations (ICLR 2026)

Abstract

Large language models (LLMs) are increasingly deployed as part of compound AI systems that coordinate multiple modules, such as retrievers, tools, and verifiers, over long-horizon workflows. Recent approaches that propagate textual feedback globally, such as TextGrad, make it feasible to optimize such pipelines, but we find that performance degrades as system depth grows.

Can Textual Gradient Work in Federated Learning?

Fri, 24 Jan 2025 00:00:00 +0000

Authors: Minghui Chen, Ruinan Jin, Wenlong Deng, Yuanyuan Chen, Zhi Huang, Han Yu, Xiaoxiao Li

Published in: The Thirteenth International Conference on Learning Representations (ICLR 2025)

Abstract

Recent studies highlight the promise of LLM-based prompt optimization, especially with TextGrad, which automates “differentiation” via texts and backpropagates textual feedback provided by LLMs. This approach facilitates training in various real-world applications that do not support numerical gradient propagation or loss calculation. It opens new avenues for optimization in decentralized, resource-constrained environments, suggesting that users of black-box LLMs (e.g., ChatGPT) could enhance components of LLM agentic systems (such as prompt optimization) through collaborative paradigms like federated learning (FL).

Prompt Optimization on Minghui Chen

Textual Equilibrium Propagation for Deep Compound AI Systems

Abstract

Can Textual Gradient Work in Federated Learning?

Abstract