Abstract
Current LLM agents exhibit limited capacity for sustained self-improvement without external data or retraining. Existing approaches—such as reflective memory systems, self-generated tasks, and recursive agent rewriting—demonstrate incremental gains but remain bounded by static representations and brittle feedback loops . This paper introduces Chrono-Semantic Self-Distillation (CSSD), a novel architecture in which agents restructure their internal reasoning space through temporally layered tool interactions, forming emergent meta-representations that function as synthetic priors. The system enables continuous performance improvement without additional datasets, gradient updates, or external supervision.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny, "A Zero-Data Paradigm for Autonomous LLM Agent Evolution via Tool Interaction", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10015