Proposes replacing flat conversation histories with a tree-based architecture to solve 'logical context poisoning.'
March 24, 2026
Original Paper
Conversation Tree Architecture: A Structured Framework for Context-Aware Multi-Branch LLM Conversations
arXiv · 2603.21278
The Takeaway
Current LLM interfaces suffer from context bleed where distinct topics degrade model performance. This architecture organizes conversations into discrete nodes with isolated context flows, providing a principled way to manage multi-topic, multi-turn interactions without window exhaustion.
From the abstract
Large language models (LLMs) are increasingly deployed for extended, multi-topic conversations, yet the flat, append-only structure of current conversation interfaces introduces a fundamental limitation: all context accumulates in a single unbounded window, causing topically distinct threads to bleed into one another and progressively degrade response quality. We term this failure mode logical context poisoning. In this paper, we introduce the Conversation Tree Architecture (CTA), a hierarchical