mlsanityData to 14 Oct 2026

Chain-of-Thought Shows the Path to a Tree: Realizing Branching Complexity

arXiv 2608.11716 ↗First posted 12 Aug 2026cs.LGcited by 0

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Chain of Thought (CoT) lifts the expressive ceiling of bounded-depth Transformers, with characterizations tying the number of CoT steps to circuit complexity classes. What remains largely missing are concrete instantiations with explicit, depth-bounded constructions, and the traversal procedures such characterizations presuppose. We close this gap for branching complexity. We give CoT realizations of depth-first search (DFS) and of Dijkstra algorithm, the latter subsuming breadth-first search, by unique hard-attention decoders of at most two layers, and use them as a shared computational substrate: reusing the DFS decoder yields the Strahler number of an n-vertex tree in 2n-1 steps with four layers, and reusing the Dijkstra decoder yields its width in n-1 steps with three. Since computing the Strahler number of a binary tree given as a term is textsfNCtextsuperscript1-complete, and our constructions handle arbitrary n-ary trees without layer normalization or positional encodings, this is a non-trivial witness for the linear-step regime of the CoT hierarchy. Exploiting the classical bijection between ordered trees and Dyck paths, itself realized by our DFS construction, which emits the path as it traverses, we give independent constructions for both measures on the path representation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 3 of its 47 references that are in the map. First posted on arXiv 12 Aug 2026.

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3 of 47 references.

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  1. 2025A Theory of Learning with Autoregressive Chain of Thought11 Mar

Other references · 2

  1. 2019On the Turing Completeness of Modern Neural Network Architecturescited by 129
  2. 2025On the Existence of Universal Simulators of Attentioncited by 1

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  1. 2024Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
  2. 2025To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers
  3. 2025A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
  4. 2023The Expressive Power of Transformers with Chain of Thought
  5. 2025Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought
  6. 2025Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers
  7. 2026Efficiently Representing Algorithms With Chain-of-Thought Transformers
  8. 2025Continuous Chain of Thought Enables Parallel Exploration and Reasoning