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        <title>Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search</title>
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            <namePart type="given">Viviane</namePart>
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    <abstract>Test-time scaling improves large language models (LLMs) on long-horizon reasoning tasks by allocating more compute at inference. LLM inference via tree search (LITS) achieves strong performance but is highly inefficient. We propose Chain-in-Tree (CiT), a plug-in framework that decides when to branch during search instead of expanding at every step. CiT introduces lightweight Branching Necessity (BN) evaluations, including BN-DP (direct prompting) and BN-SC (self-consistency). Integrated into Tree of Thoughts, ReST-MCTS, and RAP, BN-DP reduces token generation, model calls, and runtime by 75-85% on GSM8K and Math500, with often negligible or no accuracy loss. BN-SC typically yields substantial savings (up to 80%) generally but shows instability in 1-4 out of 14 settings, caused by a small subset of examples that produce extremely long reasoning steps. We theoretically prove that BN-DP never increases policy invocations and release unified implementations applicable across LITS frameworks. The full codebase is publicly available at https://github.com/xinzhel/chain_in_tree.</abstract>
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%0 Conference Proceedings
%T Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search
%A Li, Xinzhe
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-2026-chain
%X Test-time scaling improves large language models (LLMs) on long-horizon reasoning tasks by allocating more compute at inference. LLM inference via tree search (LITS) achieves strong performance but is highly inefficient. We propose Chain-in-Tree (CiT), a plug-in framework that decides when to branch during search instead of expanding at every step. CiT introduces lightweight Branching Necessity (BN) evaluations, including BN-DP (direct prompting) and BN-SC (self-consistency). Integrated into Tree of Thoughts, ReST-MCTS, and RAP, BN-DP reduces token generation, model calls, and runtime by 75-85% on GSM8K and Math500, with often negligible or no accuracy loss. BN-SC typically yields substantial savings (up to 80%) generally but shows instability in 1-4 out of 14 settings, caused by a small subset of examples that produce extremely long reasoning steps. We theoretically prove that BN-DP never increases policy invocations and release unified implementations applicable across LITS frameworks. The full codebase is publicly available at https://github.com/xinzhel/chain_in_tree.
%U https://aclanthology.org/2026.findings-acl.214/
%P 4365-4392

Markdown (Informal)

[Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search](https://aclanthology.org/2026.findings-acl.214/) (Li, Findings 2026)

• Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search (Li, Findings 2026)

ACL

• Xinzhe Li. 2026. Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search. In Findings of the Association for Computational Linguistics: ACL 2026, pages 4365–4392, San Diego, California, United States. Association for Computational Linguistics.