@inproceedings{fang-etal-2026-answer,
title = "Answer Probing-Guided Search for Diverse Solution Exploration of {LLM}s",
author = "Fang, Yi and
Shen, Que and
Li, Chengpeng and
Deng, Boyi and
Shi, Wei and
Wang, Wenjie and
Feng, Fuli and
Xu, Fengli and
Liu, Dayiheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1383/",
pages = "31571--31587",
ISBN = "979-8-89176-459-0",
abstract = "Generating multiple diverse and high-quality solutions is valuable for many applications, such as code-test generation and drug discovery. However, Large Language Models (LLMs) tend to converge on a single high-confidence solution during inference, limiting exploration of alternative valid solution paths. Existing test-time methods promote diversity through tree-like search and prune semantically similar branches using response-level semantic embeddings. However, we find that such embeddings are easily confounded by linguistic and stylistic similarities, making it difficult to distinguish genuinely distinct solution paths. To address this, we introduce Answer Probing, which probes the potential answer an LLM would reach from an intermediate reasoning path. We demonstrate that the hidden states of probed answers more effectively differentiate distinct solution paths than semantic embeddings, and the perplexity of probed answers serves as a practical proxy for reasoning correctness. Based on these findings, we propose Answer Probing-Guided Tree Search (APTS), which guides the tree search by the probed answers' hidden state similarity and perplexity. Experiments on three reasoning tasks across two LLMs show that APTS consistently enhances solution diversity, demonstrating its effectiveness and robustness."
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<abstract>Generating multiple diverse and high-quality solutions is valuable for many applications, such as code-test generation and drug discovery. However, Large Language Models (LLMs) tend to converge on a single high-confidence solution during inference, limiting exploration of alternative valid solution paths. Existing test-time methods promote diversity through tree-like search and prune semantically similar branches using response-level semantic embeddings. However, we find that such embeddings are easily confounded by linguistic and stylistic similarities, making it difficult to distinguish genuinely distinct solution paths. To address this, we introduce Answer Probing, which probes the potential answer an LLM would reach from an intermediate reasoning path. We demonstrate that the hidden states of probed answers more effectively differentiate distinct solution paths than semantic embeddings, and the perplexity of probed answers serves as a practical proxy for reasoning correctness. Based on these findings, we propose Answer Probing-Guided Tree Search (APTS), which guides the tree search by the probed answers’ hidden state similarity and perplexity. Experiments on three reasoning tasks across two LLMs show that APTS consistently enhances solution diversity, demonstrating its effectiveness and robustness.</abstract>
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%0 Conference Proceedings
%T Answer Probing-Guided Search for Diverse Solution Exploration of LLMs
%A Fang, Yi
%A Shen, Que
%A Li, Chengpeng
%A Deng, Boyi
%A Shi, Wei
%A Wang, Wenjie
%A Feng, Fuli
%A Xu, Fengli
%A Liu, Dayiheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fang-etal-2026-answer
%X Generating multiple diverse and high-quality solutions is valuable for many applications, such as code-test generation and drug discovery. However, Large Language Models (LLMs) tend to converge on a single high-confidence solution during inference, limiting exploration of alternative valid solution paths. Existing test-time methods promote diversity through tree-like search and prune semantically similar branches using response-level semantic embeddings. However, we find that such embeddings are easily confounded by linguistic and stylistic similarities, making it difficult to distinguish genuinely distinct solution paths. To address this, we introduce Answer Probing, which probes the potential answer an LLM would reach from an intermediate reasoning path. We demonstrate that the hidden states of probed answers more effectively differentiate distinct solution paths than semantic embeddings, and the perplexity of probed answers serves as a practical proxy for reasoning correctness. Based on these findings, we propose Answer Probing-Guided Tree Search (APTS), which guides the tree search by the probed answers’ hidden state similarity and perplexity. Experiments on three reasoning tasks across two LLMs show that APTS consistently enhances solution diversity, demonstrating its effectiveness and robustness.
%U https://aclanthology.org/2026.emnlp-main.1383/
%P 31571-31587
Markdown (Informal)
[Answer Probing-Guided Search for Diverse Solution Exploration of LLMs](https://aclanthology.org/2026.emnlp-main.1383/) (Fang et al., EMNLP 2026)
ACL
- Yi Fang, Que Shen, Chengpeng Li, Boyi Deng, Wei Shi, Wenjie Wang, Fuli Feng, Fengli Xu, and Dayiheng Liu. 2026. Answer Probing-Guided Search for Diverse Solution Exploration of LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31571–31587, Budapest, Hungary. Association for Computational Linguistics.