@inproceedings{bitan-etal-2026-latent,
title = "Latent Reasoning Guidance for Parallel Code Translation",
author = "Bitan, Tomer and
Kaplan, Erel and
Bar-Yadin, Roee and
Ghrayeb, Lian and
Chen, Le and
Jhaveri, Samyak and
Hasabnis, Niranjan and
Oren, Gal",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2359/",
pages = "50714--50727",
ISBN = "979-8-89176-462-0",
abstract = "Tackling complex coding tasks often requires autonomous agents and iterative repair pipelines. These increasingly rely on large amounts of test-time computation, often spending many decoding and repair steps before discovering whether a program compiles, runs, or validates. Executable parallel-code translation is an effective setting for earlier guidance because success is behavioral rather than textual. However, most guidance methods act only after complete programs or textual traces are decoded. This motivates the question: can latent reasoning provide an earlier intervention point, before the model commits to code? We study a test-time latent guidance method for this setting that trains a smaller Process Reward Model (PRM) over continuous latent prefixes and uses it to select among alternate hidden-state trajectories before final code decoding, separately from but compatible with post-decoding optimization. On a 76-task ParaTrans benchmark evaluation, latent PRM guidance improves mean validation rate from 32.89{\%} with unguided latent reasoning to 42.1{\%}, outperforming fine-tuned and vanilla baselines in the same setting. These gains persist under the same three-iteration repair loop. These results provide bounded evidence that useful alternative latent continuations exist and that PRM-scored latent branch selection can improve executable outcomes in this setting without retraining the main generative model."
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<abstract>Tackling complex coding tasks often requires autonomous agents and iterative repair pipelines. These increasingly rely on large amounts of test-time computation, often spending many decoding and repair steps before discovering whether a program compiles, runs, or validates. Executable parallel-code translation is an effective setting for earlier guidance because success is behavioral rather than textual. However, most guidance methods act only after complete programs or textual traces are decoded. This motivates the question: can latent reasoning provide an earlier intervention point, before the model commits to code? We study a test-time latent guidance method for this setting that trains a smaller Process Reward Model (PRM) over continuous latent prefixes and uses it to select among alternate hidden-state trajectories before final code decoding, separately from but compatible with post-decoding optimization. On a 76-task ParaTrans benchmark evaluation, latent PRM guidance improves mean validation rate from 32.89% with unguided latent reasoning to 42.1%, outperforming fine-tuned and vanilla baselines in the same setting. These gains persist under the same three-iteration repair loop. These results provide bounded evidence that useful alternative latent continuations exist and that PRM-scored latent branch selection can improve executable outcomes in this setting without retraining the main generative model.</abstract>
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%0 Conference Proceedings
%T Latent Reasoning Guidance for Parallel Code Translation
%A Bitan, Tomer
%A Kaplan, Erel
%A Bar-Yadin, Roee
%A Ghrayeb, Lian
%A Chen, Le
%A Jhaveri, Samyak
%A Hasabnis, Niranjan
%A Oren, Gal
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F bitan-etal-2026-latent
%X Tackling complex coding tasks often requires autonomous agents and iterative repair pipelines. These increasingly rely on large amounts of test-time computation, often spending many decoding and repair steps before discovering whether a program compiles, runs, or validates. Executable parallel-code translation is an effective setting for earlier guidance because success is behavioral rather than textual. However, most guidance methods act only after complete programs or textual traces are decoded. This motivates the question: can latent reasoning provide an earlier intervention point, before the model commits to code? We study a test-time latent guidance method for this setting that trains a smaller Process Reward Model (PRM) over continuous latent prefixes and uses it to select among alternate hidden-state trajectories before final code decoding, separately from but compatible with post-decoding optimization. On a 76-task ParaTrans benchmark evaluation, latent PRM guidance improves mean validation rate from 32.89% with unguided latent reasoning to 42.1%, outperforming fine-tuned and vanilla baselines in the same setting. These gains persist under the same three-iteration repair loop. These results provide bounded evidence that useful alternative latent continuations exist and that PRM-scored latent branch selection can improve executable outcomes in this setting without retraining the main generative model.
%U https://aclanthology.org/2026.findings-emnlp.2359/
%P 50714-50727
Markdown (Informal)
[Latent Reasoning Guidance for Parallel Code Translation](https://aclanthology.org/2026.findings-emnlp.2359/) (Bitan et al., Findings 2026)
ACL
- Tomer Bitan, Erel Kaplan, Roee Bar-Yadin, Lian Ghrayeb, Le Chen, Samyak Jhaveri, Niranjan Hasabnis, and Gal Oren. 2026. Latent Reasoning Guidance for Parallel Code Translation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 50714–50727, Budapest, Hungary. Association for Computational Linguistics.