Challenge: Existing methods to generate intermediate steps (CoT) are limited by the maximum context size due to various reasons.
Approach: They propose a new inference framework that introduces several special tokens that the models can output to trigger context-related operations.
Outcome: Extensive experiments with multiple architectures including GPT-3 show that the proposed framework significantly improves LMs’ inference capability.

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Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision (2025.findings-emnlp)

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Challenge: Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks.
Approach: They propose a chain-of-thought framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance.
Outcome: The proposed framework generalizes across most long-context scenarios and amplifys with increasing context length.
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (2023.emnlp-main)

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Challenge: Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes.
Approach: They propose a two-step reasoning framework based on prompt tuning to implement step-by-step thinking for MLMs on NLU tasks.
Outcome: The proposed framework outperforms baselines and achieves state-of-the-art performance on two NLU tasks.
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs (2026.findings-acl)

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Challenge: Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but is vulnerable to exposure bias and error accumulation.
Approach: They propose a diffusion-styled CoT framework that reformulates CoT reasoning as an iterative denoising process.
Outcome: The proposed framework outperforms existing methods on three multi-step CoT reasoning benchmarks.
GoT: Effective Graph-of-Thought Reasoning in Language Models (2024.findings-naacl)

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Challenge: Recent advances in Large Language Models (LLMs) have been advancing at an unprecedented pace.
Approach: They propose a graph-based approach which models human thought processes as a chain and as 'graphs' by representing thought units as nodes and connections between them as edges, they capture the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes.
Outcome: The proposed model improves on a text-only reasoning task and a multimodal reasoning task.
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)

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Challenge: Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT).
Approach: They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions.
Outcome: The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks.
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models (2023.emnlp-main)

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Challenge: Chain-of-Thought (CoT) prompting relies on the initial decisions, causing errors in early steps to accumulate and impact the final answers.
Approach: They propose a divide-and-conquer style algorithm that leverages large language models to raise and answer sub-questions until collecting enough information to tackle the original one.
Outcome: The proposed algorithm is more robust to errors and errors than CoT prompting and Tree-of-Thought prompting methods.
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)

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Challenge: Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer.
Approach: They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs).
Outcome: The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation.
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models (2024.findings-naacl)

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Challenge: Chain-of-thought (CoT) prompting is a technique to enhance the reasoning abilities of Large language models (LLMs) however, the reasoning chains of demonstrations are observed to be prone to errors, which can lead to incorrect reasoning during inference.
Approach: They propose an iterative bootstrapping technique to enhance the reasoning abilities of Large language models (LLMs) by generating a series of reasoning steps to obtain the answer, and using the reasoning chains as exemplars to demonstrate the task.
Outcome: The proposed method improves the performance of Large language models (LLMs) on three reasoning tasks on ten datasets.
A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (2024.findings-naacl)

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Challenge: Existing methods for eliciting Large Language Models (LLMs) to solve complex tasks are limited to English due to the imbalance in the distribution of pre-training data.
Approach: They propose a method for aligning Cross-lingual CoT reasoning across languages . they propose eliciting Large Language Models to solve complex tasks step-by-step .
Outcome: The proposed method outperforms existing prompting methods by reducing interactions and achieving state-of-the-art performance.
ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting (2024.lrec-main)

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Challenge: Existing CoT synthesis approaches focus on simpler reasoning tasks and result in inconsistent CoT prompts.
Approach: They propose a framework for automatic generation of superior CoT prompts based on three major evolution strategies . they propose 'step-level debating' method where multiple debaters discuss each reasoning step to arrive at the correct answer.
Outcome: The proposed framework can generate superior CoT prompts from a CoT dataset.

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