Papers with transparent reasoning process
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation (2022.acl-long)
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| Challenge: | Existing approaches to interpret task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. |
| Approach: | They propose a neuro-symbolic approach that performs explicit reasoning that justifies model decisions by reasoning chains. |
| Outcome: | The proposed approach achieves better results and introduces an interpretable decision process. |
AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) lack the end-to-end optimization needed to learn a coherent strategy from market feedback. |
| Approach: | They propose a single-agent framework that uses reinforcement learning to learn a dynamic policy over a transparent decision workflow. |
| Outcome: | The proposed framework achieves state-of-the-art performance on key financial metrics. |
CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning (2025.emnlp-main)
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| Challenge: | Mathematical reasoning remains a significant challenge for large language models (LLMs), despite advances in prompting techniques such as Chain-of-Thought (CoT). |
| Approach: | They propose a framework that enhances reasoning through two stages: Symbolic Conversion and Reasoning Execution. |
| Outcome: | The proposed framework outperforms traditional CoT on six out of seven benchmarks across four LLMs. |