Papers by Sophia Simeng Han
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Recent advances on prompting and post-training have enabled LLMs to perform step-wise reasoning tasks, but they tend to explore unproductive solution paths without effective backtracking or strategy adjustment. |
| Approach: | They propose a framework that empowers LLMs to “think about how to think” and dynamically adapts reasoning strategies in real-time. |
| Outcome: | The proposed framework outperforms previous SOTA methods by 9-12% in accuracy while reducing inference time by 28-35% under the same compute budget. |
Evaluating Legal Reasoning Traces with Legal Issue Tree Rubrics (2026.acl-long)
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| Challenge: | Evaluating the quality of LLM-generated reasoning traces in expert domains is essential for ensuring credibility and explainability, yet remains challenging due to the inherent complexity of such reasoning tasks. |
| Approach: | They propose a large-scale legal reasoning dataset with an emphasis on reasoning trace evaluation that converts court judgments into hierarchical trees of opposing parties’ arguments and the court’s conclusions. |
| Outcome: | The proposed model improves the quality of LLM-generated reasoning traces in legal domains, whereas RL improves correctness albeit with reduced coverage. |
CourtReasoner: Can LLM Agents Reason Like Judges? (2025.emnlp-main)
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Sophia Simeng Han, Yoshiki Takashima, Shannon Zejiang Shen, Chen Liu, Yixin Liu, Roque K. Thuo, Sonia Knowlton, Ruzica Piskac, Scott J Shapiro, Arman Cohan
| Challenge: | Large language models (LLMs) have achieved impressive results across mathematical, logical, and commonsense reasoning tasks. |
| Approach: | They propose a novel expert-annotated judicial reasoning benchmark to measure LLMs' ability to construct goal-oriented legal reasoning. |
| Outcome: | The proposed benchmark measures the LLM agent’s ability to construct goal-oriented legal reasoning. |