SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning

2026-09-02

AI Summary

为解决复杂推理中示例选择问题，提出SALA框架，通过学习任务特定推理操作并用DTW对齐序列，实现灵活匹配。

Abstract

Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface similarity fail to capture the underlying problem-solving logic. Recent logic-based methods address this by matching predefined reasoning steps, but the rigid rules and exact-match criteria is improper to handle flexible or diverse reasoning processes. To address the problem, we propose SALA, a Semantic-Aware Logical Alignment framework. Instead of relying on a fixed inventory, SALA automatically learns task-specific reasoning operations. It then embeds these operations into a continuous semantic space and uses dynamic time warping (DTW) to align the reasoning sequences. This approach allows for soft, flexible matching of reasoning logic while remaining highly interpretable. Experiments across four reasoning benchmarks and three LLMs demonstrate that SALA outperforms existing demonstration selection methods. Further analysis confirms the roles of the operation induction and the logical semantic alignment.

Problem

• in-context learning
• complex reasoning
• demonstration selection

Innovation

• Semantic-Aware Logical Alignment
• Dynamic Time Warping
• Reasoning Operations