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

arXiv:2609.02336 · cs.AI, cs.CL · Submitted 2026-09-02 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-02

Updated: 2026-09-02

Comments: Accepted for publication in Findings of EMNLP 2026

Code: https://github.com/ByteDance-Seed/seed-oss

License: http://creativecommons.org/licenses/by/4.0/

The gist: Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations.

Terminology

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.

Sources

Related papers