This paper tackles the fundamental failure of Large Language Models (LLMs) to solve new tasks when prompted with a sufficient, yet overly complex, set of multi-modal episodes. This failure stems from the model's inability to distill underlying patterns from the noisy experiences. We propose Hypothesis-Driven Reasoning (HDR), a framework that enhances LLM reasoning by building an explicit semantic memory—a set of hypotheses induced from the multi-modal episodes. HDR employs a two-stage pipeline. It first extracts potential factors from the episodes and then iteratively refines hypotheses by generate-verify loop with the factors. We first empirically demonstrates this failure and the potential of sematic memory, showing that oracle hypotheses can boost accuracy from 35.3% to 92.0% on a novel task we designed. We then evaluate our HDR, achieving near-oracle performance and significantly outperforming baselines, especially on smaller models. This paper validates a shift from unstructured in-context recall to explicit knowledge abstraction for robust reasoning.

Aakash Kumar Agarwal, Saprativa Bhattacharjee, Mauli Rastogi, Jemima S. Jacob, Biplab Banerjee, Rashmi Gupta, Pushpak Bhattacharyya. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.