PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning

2026-08-03

AI Summary

This work addresses the limitations of existing in-context learning approaches, which treat prompts merely as semantic cues and thus fail to enable task-adaptive dynamic computation, resulting in shallow and uninterpretable reasoning. To overcome this, the authors propose PromptPath, a novel framework that directly integrates prompt information into the model’s inference architecture. PromptPath employs a prompt-conditioned routing mechanism to dynamically activate and compose lightweight low-rank expert modules, thereby constructing task-specific computational pathways. This approach achieves dynamic adaptation at the computational level, significantly outperforming current methods on both 3D point cloud and 2D visual recognition benchmarks while demonstrating strong cross-domain and cross-task generalization capabilities.

Abstract

In-context learning (ICL) has attracted increasing attention for enabling models to perform new tasks using only a few ``input--output'' prompt examples. However, existing approaches suffer from \textbf{shallow task adaptation}, where prompts are primarily used as contextual cues to implicitly infer task intent through semantic representations, while the underlying computational process remains unchanged. This limitation restricts task-specific adaptation and compromises inference interpretability. We argue that prompts should not only condition feature representations but also dynamically regulate the model's computation pathways. To this end, we propose \textbf{PromptPath}, an adaptive ICL framework that enables computation-level adaptation through prompt-conditioned dynamic pathways. Specifically, PromptPath introduces a prompt-driven routing mechanism to selectively activate and compose lightweight low-rank experts, forming task-specific computational pathways tailored to different prompts. By integrating prompt information directly into the inference process, PromptPath dynamically reconfigures model computation to enhance task specialization and interpretability. Extensive experiments on 3D point cloud and 2D visual recognition benchmarks demonstrate that PromptPath consistently outperforms state-of-the-art ICL baselines while exhibiting strong cross-domain and cross-task generalization.

Problem

• in-context learning
• task adaptation
• computational pathways
• prompt conditioning
• model interpretability

Innovation

• Prompt-Adaptive Computation
• In-Context Learning
• Dynamic Routing
• Low-Rank Experts
• Computational Pathways