Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

2026-06-29

AI Summary

Large language models (LLMs) acting as agents lack persistent procedural memory, hindering their ability to reliably execute tasks over extended interactions. This work proposes a training-free, implicit activation-guidance framework that, for the first time, models procedural memory as guidance vectors in the model’s activation space. By distilling task-specific skills from historical experiences through contrastive learning, the approach directly activates relevant internal neural mechanisms without relying on explicit instructions. This design circumvents the semantic gap between symbolic instructions and executable actions, enabling tight coupling between memory and execution while remaining complementary to explicit methods such as retrieval-augmented generation (RAG). Experiments demonstrate that the proposed method achieves performance on par with explicit instruction-based approaches across four agent benchmarks; when combined with such methods, it significantly enhances robustness. Moreover, the guidance vectors exhibit structured task logic within the activation space.

Abstract

While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires continuous environmental interaction, yet current agents lack the necessary persistent procedural memory. Existing approaches predominantly employ Retrieval-Augmented Generation (RAG) to inject explicit textual guidelines into model contexts. However, relying solely on symbolic instructions can introduce a text-action disconnect, frequently failing to activate the internal representations necessary for correct task execution. To address this, the paper introduces Neural Procedural Memory (NPM), a training-free framework that represents agent memory through implicit activation steering rather than explicit instructions. By distilling procedural skills from historical contrastive experiences into steering vectors in the activation space, NPM directly activates the task-relevant neural mechanisms to guide task execution. Evaluations across four agent benchmarks show that NPM performs comparably to baselines using explicit textual instructions. Furthermore, the results show that combining implicit steering with explicit workflows provides complementary advantages, leading to more robust task execution. Representational analyses indicate that these steering vectors encode consistent task logic, forming organized structures within the activation space. These findings suggest that implicit activation steering provides a promising approach for managing agent memory.

Problem

• procedural memory
• large language models
• agent memory
• activation steering
• text-action disconnect

Innovation

• Neural Procedural Memory
• Implicit Activation Steering
• Activation Space
• LLM Agents
• Procedural Memory