Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation

Hugging Face Daily Papers Papers
llm-agents procedural-memory benchmark cross-task-transfer enterprise-tasks skill-transfer

Summary

Introduces AFTER, a benchmark of 382 enterprise tasks to evaluate procedural memory in LLM agents, showing that skill transfer improves performance across tasks, roles, and model backbones, with some skills generalizing broadly while others specialize.

Procedural memory is increasingly used to improve LLM agents on recurring workplace tasks, yet its ability to produce reusable skills remains poorly understood. We introduce AFTER, a benchmark of 382 realistic enterprise tasks spanning six professional roles and 22 procedural skills, designed to evaluate how skills transfer across tasks, roles, and model backbones. The benchmark includes controlled evaluation settings for local improvement, cross-task transfer, cross-role transfer, and cross-model generalization. Experiments show that procedural memory delivers consistent gains in industrial workflows: a single refinement round improves aggregate performance by 3.7-6.7 points, while skills evolved from diverse multi-model execution traces achieve 73.1% cross-model test accuracy, outperforming all single-model trace sources. We further find that some skills generalize broadly across tasks and models, whereas others become specialized to role-specific workflows and lose effectiveness under transfer. These results provide practical guidance for building, evaluating, and deploying procedural memory systems in production agent platforms.
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Paper page - Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation

Source: https://huggingface.co/papers/2606.23127

Abstract

Procedural memory enhances LLM agents on workplace tasks through skill transfer across roles and models, with varying generalization capabilities affecting deployment strategies.

Procedural memoryis increasingly used to improveLLM agentson recurring workplace tasks, yet its ability to produce reusable skills remains poorly understood. We introduce AFTER, a benchmark of 382 realisticenterprise tasksspanning six professional roles and 22procedural skills, designed to evaluate how skills transfer across tasks, roles, and model backbones. The benchmark includes controlled evaluation settings for local improvement,cross-task transfer,cross-role transfer, andcross-model generalization. Experiments show thatprocedural memorydelivers consistent gains in industrial workflows: a single refinement round improvesaggregate performanceby 3.7-6.7 points, while skills evolved from diversemulti-model execution tracesachieve 73.1% cross-model test accuracy, outperforming all single-model trace sources. We further find that some skills generalize broadly across tasks and models, whereas others become specialized to role-specific workflows and lose effectiveness under transfer. These results provide practical guidance for building, evaluating, and deployingprocedural memorysystems in production agent platforms.

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