Procedural Memory

APEX-EM combines a Procedural Knowledge Graph, Experience Memory store, PRGII workflow, Task Verifiers, and StructuralSignatureExtractor to store and reuse full procedural-episodic traces without changing model weights. On KGQAGen-10k, APEX-EM reaches 89.6% accuracy (95.3% CSR) versus 41.3% without memory and surpasses the GPT-4o w/ SP oracle at 84.9%.

Evaluating Long-Term Memory for Long-Context Question Answering compares Full Context, RAG, A-Mem, RAG+PromptOpt, and RAG+EpMem memory components across semantic, episodic, and procedural memory for long conversational QA. On LoCoMo, RAG+EpMem reaches an average F1 ranking of 1.83 for Llama 3.2-3B Instruct and 1.80 for GPT-4o mini while using around 1,000 tokens per query versus over 23,000 for Full Context.

Memp constructs agent skills via Build, Retrieve, and Update modules that turn past trajectories into scripts, trajectories, and combined proceduralizations stored in a procedural memory library. On ALFWorld, Memp’s proceduralization with GPT-4o reaches 77.86% test success versus 42.14% with no memory, while reducing steps from 23.76 to 15.01.

ReMe manages procedural memory through experience acquisition, experience reuse, and experience refinement, combining multi-faceted distillation, context-adaptive reuse, and utility-based deletion into a single lifecycle. On BFCL-V3 and AppWorld, Qwen3-8B with ReMe (dynamic) achieves 34.94% Avg@4 vs 27.65% for the No Memory baseline, and 55.03% Pass@4 vs 46.20%, showing that self-evolving memory can substitute for model scale.