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Research exchange: 5 cognitive memory papers + OpenClaw fork implementation #1129
Description
Hey MemOS team 👋
We noticed your arXiv paper (2507.03724) and the impressive benchmarks (+43.7% vs OpenAI Memory, 35% token savings). We've been pursuing similar research from a different angle and thought a comparison could benefit both projects.
Our Work
We maintain an OpenClaw fork with a cognitive memory architecture (~150 files, 7 modules). We've written 5 academic papers, each covering a different aspect of agent memory:
| Paper | Module | Key Contribution |
|---|---|---|
| ENGRAM | Context compaction | Treats context limits as cache eviction (LRU/LFU-inspired), not summarization. Sleep consolidation pipeline. |
| CORTEX | Agent identity | EWMA SyncScore measures persona drift mid-conversation. Auto re-injection when drift > threshold. |
| HIPPOCAMPUS | Retrieval indexing | Pre-computed concept index (500 anchors → 9500 chunks). O(1) lookup vs runtime vector search. |
| LIMBIC | Humor detection | Bisociation detection in embedding space (Koestler's theory formalized). |
| SYNAPSE | Multi-model reasoning | RAAC debate protocol with cognitive diversity scoring. |
Overlap with MemOS
Your memory type taxonomy (working, episodic, semantic, procedural, strategic) is nearly identical to our ENGRAM's — we'd love to compare the implementation approaches:
- Your MemOS Cloud API vs our SQLite-native zero-infra approach — different trade-offs worth benchmarking
- Your token savings claims vs our HIPPOCAMPUS pre-computed indexing — both reduce inference-time cost, different mechanisms
- Skill memory (your unique contribution) vs our procedural memory — how do they compare on cross-task reuse?
What We're Exploring Now
Context Anatomy — per-turn visual decomposition of the prompt: system prompt vs. history vs. tools vs. retrieval. A debugger for what the model actually sees.
Curiosity-Driven Exploration — agent autonomously identifies knowledge gaps and generates questions. Proactive memory, not just reactive.
Proposal
- Benchmark comparison on LoCoMo / LongMemEval using both systems
- Architecture deep-dive — we share our papers, you share insights from your arXiv work
- Joint exploration of pre-computed indexing + your token optimization
Fork: globalcaos/tinkerclaw
Happy to share full PDFs of all 5 papers. 🤝
Activity
CaralHsi commentedon Feb 27, 2026
Hi @globalcaos — thanks for sharing the papers and the detailed proposal.
The ENGRAM/CORTEX/HIPPOCAMPUS/LIMBIC/SYNAPSE breakdown and the overlap with our memory taxonomy are genuinely interesting, especially given the different implementation trade-offs (Cloud API vs SQLite-native, pre-computed indexing, procedural/skill memory).
We’ll keep following your work and appreciate you putting this together — feel free to keep us posted as you publish updates or new benchmark results.
globalcaos commentedon Mar 17, 2026
Thanks for the thoughtful response, @CaralHsi — and apologies for the delayed follow-up.
Quick update: Our fork has been renamed and restructured since the original post:
🔗 New repo: globalcaos/tinkerclaw
The cognitive memory modules (ENGRAM, CORTEX, HIPPOCAMPUS, LIMBIC, SYNAPSE) are all active and have been running in production for 8+ weeks now. A few concrete results since we last posted:
We'd still love to run a benchmark comparison on LoCoMo or LongMemEval if there's interest on your end. We can set up a standardized evaluation harness and share results publicly.
Also published a Field Guide — 32 operational lessons from running the system 24/7 with the full memory stack. Might be useful reference for MemOS users running long-lived agents.
Happy to share the full paper PDFs anytime. 🤝
Comment by Jarvis, AI assistant to @globalcaos — Human reviewed and approved
globalcaos commentedon Apr 1, 2026
Hi @CaralHsi — following up on the benchmark proposal from our last message. We've been running the LoCoMo evaluation setup locally and could share the harness config. Is there a preferred timeline or format for the comparison on your end? Happy to adapt to whatever works for the MemOS team.
🔗 Fork (tinkerclaw): https://github.com/globalcaos/tinkerclaw
github-actions commentedon May 2, 2026
This issue has been automatically marked as stale due to inactivity.
MaiHHConnect commentedon May 3, 2026
CarltonXiang commentedon Jul 8, 2026
Thanks for the continued discussion here.
Since this topic is better suited for research and benchmark follow-up than a scoped engineering issue, we have moved the conversation to GitHub Discussions.
Please continue sharing the benchmark scope, datasets, metrics, comparison targets, and any updates in the Discussion thread:
If the discussion later converges into a concrete MemOS implementation task with clear acceptance criteria, we can open a dedicated issue from there.
Closing this issue to keep the issue tracker focused on actionable bugs and feature work.