Memory-R1
https://aclanthology.org/2026.acl-long.583/
Uses RL (PPO/GRPO) to train a Memory Manager that learns ADD/UPDATE/DELETE/NOOP operations and an Answer Agent that filters retrieved memories. Beats heuristic memory baselines on LoCoMo, MSC and LongMemEval from just 152 training QA pairs. Traction: ACL 2026 long paper; LMU/Oxford/TUM; widely cited RL-for-memory work
Active · Verified 2026-10-23 · fresh as of 2026-10-23 · 7 cited facts, 0 with a third-party source · 1 recorded change · Suggest a correction
At a glance
- Tier
- T3
- Created
- searched not found fix
- Latest release
- searched not found fix
- GitHub
- not applicable — no GitHub repo fix
- Pricing
- searched not found fix
- Funding
- searched not found fix
- Freshness
- 2026-10-23 fix
- Layer
- agent-infra repo fix
Memory taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- long-term
- update
- agent-controlled
- unit
- fact
- governance
- plaintext
- conflict
- llm-arbitrate
When to use
Optimised for: searched not found
Anti-fit: searched not found
Pros & cons
Pros
searched not found
Cons
searched not found
Claims & capabilities
Sikuan Yan , Xiufeng Yang , Zuchao Huang , Ercong Nie , Zifeng Ding , Zonggen Li , Xiaowen Ma , Jinhe Bi , Kristian Kersting , Jeff Z. Pan , Hinrich Schuetze , Volker Tresp , Yunpu Ma
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
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- Embedding model
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- Multi-tenancy
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- MCP
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- A2A
- searched not found
- OpenTelemetry
- searched not found
Similar systems
Other recent method papers — theorized, no distinct product in the catalog, ranked by inbound references.
- Compressive Transformer T3
Maintains recent states in full resolution while compressing older memories with learned compression functions. DeepMind.
- MemGPT v2 / agent-tools T3
Already in catalog as the foundational MemGPT paper. Note: Letta is the productionised successor (cross-listed).
- Transformer-XL T3
Extends context through segment-level recurrence + caching of hidden states from prior segments. Foundational long-context architecture.
- Generative Agents T3
Park et al. — landmark agent-simulation paper. Reflection + memory stream + retrieval enable believable agent behavior.
- MemoryBank T3
Enhances LLMs with long-term memory. Early influential paper.
- Reflexion T3
Language agents with verbal reinforcement learning.
Sources (6 cited facts, 0 third-party)
Each fact links to the page it came from; the badge says who published it (how to read sources).
- What it is Paper vendor
- What it does & what's distinct Uses RL (PPO/GRPO) to train a Memory Manager that learns ADD/UPDATE/DELETE/NOOP operatio… vendor
- Claims / benefits Sikuan Yan , Xiufeng Yang , Zuchao Huang , Ercong Nie , Zifeng Ding , Zonggen Li , Xiaow… vendor
- License unknown vendor
- Project & sources https://aclanthology.org/2026.acl-long.583/ vendor
- taxonomy-three-layer-rollup agent-infra repo
What changed
Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)
- 2026-10-23 Added to the catalog #222