Catalog Systems Research / specialised systems Titans (Google)

Titans (Google)

https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/

Neural long-term memory module that learns to memorise at test time. Uses gradient-of-loss as "surprise" signal; adaptive weight-decay forgetting. Three variants: MAC (memory-as-context), MAG (memory-as-gate), MAL (memory-as-layer).

Active · Verified 2026-10-28 · fresh as of 2026-10-28 · 20 cited facts, 0 with a third-party source · Suggest a correction

At a glance

Type
Neural fast/slow + surprise gating vendor fix
Tier
T4
Section
Research / specialised systems
Created
2025-01 paper fix
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Pricing
not applicable — not commercial fix
Funding
not applicable — not commercial fix
Freshness
2026-10-28 fix
Layer
agent-infra repo fix

Memory taxonomy

storage
parametric
retrieval
attention
persistence
session
update
parametric-edit
unit
kv-token
governance
opaque
conflict
n/a

When to use

Optimised for: research positioning (see memory_model)

Anti-fit: not applicable - research paper

Pros & cons

Pros

Google's response to Mamba — long-term memory module learns at test time; widely discussed in 2025.

Cons

Closed weights; reproducibility limited; benchmark coverage narrower than published claims suggest.

Claims & capabilities

Scales to 2M+ tokens. Reports outperforming Transformers + recent linear-recurrent models on language modeling, common-sense reasoning, genomics, and time series.

Technical surface

API surface
not applicable — research paper
Backend storage
not applicable — research paper
Deployment
not applicable — not a deployable product
Embedding model
not applicable — research paper
Multi-tenancy
not applicable — research paper
MCP
not applicable — research paper, no deployed product
A2A
not applicable — research paper, no deployed product
OpenTelemetry
not applicable — research paper, no deployed product

Similar systems

Other research / specialised systems in the catalog, ranked by inbound references.

  • A-MEM T3

    Treats memories as atomic linkable notes — explicit nod to Zettelkasten knowledge management. Dynamic linking; retroactive memory revision.

  • BAI-LAB MemoryOS T3

    Hierarchical "OS" with Storage / Updating / Retrieval / Generation modules. Short-term → mid-term via FIFO dialogue-chain; mid-term → long-term via segmented paging.

  • EverMemOS T4

    Self-organizing memory OS for structured long-horizon reasoning. Three-phase model: episodic, semantic, reconstructive.

  • EVOLVE-MEM T3

    Dynamic Memory Network + Hierarchical Memory Manager + Self-Improvement Engine. L0 raw embeddings, L1 contextual summaries, L2 high-level principles. NeurIPS 2025 (Scaling Environments for Agents workshop).

  • LiCoMemory T4

    Lightweight hierarchical graph (CogniGraph) with entities and relations as semantic indexing layers. Incremental graph construction, fast updates, low-latency inference. Nov 2025.

  • MemoRAG T3

    RAG framework on top of a long-context memory model. Builds global memory once, generates contextual clues at query time. TheWebConf 2025.

Sources (19 cited facts, 0 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

Related systems

References (5)

Referenced by (3)