Catalog Systems Recent method papers — theorized, no distinct product WorkMATe

WorkMATe

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316453

Kruijne et al. RL-learned gating policies open/close multiple working-memory slots independently. Handles hierarchical 12-AX task with multiple concurrent context levels; all-or-nothing slot updates.

Active · Verified 2026-10-28 · fresh as of 2026-10-28 · 19 cited facts, 1 with a third-party source · Suggest a correction

At a glance

Type
Bio-inspired RL-gated memory slots vendor fix
Tier
T3
Section
Recent method papers — theorized, no distinct product
Created
2021-01 (Neural Computation 33(1):1-40; published January 2021) third-party 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
kv-cache
retrieval
attention
persistence
session
update
agent-controlled
unit
kv-token
governance
n/a
conflict
n/a

When to use

Optimised for: not applicable - research paper

Anti-fit: not applicable - research paper

Pros & cons

Pros

Explicit working-memory framework with managed buffer.

Cons

Research-stage; less popular than implicit context-window approaches.

Claims & capabilities

Working memory model with complex gated memory stores for sensory stimuli updated all-or-nothing; output module gates whether stimuli are encoded or discarded; comparison model in PLOS ONE study, where authors note WorkMATe stored stimuli override previous content making memorize/forget less flexible than RECOLLECT

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 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 (18 cited facts, 1 third-party)

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