Catalog Systems Recent method papers — theorized, no distinct product Dual-Process Agent (DPA)

Dual-Process Agent (DPA)

https://www.mdpi.com/2079-9292/15/6/1232

System 1: fast retrieval path that pulls compressed, relevant memories and conditions generation. System 2: slow reflection pass that evaluates outcomes and writes curated updates back through a conservator gate. No model-weight changes.

Active · Verified 2026-05-14 · fresh as of 2026-05-14 · 19 cited facts, 0 with a third-party source · Suggest a correction

At a glance

Type
Kahneman System 1 / System 2 vendor fix
Tier
T3
Section
Recent method papers — theorized, no distinct product
Created
2026-03-16 (MDPI Electronics 15(6) 1232; published March 16 2026) vendor 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
2026-05-14 fix
Layer
agent-infra repo fix

Memory taxonomy

storage
vector
retrieval
similarity
persistence
long-term
update
consolidation
unit
summary
governance
inspectable
conflict
llm-arbitrate

When to use

Optimised for: not applicable - research paper

Anti-fit: not applicable - research paper

Pros & cons

Pros

System-1 / System-2 dual-process model applied to LLM agents.

Cons

Dual-process framing is descriptive; production tradeoffs against single-loop agents are unclear.

Claims & capabilities

Outperforms vanilla prompting on six benchmarks with both GPT and Llama backbones. MDPI Electronics 2025.

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, 0 third-party)

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