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

ExpeL

LLM agents are experiential learners. AAAI 2024.

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
LLM agents as experiential learners paper fix
Tier
T3
Section
Recent method papers — theorized, no distinct product
Created
2023-08-20 (arxiv 2308.10144 submitted August 20 2023; AAAI 2024) 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
vector
retrieval
similarity
persistence
cross-session
update
extraction
unit
episode
governance
inspectable
conflict
llm-arbitrate

When to use

Optimised for: not applicable - research paper

Anti-fit: not applicable - research paper

Pros & cons

Pros

Defines LLM agents as experiential learners — explicit experience-extraction across tasks; AAAI 2024.

Cons

Extraction quality bounded by base model; experience storage scales with task count.

Claims & capabilities

LLM agent extracts knowledge as natural language from training tasks without parameter updates; in-context learning over accumulated experiences; AAAI 2024. Headline: 59% success on ALFWorld, beating ReAct (40%) and matching/exceeding Reflexion without task reattempts; baselines: ReAct and Act agents; primary datasets: HotpotQA (100 val), ALFWorld (134 solvable), WebShop (100), FEVER.

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 (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 (1)

  • Reflexion cites — S2 isInfluential citation

Referenced by (4)