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

ELDER

Enhances lifelong model editing with mixture-of-LoRA. AAAI 2025.

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
Mixture-of-LoRA model editing paper fix
Tier
T3
Section
Recent method papers — theorized, no distinct product
Created
2024-08 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
parametric-recall
persistence
parametric-permanent
update
parametric-edit
unit
fact
governance
opaque
conflict
llm-arbitrate

When to use

Optimised for: not applicable - research paper

Anti-fit: not applicable - research paper

Pros & cons

Pros

Mixture-of-LoRA approach for lifelong model editing — adds task-specific LoRA adapters; AAAI 2025.

Cons

Adapter count grows with task count; adapter routing decisions are model-dependent.

Claims & capabilities

Mixture-of-LoRA for lifelong model editing with router network and continuous data-adapter mapping; AAAI-25. Headline: 96.08 generalization on ZsRE with GPT2-XL, outperforming 8 baselines by over 10%; baselines: GRACE and MELO (plus 6 others); primary datasets: ZsRE and CounterFact (1000 sequential edits with semantic rephrasings).

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.

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  • 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).