CL of LLMs Survey
https://dl.acm.org/doi/10.1145/3735633
Comprehensive survey. ACM Computing Surveys 2025. Anchor reference for the continual-learning-of-LLMs subfield.
At a glance
- Type
- Continual learning of LLMs (survey)
- Tier
- T3
- Created
- 2024-04
- Latest release
- no releases
- License
- Unlicensed
- GitHub
- 542★
- Pricing
- not applicable — research paper
- Funding
- not applicable — not commercial
Taxonomy
- storage
- n/a
- retrieval
- n/a
- persistence
- n/a
- update
- n/a
- unit
- n/a
- governance
- n/a
- conflict
- n/a
When to use
Optimised for: not applicable - research paper
Anti-fit: not applicable - research paper
Pros & cons
Pros
Comprehensive survey of continual learning specifically for LLMs.
Cons
Survey-only; no new method; recommendations are general.
Claims & capabilities
Comprehensive survey of continual learning of LLMs covering vertical (general->specific) and horizontal (across time/domains) continuity; structured around Continual Pre-Training, Domain-Adaptive Pre-training, and Continual Fine-Tuning.
Technical surface
- API surface
- not applicable — research paper
- Backend storage
- not applicable — research paper
- Deployment
- not applicable — research paper
- 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.
Related systems
References (2)
- EWC (Elastic Weight Consolidation) cites — S2 isInfluential citation
- LoRA cites — S2 isInfluential citation