Wayve GAIA-2 / GAIA-3
https://wayve.ai/thinking/gaia-3/
All surround-camera views encoded through video tokeniser to continuous latent; past latent sequences serve as explicit temporal context via space-time factorised transformer. GAIA-3 (15B params, 2× GAIA-2 size, 10× training data) adds safety-critical scenario generation + embodiment transfer.
At a glance
- Type
- Multi-view latent diffusion world model
- Tier
- T1
- Created
- 2025-03
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- searched not found
- Funding
- $2.5B total Series D · 2026-02
Taxonomy
- storage
- vector
- retrieval
- attention
- persistence
- session
- update
- append-only
- unit
- episode
- governance
- opaque
- conflict
- append
When to use
Optimised for: real-time perception + spatial reasoning + multi-modal sensor fusion
Anti-fit: not for non-embodied / non-physical use cases
Pros & cons
Pros
Generative world model trained on real driving data — memory is the world simulation itself; strong generalization across geographies.
Cons
Autonomous driving scope only; closed weights; production deployment in vehicles still in pilot.
Claims & capabilities
5× lower synthetic-test rejection rate vs GAIA-2 (Wayve internal eval).
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Research/internal tool — not sold separately
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- no MCP support advertised — vertical product, no MCP server / client integration documented
- A2A
- no A2A protocol support advertised — vertical product, no A2A integration documented
- OpenTelemetry
- no OpenTelemetry integration advertised — vendor logs/observability not publicly documented
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Other vertical / domain-specific ai memory in the catalog, ranked by inbound references.
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