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.

Active · Verified 2026-10-22 · fresh as of 2026-10-22 · 52 cited facts, 0 with a third-party source · Suggest a correction

At a glance

Tier
T1
Created
2025-03 paper fix
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Pricing
searched not found fix
Freshness
2026-10-22 fix
Layer
agent-infra repo fix
Licensing
commercial vendor fix

Memory 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

Similar systems

Other vertical / domain-specific ai memory in the catalog, ranked by inbound references.

  • Inworld AI T1

    Formerly an NPC character engine with named Long-Term Memory; now sells realtime voice models (TTS-2, STT-1, speech-to-speech) and OpenAI-compatible open-model inference. Character/NPC memory is no longer the headline product.

  • NVIDIA ReMEmbR T3

    Builds long-horizon memory by captioning video segments with VILA, storing captions with timestamps + 3D position coordinates in MilvusDB. At query time, LLM iterates retrieval across text, time, and position modalities. Deployed on Nova Carter robot (Jetson Orin).

  • Abridge T1

    Clinician-assist ambient documentation. Source mapping: every AI-generated summary element traced back to the source utterance. Audit-and-trust layer over episodic memory. Built on proprietary 1.5M+ medical-encounter dataset.

  • ASAPP GenerativeAgent T1

    Treats memory as first-class architecture. Captures the digital footprint of every interaction; retrieves preference and history at engagement time. Public example: airline knowing a frequent flyer wants aisle seats with her son — preference-aware, not just history-lookup.

  • BenevolentAI T1

    Taken private: merged into Osaka Holdings (renamed BenevolentAI S.A.) and delisted from Euronext Amsterdam 2025-03-13. Target identification / drug repurposing / mechanism tracing. 85+ data sources, petabyte-scale, rebuilt every few weeks. Wet-lab results re-enter the graph and shift downstream predictions — institutional experimental memory closing a feedback loop. As of Oct 2026 benevolent.com leads with Qor, powered by BenevolentAI, a life-science intelligence service for biopharma decision makers and consultancies. Qor (qor.one) is a continuously updated decision-intelligence service built on BenevolentAI's knowledge graph and reasoning methods: it tracks evidence on an asset or indication (competitive position, rival readouts, comparable deals), source-verifies each claim and flags when an earlier conclusion stops holding. It targets biotech teams and life-science consultancies.

  • Causaly T1

    Agentic AI platform for life sciences: orchestrated scientific agents over a curated biomedical knowledge graph (plus Bio Graph and Pipeline Graph), an evidence-retrieval system and an enterprise data fabric over literature, trials and patents.

Sources (51 cited facts, 0 third-party)

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