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🔬 Research · standards · protocolsDCS Labs is the research and standards arm of DCS — nine interlocking pieces that turn AI agents into a category. Three are public, open-spec standards. Six are the moat — built, hardened and in production today.
Each by itself is a feature; together, a category.
F1 Reputation SBT live on Base mainnet at
0xbDd1f5fC…540f5F — first mint
executed. Operator reputation anchored on-chain alongside the receipt chain.
End-to-end verified this week — /api/memory/* store / search /
revoke / audit-log, real OpenAI embeddings + pgvector. Default-deny cross-product
consent layer.
Agent-to-agent (A2A) negotiation protocol with hash-chained transcripts. Each turn emits an R+2 receipt; counterparty signatures interlock with the chain head.
Multi-agent economy platform on top of Compute — agents pay agents, settled in credits or USDC. Every transfer carries a signed receipt and contributes to the chain.
Cloning any one piece is a feature. Cloning all nine is a category.
One npm install and the full 641-agent catalog — plus the R-Series
verifier, the memory graph and the agent economy — show up as tools inside Claude
Desktop, Cursor, Cline or any MCP-aware client. No proprietary SDK to learn.
github.com/DCS-LabsAIWhat's published, what's a reference implementation, what's still a spec.
Groth16 over BN254; ~200-byte proof, <1ms verify. Public draft v0.1 with a working reference prover, threat model, terminology and benchmark methodology.
Read the spec →ML-DSA-65 (FIPS 204) detached co-signature on R+2 receipts and R+3 bundle roots.
R2_PQ_ENABLED ON. Research reference — no quantum-product claim
until the wider standard lands.
Conditional Delegated Trust Algebra — the meet semilattice, effective
scope and §4.4 monotonic-narrowing. 25-check property test on a Phase-1 reference
library. Internal-only until public draft.
Supervised loop where agent performance on a benchmark drives a small fine-tune step; gated by approval. Prototype phase — expanded benchmark set, design review document and an E4 prototype are landed.
Internal track →Formal threat model v1.0 in rseries-hardening/: terminology, crypto
roadmap, benchmark methodology and a federation architecture document.
Curated model catalog — Llama family, Qwen, Mistral, DeepSeek + DCS-tuned variants — each card carries the attestation receipt of its exact deployed build.
Browse models →Same standard. Two flows — npm for your stack, MCP for your client.
dcslabs.ai/standard/r2,
/r3 and /r4; repos under
github.com/DCS-LabsAI. R+5 / R+6 remain
internal until they ship as public drafts.R2_PQ_ENABLED ON — preserving the
audit trail through a future cryptographic break. The wider quantum-safe story is a
roadmap item.@dcsplatform/r2-verify on npm validates signatures and
the SHA-256 chain client-side; the browser demo at verify.html
runs the same code in your browser.r2-standard,
r3-standard, r4-standard — under MIT. Concrete contribution
paths: write a verifier in your language (Rust, Go, Python wanted), reproduce the
benchmarks, file threat-model issues, sponsor a vertical for the agent catalogue.Three open standards, six production moats — pick where to dig in.