NRP Implementation Status

monad.ai v2.1.1 — current local implementation status

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What Is Implemented

The current NRP work is no longer only an HTTP resolver. It is a mesh-aware runtime path:

1. Parse me://namespace:read/path
2. Discover monads that claim the namespace
3. Filter by optional selector constraints
4. Score eligible monads
5. Forward to the selected endpoint
6. Learn from the request outcome
7. Explain and optionally log the decision

This is the current operational loop:

request
  → parseBridgeTarget
  → findMonadsForNamespaceAsync
  → matchesMeshSelector
  → computeScoreDetailed
  → fetch selected monad
  → recordForwardResult
  → correlateOutcome
  → analyze-decisions

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Phase Summary

Phase	Status	Result
Phase 1	done	Namespace reads/writes and claim/open lifecycle
Phase 2	done	Self mapping, monad identity, __surface, disclosure envelopes
Phase 3	done	Mesh index, /.mesh/monads, /.mesh/resolve, bridge forwarding
Phase 4	done	Selector-aware mesh routing ([device:macbook], [host:edge])
Phase 5	done	Production scoring engine with open .me claim metadata
Phase 5.5	done	Score introspection, runner-up tracking, margin logging
Phase 5.6	done	Decision log, biased sampling, offline analyzer
Phase 6	done	Continuous reward and epsilon-greedy exploration for fragile choices
Phase 7	done	Global adaptive weights and live /.mesh/weights observability
Phase 8	done	Patch bay: controlled feature composition with learned weights
Phase 9	done	Namespace-local weights with maturity blending and global background learning

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Current Runtime Surfaces

Endpoint	Purpose
GET /<path>	Read a path in the namespace selected by Host
POST /	Write/claim/open depending on body operation
GET /__surface	Self description and monad continuity proof
GET /.mesh/monads	List locally known and CLI-known monads
GET /.mesh/resolve?namespace=...	Discover monads claiming a namespace
GET /.mesh/resolve?monad=frank	Discover a monad by name/id
GET /.mesh/weights	Inspect global adaptive scorer weights
GET /.mesh/weights?namespace=...	Inspect namespace-local weights and blended request weights
GET /resolve?target=me://ns:read/path	Bridge a canonical NRP target

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Current Selection Model

The selector is not part of the semantic path. It constrains execution.

me://frank.local[device:macbook]:read/profile

Resolution:

1. semantic namespace: frank.local
2. semantic path: profile
3. execution constraint: device:macbook
4. winner: highest scored eligible monad, unless low-margin exploration fires

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Current Learning Model

The fast index remains simple:

claimed_namespaces: string[]
tags: string[]
type: "desktop" | "mobile" | "server" | "browser-tab" | "node"

The open .me subtree holds rich learned metadata:

_.mesh.monads.<id>.claimed.<namespace> = {
  resonance: 42,
  effectiveResonance: 31.5,
  avgLatencyMs: 85,
  forwardCount: 100,
  failureCount: 4,
  geopoliticalZone: "mx-east",
  energyProfile: "low-power"
}

The index answers: who could answer?

The scoring engine answers: who should answer?

Adaptive weights now use two layers:

_.mesh.adaptiveWeights          global prior
_.mesh.nsWeights.<namespace>    namespace-local posterior

Reads blend them by namespace maturity:

maturity = min(1, sampleCount / 200)
weights = global * (1 - maturity) + namespace * maturity

Writes split the same reward gradient:

globalShare = max(0.05, 1 - maturity)
nsShare = maturity

The global prior remains alive at full namespace maturity, so cross-namespace trends still propagate.

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Operational Flags

Env var	Default	Effect
MONAD_MESH_STALE_MS	300000	Exclude stale monads from mesh claim selection
MONAD_DEBUG_SCORING	unset	Log every scoring decision when set to 1
MONAD_SCORE_SAMPLE_RATE	0	Randomly log a fraction of scoring decisions
MONAD_SCORE_MARGIN_THRESHOLD	0.05	Always log fragile low-margin decisions
MONAD_EXPLORATION_RATE	0	Route some fragile decisions to runner-up
MONAD_DECISION_LOG	unset	JSONL path for correlated decision outcomes
MONAD_LEARNING_QUALITY_WEIGHT	0.7	Quality/latency split in reward
MONAD_DEBUG_WEIGHTS	unset	Log adaptive weight changes after every forward

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Verification

Current regression suite:

19 test files
154 tests

Primary commands:

npm run build
npm test
npm run docs:api

Decision analysis:

MONAD_DECISION_LOG=~/.monad/decisions.jsonl npm run dev
tsx scripts/analyze-decisions.ts ~/.monad/decisions.jsonl

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Not Yet Implemented

These remain design targets, not production guarantees:

• multi-hop mesh traversal
• distributed cross-machine monad index sync
• .me-defined scoring pipeline
• auto-tuning of weights from reward history
• durable decision log beyond JSONL
• challenge/nonce validation for stronger monad proof freshness