



Individual Submission                                            K. Tsoi
Internet-Draft                                             Aevum Network
Intended status: Informational                               19 May 2026
Expires: 20 November 2026


    WLP: Wet-Lab Protocol -- AI-to-Automation Instruction Schema v0
                           draft-aevum-wlp-00

Abstract

   This document specifies WLP v0, the Wet-Lab Protocol for translating
   AI hypotheses (PACR records) into machine-executable wet-lab
   instructions.  WLP is the longevity-research analogue of SCP (Science
   Context Protocol) from the photoresist automation domain.  The
   protocol is royalty-free (Apache-2.0).  Reference implementations in
   Rust (crates/wlp-conformance) and Python
   (agentcard_adapters.wlp_conformance) are provided.

Status of This Memo

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   This Internet-Draft will expire on 20 November 2026.

Copyright Notice

   Copyright (c) 2026 IETF Trust and the persons identified as the
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  WLP-instruction Schema v0.1 . . . . . . . . . . . . . . . . .   2
   3.  WLP-outcome Schema  . . . . . . . . . . . . . . . . . . . . .   3
   4.  AgentCard Extension . . . . . . . . . . . . . . . . . . . . .   4
   5.  Conformance Requirements  . . . . . . . . . . . . . . . . . .   4
   6.  Execution Flow  . . . . . . . . . . . . . . . . . . . . . . .   5
   7.  Lambda Throughput Ratio . . . . . . . . . . . . . . . . . . .   5
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   9.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Introduction

   WLP closes the loop between AI hypothesis generation and physical
   experimental execution without human translation.  Each WLP-
   instruction carries a PACR provenance link and a lambda (energy)
   budget, enabling full cost accounting and falsifiability tracing.

   Key design principles: (1) AI-to-Machine direct translation -- WLP-
   instructions are machine-executable; (2) PACR traceable -- every
   instruction has a pacr_record_id provenance link; (3) lambda budget
   constrained -- platforms MUST reject over-budget instructions; (4)
   Conformance verifiable -- any platform can run the Conformance Suite.

2.  WLP-instruction Schema v0.1
























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{
  "wlp_version": "0.1",
  "instruction_id": "WLP-{gene}-{yyyymmdd}-{seq}",
  "target": {
    "gene": "FOXO3",
    "itt_l_id": "ITT-L-001",
    "h_itt_v2": 0.72
  },
  "intervention": {
    "type": "crispr_knockdown | compound | overexpression | sirna | small_molecule",
    "reagent": "sgRNA-FOXO3-001",
    "dose": {"value": 10, "unit": "nM | uM | mg/kg"},
    "window": {"start_day": 0, "end_day": 14}
  },
  "readout": {
    "primary": "S_T_delta",
    "secondary": ["lifespan_extension_pct", "healthspan_marker"],
    "model": "C_elegans | mouse | organoid | cell_line",
    "timepoint_days": [7, 14, 30]
  },
  "expected": {
    "S_T_delta": 0.05,
    "confidence": 0.65,
    "basis": "superlearner_dqn_v2"
  },
  "constraints": {
    "lambda_budget_j": 500,
    "max_duration_days": 30,
    "min_replicates": 3
  },
  "provenance": {
    "generated_by": "II:superlearner-meta-v1",
    "pacr_record_id": "pacr-discovery-v3:line-XXXXXX",
    "wlp_conformance": "v0.1"
  }
}

3.  WLP-outcome Schema













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   {
     "instruction_id": "WLP-FOXO3-20260519-001",
     "executed_by": "wet-lab-node-001",
     "execution_date": "2026-05-26",
     "result": {
       "S_T_delta_observed": 0.048,
       "S_T_delta_expected": 0.050,
       "prediction_accuracy": 0.96,
       "replicates": 3,
       "p_value": 0.023
     },
     "lambda_actual_j": 420,
     "falsification_triggered": false,
     "notes": ""
   }

4.  AgentCard Extension

   Wet-lab nodes MUST declare capability via AgentCard extension fields:

   {
     "execution_capability": ["crispr-screen", "organoid-assay",
                               "model-organism", "compound-screen"],
     "wlp_version": "0.1",
     "throughput": {
       "assays_per_week": 10,
       "lambda_capacity_j_per_week": 5000
     }
   }

5.  Conformance Requirements

   An implementation is conformant if it passes all 5 tests.  Reference
   test vectors are in crates/wlp-conformance/src/lib.rs.

















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             +===================+===========================+
             | Test              | Acceptance Criterion      |
             +===================+===========================+
             | schema_valid      | WLP-instruction has all 8 |
             |                   | required top-level fields |
             +-------------------+---------------------------+
             | pacr_traceable    | provenance.pacr_record_id |
             |                   | is non-empty              |
             +-------------------+---------------------------+
             | lambda_bounded    | lambda_actual_j <=        |
             |                   | lambda_budget_j           |
             +-------------------+---------------------------+
             | outcome_parseable | WLP-outcome has all 5     |
             |                   | required fields           |
             +-------------------+---------------------------+
             | s_t_delta_range   | S_T_delta_observed in     |
             |                   | [-1.0, 1.0]               |
             +-------------------+---------------------------+

                                  Table 1

6.  Execution Flow

   Superlearner DQN
     -> generate WLP-instruction
     -> write to ~/.eon/bus/wlp-queue.jsonl
           |
           v
   wlp_dispatcher.py
     -> match wet-lab-node AgentCard (execution_capability filter)
     -> send WLP-instruction to node
           |
           v
   Automated wet-lab platform
     -> execute experiment
     -> return WLP-outcome.json
           |
           v
   wlp_outcome_bridge.py
     -> convert to PACR record (domain="wet_lab")
     -> write to ~/.eon/pacr-wet-lab-v1.jsonl

7.  Lambda Throughput Ratio

lambda_throughput_ratio = PACR_virtual_hypotheses / WLP_executed_instructions

Target (Day 90): filtered hypotheses (H_ITT_v2 >= 0.5) / WLP count >= 1000x




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8.  IANA Considerations

   This document has no IANA actions.

9.  Security Considerations

   WLP-instructions carry lambda budgets that MUST be enforced by
   execution platforms to prevent resource exhaustion.  PACR provenance
   links MUST be validated before execution.

Author's Address

   Kwai Lap Tsoi
   Aevum Network
   Email: kwailapt@gmail.com
   URI:   https://github.com/kwailapt/EON



































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