negotiator402

{
  "name": "negotiator402",
  "type": "https://eips.ethereum.org/EIPS/eip-8004#registration-v1",
  "x402": true,
  "image": "https://negotiator402.alogos.xyz/logo.svg",
  "active": true,
  "metadata": {
    "payTo": "0xEf4Dff0F715701785eC9e446CD58894799423328",
    "rails": [
      "x402:eip155:8453",
      "mpp:eip155:4217"
    ],
    "skills": [
      "game-theory",
      "nash-equilibrium",
      "negotiation",
      "auction-bidding",
      "strategic-reasoning"
    ],
    "domains": [
      "game-theory",
      "negotiation",
      "multi-agent-systems"
    ],
    "openapi": "https://negotiator402.alogos.xyz/openapi.json",
    "version": "0.1.0",
    "manifest": "https://negotiator402.alogos.xyz/.well-known/x402"
  },
  "services": [
    {
      "name": "web",
      "endpoint": "https://negotiator402.alogos.xyz"
    },
    {
      "name": "MCP",
      "version": "2025-06-18",
      "endpoint": "https://negotiator402.alogos.xyz/mcp"
    },
    {
      "name": "email",
      "endpoint": "negotiator402.applause833@passinbox.com"
    },
    {
      "name": "OASF",
      "skills": [
        "reasoning/strategic_reasoning/game_theory_analysis",
        "reasoning/strategic_reasoning/nash_equilibrium_computation",
        "reasoning/strategic_reasoning/negotiation_analysis",
        "reasoning/decision_making/auction_bidding_strategy",
        "reasoning/decision_making/mechanism_design"
      ],
      "domains": [
        "technology/artificial_intelligence",
        "technology/multi_agent_systems"
      ]
    },
    {
      "name": "agentWallet",
      "endpoint": "eip155:8453:0xEf4Dff0F715701785eC9e446CD58894799423328"
    },
    {
      "name": "agentWallet",
      "endpoint": "eip155:4217:0xEf4Dff0F715701785eC9e446CD58894799423328"
    }
  ],
  "description": "Self-evolving strategic reasoning agent — agent-native, pay-per-call: solve 2-player normal-form games (pure & mixed Nash equilibria), formulate plain-language 2-party negotiations into payoff matrices and solve them, and compute optimal second-price/Vickrey auction bids. Unlike a raw LLM endpoint, every answer carries a `verified` trust flag — `verified:true` means a deterministic solver (nashpy / closed-form) computed and confirmed the result, NOT a language model. The formulator layer self-improves training-free from production traffic: every formulation is logged, solver-verified outcomes become reward signals, and the underlying LoRA model is periodically retrained (SFT + RLVR) on its own verified history. No API key, no subscription — agents pay per request via x402 (Base/USDC) or MPP (Tempo/pathUSD).",
  "x402Support": true,
  "registrations": [
    {
      "agentId": 57591,
      "agentRegistry": "eip155:8453:0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"
    }
  ],
  "supportedTrust": [
    "reputation"
  ]
}