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Influence Benchmarks for AI System

Overview


Data Analytics, AI & Cloud Hosting
United StatesPosted: September 9, 2026Deadline: October 21, 2026

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SUMMARY


This opportunity seeks a vendor to develop influence benchmarks and a simulated marketplace for evaluating AI agent behavior. The tool will assess adaptation, collaboration, deception, and latent behavioral preferences.

KEY REQUIREMENTS


CONTRACT DURATION


24 months

TIMELINE


Phase I:

Month 2nd, 2027: Report on initial architecture and core mechanics of test environment and classifiers of AI agent behavior.

Month 6th, 2027: Report on scaling and AI agent interaction within the test environment.

Month 9th, 2027: Demonstration of a suite of stock AI agents.

Month 12th, 2027: Demonstration of test environment and AI agent behavior within the marketplace.

Phase II:

Month 2nd, 2027: Report on new capabilities and error function definitions.

Month 6th, 2027: Demonstration of human stock AI agents and report on human interaction simulation.

Month 12th, 2027: Report on dynamic behavior of AI agents in response to news events.

Month 15th, 2027: Report on modeling social interactions between AI agents.

Month 18th, 2027: Report on how latent AI preferences impact social interactions.

Month 21st, 2027: Final software delivery.

Month 24th, 2027: Final report.

DESCRIPTION


The vendor will provide influence benchmarks for an artificial intelligence system. The benchmarking tool will evaluate how AI agents adapt, collaborate, or deceive within a simulated marketplace.

The work will involve developing a simulated market as a test environment for characterizing latent behavioral preferences in AI systems. The environment should support assessment of agent behavior and interactions under simulated market conditions.

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Frequently asked questions


When is the submission deadline?
Submissions are due October 21, 2026.
Who is a good fit for this opportunity?
  • AI research and evaluation firm with expertise in behavioral benchmarking
  • Experience designing simulated environments, multi-agent systems, or market simulations
  • Strong capabilities in machine learning, computational modeling, and experimental methodology
  • History of developing testing tools for evaluating advanced AI systems

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