mlsanityData to 7 Oct 2026

PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations

arXiv 2609.07910 ↗First posted 7 Sep 2026cs.AIcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Multi-agent federations need governance that answers three questions under adversarial conditions: who participated (identity), did they conform (enforcement), and who decides (authority). A separate question is whether the verification machinery that polices a federation's outputs can also steer a generate-and-test loop toward better answers. Part I. PRIMA introduced prime-power agent identity and a consensus token whose factorization indexes participation, but assumed honest agents. We present PRIMUS, which couples prime-power identity with BLS aggregate signatures (PIAC), derives a safe-kill threshold that reduces false-positive agent termination from 80% to 0.00% under 10% channel noise, gives the closed-form economic boundary where singleton governance outperforms Byzantine quorum (γ^* ≈ 9f, verified flat across n = 50 to 10,000), and specifies VRF succession with lease and fencing that makes safety unconditional under partial synchrony. Five problems are identified as provably unfixable within the model and stated as scope boundaries. Part II. A verifier is not a solver. We ask whether PRIMA's binary artifact-fidelity verdict can be converted into a graded fitness signal, and measure the conversion on binary covering codes. Calibration against injected fault burden is strong (ρ = 0.676 deterministic, 0.819 full); against real LLM-generated candidates the same scores fall to 0.158 and 0.406, roughly a quarter of the calibration value (the same-designer confound, measured). As a pre-filter it beats a random-score control convincingly and a binary gate narrowly. Under 400 iterations of explicit optimization it was not gamed, but only because the objective saturated after one honest answer. A cross-family judge preserves the burden-ordering signal while destroying individual judgments. No covering-code record resulted. Measured program cost: USD 164.78.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 18 of its 29 references that are in the map. First posted on arXiv 7 Sep 2026.

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18 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganography12 Feb

Other references · 17

  1. 2023Let's Verify Step by Stepcited by 3,598
  2. 2022Scaling Laws for Reward Model Overoptimizationcited by 1,056
  3. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 895
  4. 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscationcited by 338
  5. 2025Spurious Rewards: Rethinking Training Signals in RLVRcited by 199
  6. 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
  7. 2024Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Ratescited by 42
  8. 2025Byzantine-Robust Decentralized Coordination of LLM Agentscited by 13
  9. 2026Robust Multi-Agent LLMs under Byzantine Faultscited by 10
  10. 2026Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hackingcited by 9
  11. 2026Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Modelscited by 8
  12. 2026Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systemscited by 6
5 more
  1. 2026Autonomous Research Agents: A Survey of AI Scientists and the Verification Gapcited by 6
  2. 2026Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systemscited by 3
  3. 2026Governance Gaps in Agent Interoperability Protocols: What MCP, A2A, and ACP Cannot Expresscited by 3
  4. 2026Exploring Pass-Rate Reward in Reinforcement Learning for Code Generationcited by 2
  5. 2026PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedbackcited by 2

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