Physics-constrained blockchain agents in decentralized energy markets can detect and mitigate hallucination propagation in multi-agent LLM systems by enforcing thermodynamic consistency checks on gas distribution network data, improving trustworthiness metrics by ≥25% in SolarChain-Eval benchmarks.

In a decentralized energy-market simulation where multiple LLM agents propose gas/power distribution actions, augmenting a blockchain consensus layer with physics-constrained validators (enforcing mass/energy conservation, Weymouth gas-flow equations, and nodal pressure/temperature bounds on claimed network states) will detect ≥90% of synthetically injected LLM hallucinations (fabricated network states violating thermodynamic consistency) and will improve an aggregate "trustworthiness score" (composite of factual consistency, decision accuracy, and downstream cascading-error rate) on a SolarChain-Eval-style benchmark by ≥25% relative to an unconstrained multi-agent LLM baseline, at matched task-completion latency (≤2× baseline) and consensus overhead (≤500ms added per block).

Design: 2×2×3 factorial simulation study.

• Factor A: Validator type (physics-constrained blockchain validator vs. no validator / vs. naive statistical-anomaly validator baseline)
• Factor B: LLM backbone (GPT-4o-class vs. open-weight Llama-3.1-70B-class) to test generalization across hallucination profiles
• Factor C: Hallucination injection rate (5%, 20%, 40%) to test dose-response and boundary robustness
• Outcome measures: (1) hallucination detection precision/recall/F1, (2) SolarChain-Eval trustworthiness composite (factual consistency + decision accuracy + cascading-error rate), (3) consensus/validation latency (ms/block), (4) false-positive rate on legitimate edge-case states.
• N = 30 independent simulation runs per cell (5,400 total agent-decision episodes minimum) to power a two-sided test for a 25% relative effect size at α=0.05, power=0.8 (estimated via bootstrap given non-normal latency/error distributions).
• Pre-registration of the 25% improvement threshold and detection-rate threshold BEFORE running full-scale experiments (minimum viable test first, see Phase 0 below).

# Phase 0: Minimal viable test (Days 1-5)
gas_net = load_gaslib("GasLib-40")
checker = WeymouthConstraintValidator(gas_net)
agent_out = llm_agent.propose_flow_state(gas_net, prompt_template)
injected = inject_hallucinations(agent_out, rate=0.2, seed=42)
labels = injected.ground_truth_flags
preds = [checker.is_consistent(s) for s in injected.states]
f1 = compute_f1(labels, preds)
assert f1 > 0.6, "ABORT: base physics-check formulation not discriminative"

# Phase 1-3: Full multi-agent blockchain simulation
class PhysicsConstrainedValidatorContract:
    def validate_transaction(self, agent_action, network_state):
        residual = weymouth_residual(agent_action, network_state)
        energy_balance = check_conservation(agent_action, network_state)
        pressure_bounds = check_nodal_bounds(agent_action)
        violation_score = weighted_sum(residual, energy_balance, pressure_bounds)
        return violation_score < THRESHOLD  # tunable, calibrated on held-out non-hallucinated data

for seed in range(30):
    for llm in [gpt4o, llama3_70b]:
        for validator in [none, naive_statistical, physics_constrained]:
            for injection_rate in [0.05, 0.20, 0.40]:
                agents = spawn_agents(n=10, llm_backbone=llm)
                env = EnergyMarketSimEnv(gas_net, power_net, blockchain_testnet)
                trace = env.run_episode(agents, validator, injection_rate, seed)
                metrics = score_trustworthiness(trace, solarchain_eval_rubric)
                log_results(metrics, config=(seed, llm, validator, injection_rate))

# Analysis
mixed_effects_model(outcome=trustworthiness_score,
                     fixed=["validator_type","llm_backbone","injection_rate"],
                     random=["seed"])
bootstrap_ci(effect="physics_constrained vs none", n_boot=10000)