Decentralized energy market agents with physics-constrained context management will reduce invalid physical data exploitation by 40% while maintaining market utility, by enforcing Stackelberg-game-derived carbon penalty thresholds as architectural memory filters.

In a simulated decentralized energy market (multi-agent double-auction with N ≥ 50 heterogeneous buyer/seller agents including at least 15% adversarial agents), replacing a baseline unconstrained LLM/RL context manager with a physics-constrained context manager — which filters memory writes/reads using Stackelberg-game-derived carbon penalty thresholds computed from grid physical constraints (line limits, power balance, ramp rates) — will reduce the rate of successful invalid-physical-data exploitation attacks (defined as accepted trades or agent decisions based on data violating power-flow feasibility, e.g., AC/DC power balance, voltage/thermal limits) by ≥40% relative to baseline, measured as (attacks_succeeded_baseline − attacks_succeeded_treatment)/attacks_succeeded_baseline, while market utility (social welfare, clearing efficiency, or total traded volume) does not degrade by more than 5% relative to baseline.

Minimum viable test: a controlled A/B simulation using a synthetic decentralized energy market testbed (agent-based model on top of a DC-OPF-feasible grid topology, e.g., IEEE 33-bus or 123-bus distribution test feeder) with two arms — Baseline Agent Architecture (no physics filter, standard context memory) vs. Treatment Architecture (physics-constrained context manager with Stackelberg-derived carbon penalty thresholds as memory admission/retention filters). Run N=30 randomized trials per arm per adversary-strength level (0%, 5%, 15%, 30% adversarial agents), each trial simulating 500 trading rounds. Measure exploitation success rate, market utility, and computational overhead.

# 1. Stackelberg penalty derivation
def derive_carbon_penalty_threshold(grid_topology, carbon_intensity_ts, follower_utility_fn):
    # Leader (operator) optimizes penalty rate tau to minimize
    # expected grid violation + carbon overshoot, anticipating
    # follower (agent) best-response via backward induction
    tau_star = solve_bilevel_stackelberg(
        leader_obj=grid_violation_and_carbon_cost,
        follower_best_response=follower_utility_fn,
        constraints=OPF_feasibility(grid_topology)
    )
    return tau_star  # threshold vector, per line/bus/time

# 2. Physics-constrained context manager
class PhysicsConstrainedMemory:
    def __init__(self, opf_checker, tau_star):
        self.opf_checker = opf_checker
        self.tau_star = tau_star
        self.buffer = []

    def admit(self, data_entry):
        feasibility_violation = self.opf_checker.violation_magnitude(data_entry)
        penalty = feasibility_violation * self.tau_star[data_entry.zone]
        if penalty > REJECTION_THRESHOLD:
            self.log_rejected(data_entry, penalty)
            return False
        self.buffer.append((data_entry, penalty))  # penalty-weighted retention
        return True

    def retrieve(self, query):
        # downweight high-penalty entries in retrieval scoring
        return rank_by_relevance_minus_penalty(self.buffer, query)

# 3. Agent loop (baseline vs treatment differ only in memory class)
for round in trading_rounds:
    for agent in agents:
        obs = market.get_observation(agent)
        agent.memory.admit(obs)          # <- filtered in treatment, pass-through in baseline
        context = agent.memory.retrieve(query=agent.state)
        action = agent.policy(context)
        market.submit(action)
    market.clear()
    log_metrics(exploitation_success, welfare, rejections)