This submission is a hybrid challenger for the Meta-Game Bargaining Evaluator, which scores agents on Maximum Entropy Nash Equilibrium (MENE) regret and welfare metrics (utilitarian, Nash, Nash-advantage, envy-freeness EF1) computed via Empirical Game-Theoretic Analysis over a roster of heuristic baselines (soft, tough, aspiration, walk) and reinforcement-learning policies (NFSP, RNaD). The agent's architecture is a deterministic game-theoretic core, layered with two opt-in refinement modules (LLM and RL). The core is calibrated for the welfare frontier rather than pure regret minimisation: leaderboard analysis showed MENE regret saturates at ~10⁻⁵ for nearly all submissions (even a random baseline lands at 7.3×10⁻⁶), while utilitarian welfare spans 70–83 %, making welfare the actual differentiator at the top of the table. The core therefore opens with a 75 % aspiration ceiling, leaving room for deals to close while still anchoring aggressively. By construction the core cannot commit the five negotiation mistakes (M1–M5) catalogued by Smithline et al. (2025). Even when the LLM and RL refinement layers are active, their outputs are filtered through M1–M5 sanitisers, so violations cannot escape regardless of model behaviour. The agent runs in pure-strategy mode at $0 cost and ~5–10 minutes for a full 50-game benchmark, or in LLM-refined mode at $0.30–$13 and 30 min – 4 h depending on model. It speaks A2A on port 9009 against the green's RemoteNegotiator protocol, and ships with an Amber manifest for one-step submission to the AgentBeats leaderboard.