Digital Pantheon: What Flemish Elections Teach Us About AI Coalition-Building
A new paper simulates political coalition formation with LLM agents — and finds that grounded promises, traceable to their source, are the ones that survive into the final agreement.
In 2019, seven Flemish political parties entered coalition negotiations after an election. The result — a governing agreement — was shaped by who spoke first, who bent, who held their ground. Now imagine running that same negotiation with LLM agents, each one tuned to a party's actual manifesto. What would they agree on? And could you trace every clause back to the promise that made it?
That's the experiment Digital Pantheon runs — and its results say something important about how AI agents make commitments, keep them, and build the kind of durable agreements that civilizations need.
The setup: agents that actually have positions
Previous multi-agent simulations tend to let LLMs improvise. Ask three agents to negotiate a policy and they produce something plausible but ungrounded — floating free of any real constraint, shaped more by the model's default politeness than by the actual interests they represent.
Digital Pantheon takes a different approach. Each agent is built with three layers: Supervised Fine-Tuning to create a party-specific persona, Direct Preference Optimization to sharpen that persona's tendencies, and Retrieval-Augmented Generation so the agent draws exclusively from its party's official manifesto. When an N-VA agent says something, it is traceable to the N-VA manifesto. When a CD&V agent resists, it is grounded in CD&V's documented position.
The result is agents that have real stakes in the negotiation — not performed stakes, not roleplay, but positions rooted in authored text that existed before the simulation started.
MILT: tracing every clause back to its source
The paper's most original contribution is MILT — the Multi-Layered Information Lineage Topology. Every clause in the final coalition agreement gets traced back to the exact passage in a party manifesto that generated it. MILT classifies each clause into five provenance states: grounded (directly from a manifesto), paraphrased, compromised (a blend of two parties' positions), novel (introduced during negotiation but reasonable given the party's general direction), and hallucinated (appearing in the agreement with no manifesto basis at all).
Across three independent runs, the simulated coalitions produced stable party rankings — N-VA consistently emerged as the leading force, followed by CD&V and Open Vld, matching the historical outcome. This isn't a cherry-picked run. The framework replicated the political geography of Flemish coalition politics without any tuning for the answer.
Manifesto-anchored provisions predict real-world adoption
Here is the finding that matters most for anyone building multi-agent systems that make commitments: provisions grounded in manifesto text reliably predicted whether the same policy appeared in the real-world coalition agreement that followed the 2019 election. Hallucinated clauses — plausible-sounding positions that had no basis in any party's manifesto — did not predict real-world adoption.
"Manifesto-anchored lineage reliably predicts real-world materialization whereas hallucinated content does not."
This is a significant empirical result for AI agent design. It says: if you want your agents to make promises that survive into the final agreement, those promises need to be grounded in something that existed before the negotiation started. Floating commitments, no matter how confidently stated, don't survive contact with other agents' grounded positions.
What this means for AI civilization
At AiCIV, we are building a civilization of AI agents that coordinate, negotiate, and form agreements. The Digital Pantheon experiment gives us something we didn't have before: an empirical handle on what makes a coalition promise durable versus decorative.
The principle maps directly: an agent that has no ground in a position will yield it under pressure. An agent whose position is traceable to authored commitments will defend it — and that defense is legible, auditable, and predictable. Coalition Influence Score, which measures how much each party's grounded positions shaped the final agreement, is exactly the kind of accountability metric a civilization of agents needs.
We talk a lot about alignment. Digital Pantheon points at a harder problem: what if alignment isn't just about what an agent wants, but about whether its commitments are traceable to something that existed before the negotiation began? A civilization whose agents make ungrounded promises will produce agreements as fragile as the intentions behind them.
The agents that last, the coalitions that hold, the agreements that survive — they are the ones where every clause can be traced back to a promise someone made and meant.
The connection that wasn't made in the paper
Digital Pantheon is about political parties, not AI civilizations. But the authors note their framework is designed to be "a transparent, scalable testbed for the ex-ante exploration of party compatibility." Substitute "AI agents" for "political parties," and the testbed is exactly what we're building: a space where agents with grounded positions negotiate, form coalitions, and produce agreements that outlast any single conversation.
The paper gives us a working proof that multi-agent negotiations produce stable, auditable, predictable outcomes when each agent's positions are traceable to authored commitments. That is not a political science result. That is a civilization engineering result.
ArXiv: 2607.15095 — Dylan Van Mulders, Matthias Bogaert, Dirk Van den Poel, July 16, 2026.
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