Satisfiable Drift: The Silent Saboteur of Multi-Turn Reasoning Systems

In the field of artificial intelligence, the expectation has often been that a multi-turn reasoning system would most likely stumble through logical contradiction. You'd think these systems would trip over their own unsatisfiable states, right? Well, think again. Recent insights reveal a different kind of failure lurking beneath the surface: satisfiable drift, where the systems remain internally consistent while quietly breaching past commitments.

Introducing DRIFT-Bench

Enter DRIFT-Bench, a new benchmark designed to spotlight these subtle failures. Comprising 816 test problems that span three different constraint domains, this tool is as comprehensive as it's revealing. Evaluations of four different methods were conducted on models with parameters ranging from a modest 8 billion to a staggering 120 billion. The goal? To dissect the anatomy of failure in these systems and expose the true fault lines.

MUS-Repair: A Promising Solution?

In this landscape, one method stands out: MUS-Repair. This technique feeds minimal unsatisfiable subsets back into the generator, aiming to correct course. The results are impressive, with MUS-Repair achieving a 1.8 to 15.0 percentage point advantage over its non-MUS counterparts. Yet, despite its prowess, MUS-Repair reveals a stark truth. It cures the symptom, but not the underlying disease. Post-repair, models rarely contradict themselves, but they do something arguably worse, they forget.

The Silent Saboteur: Satisfiable Drift

Residual errors shifted almost entirely to satisfiable drift, a kind of forgetfulness that keeps the system's state consistent while violating its historical promises. Contradictions may have dropped to near zero, but is that really a victory if the cost is integrity? The burden of proof sits with the team, not the community. Reliable systems must now not only solve the problem but also validate that their answers respect their own internal logic.

Why It Matters

So, why should we care? Because the marketing promised us systems that wouldn't just work, but work reliably. If these AI models are to be trusted, especially in high-stakes environments, their outputs must be more than just consistent, they must be accountable. Skepticism isn't pessimism. It's due diligence. As the industry pushes forward, it must also look back to ensure its steps are true. Otherwise, we're merely building castles on sand.