Why Coherent AI Is Harder Than It Looks

The promise of large language models (LLMs) often dazzles with the ability to stitch together coherent narratives from disparate information. But a new study highlights a key flaw: these models can fail basic probability tests even when each part of their output seems locally sound.

Local Soundness, Global Chaos

Imagine trying to assemble a jigsaw puzzle where each piece fits perfectly in its small cluster but the whole picture is skewed. That's essentially what's happening with multi-component LLM agents. Each component processes part of a problem, but the final composition can violate probability axioms. This discrepancy is formalized as the compositional residual, or eps*, which measures how far off the mark the assembled claims are from a coherent whole.

In real-world terms, the research found eps* deviations in 33% to 94% of agent groupings across 1,876 test scenarios. That's not just a rounding error. It's like a GPS consistently steering us slightly off course, not enough to crash, but enough to lead us astray over time.

The Cost of Incoherence

Why should this matter? Because it’s all about trust. If these models are used in critical domains like finance or healthcare, small errors can accumulate into significant risks. The study quantified this 'regret', a measure of lost potential, in betting scenarios. Even with corrective measures, bettors incurred a 0.115 nats per bet regret, a metric of inefficiency that echoes broader implications.

Attempted fixes, like improved prompting or retrieving more relevant data, largely floundered. In practice, these band-aid solutions either failed outright or regressed. So, what's the real fix?

What's Next for AI?

Is it time to rethink how we build and deploy these systems? Absolutely. While hierarchical projections like the Boyle-Dykstra method offer some salvation, they’re not a silver bullet. They repair after the fact rather than preventing the initial fumble.

AI needs to evolve from simply mimicking coherence to understanding context deeply and holistically. It's not about whether AI can sound human. We need it to reason like one too. So, when will AI finally close the gap between local soundness and global coherence? That's the billion-dollar question.

Mobile money came first. AI is the second wave. But it seems this wave has a few rocks to navigate around before it can truly change the landscape.