Locally Coherent, Globally Incoherent: Bounding Compositional Incoherence in Multi-Component LLM Agents
Anany Kotawala
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Key claim
Coherence failures in LLM agents can lead to significant performance loss.
This paper presents a novel approach to address the coherence failures in multi-component LLM agents. It introduces the concept of compositional residuals and provides empirical evidence of the effectiveness of proposed mitigations. The key finding is that coherence issues can significantly impact performance, with a notable regret metric observed.
In plain English
This paper presents a novel approach to address the coherence failures in multi-component LLM agents. It introduces the concept of compositional residuals and provides empirical evidence of the effectiveness of proposed mitigations. The key finding is that coherence issues can significantly impact performance, with a notable regret metric observed.
The paper introduces a new framework for understanding and addressing coherence issues in multi-component LLM agents.
The claims are supported by empirical results across a substantial number of ensemble cliques, though some limitations in the evaluation scope exist.
Deep reliability assessment
The methodology supports a formal, runtime certificate and deterministic projection repair for probabilistic incoherence when cross-component coupling constraints are explicitly declared as finite linear constraints. It is overclaimed if read as solving calibration, truthfulness, or free-form agent reliability, since ε⋆ only certifies coherence of the assembled probabilities and cannot be computed without recovering the coupling set C.
Reproducibility
No open-source code repository is mentioned in the provided abstract, introduction, results, limitations, or conclusion. The evaluation uses Paleka and Polymarket-derived cliques and resolved bets, but the implementation/harness is not linked.
Discussion questions
- 1.How realistic is the core assumption that the cross-component coupling set C is explicitly known in deployed agent systems, rather than implicit in prompts, tool traces, or natural-language plans?
- 2.For builders, should coherence projection be applied automatically before downstream decisions, or should large ε⋆ instead trigger re-routing, re-querying, or human review because projection may hide specialist disagreement?
- 3.What empirical result would falsify the paper’s central claim: low ε⋆ failing to reduce Dutch-book exposure, Rayleigh residual predictions breaking on broader relation classes, or LLM-side mitigations reliably eliminating incoherence without projection?
Key figure
Figure 1 shows four specialist LLM components independently assigning probabilities to mutually exclusive IPO-sector outcomes whose probabilities should sum to 1, but their assembled probabilities sum to 2.50, yielding a certified incoherence residual ε⋆ = 0.749.