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This paper proposes "generative interpretability" as a necessary architectural shift for agentic AI systems, arguing that post-hoc explanations are insufficient when model outputs trigger irreversible real-world actions. By integrating neuro-symbolic models, the research aims to create inference passes that natively expose human-understandable checkpoints capable of causal intervention before commitment. This approach directly addresses critical safety and trustworthiness challenges in the transition from chatbots to autonomous agents.
Generative Interpretability via Scalable Neuro-Symbolic Models
arXiv:2609.13529v1 Announce Type: cross Abstract: As the use of Large Language Models moves from chatbots into agentic systems, where outputs become actions with irreversible consequences on reality, the existing paradigm on AI Interpretability research, post-hoc interpretability, is structurally inadequate for safe and trustworthy model deployment: it explains behavior after the fact but cannot audit or intervene in an inference computation before it commits to an output. We therefore argue for a shift toward \emph{generative interpretability}, an architectural property under which a model's inference pass natively exposes semantically meaningful checkpoints that are human-understandable and amenable to causal intervention. We show the merits of generative interpretability as comparison to other interpretability research paradigms, and propose Neuro-Symbolic Models as a concrete instantiation.
Abstract:As the use of Large Language Models moves from chatbots into agentic systems, where outputs become actions with irreversible consequences on reality, the existing paradigm on AI Interpretability research, post-hoc interpretability, is structurally inadequate for safe and trustworthy model deployment: it explains behavior after the fact but cannot audit or intervene in an inference computation before it commits to an output. We therefore argue for a shift toward \emph{generative interpretability}, an architectural property under which a model's inference pass natively exposes semantically meaningful checkpoints that are human-understandable and amenable to causal intervention. We show the merits of generative interpretability as comparison to other interpretability research paradigms, and propose Neuro-Symbolic Models as a concrete instantiation.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Symbolic Computation (cs.SC)
arXiv:2609.13529 [cs.LG]
(or arXiv:2609.13529v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.13529 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3806096.3844850 Focus to learn more DOI(s) linking to related resources
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