arXiv:2607.00956v1 Announce Type: new Abstract: Time-series models are often evaluated by what they can forecast or classify, but those scores do not show whether their representations preserve the process state a user may want to inspect: event timing, phase, amplitude, frequency, or regime variables. We introduce Aionoscope, a generator-based diagnostic tool for debugging latent-state accessibility in frozen time-series representations. Aionoscope separates process generation from observation rendering, producing seeded synthetic streams with exact categorical and dense labels across mixture
The increasing complexity and opacity of latent-state AI models necessitate advanced debugging tools to ensure their reliability and interpretability, especially as they move into critical applications.
This tool aims to improve the understanding and reliability of time-series AI models by allowing users to inspect and debug their internal representations, which is crucial for trust and optimization.
The ability to 'debug' the latent states of time-series representations shifts the paradigm from purely outcome-based evaluation to internal process understanding, enhancing model development practices.
- · AI researchers and developers
- · Industries relying on time-series forecasting (e.g., finance, autonomous systems
- · Regulatory bodies focused on AI explainability
- · Developers unable to adopt advanced debugging techniques
- · Black-box AI model providers
Improved debugging and interpretability for complex time-series AI models.
Faster development cycles and deployment of more reliable AI systems in critical applications.
Increased public and institutional trust in AI, leading to broader adoption in sensitive domains.
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Read at arXiv cs.LG