arXiv:2607.06155v1 Announce Type: cross Abstract: Modern sequence models are increasingly deployed as agents that interleave token generation with calls to external tools. We give an exact, architecture-level account of when such tool access increases computational expressivity. We model any fixed finite-precision recurrent sequence model, including finite-precision state-space models (SSMs) with $B$ bits of internal state, as a deterministic finite-state controller interacting with an oracle through a finite command/observation interface. Our results form a sharp dichotomy. First, tools that
The proliferation of AI agents that integrate external tools into their operations necessitates a theoretical understanding of how such tool use enhances their computational capabilities.
Understanding the precise conditions under which tool use increases the expressive power of AI models is crucial for designing more capable and efficient AI systems, especially in agentic applications.
This research provides a foundational 'architecture-level account' of tool augmentation, shifting the conceptual landscape for developing finite-precision recurrent models with external interfaces.
- · AI agents developers
- · Applied AI researchers
- · Cloud service providers
- · Companies adopting AI agents
- · Inefficient AI model architectures
- · Companies neglecting tool-augmented AI
More robust and generalizable AI agents will become feasible and widespread.
Automation of complex white-collar tasks accelerates dramatically, impacting service industries.
The definition of 'intelligence' in AI systems expands to incorporate sophisticated resource and tool management, leading to new benchmarks and competitive landscapes.
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