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ARXIV:2601.22352 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.22352AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure.
Opportunity summary
Pain Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure.
Evidence 0 refs | 0 sources | 17% coverage
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Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure. We present a predictive theory that resolves this gap by showing that recoverability follows a measurable…
Language model agents often appear capable of self-recovery after failing tool call executions, yet this behavior lacks a formal explanation. We present a predictive theory that resolves this gap by showing that recoverability follows…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results reveal that recoverability is not an artifact of model scale or architecture, but a governed property of interaction dynamics, providing a theoretical…
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure.
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10.48550/arXiv.2601.22352Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure.
Abstract
Language model agents often appear capable of self-recovery after failing tool call executions, yet this behavior lacks a formal explanation. We present a predictive theory that resolves this gap by showing that recoverability follows a measurable law. To elaborate, we formalize recoverability through Expected Recovery Regret (ERR), which quantifies the deviation of a recovery policy from the optimal one under stochastic execution noise, and derive a first-order relationship between ERR and an empirical observable quantity, the Efficiency Score (ES). This yields a falsifiable first-order quantitative law of recovery dynamics in tool-using agents. We empirically validate the law across five tool-use benchmarks spanning controlled perturbations, diagnostic reasoning, and real-world APIs. Across model scales, perturbation regimes, and recovery horizons, predicted regret under the ERR-ES law closely matched observed post-failure regret measured from Monte Carlo rollouts, within delta less than or equal to 0.05. Our results reveal that recoverability is not an artifact of model scale or architecture, but a governed property of interaction dynamics, providing a theoretical foundation for execution-level robustness in language agents.
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PROBLEM
Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure. We present a predictive theory that resolves this gap by showing that recoverability follows a measurable law.
METHOD
Language model agents often appear capable of self-recovery after failing tool call executions, yet this behavior lacks a formal explanation. We present a predictive theory that resolves this gap by showing that recoverability follows a measurable law.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results reveal that recoverability is not an artifact of model scale or architecture, but a governed property of interaction dynamics, providing a theoretical foundation for execution-level robustness...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure. We present a predictive theory that resolves this gap by showing that recoverability follows a measurable law.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Language model agents often appear capable of self-recovery after failing tool call executions, yet this behavior lacks a formal explanation. We present a predictive theory that resolves this gap by showing that recoverability follows a measurable law.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results reveal that recoverability is not an artifact of model scale or architecture, but a governed property of interaction dynamics, providing a theoretical foundation for execution-level robustness in language agents.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Explore the governed property of interaction dynamics for recoverability in tool-augmented language model agents via the ERR measure.
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