[2606.06660] AEGIS: A Backup Reflex for Physical AI

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Computer Science > Artificial Intelligence

arXiv:2606.06660 (cs)

[Submitted on 4 Jun 2026]

Title:AEGIS: A Backup Reflex for Physical AI

Authors:Josef Chen<br>View a PDF of the paper titled AEGIS: A Backup Reflex for Physical AI, by Josef Chen

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Abstract:Long-horizon robot manipulation tends to fail gradually: one bad step degrades the state, and the policy spirals into a basin from which it cannot recover. The failure is often visible before it happens. We introduce AEGIS (Activation-probe Early-warning, Gated Inference Switching), a selective escalation method that uses a lightweight probe on a weak policy's frozen activations to detect high-risk steps while there is still time to act. When the probe flags a step, control switches to a stronger separate policy, but only for the steps that need it. On LIBERO-Spatial, AEGIS recovers 10.1% of the trajectories the weak policy alone loses, versus 4.6% for budget-matched blind escalation and 5.1% for a random-trigger placebo. These gains are significant under one-sided exact paired McNemar tests with Holm-Bonferroni adjustment over three pre-registered contrasts: +5.4pp over blind escalation, p=8.5e-6; +5.0pp over random triggering, p=1.0e-4; paired-trajectory bootstrap CIs exclude zero. AEGIS activates the stronger policy on only 38% of steps, so the lever is timing rather than compute. The probe clears its precondition with an early-window AUROC of 0.764, 95% CI [0.70, 0.84], read from the weak-policy path over the first 30% of trajectory steps before any handoff. We pre-register the full analysis plan, including a conditional recovered-task-rate estimand and explicit kill criteria, and confirm the result on 700 common-random-number episodes per arm, with nA-fail=646.

Subjects:

Artificial Intelligence (cs.AI); Performance (cs.PF); Robotics (cs.RO)

Cite as:<br>arXiv:2606.06660 [cs.AI]

(or<br>arXiv:2606.06660v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.06660

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Josef Liyanjun Chen [view email]<br>[v1]<br>Thu, 4 Jun 2026 19:09:22 UTC (3,414 KB)

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