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Rajshahi University of Engineering and Technology

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researchsquare.com Oct 14, 2026
Cross-day speech decoding without retraining the network: 65,792 input parameters lift all six T12 sessions
Across days, neural drift degrades a fixed intracortical speech decoder. One study confined adaptation to a 256×256 affine transform and bias at the neural input, updating 65,792 parameters, under 0.05% of the full model, while the recurrent encoder and phoneme classifier stayed frozen, using 40 or 80 labeled trials for supervised calibration. On the public T12 dataset, macro phoneme error rate fell 11.37% and 15.51% across six chronologically held-out sessions, with all six improving under all three seeds; on the independent T15 dataset the same frozen rule gave 2.72% and 10.05% reductions, with more session-to-session variability and neither budget below the nominal 0.05 reference threshold.
Across days, neural drift degrades a fixed intracortical speech decoder. One study confined adaptation to a 256×256 affine transform and bias at the neural input, updating 65,792 parameters, under 0.05% of the full model, while the recurrent encoder and phoneme classifier stayed frozen, using 40 or 80 labeled trials for supervised calibration. On the public T12 dataset, macro phoneme error rate fell 11.37% and 15.51% across six chronologically held-out sessions, with all six improving under all three seeds; on the independent T15 dataset the same frozen rule gave 2.72% and 10.05% reductions, with more session-to-session variability and neither budget below the nominal 0.05 reference threshold.
Rajshahi University of Engineering and Technology Bangladesh University of Engineering and Technology
Dibakar Roy（First author, Corresponding author）
Cross-session intracortical speech decoding Affine-only calibration Parameter-efficient adaptation Connectionist Temporal Classification
arXiv Oct 7, 2026
TGCC uses cross-layer trust gating so a compromised LLM agent self-revokes within steps instead of escalating
The work turns a descriptive layered trust model into an operational controller: a cross-layer synergy operator propagates prerequisite-layer deficits into dependent layers, a no-regret online estimator grounds per-layer weights in observed failures, and Trust-Gated Capability Control issues short-lived, revocable grants only when composite trust and the relevant prerequisite layers clear capability-specific thresholds; the authors prove and numerically confirm that a stealthy compromise inflating behavioral trust while degrading a prerequisite layer cannot escalate privilege and instead self-revokes within a few interactions.
The work turns a descriptive layered trust model into an operational controller: a cross-layer synergy operator propagates prerequisite-layer deficits into dependent layers, a no-regret online estimator grounds per-layer weights in observed failures, and Trust-Gated Capability Control issues short-lived, revocable grants only when composite trust and the relevant prerequisite layers clear capability-specific thresholds; the authors prove and numerically confirm that a stealthy compromise inflating behavioral trust while degrading a prerequisite layer cannot escalate privilege and instead self-revokes within a few interactions.
BRAC University Rajshahi University of Engineering and Technology Ahsanullah University of Science and Technology
Mehedi hasan Nipu（First author）
Trust-Gated Capability Control Multi-Agent LLM Systems Agent Safety

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