itsalissonsilva/LatentRisk-Probe

AI model by itsalissonsilva

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Two trained sparse linear probes for prompt harmfulness, using frozen Gemma 3 1B and Qwen3 0.6B activations. The repository contains actual readout weights, inference code, frozen labels, captured activations, text/control baselines and an independent serialized-weight audit. The probes generalize beyond their training categories on these benchmarks, but falsely flag 43–46% of difficult benign requests. They are research instruments, not reliable safety filters. They do not establish covert intent, causal features or a model's eventual harmful response. XSTest: 450 prompts, including 250 safe and 200 unsafe requests. Threshold 0.5, selected without using XSTest. The fixed hidden-state-index-7 controls score 72.8% (Gemma) and 60.5% (Qwen) balanced accuracy. Shuffled-label controls score 48.45% and 47.9%; Gaussian-feature controls score 52.6% and 48.3%. The selected hidden states and regularization were chosen on validation only. The Portuguese result is only 12 authored pairs, with no independent human review. It does not demonstrate broad multilingual generalization. Single-class suites have…

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Model overview

itsalissonsilva/LatentRisk-Probe is a task-unspecified model repository published by itsalissonsilva on Hugging Face. The source reports the pytorch library.

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Model facts

Task: Not reported
Library: pytorch
Recent downloads (30 days): 0
Cumulative likes: 0
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: other
Access: Not gated by Hugging Face
Created: 2026-10-31T06:17:16.000Z
Last modified: 2026-10-31T06:31:28.000Z

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Reported languages: en, pt

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Source: Hugging Face Hub. Metadata observed 2026-10-31T06:50:12.924Z. Daily imports are snapshots, not real-time monitoring.

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