ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation

• URL: http://arxiv.org/abs/2609.13737v1
• Date: Sat, 12 Sep 2026 06:15:17 GMT
• Title: ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation
• Abstract summary: ForeSight is a first-token output-risk forecasting framework.<n>It distills weak and redundant early safety signals into compact, layer-aware risk representations.
• Score: 16.209377275700568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) are increasingly deployed, the generation of harmful content has become a critical safety concern. Existing safeguards operate at the input, output, or streaming-generation stages, while early-risk methods that rely on surface tokens or output logits may suffer from weak initial signals, and internals-based detectors using dense representations may retain highly entangled and redundant safety-irrelevant information. It therefore remains unclear whether the earliest post-generation hidden states already contain reliable signals about final-response harmfulness. To address this gap, we propose ForeSight, a first-token output-risk forecasting framework that distills weak and redundant early safety signals into compact, layer-aware risk representations. Experiments on five safety benchmarks and two target models demonstrate that ForeSight achieves superior and efficient early-risk forecasting while relying solely on first-token hidden states. The code is available at: https://github.com/Scabbards1500/Foresight

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