ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
arXiv:2609.13737 (cs)
[Submitted on 12 Sep 2026]
Title:ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation
Authors:Hanling Wang, Chenlong Wei, Ling Xu, Hanyan Niu, Qi Cao, Shizhou Huang, Yang Yang, Xiaohui Zhu, Yao Zhu
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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: this https URL
| Comments: | 17 pages, 12 figures. Accepted to Findings of EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.13737 [cs.CL] |
| (or arXiv:2609.13737v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.13737
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation, by Hanling Wang and 8 other authors
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