@inproceedings{wu-etal-2026-sirf,
title = "{SIRF}: A Spec-Internalized Risk Foundation Model for Industrial Content Risk Control",
author = "Wu, Suwan and
Lin, Yumeng and
Yuan, Pengcheng and
Jiang, Xiaolong",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.197/",
pages = "3308--3321",
ISBN = "979-8-89176-460-6",
abstract = "For industrial content risk control, the real deployment constraint is not average accuracy but how much risk can be auto-handled under high precision and second-level latency. We present SIRF (Spec-Internalized Risk Foundation Model), which internalizes a platform{'}s complex policies, synthesized label-free via EntiGraph, MAGA rewriting, and account-level CoT, into the weights via continued pretraining, so rules are applied at high precision under an ultra-low-latency, classification-only deployment. A controlled same-source comparison (Qwen3-8B-SFT vs. SIRF-8B-SFT, identical policy injection and classification-only form, differing only in policy-grounded CPT) attributes the gain to internalization: SIRF-8B-SFT reaches 71.3 Black Recall@P95, +15 pp over the baseline, using only about 70M CPT tokens without harming general ability, and among included, logprob-available models under this interface it matches or exceeds far larger systems. SIRF is deployed as a tree-model adjudication layer (+20{\%} more mis-penalized samples recovered) and transfers to a freezing scenario at low cost ({\textasciitilde}70{\%} relative mis-penalization reduction)."
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<abstract>For industrial content risk control, the real deployment constraint is not average accuracy but how much risk can be auto-handled under high precision and second-level latency. We present SIRF (Spec-Internalized Risk Foundation Model), which internalizes a platform’s complex policies, synthesized label-free via EntiGraph, MAGA rewriting, and account-level CoT, into the weights via continued pretraining, so rules are applied at high precision under an ultra-low-latency, classification-only deployment. A controlled same-source comparison (Qwen3-8B-SFT vs. SIRF-8B-SFT, identical policy injection and classification-only form, differing only in policy-grounded CPT) attributes the gain to internalization: SIRF-8B-SFT reaches 71.3 Black Recall@P95, +15 pp over the baseline, using only about 70M CPT tokens without harming general ability, and among included, logprob-available models under this interface it matches or exceeds far larger systems. SIRF is deployed as a tree-model adjudication layer (+20% more mis-penalized samples recovered) and transfers to a freezing scenario at low cost (~70% relative mis-penalization reduction).</abstract>
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%0 Conference Proceedings
%T SIRF: A Spec-Internalized Risk Foundation Model for Industrial Content Risk Control
%A Wu, Suwan
%A Lin, Yumeng
%A Yuan, Pengcheng
%A Jiang, Xiaolong
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F wu-etal-2026-sirf
%X For industrial content risk control, the real deployment constraint is not average accuracy but how much risk can be auto-handled under high precision and second-level latency. We present SIRF (Spec-Internalized Risk Foundation Model), which internalizes a platform’s complex policies, synthesized label-free via EntiGraph, MAGA rewriting, and account-level CoT, into the weights via continued pretraining, so rules are applied at high precision under an ultra-low-latency, classification-only deployment. A controlled same-source comparison (Qwen3-8B-SFT vs. SIRF-8B-SFT, identical policy injection and classification-only form, differing only in policy-grounded CPT) attributes the gain to internalization: SIRF-8B-SFT reaches 71.3 Black Recall@P95, +15 pp over the baseline, using only about 70M CPT tokens without harming general ability, and among included, logprob-available models under this interface it matches or exceeds far larger systems. SIRF is deployed as a tree-model adjudication layer (+20% more mis-penalized samples recovered) and transfers to a freezing scenario at low cost (~70% relative mis-penalization reduction).
%U https://aclanthology.org/2026.emnlp-industry.197/
%P 3308-3321
Markdown (Informal)
[SIRF: A Spec-Internalized Risk Foundation Model for Industrial Content Risk Control](https://aclanthology.org/2026.emnlp-industry.197/) (Wu et al., EMNLP 2026)
ACL