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ARBITRA: Robust Document-Level ATT&CK Extraction via Multi-Expert Argumentation

Paper recorded by Signals 4 on 2026-10-29 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CL · 自然语言处理 · first seen 2026-10-30

Abstract

Cyber threat intelligence (CTI) reports describe adversary behavior in natural language, but converting them into structured MITRE ATT&CK mappings remains difficult: existing methods decompose reports to sentence-level granularity for extraction, while modern LLMs can work with full reports in a single pass but with noticeable performance degradation. We present ARBITRA, which orchestrates multipl

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#84 most recent of 628 cs.CL papers we have recorded · ↑ newer: Integrating Heterogeneous Echocardiography AI Outputs in Language Spac · ↓ older: Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix
Cite this page: ARBITRA: Robust Document-Level ATT&CK Extraction via Multi-Expert Argumentation: the #84 most recent of 628 cs.CL papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/arbitra-robust-document-level-att-ck-extraction-via-multi-expert-argumentation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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