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WritersLogic Inc. wins PAN 2026 detection tasks with novel approach

By PulseAugur Editorial · [1 sources] ·

WritersLogic Inc. presented its systems for three PAN at CLEF 2026 shared tasks, focusing on feature robustness under distribution shift. Their approach, which prioritizes the generation process over content, achieved first place in Reasoning Trajectory Detection and third in safety classification. For the Voight-Kampff Generative AI Detection task, a calibrated ensemble of models including DeBERTa-v2 and LightGBM achieved strong results. The team also developed a system for Multi-Author Writing Style Analysis, though a platform issue prevented its official evaluation. AI

IMPACT Demonstrates robust AI detection methods that generalize across different domains and tasks.

RANK_REASON Academic paper detailing systems and results for competitive AI detection tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

WritersLogic Inc. wins PAN 2026 detection tasks with novel approach

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Academic paper detailing systems and results for competitive AI detection tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · David L. Condrey ·

    Writerslogic at PAN 2026: Process over Content for Robust Detection under Domain Shift

    arXiv:2610.03565v1 Announce Type: new Abstract: We describe the Writerslogic systems for three PAN at CLEF 2026 shared tasks (Reasoning Trajectory Detection, Voight-Kampff Generative AI Detection, and Multi-Author Writing Style Analysis), unified by a shared analytical framework:…

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