[{"data":1,"prerenderedAt":61},["ShallowReactive",2],{"site-nav":3,"paper-\u002Fpapers\u002Fan-efficient-long-context-ranking-architecture-with-calibrated-llm-distillation-application-to-person-job-fit":39},{"id":4,"extension":5,"items":6,"meta":25,"stem":37,"__hash__":38},"nav\u002Fnav.md","md",[7,10,19,22],{"label":8,"to":9},"Papers","\u002Fpapers",{"label":11,"items":12},"Benchmark",[13,16],{"label":14,"to":15},"Retrieval","\u002Fretrieval",{"label":17,"to":18},"Candidate Assesment","\u002Fcandidate-assessment",{"label":20,"to":21},"Team","\u002Fteam",{"label":23,"to":24},"Careers","https:\u002F\u002Fcareers.malt.com\u002F",{"body":26},{"type":27,"value":28,"toc":33},"minimark",[29],[30,31,32],"p",{},"Navigation links for the site header. This file is loaded by the default layout and is not meant as a standalone page.",{"title":34,"searchDepth":35,"depth":35,"links":36},"",2,[],"nav","4zzqubtdeLCKskuzs3cOPP7CYzCZzJ_6gUl4De3Xamc",{"id":40,"title":41,"authors":42,"body":46,"date":53,"description":50,"extension":5,"image":34,"link":54,"meta":55,"navigation":56,"path":57,"seo":58,"stem":59,"summary":34,"__hash__":60},"papers\u002Fpapers\u002Fan-efficient-long-context-ranking-architecture-with-calibrated-llm-distillation-application-to-person-job-fit.md","An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit",[43,44,45],"Warren Jouanneau","Emma Jouffroy","Marc Palyart",{"type":27,"value":47,"toc":51},[48],[30,49,50],{},"Finding the most relevant person for a job proposal in real time is challenging, especially when resumes are long, structured, and multilingual. In this paper, we propose a re-ranking model based on a new generation of late cross-attention architecture, that decomposes both resumes and project briefs to efficiently handle long-context inputs with minimal computational overhead. To mitigate historical data biases, we use a generative large language model (LLM) as a teacher, generating fine-grained, semantically grounded supervision. This signal is distilled into our student model via an enriched distillation loss function. The resulting model produces skill-fit scores that enable consistent and interpretable person-job matching. Experiments on relevance, ranking, and calibration metrics demonstrate that our approach outperforms state-of-the-art baselines.",{"title":34,"searchDepth":35,"depth":35,"links":52},[],"2026-01-16","https:\u002F\u002Farxiv.org\u002Fabs\u002F2601.10321",{},true,"\u002Fpapers\u002Fan-efficient-long-context-ranking-architecture-with-calibrated-llm-distillation-application-to-person-job-fit",{"title":41,"description":50},"papers\u002Fan-efficient-long-context-ranking-architecture-with-calibrated-llm-distillation-application-to-person-job-fit","yVxbqZBWMW5P3fpRpykGgVyFPmeLzy2cGOSDi5lETM0",1776860002889]