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RMIT-ADM+S team explores retrieval agents in TREC RAG 2026

By PulseAugur Editorial · [1 sources] ·

The RMIT-ADM+S team participated in the TREC 2026 Retrieval-Augmented Generation (RAG) track, submitting seven research-agent runs that utilized both proprietary OpenAI models and open-weight alternatives. Their research focused on exploring various retrieval strategies, writer models, and agent workflows, with pre-submission tests indicating that writer selection and adjacent-chunk expansion significantly impacted answer quality. In the official NIST evaluation, six of their seven runs surpassed the track median, and their top-performing run achieved the highest all-item coverage among their submissions, tying the track maximum for vital coverage in one batch. AI

IMPACT Highlights the potential of open-weight models for research-agent systems and differences between automated metrics and human preferences.

RANK_REASON Academic paper detailing participation in a research competition and model performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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

RMIT-ADM+S team explores retrieval agents in TREC RAG 2026

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Academic paper detailing participation in a research competition and model performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Shuai Wang ·

    RMIT-ADM+S at TREC RAG 2026: Research Agents, Retriever Ablations, and Open-Weight Replication

    We describe the RMIT-ADM+S team's participation in the TREC 2026 Retrieval-Augmented Generation (RAG) track using the ClimbMix corpus. We submitted seven research-agent runs using proprietary OpenAI and open-weight models, exploring different retrieval strategies, writer models, …

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