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    <abstract>Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. To bridge this gap, we present a benchmark for reasoning-based conversational information retrieval comprising 707 conversations (2,971 turns) across eleven domains. To ensure quality, our Decomposition-and-Verification framework transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation, where atomic facts are verified against sources and explicit retrieval reasoning is generated for each turn. Comprehensive evaluation reveals that combining conversation history with reasoning doubles retrieval performance (Baseline .236 \rightarrow History+Reasoning .479 nDCG@10), while reasoning-specialized models substantially outperform dense encoders. Despite these gains, further analysis highlights that implicit reasoning remains challenging, particularly when logical connections are not explicitly stated in the text. [\ensuremath&lt;https://github.com/RECOR-Benchmark/RECOR\ensuremath&gt;]</abstract>
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%0 Conference Proceedings
%T RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark
%A Ali, Mohammed
%A Abdallah, Abdelrahman
%A Agarwal, Amit
%A Patel, Hitesh Laxmichand
%A Jatowt, Adam
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ali-etal-2026-recor
%X Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. To bridge this gap, we present a benchmark for reasoning-based conversational information retrieval comprising 707 conversations (2,971 turns) across eleven domains. To ensure quality, our Decomposition-and-Verification framework transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation, where atomic facts are verified against sources and explicit retrieval reasoning is generated for each turn. Comprehensive evaluation reveals that combining conversation history with reasoning doubles retrieval performance (Baseline .236 \rightarrow History+Reasoning .479 nDCG@10), while reasoning-specialized models substantially outperform dense encoders. Despite these gains, further analysis highlights that implicit reasoning remains challenging, particularly when logical connections are not explicitly stated in the text. [\ensuremath<https://github.com/RECOR-Benchmark/RECOR\ensuremath>]
%U https://aclanthology.org/2026.findings-acl.129/
%P 2688-2723

Markdown (Informal)

[RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark](https://aclanthology.org/2026.findings-acl.129/) (Ali et al., Findings 2026)

• RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark (Ali et al., Findings 2026)

ACL

• Mohammed Ali, Abdelrahman Abdallah, Amit Agarwal, Hitesh Laxmichand Patel, and Adam Jatowt. 2026. RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark. In Findings of the Association for Computational Linguistics: ACL 2026, pages 2688–2723, San Diego, California, United States. Association for Computational Linguistics.