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Qingcheng Zeng

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@SteveZeng7

Final-year PhD on job market / I do agent RL and search / @NorthwesternU @TencentGlobal @Snowflake @AlibabaGroup / Big fan of @Arsenal / Christian
Evanston, IL qcznlp.github.io Joined June 2019
3K Following 1.4K Followers 217 Posts

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Qingcheng Zeng Verified account @SteveZeng7
We’re building a scientific computer-use benchmark for AI agents and welcome contributions from researchers across all scientific disciplines!
Tianyu Liu @meSuper8 Sep 29
🎮 What are the limits of agents’ ability to solve scientific problems? Can they use research software as effectively as scientists?
🧪 Introducing 𝗢𝗦𝗪𝗼𝗿𝗹𝗱-𝗦𝗰𝗶𝗲𝗻𝗰𝗲: a Computer-Use Agent / GUI Agent Benchmark designed for real-world scientific research scenarios.
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Qingcheng Zeng Verified account @SteveZeng7
Excited to see Jev and RLCD bringing calibration into the spotlight.

This connects closely to a question we’ve been exploring since last year: how can we build models whose confidence we can actually trust?

In two EMNLP 2025 papers, we evaluated calibration in reasoning models and VLMs. We found that stronger capabilities don’t automatically translate into more reliable confidence. Task and modality matter.

Reasoning models: aclanthology.org/2025.emnlp-mai…
Vision-language models: aclanthology.org/2025.emnlp-mai…

In our ACL 2026 work, we took this into agentic RL: explicitly optimizing calibration alongside task performance in tool-using agents.

The Confidence Dichotomy: aclanthology.org/2026.acl-long.…

For me, the excitement around Jev/RLCD highlights why this direction matters. As confidence starts driving real actions, calibration becomes a core capability to train and evaluate.

Really looking forward to seeing more work on calibration. Excited to keep pushing this direction forward!
aclanthology.org Thinking Out Loud: Do Reasoning Models Know When They’re Right? Qingcheng Zeng, Weihao Xuan, Leyang Cui, Rob Voigt. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.
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Qingcheng Zeng Verified account @SteveZeng7
Excited to share what we built! Try it!
Tianyu Liu @meSuper8 Sep 14
35B total. 3B active. Built for serious agentic work. Excited to share Occamy-1.0, our team's first open-source agentic model. Occamy delivers strong results across long-horizon co-work, automation, business, tool-use,while sitting near the low-cost knee of the Pareto frontier.
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Qingcheng Zeng Verified account @SteveZeng7
Work done during internship at Snowflake ❄️ accepted at #EMNLP2026🇭🇺.

We present a strikingly easy and intuitive method to enhance retrievers' instruction-following information retrieval capability. Read below and more works at EMNLP coming soon!

arxiv.org/abs/2604.18845
arxiv.org Dual-View Training for Instruction-Following Information Retrieval Instruction-following information retrieval (IF-IR) studies retrieval systems that must not only find documents relevant to a query, but also obey explicit user constraints such as required...
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