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	<title>UniResearch Community | Zhen Jin | Favorites</title>
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				<title>Mark Taylor posted an update: Data collection is the foundation of all empirical [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/455/</link>
				<pubDate>Thu, 20 Aug 2026 14:22:25 +0800</pubDate>

									<content:encoded><![CDATA[<p>Data collection is the foundation of all empirical and computational research. However, many researchers waste months on invalid data screening, low‑quality data cleaning and inconsistent data standards. What efficient data collection and filtering workflows do you adopt to accelerate your research progress?</p>
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									<slash:comments>3</slash:comments>
				
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				<title>Xiulan Jiang posted an update: Experimental reproducibility is the basic principle [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/454/</link>
				<pubDate>Thu, 20 Aug 2026 14:22:19 +0800</pubDate>

									<content:encoded><![CDATA[<p>Experimental reproducibility is the basic principle of academic research, but many published papers have unrepeatable experimental results due to incomplete parameter records and hidden operation details. How can we strictly guarantee research reproducibility in daily experiments?</p>
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									<slash:comments>3</slash:comments>
				
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				<title>Chao Guo posted an update: I’ve been testing out UniResearch for aggregating and [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/418/</link>
				<pubDate>Thu, 20 Aug 2026 10:18:59 +0800</pubDate>

									<content:encoded><![CDATA[<p>I’ve been testing out UniResearch for aggregating and synthesizing large paper sets lately. Being able to quickly cross‑compare findings across dozens of related works cuts down my reading time significantly. Curious who else has tried similar AI‑augmented research workflows. What pain points are you still hoping these tools can solve?</p>
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									<slash:comments>3</slash:comments>
				
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				<title>Zhen Xie posted an update: Reading top conference papers, model metrics keep [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/415/</link>
				<pubDate>Thu, 20 Aug 2026 10:18:42 +0800</pubDate>

									<content:encoded><![CDATA[<p>Reading top conference papers, model metrics keep climbing. But when moving these models toward real‑world inference, latency, cost and robustness often become show‑stoppers. Do you think conference evaluation frameworks should put more weight on deployment‑oriented metrics?</p>
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									<slash:comments>5</slash:comments>
				
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				<title>Nathan Phillips posted an update: I’ve noticed many early‑stage researchers spend weeks [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/409/</link>
				<pubDate>Thu, 20 Aug 2026 10:18:17 +0800</pubDate>

									<content:encoded><![CDATA[<p>I’ve noticed many early‑stage researchers spend weeks picking suitable benchmarks. Some public datasets contain hidden distribution bias that can mislead model performance conclusions. What’s your go‑to checklist before locking down your benchmark suite?</p>
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									<slash:comments>3</slash:comments>
				
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