Original Summary

Earlier, I mentioned how most of the research tools, be it AI, start with the question you ask today, but not with the history behind it. I have been thinking and building Aevron around this core concept . Finding papers, summarising them, and creating a well-cited report is not actually research. The actual research starts after the retrieval, connections, and relevant information have been surfaced. Then we get into which findings are strong to rely on, which claims or information is interpretation or an evidence to the research. Do any of the collected information, evidence, or interpretations contradict each other, etc. This is where most of the AI research tools, or, in general, AI tools, flatten the entire process of doing the research. Usually, they follow a very streamlined process from question to search to source and then summarise. But but take a look at the different approach. Let's say: Evidence to compare, to challenge, to connect, to decide what follows. That distinction is way more important and is how Aevron is being built. How I've designed Aevron is that it doesn't treat the source as the sole answer, but it treats it as evidence inside an existing inquiry. A paper, question, assumption, hypothesis, can support, we can contradict, or change nothing at all. and if something useful gets captured, then Aevron can surface it as a possibility contributing to the research rather tnan an evidence to conclude. I don’t want the AI to collapse: “the evidence suggests X” into “you believe X.” Search finds information. Research is deciding what that information actually allows you to say. That’s the layer I think AI research tools still have a lot of room to improve.   submitted by   /u/mercurias98 [link]   [comments]

  • 情报分类：商业与市场研究
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  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/25 17:14:51