HN Reader

I was hoping for this to announce a tool for research.

Anyone know of the best way to do something like:

"Find most relevant papers related to topic XYZ, download them, extract metadata, generate big-picture summary and entity-relationship graph"?

Having a nice workflow for this would be the best thing since sliced bread for hobbyists interested in niche science topics.

Recently found https://minicule.com which is free and lets you search + import, but it focuses more on "concept-extraction" than LLM synthesis/summary.

I like zotero, I started vibe coding some integration for my workflow, the project is a bit clunky to build and iterate the development specially with gemini & claude. But I think that is the direction to take instead of reinvent from scratch something

Was expecting a product I can try out. But still, not disappointed.

Is there any intership opportunity for me

AI getting into scientific research is definitely impressive. But the more we use it, the more it feels like we're slowly getting too lazy to think on our own. Human judgment and intuition seem to be fading bit by bit.

I wonder how well these models will hold up in messy, interdisciplinary real-world projects

AI for Scientific Search yes. LLM for Scientific Search I am not sure. AI is not equivalent with LLM. I dislike it when people do it.

AI will have a brand crisis once LLMs get abandoned and researchers need to explain the public that the new AI (not LLM based) is different than the old AI (LLM based) which is different from the old AI (GOFAI)

From the title, I had thought that this would be a new tool for searching science, such as searching the arxiv. But this is actually a survey.

I quote the conclusion of the survey:

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In conclusion, rapid advancements in artificial intelligence, particularly large language models like OpenAI-o1 and DeepSeek-R1, have demonstrated substantial potential in areas such as logical reasoning and experimental coding. These developments have sparked increasing interest in applying AI to scientific research. However, despite the growing potential of AI in this domain, there is a lack of comprehensive surveys that consolidate current knowledge, hindering further progress. This paper addresses this gap by providing a detailed survey and unified framework for AI4Research. Our contributions include a systematic taxonomy for classifying AI4Research tasks, identification of key research gaps and future directions, and a compilation of open-source resources to support the community. We believe this work will enhance our understanding of AI’s role in research and serve as a catalyst for future advancements in the field.

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I jumped at this because I'm a mathematician who has been complaining about the lack of effective mathematical search for several years.

Always worth noting where the authors are affiliated and I don't remember ever hearing of bytedance breaking new ground in chemical or materials research so I'm sceptical about reading this...