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title: Surviving the paper deluge: Notes from an ICRA panel on...
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# Surviving the paper deluge: Notes from an ICRA panel on publishing, LLMs, and the future of peer review

**[Robohub](https://daily.dev/sources/robohub)** · 7 min read · 0 upvotes · 0 comments

## Summary

A panel at the ICRA robotics conference, titled 'Surviving the Paper Deluge,' examined the exponential growth of robotics publications (an estimated 70,000 papers in 2025 containing the word 'robotics'). Panelists including Aude Billard, Kunpeng Yao, Nadia Figueroa, Greg Dudek, Renaud Detry, Dongheui Lee, and Shigeki Sugano discussed causes like salami-slicing and reward incentives, the risks of LLM-driven literature review (including first, second, and third-order hallucination), the need for differentiated publication tracks for different contribution types, and radical proposals such as replacing peer review with open community evaluation. No consensus fix emerged, but principles included reading selectively, rewarding quality over count, and treating visibility as an indicator rather than a verdict of research worth.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://robohub.org/surviving-the-paper-deluge-notes-from-an-icra-panel-on-publishing-llms-and-the-future-of-peer-review>

## Questions this post answers

### What percentage of robotics papers on learning from demonstration were judged to make notable contributions in a 2024 review study?

About 20 percent, or 69 out of 347 screened papers, were judged notable in a study of 2024 learning-from-demonstration papers funded by the IEEE RAS Science and Technology Watch Board. Notable papers offered new formulations, theoretical guarantees, strong comparisons against state of the art, clear robustness gains, and convincing real-robot validation, rather than simply relabeling existing approaches.

_Researchers sorting signal from noise in robotics literature can track these publishing-culture debates on daily.dev._

### What are the different types of hallucination that occur when using LLMs for academic literature review?

Three types were identified: first-order hallucination is fake or incorrect references, second-order hallucination is when a cited reference is real but the model misstates what the paper actually did, and third-order hallucination is when the model invents plausible but false commonalities across multiple papers. The last two are considered more dangerous because they produce fluent, structured, but wrong understanding rather than obviously fake citations.

_Anyone weighing LLM tools against manual review can follow this discussion on daily.dev._

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---

Tags: [#llm](https://daily.dev/tags/llm), [#robotics](https://daily.dev/tags/robotics)

[View this post on daily.dev](https://daily.dev/posts/surviving-the-paper-deluge-notes-from-an-icra-panel-on-publishing-llms-and-the-future-of-peer-rev-ecu35qej1)

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