A recent ICRA panel titled “Surviving the Paper Deluge” brought together leading robotics researchers who have grappled with the overwhelming number of robotics papers being published today.
The discussion ranged from hard numbers on publication growth, through the promises and risks of large language models (LLMs), to radical proposals for reshaping peer review as we know it.
Panel chair Aude Billard pointed to rapid growth across major IEEE Robotics and Automation Society venues, with a roughly exponential curve beginning around 2017. An estimate for 2025 suggests around 70,000 papers containing the word “robotics.”
Billard noted that while in some fields, extreme specialization may be an acceptable survival strategy, robotics is inherently different. A robot is an integration of perception, control, manipulation, learning, hardware, interaction, safety, and deployment. If researchers can only stay current within narrow silos, the field risks losing one of its core strengths: the ability to connect ideas across domains.
Kunpeng Yao presented a case study funded by IEEE RAS Science and Technology Watch Board. The team tried to do something that borders on heroic in today’s publishing environment; that is, reading an entire subfield carefully over a single year.
Focusing on papers related to learning from demonstration, the team searched IEEE Xplore for relevant 2024 papers, screened the results manually, and identified 347 relevant papers. Of those, only 69, or about 20 percent, were judged to have made notable contributions.
According to the criteria used, ‘notable’ papers tended to offer new formulations, mechanisms, or theoretical guarantees; serious comparisons against strong state of the art; clear gains in robustness, transfer, or failure recovery; new teaching or data collection modalities; and convincing real-robot validation. Papers were less compelling when they simply relabelled existing approaches or failed to demonstrate their claims.
Yao summarized one lesson neatly: notability should be judged against the state of the art, not against “the newest vocabulary.”
That distinction is of particular importance in a field where new labels can travel quickly and a paper may sound fresh without actually moving the frontier. Conversely, useful work is sometimes hidden in less fashionable venues or written by less visible groups.
Degrees of hallucination
One question is whether large language models can help with literature review. Nadia Figueroa explained that LLMs can improve the mechanics of literature review. That is, they can help with search, retrieval, clustering, tables, summaries, and rough conceptual maps. Work that once took weeks or months can now sometimes be done in hours. For a new PhD student entering a field, that is a genuine reduction in the barrier to entry.
But Figueroa also identified hallucination as a major issue. First-order hallucination involves fake or incorrect references. More subtle is second-order hallucination, where the reference is real but the model misstates what the paper actually did. Also dangerous is third-order hallucination, where the LLM invents plausible but false commonalities across papers.
“The danger currently is not fake citations,” observed Figueroa. “It’s fake understanding.”
An LLM-based literature review can contain real papers and still misrepresent the field. It can be fluent, structured, and wrong. If researchers outsource not just the mechanics of reading but the cognitive act of comparison, judgment, and doubt, the field may produce more text while developing less understanding.
Salami and sandcastles
Meanwhile, Greg Dudek described the familiar problem of “salami slicing,” where a larger body of work is divided into “the thinnest possible slices” and spread across workshops, conferences, and journals.
For Dudek, the system rewards this behaviour. Students need papers to graduate. Early-career researchers need papers for jobs. Committees often face too much material to read deeply, so titles, venues, counts, and indices become tempting shortcuts.
But the result is costly for readers. Multiple papers repeat the same background, divide one contribution into fragments, and make it harder to reconstruct the full story.
His preferred remedy is simple in principle but difficult in practice: publish fewer, more integrated papers.
Dudek is under no illusion about how hard this will be. “There is no fix,” he said, if by fix we mean a return to a quieter world. Incremental reforms may help, but the flood is still rising. He compared small procedural fixes to building better walls around a sandcastle while “there’s a tsunami coming from the back of the room.”
Re-designing peer review?
Renaud Detry argued that the growth in publications partly reflects the growing presence of applied systems work in academic venues. That is not necessarily bad. Robotics advances through real systems, data, benchmarks, platforms, and the last-mile effort needed to make ideas work outside idealized settings.
The problem is that the same evaluation machinery often judges very different contribution types. A new algorithm, a carefully engineered system, a benchmark, a dataset, and an industrially relevant validation study should not all have to pretend to be the same kind of paper. Robotics may need clearer tracks for fundamental science, applications, infrastructure, benchmarks, and technical correctness.
Visibility is not value
Dongheui Lee addressed another vital part of the new publication ecosystem: arXiv, open-source releases, project pages, videos, blogs, and social media. These tools can broaden access, improve reproducibility, and help readers decide what to examine closely. But they also distort attention. Visibility can reflect institutional prestige, networks, speed, and self-promotion as much as scientific value.
Lee’s suggestion was pragmatic: keep expert peer review as a quality filter but use its signals better. Editorial boards and media teams could do more to promote strong papers that receive excellent reviews but do not come from famous labs.
Beyond the gatekeeper model
The most radical question came from Shigeki Sugano, who asked “What if we were to abandon peer review altogether?” In Sugano’s model, legitimate robotics and AI manuscripts would be uploaded first to an open archive, along with videos, code, and data.
Community evaluation, under verified identities, would happen in public. Journals and conferences would then certify high-value work rather than deciding what gets to exist.
“The question is not whether peer review is valuable,” he argued. “The question is whether it must remain the only gate to visibility.”
Not everyone agreed. Popularity bias, gaming, reputation effects, and low-quality papers flooding the system were highlighted by other panelists as possible risks. Still, Sugano’s radical proposal addressed real challenges around the ability of traditional accept-or-reject peer review models to scale to meet the deluge of papers flooding the robotics community’s information space.
No one on the panel pretended there was a clean fix. Instead, it emerged that the paper deluge is a tangle of volume, incentives, tools, evaluation, visibility, and culture.
Nevertheless, several useful principles emerged. LLMs may provide some mechanical support but carry risks of intellectual outsourcing. Read selectively and deeply. Reward quality before publication counts. Recognize different kinds of value in robotics research. Treat visibility as a possible indicator of worth, not as a verdict.
The open question
One major question remains: what happens to robotics research if papers continue to be published at the current rate?
There are clear benefits. More papers, more preprints, and more routes into publication can lower barriers to entry, help new researchers find a foothold, and make the field more open to a wider range of voices. Platforms such as arXiv have also made it easier for work to circulate before, or outside of, traditional publication channels.
But the risks are equally clear. Robotics is an integrative field, bringing together perception, control, manipulation, locomotion, hardware, safety, learning, and human-robot interaction. If researchers can only keep up with narrow slices of that landscape, the field may become more siloed. One result could be a growing reliance on off-the-shelf tools, including black-box commercial systems, without a deep understanding of the foundations on which those tools depend.
There is also the problem of memory. At a certain scale, no individual researcher can read far enough back, or broadly enough across adjacent domains. That increases the risk of duplicated work, overlooked insights, and important papers disappearing beneath the next wave of more fashionable topics.
Are there too many papers being published? What measures could we take to improve the publication environment for students, researchers, and reviewers? Let us know your thoughts at [email protected]
