When peer review is this noisy, is aiming above your venue a sensible long shot rather than vanity?

This explores why authors aim submissions above where their work is likely to land. The corpus's most direct answer is that the gamble pays off because peer review is noisy, and authors are betting on that noise. A model of two journals shows how this sustains itself Does peer review quality collapse under submission overload?. As submissions rise, unpaid reviewers get overtaxed. Journals either recruit less-qualified reviewers or overload the ones they have, so review accuracy drops. Less accurate review makes a long-shot submission more likely to get through, so authors submit more speculatively, and that pushes submissions up again. In this model, reaching beyond your venue is a reasonable response to a system that has become less reliable, not vanity. The authors note that the mechanism is plausible but hasn't been measured empirically yet.

The surprising part is that authors often know which of their papers are weaker. At ICML 2023, 1,342 researchers ranked their own submissions, and those rankings predicted future citations better than the official review scores did Can authors rank their own papers better than peer reviewers?. If authors can judge their papers' quality that well, many long-shot submissions aren't misjudgments. The corpus doesn't measure motive, but this fits the idea that authors submit their weaker papers anyway because they expect review to be imperfect.

AI makes the bet cheaper and more tempting. Evaluators have rated AI-generated documents as better than human-written ones, so polished prose can be mistaken for quality Does polished writing actually signal better quality work?. Across more than 125,000 reviews, LLM-assisted papers clustered among the weaker submissions, and LLM-assisted reviewers were more lenient toward lower-quality work in general Do LLM reviewers actually favor LLM-written papers?. That is the kind of soft spot a speculative submitter hopes to hit. Some authors try to push the odds directly: 18 arXiv manuscripts hid instructions telling AI reviewers to rate them positively Are hidden AI prompts in preprints a deceptive research practice?. Sakana AI showed how low the bar can be when it sent fully AI-generated papers to an ICLR workshop. One of three scored above the acceptance threshold, though its own authors judged none of them good enough for the main conference Can AI-generated papers pass peer review undetected?.

At the extreme, reaching beyond your venue becomes an organized business. Paper mills work through coordinated networks of brokers and cooperating editors, and they move to new journals when old ones lose indexing Does scientific fraud operate through organized networks or individual actors?. Venues are starting to push back at the points they can enforce. ICLR 2026 sent AI-detector flags to human area chairs rather than auto-rejecting papers, but it desk-rejected papers with confirmed fabricated references How can conferences detect and handle LLM misuse in peer review?. Inference-scaled AI reviewers have caught proof errors that got past human review at top venues Can inference scaling help reviewers catch errors humans miss?. If review gets more accurate, the long-shot bet should pay off less often. Which venue publishes a paper may also matter less than authors think, since an unreviewed preprint can shape a field's debate before any venue rules on it Can unreviewed preprints shape scientific debate before peer review?.

The corpus is thin on the human side: career pressure, prestige, and how authors themselves explain their choices. What it shows well is the system: review noise makes long shots rational, and long shots add to the noise.