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arXiv 最近完成扫描:等待完成 · 全站最近入库:2026-11-02 00:39各信源进度 →

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版本更新 · v2arXiv:2609.32963

More of the same? Are scientific papers losing originality?

Ilan Doron-Arad, Elchanan Mossel

Scientific output is growing rapidly, but it is unclear whether the expanding literature remains original or is increasingly repeating itself. Originality has many dimensions. One dimension can now be measured directly: how textually distinct a paper is from the work that came before it. Using semantic language models, we screened more than 26,000 full-text papers across eight fields in the CORE database (2015--2025) for passages resembling recent literature. We compared every paper against a fixed-size sample of earlier work, so increases in resemblance are not a by-product of literature growth. We find that the share of a paper's text resembling recent work was stable before 2021, then rose sharply, more than tripling by 2025. The increase came not from a few papers borrowing more heavily but from resemblance spreading across the literature: more papers now contain passages that echo recent work, and almost none of this growth is explained by cited work. We also found an analogous increase in passages expressing the same scientific idea. The pattern held across robustness checks and replicated in a second, independent database. Along this one measurable axis of originality, scientific writing has become less distinct from the work preceding it.

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cs.DL
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36970

On the VC Dimension of Autoregressive Linear Threshold Functions

Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel

Linear threshold functions (LTFs) are a canonical class of Boolean predictors. In the autoregressive setting, the same LTF reads the most recent $d$ bits, generates the next bit, and returns the bit produced after $T$ steps. While the one-step class has VC dimension $d$, for general $T$ the counting upper bound is $O(d^2)$. We prove that the VC dimension is $Θ(d^2)$ for every $T\ge d^2$, resolving the quadratic gap for deterministic autoregressive LTFs. We then adapt the same technique to show an exponential separation between deterministic and stochastic autoregressive models: replacing the sign of LTFs by logistic sampling can increase the VC dimension of the final majority prediction class from quadratic to exponential in $d$.

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cs.LG
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