The NLLG (Natural Language Learning&Generation) arXiv reports assist in navigating the rapidly evolving landscape of NLP and AI research across cs.CL, cs.CV, cs.AI, and cs.LG categories. This fourth installment captures a transformative period in AI history - from January 1, 2023, following ChatGPT's debut, through September 30, 2024. Our analysis reveals substantial new developments in the field - with 45% of the top 40 most-cited papers being new entries since our last report eight months ago and offers insights into emerging trends and major breakthroughs, such as novel multimodal architectures, including diffusion and state space models. Natural Language Processing (NLP; cs.CL) remains the dominant main category in the list of our top-40 papers but its dominance is on the decline in favor of Computer vision (cs.CV) and general machine learning (cs.LG). This report also presents novel findings on the integration of generative AI in academic writing, documenting its increasing adoption since 2022 while revealing an intriguing pattern: top-cited papers show notably fewer markers of AI-generated content compared to random samples. Furthermore, we track the evolution of AI-associated language, identifying declining trends in previously common indicators such as"delve".

In the quiet backwaters of cs.CV, cs.LG and stat.ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics-learning systems with no need for external supervision. To date, little thought has been given to how these self-supervised learners have sprung into being or the principles that govern their continuing diversification. After a period of deliberate study and dispassionate judgement during which each author set their Zoom virtual background to a separate Galapagos island, we now entertain no doubt that each of these learning machines are lineal descendants of some older and generally extinct species. We make five contributions: (1) We gather and catalogue row-major arrays of machine learning specimens, each exhibiting heritable discriminative features; (2) We document a mutation mechanism by which almost imperceptible changes are introduced to the genotype of new systems, but their phenotype (birdsong in the form of tweets and vestigial plumage such as press releases) communicates dramatic changes; (3) We propose a unifying theory of self-supervised machine evolution and compare to other unifying theories on standard unifying theory benchmarks, where we establish a new (and unifying) state of the art; (4) We discuss the importance of digital biodiversity, in light of the endearingly optimistic Paris Agreement.

Abstract Kearney, C, Coughlan, EK, O'Connell, A, Lacey, S, and Burns, C. Bigger but are they better? Investigating growth, maturation, and physical performance metrics in underage Gaelic games development squads. J Strength Cond Res 39(6): e806–e814, 2025—The purpose of this study was to examine (a) maturation status distribution across intercounty underage Gaelic games development squads and (b) differences in physical performance metrics relative to maturation status. The study is the first to assess physical performance metrics in intercounty development squad Gaelic Games players, with reference to maturation status. The Khamis & Roche method was used to establish maturation status in 254 male U15 and U16 Gaelic Games development squad players. Tests for lower body power (countermovement jump [CMJ] height), linear speed (10, 20 m), upper body strength (maximum chin-up test), and aerobic endurance (GAA modified Bronco) were conducted. One-way multivariate analysis of variance and analysis of variance were conducted, with an alpha level of p < 0.05 set to determine statistical significance. In both U15 and U16 groups, early developers (EDs) comprised 64.9 and 64.0%, respectively, while on-time (OT) developers comprised 31.5% (U15) and 33.7% (U16), and late developers (LDs) comprised 3.6% (U15) and 2.3% (U16). For U15, ED exhibited significantly faster 10 m speed than LD (p = 0.045) and faster 20 m speed than both OT (p = 0.007) and LD (p = 0.006). After post hoc tests for U16, CMJ scores showed no significant differences (p > 0.05), while U16 ED and OT displayed faster 20 m speed than LD (p = 0.023, p = 0.024, respectively). Coach education around growth and maturation and strategies such as biobanding should be used in talent development settings. Practitioners should interpret speed times relative to maturation status as opposed to chronological age.

In this chapter we review the current theoretical state of the art of small black holes at the LHC. We discuss the production mechanism for small non thermal black holes at the LHC and discuss new signatures due to a possible discrete mass spectrum of these black holes.

We solve the dynamic equation for the kinetic spherical model that initially is in an arbitrary equilibrium state and then is left to evolve in a heat-bath with another temperature. Flows of the Renormalizational group are determined.

We present the statistical approach to the combining of signal significances.

The optimum interval method for finding an upper limit of a one-dimensionally distributed signal in the presence of an unknown background is extended to the case of high statistics. There is also some discussion of how the method can be extended to the multiple dimensional case.

An introduction to numerical statistics.

We point out that in Granato & Danese 1994 and Granato et al. 1997 we predicted maximum observable sizes for the putative torus in NGC1068 of 10-20 pc, not "hundreds of parsecs" as stated by M. Elitzur in astro-ph/0512025.

This article considers state estimation and veri cation problems for an important class of man-made cyber-physical systems called Discrete-Event Systems (DES).