Analysis of 56,800 AI conference papers shows documentation practices improved from 2014 to 2024. Papers sharing both code and data increased from 11% to 64%, and estimated reproducibility rose from 28% to 64%. These improvements predate formal reproducibility checklists, indicating a broader shift toward open science.
Experiments on EC 2025's 'Stable Menus of Public Goods' show that human-intuition prompts improve LLM performance and multi-turn interactions enhance ambitious steps. However, when compared to a first-year PhD student using an unpublished manuscript, the LLM is found to be slightly less effective.
Public AI evaluation archives show that a single terminal result can arise from two distinct pre-terminal histories, with estimated times to reach 95% of performance ceilings at 23.03 or 75.13. A candidate selection-aware frontier model fails synthetic recovery and uncertainty calibration, and is rejected by fixed audit gates. An archive-and-adjudication protocol verifies timing boundaries and falsifies unsupported frontier claims.
HAMON uses passive optical components to perform long-horizon time-series forecasting, outperforming top digital models on ETTm2 across all horizons and on ETTh2 at all but the longest horizon. It achieves up to 14% lower MSE and relies on physical optical propagation without trainable digital layers, demonstrating that passive optical mixing can produce competitive forecasts.
The study introduces PReLU-IPM, a new integral probability metric based on a neural network discriminator with a single node. The resulting PReLU-TST test is nonparametric, consistent, and asymptotically equivalent to standard IPM-based tests, showing higher power or competitive performance on simulated and real datasets.
A new activation function enables fixed-size neural networks to approximate any function in Sobolev spaces $W^{s,\infty}((a,b)^d)$ with arbitrary accuracy in the $W^{s-1,\infty}$-norm. The results use elementary activations like EUAF and DUAF$_\infty$, with explicit width and depth bounds, and extend to sigmoidal variants $\widetilde{\mathrm{DUAF}}_n$ preserving accuracy for all $1\leq s\leq n$.
A compact spectral representation using Betti numbers, spectral gap, and analytic torsion distills persistent Laplacians into three mathematically grounded invariants. This approach captures essential predictive signals from the full spectrum, outperforms it in some cases, and reduces computational overhead on datasets like MNIST, QM-3D, and SKEMPI WT.
A new multi-center benchmark enables abdominal disease diagnosis and report generation from non-contrast CT by synthesizing contrast-enhanced findings. The dataset includes paired NCCT-CECT studies and reports from two centers, showing NCCT achieves average multi-organ AUCs of 69.1% internally and 63.1% externally. The benchmark and code are publicly released to support research into safer, contrast-free abdominal imaging workflows.
HAMON uses passive optical diffraction to generate forecasts, outperforming digital baselines on ETTm2 at all horizons and ETTh2 at all but the longest horizon. It achieves up to 14% lower MSE and operates without trainable digital mixing, relying instead on physical optical propagation.
A new attack, RING, exploits differential privacy in federated learning to conceal backdoor signals while maximizing impact. It achieves 90.3% attack success against state-of-the-art defenses, up to 26.08x over baseline methods, and reveals a critical security gap in DP-FL due to inherent masking of malicious updates.