As several mathematical conjectures have recently been settled using large language models (LLMs), the mathematical community has formulated norms and recommendations regarding the publishing of such results. These norms do not cover the disclosure of the prompts and precise software setup used to obtain those results, nor do they require that results be formalized in a manner that allows for machine verification. I argue that both of these are essential. In addition, since LLM-obtained results may be hard to understand, human authors have the responsibility to invent intuitive explanations.
FiLM-GPNet is proposed, a geometry-conditioned network for wrapped-phase restoration that explicitly adapts to acquisition differences using Feature-wise Linear Modulation (FiLM) and a 7D per-pair geometry descriptor, supporting geometry-conditioned restoration as an effective alternative to fixed classical filtering across heterogeneous stacks.
Getnet Demil, Muhammad Farhan Humayun, Tomi Westerlund et al.· 0 citations
This work compares a ~28M-parameter autoregressive Tiny Recursive Model (TRM-AR) on natural-language-to-Python code generation against parameter-matched and depth-matched controls, tracking fit and generation across 40 epochs and three seeds.
This work gives the first known exploration of the connections between sparse matrix computation and spectral analysis by treating sparse matrices as two-dimensional signals and analyzing their frequency-domain representations through Fast Fourier Transform.
Ruifeng Zhang, Xipeng Shen· 0 citations
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APPSolver is introduced, a point-wise flow-prediction framework built around Adaptive Patch Partitioning (APP), a deterministic quadtree representation for fixed two-dimensional horizontal slices extracted from ship CFD simulations, characterized as a compact spatial representation with an explicit accuracy--efficiency trade-off.
Wen-Hua Huo, Fenglei Han, Wangyuan Zhao et al.· 0 citations
This work proposes that LLM web agents can learn simple environment observations at test time, and introduces trial steps for agents to decompose a complex environment observation into sub-modules, and implements a label-free learning method, Test-Time Environment Decomposition (TTED), to adapt agent behaviors with experience during inference.
Jun-Xuan Li, Zijun Liu, Zi-Yi Huang et al.· 0 citations
The signed random Fourier features (SRFF) technique is introduced, a generalization of RFF compatible with indefinite kernels whose inverse Fourier transform is absolutely integrable and speed up KDE in the case of multivariate compact kernels, which are generally not positive definite.
Common-Trace Factorized Constrained Proximal Policy Optimization (CT-PPO), a constrained Markov decision process that retains a return advantage at low, nominal, and high arrival loads, with the strongest gain under clustered arrivals.
A cooperative multi-agent orchestration framework, in which each edge node is equipped with a scheduling agent to route tasks to local ASPs or neighboring edge nodes, and an attention-guided centralized critic to estimate per-agent values from cross-agent states under GPU memory heterogeneity is proposed.
Chong-Zhi Wu, Zheng-Tao Li, Jia-Wen Kang et al.· 0 citations
QCell is presented, a novel query-based model that de-overlaps cell instances in microscopy scenes and outperforms state-of-the-art methods across multiple benchmarks, achieving +2.2 AP and +2.7 AJI on ISBI2014.
Yaroslav Prytula, A. Popov, Dmytro Fishman· 0 citations
This paper provides a practical, auditable, and application-agnostic framework for validating LLM-augmented recipe data, thereby strengthening the foundations of machine-readable food knowledge infrastructures in the era of LLM-generated content.
A novel metric derived from the flow matching trajectory curvature is introduced to quantify action generation confidence during inference and enables the dynamic reduction of inference steps and the adaptive adjustment of MLP pruning ratios through an efficiently computed importance evaluation, requiring no access to training data.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
