machine learning

Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs

ESNN is introduced, an Equivariant Sheaf Neural Network that enriches this interaction by learning directed, matrix-valued transport between neighboring vector features while preserving exact Euclidean equivariance.

Curvature Cryptanalysis of Smooth Transformer Feed-Forward Networks

This work formalizes resulting Hessian collection as a partially symmetric decomposition to establish conditions for local identifiability and stability to exploit vector-output stencil reuse to reduce the structural query cost by a factor of 16.

Unsupervised Latent Space Alignment with Hyperspherical Geodesic Matching

HGA (Hyperspherical Gaussian Alignment), a method that directly optimizes a transformation between two latent spaces by maximizing a geometric measure of "fit" between them.

ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning

A formal analysis showing that joint optimization of the two objectives induces gradient conflict in early training, motivating the sequential design of ERR+, a two-phase RLVR framework grounded in this observation.

TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition

TransfHAR is implemented as a real-time smartwatch application that lets users define and expand their own activity set for personalized recognition from only a few demonstrations, and indicates that broad self-supervised wrist pretraining provides an effective foundation for on-demand fine-grained activity recognition.

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

This work introduces VBVR-Pro, a closed-loop testbed that makes native visual reasoning through generation trainable, verifiable, optimizable, and experimentally controllable, and identifies recurring failure modes of the prevalent VLM-as-a-judge paradigm.

PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics

Experiments on real deformable-object manipulation sequences show that PhysCoRe outperforms state-of-the-art baselines in prediction accuracy, and that its predicted confidence forms a reliable distribution across the object's geometry, providing a natural signal for future confidence-guided exploration.

Learning from agile software development

Self-organized Learning in Software Factory: Experiences and Lessons Learned

A set of themes that can potentially explain self-organization from the learning viewpoint are identified, which include self-decided learning goals and personalized learning outcomes, peer teaching through active collaboration, diversity is the key and the personal attitude towards the learning matters.

What Can Software Startuppers Learn from the Artistic Design Flow? Experiences, Reflections and Future Avenues

This paper aims contributing to this gap by studying the artistic design flow and the tools utilized by architects, industrial designers and artists, and proposes concrete ways to improve the current state-of-practice.

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.