Papers where the original authors have engaged with our plain-language explanation.
For every paper on this page, at least one of the original authors has seen our plain-language explanation and engaged with it — either confirming it reads accurately or requesting corrections that we then applied. An endorsement does not mean the authors formally approve every sentence, but it does mean the explanation has passed the eyes of the people who wrote the paper.
956 papers reviewed by authors · 551–560 / 956
This paper presents the first systematic evaluation of transformer-based dementia detection in Filipino-English code-switched speech, demonstrating that while monolingual models fail to generalize across languages, bilingual fine-tuning effectively eliminates cross-lingual performance degradation and achieves high accuracy regardless of model architecture.
This paper introduces SiST-GNN, a novel dynamic graph neural network that unifies spatial and temporal message passing into a single operation by augmenting the graph with cross-time edges, thereby achieving state-of-the-art performance in link prediction and node classification across diverse benchmarks.
This paper introduces orthogonal bottlenecks, a lightweight, architecture-agnostic mechanism that constrains reinforcement learning representations to low-dimensional subspaces via fixed orthonormal projections, demonstrating both theoretically and empirically that task-relevant value functions can be preserved and often improved upon with minimal dimensionality while stabilizing feature geometry.
This paper presents a kinematic and transmission-aware design framework for a serial spherical microsurgical tool, featuring an analytical workspace formulation and a dynamics-informed methodology for self-locking transmissions, which are validated through experiments on a purpose-built robotic system for vitreoretinal surgery.
This paper systematically evaluates the cross-domain generalization limits of Vision Foundation Models in facial deepfake detection, revealing that while these models excel at identifying full-face synthesis, they struggle with localized editing techniques due to inherent trade-offs between pre-training paradigms and linear probe evaluation structures.
This paper proposes an energy-aware Multi-Agent Reinforcement Learning model utilizing Deep Q-Networks with individual reward functions to enhance the robustness, energy efficiency, and success rate of mission-oriented drone networks, particularly when scaling up environment size and agent numbers compared to traditional shared reward approaches.
This paper introduces a unified, branch-consistent substitution framework that converts integrals involving quadratic radicals into rational functions by expressing exponentials of principal inverse trigonometric functions in algebraic form, thereby recovering classical methods like Euler's and Weierstrass substitutions while reducing domain complexity and expression swell.
This paper introduces Test-Time Graph Search (TTGS), a lightweight, training-free planning wrapper that leverages the inherent geometric structure of existing offline goal-conditioned RL policies to dramatically improve success rates on long-horizon tasks without requiring additional supervision or parameter updates.
This study investigates the optimal hedging strategy for options on a two-asset portfolio when transaction costs are present, demonstrating through simulation that hedging with a correlated but non-underlying asset can be preferable to hedging with the correct asset if the correlation is sufficiently high and transaction costs are low, as determined by risk-adjusted value metrics.
This study demonstrates that while StyleGAN2-ADA-generated synthetic brain MRIs do not universally improve tumor classification across all model architectures, they provide a statistically significant accuracy boost for MobileViTV2 when using feature-space filtering at a 1:1 real-to-synthetic ratio, highlighting that augmentation utility depends on the specific classifier and data configuration rather than visual fidelity alone.