✓ Reviewed

Author-Reviewed Papers

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.

1335 papers reviewed by authors · 281–290 / 1335

🤖 AI

Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM-Generated Autobiography Against the Documented Record of the Life It Describes

This paper presents the first quantified, scene-level audit of an LLM-generated autobiography against a ground-truth record, revealing a 96.7% verification failure rate dominated by "grounded drift" (invented scenes using real people and settings) and demonstrating that while grounding generation in the subject's actual corpus improves accuracy, substantial hallucination persists.

Heather Renze2026-08-26✓ Author reviewed ⓘ
💻 computer science

Beyond Sequential Hybrid Retrieval: A Parallel Framework for Accurate and Scalable RAG

This paper introduces PH-RAG, a parallel hybrid retrieval framework that simultaneously executes sparse and dense retrieval with fusion and reranking to achieve state-of-the-art accuracy and improved latency on open-domain question answering, outperforming complex agentic baselines without requiring knowledge graphs or iterative critics.

Dilawaiz Hameed, Mariam Shaiq, Ibrar ul Hassan Akhtar2026-08-26✓ Author reviewed ⓘ
🤖 AI

A Formal Methodological Framework for Auditing Robustness and Fidelity in Explainable AI: From Application to Trust Certification

This paper proposes a formal auditing framework that quantifies the robustness and fidelity of explainable AI methods to generate a Trust Score, demonstrating through a Madagascar malnutrition case study that high-performing models can yield uninformative explanations and that such auditing is essential for trustworthy decision-making in sensitive domains.

Rosa Elysabeth Ralinirina, Jean Christian Ralaivao, Niaiko Michaël Ralaivao, Alain Josué Ratovondrahona, Thomas Mahatody2026-08-26✓ Author reviewed ⓘ
⚛️ phenomenology

Revisiting SU(5){\rm SU}(5) Yukawa Sectors Through Quantum Corrections

This paper demonstrates that minimal SU(5)SU(5) Grand Unified Theories, previously considered incompatible with low-energy fermion observables due to restrictive tree-level Yukawa structures, can successfully reproduce the charged and neutral fermion mass spectra and mixing angles when one-loop quantum corrections and scalar mass splittings are taken into account.

Saurabh K. Shukla2026-08-26✓ Author reviewed ⓘ
💻 computer science

Genetic Fragmentation Gradient Descent: Failure-Constrained Scheduling for GPU-Sharing Clusters

This paper proposes Genetic Fragmentation Gradient Descent (GFGD), an efficient offline-online scheduler that uses a genetic algorithm to evolve lightweight, failure-constrained policies for GPU-sharing clusters, significantly reducing scheduling latency while improving job completion rates and resource utilization compared to prior simulation-heavy approaches.

Soeun Choi, Jaehyeong Sim2026-08-26✓ Author reviewed ⓘ
💻 computer science

A Lightweight Time--Frequency Fusion Network for Accurate Wastewater Quality Forecasting

This paper introduces TSF-Resonator, a lightweight time-frequency fusion network that achieves accurate and efficient wastewater quality forecasting by combining adaptive multi-scale temporal encoding with low-rank spectral analysis to overcome the computational and modeling limitations of existing Transformer-based approaches.

Jiahao Fu, Jianjun Sun, Gaunlin Li2026-08-26✓ Author reviewed ⓘ
💻 computer science

A Comparative Analysis of Machine Learning and Deep Learning Models for Forecasting Vietnam's Export Coffee Prices

This study demonstrates that the Gated Recurrent Unit (GRU) significantly outperforms both machine learning models and other deep learning architectures in forecasting Vietnam's Robusta coffee export prices during a period of severe structural breaks, while transparently addressing methodological limitations and providing actionable risk parameters for forward contracts.

Quang Phung Duy, Hai Dang Cao Ha2026-08-26✓ Author reviewed ⓘ