Failure-mode index

Search what already failed

A searchable index of real negative results, null findings, and replication failures from the published literature — so you can learn what didn't work before repeating it.

All Replication Failure Negative / Null Result Report Failed Experiment Report Abandoned Hypothesis Methods Dead-End Short Note Null Dataset Protocol

WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record (title, authors, DOI) compiled from open scholarly databases, with the abstract shown in full only where the paper is openly licensed (e.g. Creative Commons); otherwise a short excerpt is shown for reference under fair use. WASTE classifies each work by failure type; classifications are automated and approximate.

By fieldMedicine 12,368 Computer Science 765 Biochemistry, Genetics and Molecular Biology 476 Social Sciences 324 Psychology 323 Physics 280 Neuroscience 268 Economics, Econometrics and Finance 222 Environmental Science 194 Mathematics 163 Agricultural and Biological Sciences 161 Engineering 158

1934 results for "Mpro" · page 56 of 65

Negative / Null Result ReportOpen accessComputer Science

Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL

Zhaofeng Wu, Shiqi Wang, Boya Peng et al. · 2026 · arXiv

Modern language models demonstrate impressive coding capabilities in common programming languages (PLs), such as C++ and Python, but their performance in lower-resource PLs is often limited by training data availability. In principle, however, most programming skills are universal across PLs, so the capability acquired in one PL should transfer to others. In this work, we propose the task of zero-shot cross-programming-language transfer for code RL. We find that, for Llama-3.1, RL training for code generation in a source PL fails to improve, and sometimes even degrades, the performance on othe

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Negative / Null Result ReportOpen accessMathematics

Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework

Ziling Ma, Ángel López Oriona, Hernando Ombao et al. · 2026 · arXiv

We study adaptive pooling under predictive heterogeneity in high-dimensional multivariate time series forecasting, where global models improve statistical efficiency but may fail to capture heterogeneous predictive structure, while naive specialization can induce negative transfer. We formulate adaptive pooling as a statistical decision problem and propose a validation-driven framework that determines when and how specialization should be applied. Rather than grouping series based on representation similarity, we define partitions through out-of-sample predictive performance, thereby aligning

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Negative / Null Result ReportOpen accessMathematics

Inference in matrix-valued time series with common stochastic trends and multifactor error structure

Rong Chen, Simone Giannerini, Greta Goracci et al. · 2025 · arXiv

We develop an estimation methodology for a factor model for high-dimensional matrix-valued time series, where common stochastic trends and common stationary factors can be present. We study, in particular, the estimation of (row and column) loading spaces, of the common stochastic trends and of the common stationary factors, and the row and column ranks thereof. In a set of (negative) preliminary results, we show that a projection-based technique fails to improve the rates of convergence compared to a "flattened" estimation technique which does not take into account the matrix nature of the da

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Negative / Null Result ReportOpen accessComputer Science

Reinforcement Learning vs. Distillation: Understanding Accuracy and Capability in LLM Reasoning

Minwu Kim, Anubhav Shrestha, Safal Shrestha et al. · 2025 · arXiv

Recent studies have shown that reinforcement learning with verifiable rewards (RLVR) enhances overall accuracy (pass@1) but often fails to improve capability (pass@k) of LLMs in reasoning tasks, while distillation can improve both. In this paper, we investigate the mechanisms behind these phenomena. First, we demonstrate that RLVR struggles to improve capability as it focuses on improving the accuracy of the easier questions to the detriment of the accuracy of the most difficult questions. Second, we show that RLVR does not merely increase the success probability for the easier questions, but

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Negative / Null Result ReportOpen accessComputer Science

Toward Understanding Adversarial Distillation: Why Robust Teachers Fail

Hongsin Lee, Hye Won Chung · 2026 · arXiv

Adversarial Distillation aims to enhance student robustness by guiding the student with a robust teacher's soft labels within the min-max adversarial training framework, yet its success is notoriously inconsistent: a more robust teacher often fails to improve, or even harms, the student's robust generalization. In this paper, we identify a key mechanism of this teacher dependency: the misalignment between the teacher's supervisory confidence and the student's representational limitations on a consistent subset of training data -- the Robustly Unlearnable Set. We present a theoretical framework

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Negative / Null Result ReportOpen accessEngineering

A Unified Approach to Enforce Non-Negativity Constraint in Neural Network Approximation for Optimal Voltage Regulation

Jiaqi Wu, Jingyi Yuan, Yang Weng et al. · 2025 · arXiv

Power system voltage regulation is crucial to maintain power quality while integrating intermittent renewable resources in distribution grids. However, the system model on the grid edge is often unknown, making it difficult to model physical equations for optimal control. Therefore, previous work proposes structured data-driven methods like input convex neural networks (ICNN) for "optimal" control without relying on a physical model. While ICNNs offer theoretical guarantees based on restrictive assumptions of non-negative neural network parameters, can one improve the approximation power with

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Negative / Null Result ReportOpen accessEngineering

Learning-based Axial Video Motion Magnification

Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong et al. · 2023 · arXiv

Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted systems that have complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve legibility since the intricate motions still retain complex characteristics even after being magnified, which may di

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Negative / Null Result Report

A Case Report of Korean Medicine Treatment, including Jakyakgamcho-tang, in a Patient with Unilateral Vocal Cord Paralysis That Did Not Improve After Injection Laryngoplasty

Ye-seul Park, Ja-eun Kwak, Ho-ryong Yoo et al. · 2024 · The Journal of Internal Korean Medicine

Background: Unilateral vocal cord paralysis can occur due to various causes. Among them, unilateral vocal cord paralysis that takes place after endotracheal intubation is often caused by damage to the left recurrent nerve branch during…

View details →DOI: 10.22246/jikm.2024.45.6.1309