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Browse the failure-mode index

21,770 real negative results, null findings, and replication failures. Search the index →

WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record compiled from open scholarly databases; the abstract is shown in full only where the paper is openly licensed, otherwise a short excerpt under fair use. Classifications are automated and approximate.

Negative / Null Result ReportOpen accessComputer Science

Joint-sparse recovery from multiple measurements

Ewout van den Berg, Michael P. Friedlander · 2009 · arXiv

The joint-sparse recovery problem aims to recover, from sets of compressed measurements, unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an extension of the single-measurement-vector (SMV) problem widely studied in compressed sensing. We analyze the recovery properties for two types of recovery algorithms. First, we show that recovery using sum-of-norm minimization cannot exceed the uniform recovery rate of sequential SMV using $\ell_1$ minimization, and that there are problems that can be solved with one approach but not with the other. Second, we analyze

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Negative / Null Result ReportOpen accessAgricultural and Biological Sciences

Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants

Louis Jalouzot, Alexis Thual, Yair Lakretz et al. · 2025 · arXiv

We investigate optimal strategies for decoding perceived natural speech from fMRI data acquired from a limited number of participants. Leveraging Lebel et al. (2023)'s dataset of 8 participants, we first demonstrate the effectiveness of training deep neural networks to predict LLM-derived text representations from fMRI activity. Then, in this data regime, we observe that multi-subject training does not improve decoding accuracy compared to single-subject approach. Furthermore, training on similar or different stimuli across subjects has a negligible effect on decoding accuracy. Finally, we fin

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Negative / Null Result ReportOpen accessComputer Science

The Unlearnability Phenomenon in RLVR for Language Models

Yulin Chen, He He, Chen Zhao · 2026 · arXiv

Reinforcement Learning with Verifiable Reward (RLVR) has proven effective in improving Large Language Model's (LLM) reasoning ability. However, the learning dynamics of RLVR remain underexplored. In this paper, we reveal a counterintuitive phenomenon: among hard examples that the model initially struggles with, a substantial subset remains unlearnable even when correct rollouts are present. To understand the phenomenon, we first demonstrate that existing optimization and sampling techniques fail to resolve unlearnability. With cross-example gradient analysis, we show that unlearnable examples

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Negative / Null Result ReportOpen accessComputer Science

Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?

Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li · 2025 · arXiv

Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the key factors driving MAD's effectiveness remain unclear. In this work, we disentangle MAD into two key components--Majority Voting and inter-agent Debate--and assess their respective contributions. Through extensive experiments across seven NLP benchmarks, we find that Majority Voting alone accounts for most of the performance gains typically attributed to MAD. To explain this, we propose a theoretical framework that mo

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Negative / Null Result ReportOpen accessComputer Science

Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

Mitchell A. Gordon, Kevin Duh, Nicholas Andrews · 2020 · arXiv

Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40%) do not affect pre-training loss or transfer to downstream tasks a

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Negative / Null Result ReportOpen accessComputer Science

Linguistic Features for Readability Assessment

Tovly Deutsch, Masoud Jasbi, Stuart Shieber · 2020 · arXiv

Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing th

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Negative / Null Result ReportOpen accessComputer Science

Upper Energy Limit of Heavy Baryon Chiral Perturbation Theory in Neutral Pion Photoproduction

C. Fernandez-Ramirez, A. M. Bernstein · 2012 · arXiv

With the availability of the new neutral pion photoproduction from the proton data from the A2 and CB-TAPS Collaborations at Mainz it is mandatory to revisit Heavy Baryon Chiral Perturbation Theory (HBChPT) and address the extraction of the partial waves as well as other issues such as the value of the low-energy constants, the energy range where the calculation provides a good agreement with the data and the impact of unitarity. We find that, within the current experimental status, HBChPT with the fitted LECs gives a good agreement with the existing neutral pion photoproduction data up to $\s

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Negative / Null Result ReportOpen accessComputer Science

Source Coding When the Side Information May Be Delayed

Osvaldo Simeone, Haim H. Permuter · 2011 · arXiv

For memoryless sources, delayed side information at the decoder does not improve the rate-distortion function. However, this is not the case for more general sources with memory, as demonstrated by a number of works focusing on the special case of (delayed) feedforward. In this paper, a setting is studied in which the encoder is potentially uncertain about the delay with which measurements of the side information are acquired at the decoder. Assuming a hidden Markov model for the sources, at first, a single-letter characterization is given for the set-up where the side information delay is arb

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Negative / Null Result ReportOpen accessPhysics

Breakdown of Boltzmann-type Models for the Alignment of Self-propelled Rods

Patrick Murphy, Misha Perepelitsa, Ilya Timofeyev et al. · 2023 · arXiv

Studies in the collective motility of organisms use a range of analytical approaches to formulate continuous kinetic models of collective dynamics from rules or equations describing agent interactions. However, the derivation of these kinetic models often relies on Boltzmann's hypothesis of "molecular chaos", that correlations between individuals are short-lived. While this assumption is often the simplest way to derive tractable models, it is often not valid in practice due to the high levels of cooperation and self-organization present in biological systems. In this work, we illustrated this

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Negative / Null Result ReportOpen accessPhysics

Semi-Analytical Models for Lensing by Dark Halos: I. Splitting Angles

Li-Xin Li, Jeremiah P. Ostriker · 2000 · arXiv

We use the semi-analytical approach to analyze gravitational lensing of quasars by dark halos in various cold dark matter (CDM) cosmologies, in order to determine the sensitivity of the prediction probabilities of images separations to the input assumptions regarding halos and cosmologies. The mass function of dark halos is assumed to be given by the Press-Schechter function. The mass density profile of dark halos is alternatively taken to be the singular isothermal sphere (SIS), the Navarro-Frenk-White (NFW) profile, or the generalized NFW profile. The cosmologies include: the Einstein-de Sit

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Negative / Null Result ReportOpen accessPhysics

Accurate coarse-graining of small organic molecules in melts and thin films using density-dependent potentials

Sayan Dutta, Maria C. Lesniewski, Muhammad Nawaz Qaisrani et al. · 2025 · arXiv

Conjugated organic molecules play a central role in a wide range of optoelectronic devices, including organic light-emitting diodes, organic field-effect transistors, and organic solar cells. A major bottleneck in the computational design of these materials is the discrepancy between simulation and experimental time and length scales. Coarse-graining (CG) offers a promising solution to bridge this gap by reducing redundant degrees of freedom and smoothing the potential energy landscape, thereby significantly accelerating molecular dynamics simulations. However, standard CG models are typically

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Negative / Null Result Report

No significant effect of acupuncture on assisted pregnancy

L Rose · 2011 · Focus on Alternative and Complementary Therapies

Andersen D, Løssl K, Andersen AN, Fürbringer J, Bach H, Simonsen J, Larsen EC. Acupuncture on the day of embryo transfer: a randomized controlled trial of 635 patients. Reprod Biomed Online 2010; 21: 366–372.

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Negative / Null Result Report

Does Carbon Dioxide Have a Significant Dynamic Effect on Agricultural Productivity in Indonesia?

Aliasuddin Aliasuddin, Nanda Rahmi, Fathina Almahira Sakhi · 2026 · International Journal of Energy Economics and Policy

This study investigates the dynamic influence of carbon dioxide (CO₂) emissions, labor, and capital formation on agricultural productivity in Indonesia, utilizing annual data from 2000 to 2023 and employing an Autoregressive Distributed…

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Negative / Null Result Report

Temperature Assessment of Urban Rooftop Farming for Cooling Effect to Buildings Significant Determinant of Micro-Climate Change and Urban Heat Island Effects.

Kartik Sahu · 2026 · International Journal For Multidisciplinary Research

Abstract Recently the increasing of urbanization larger number of flats, buildings apartment and hotels are establish rapidly. For that the heat is increase due to the roof of the buildings and tremendous use air condition, hot air relies…

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Negative / Null Result ReportOpen accessComputer Science

A Personalized Data-Driven Generative Model of Human Repetitive Motion

Angelo Di Porzio, Marco Coraggio · 2025 · arXiv

The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities -- such as rehabilitation therapy, sports, and manufacturing -- is expected to increase as these technologies become more pervasive. Designing cognitive architectures and control strategies to drive these agents requires realistic models of human motion. Furthermore, recent research has shown that each person exhibits a unique velocity signature, highlighting how individual motor behaviors are both rich in variability and internally consistent. However, existing models only provide simplifie

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Negative / Null Result Report

The Significant Effect of Intravenous Methylprednisolone on Visual Recovery in a Case of Acute Central Retinal Artery Occlusion: A Case Report

Indranil Saha, Raveesh Bindal · 2024 · Nigerian Journal of Ophthalmology

A 62-year-old man presented with a 1-day history of sudden vision loss in the right eye, while the left eye remained unaffected. Upon examination, the best corrected visual acuity (BCVA) was finger counting in the right eye and 20/20 (0.00…

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Negative / Null Result ReportOpen accessComputer Science

The Devil is in Fine-tuning and Long-tailed Problems:A New Benchmark for Scene Text Detection

Tianjiao Cao, Jiahao Lyu, Weichao Zeng et al. · 2025 · arXiv

Scene text detection has seen the emergence of high-performing methods that excel on academic benchmarks. However, these detectors often fail to replicate such success in real-world scenarios. We uncover two key factors contributing to this discrepancy through extensive experiments. First, a \textit{Fine-tuning Gap}, where models leverage \textit{Dataset-Specific Optimization} (DSO) paradigm for one domain at the cost of reduced effectiveness in others, leads to inflated performances on academic benchmarks. Second, the suboptimal performance in practical settings is primarily attributed to the

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