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.
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.
20289 results in Negative / Null Result Report · page 676 of 677
Myron Bander · 1996 · arXiv
Possible spin-3/2 quark resonances would have a significant effect on high E$_{\mbox{\rm t}}$ jet production through their contribution to the subprocess $q+{\bar q}\rightarrow g+g$. Such enhancements are compared to a, recently reported, anomaly in inclusive jet production at the CDF detector.
Nzar Rauf Abdullah, Gullan Ahmed Mohammed, Hunar Omar Rashid et al. · 2020 · arXiv
We investigate the electronic, thermal, and optical characteristics of graphene like SiC$_x$ structure using model calculations based on density functional theory. The change in the energy bandgap can be tuned by the Si atomic configuration, rather than the dopants ratio. The effects of the concentration of the Si atoms and the shape of supercell are kept constant, and only the interaction effects of two Si atoms are studied by varying their positions. If the Si atoms are at the same sublattice positions, a maximum bandgap is obtained leading to an increased Seebeck coefficient and figure of m
Angel J. Moreno, Federica Lo Verso · 2018 · arXiv
We present computer simulations of a realistic model of microgels. Unlike the regular network frameworks usually assumed in the simulation literature, we model and simulate a realistic and efficient synthesis route, mimicking cross-linking of functionalized chains inside a cavity. This model is inspired, e.g., by microfluidic fabrication of microgels from macromolecular precursors and is different from standard polymerization routes. The assembly of the chains is mediated by a low fraction of interchain crosslinks. The microgels are polydisperse in size and shape but globally spherical objects
A. Lançon, J. S. Gallagher, M. Mouhcine et al. · 2008 · arXiv
Like other starburst galaxies, M82 hosts compact, massive young star clusters that are interesting both in their own right and as benchmarks for population synthesis models. Can spectral synthesis models at resolutions around 1000 adequately reproduce the near-IR spectral features and the energy distribution of these clusters between 0.8 and 2.4 microns? How do the derived cluster properties compare with previous results from optical studies? We analyse the spectra of 5 massive clusters in M82, using data acquired with the spectrograph SpeX on the InfraRed Telescope Facility (NASA/IRTF) and a
Marco Cognetta, Tatsuya Hiraoka, Naoaki Okazaki et al. · 2024 · arXiv
We explore threshold vocabulary trimming in Byte-Pair Encoding subword tokenization, a postprocessing step that replaces rare subwords with their component subwords. The technique is available in popular tokenization libraries but has not been subjected to rigorous scientific scrutiny. While the removal of rare subwords is suggested as best practice in machine translation implementations, both as a means to reduce model size and for improving model performance through robustness, our experiments indicate that, across a large space of hyperparameter settings, vocabulary trimming fails to improv
Janarthanan Rajendran, Jonathan K. Kummerfeld, Satinder Singh · 2021 · arXiv
For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and time-consuming process. Instead, we show that we can use only a small amount of data, supplemented with data from a related dialog task. Naively learning from related data fails to improve performance as the related data can be inconsistent with the target task. We describe a meta-learning based method that selectively learns from the related dialog task data. Our approach leads to significant accuracy improvements in
Dhruvi Khandelwal, Anurag Basistha, Ayushi Jolotia et al. · 2026 · arXiv
Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts a constructive thought experiment to answer a fundamental inquiry: how wide must a neural network be to almost accurately approximate the ACOPF manifold? We introduce a Loss-Guided Neural Densification (LG-ND) algorithm that incrementally discovers necessary capacity by expanding only when the current deep neural network topology fails to improve further. Empirical results across various IEEE systems show that LG-ND achieves performance parity
Runzhe Zhan, Xuebo Liu, Derek F. Wong et al. · 2021 · arXiv
Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the translation performance of the domain unseen at the meta-training stage. In this paper, we aim to alleviate this issue by proposing a novel meta-curriculum learning for domain adaptation in NMT. During meta-training, the NMT first learns the similar curricula from each domain to avoid falling into a bad local optimum early, and finally learns the curricula of individualities to improve the model robustness for learning dom
Arijit Sehanobish, Charles Lovering · 2026 · arXiv
We study the \textit{parameter placement problem}: given a fixed budget of $k$ trainable entries within the B matrix of a LoRA adapter (A frozen), does the choice of which $k$ matter? Under supervised fine-tuning, random and informed subsets achieve comparable performance. Under GRPO on base models, random placement fails to improve over the base model, while gradient-informed placement recovers standard LoRA accuracy. This regime dependence traces to gradient structure: SFT gradients are low-rank and directionally stable, so any subset accumulates coherent updates; GRPO gradients are high-ran
Piyawat Lertvittayakumjorn, David Kinney, Vinodkumar Prabhakaran et al. · 2025 · arXiv
Generative large language models (LLMs) have demonstrated gaps in diverse cultural awareness across the globe. We investigate the effect of retrieval augmented generation and search-grounding techniques on LLMs' ability to display familiarity with various national cultures. Specifically, we compare the performance of standard LLMs, LLMs augmented with retrievals from a bespoke knowledge base (i.e., KB grounding), and LLMs augmented with retrievals from a web search (i.e., search grounding) on multiple cultural awareness benchmarks. We find that search grounding significantly improves the LLM p
Francis X. Diebold, Maximilian Goebel, Philippe Goulet Coulombe · 2022 · arXiv
We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regression (FELR) forecasts of Diebold and Goebel (2021), and to compare FELR forecasts to naive pure-trend benchmark forecasts. Then we introduce a much more sophisticated feature-engineered machine learning (FEML) model, and we use glide charts to evaluate FEML forecasts and compare them to a FELR benchmark. Our substantive results include the freque
Fırat Öncel, Matthias Bethge, Beyza Ermis et al. · 2024 · arXiv
In the last decade, the generalization and adaptation abilities of deep learning models were typically evaluated on fixed training and test distributions. Contrary to traditional deep learning, large language models (LLMs) are (i) even more overparameterized, (ii) trained on unlabeled text corpora curated from the Internet with minimal human intervention, and (iii) trained in an online fashion. These stark contrasts prevent researchers from transferring lessons learned on model generalization and adaptation in deep learning contexts to LLMs. To this end, our short paper introduces empirical ob
Adam Byerly, Daniel Khashabi · 2024 · arXiv
Self-consistency (SC) improves the performance of large language models (LLMs) across various tasks and domains that involve short content. However, does this support its effectiveness for long-context problems? We challenge the assumption that SC's benefits generalize to long-context settings, where LLMs often struggle with position bias, the systematic over-reliance on specific context regions-which hinders their ability to utilize information effectively from all parts of their context. Through comprehensive experimentation with varying state-of-the-art models, tasks, and SC formulations, w
Chunliang Li, Tianze Cao, Sanyuan Zhao · 2026 · arXiv
Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While existing methods accelerate inference by pruning tokens using frequency maps, their binary hard-pruning approach is fundamentally limited and fails to improve quality even with better frequency estimation. Observing that VAR models possess significant depth redundancy, we propose a paradigm shift from pruning entire tokens to adaptively allocating per-token computational depth. To this end, we introduce DepthVAR, a training-free framework that dy
Chiara Lanza, Roberto Pereira, Marco Miozzo et al. · 2026 · arXiv
Centralized training is the standard paradigm in deep learning, enabling models to learn from a unified dataset in a single location. In such setup, isotropic feature distributions naturally arise as a mean to support well-structured and generalizable representations. In contrast, continual learning operates on streaming and non-stationary data, and trains models incrementally, inherently facing the well-known plasticity-stability dilemma. In such settings, learning dynamics tends to yield increasingly anisotropic feature space. This arises a fundamental question: should isotropy be enforced t
Zhou, Cao, You et al. · 2026 · BMC oral health
Photogrammetry technique may provide a promising approach compared to conventional techniques for multiple implants. However, the accuracy of photogrammetric technique for implant-supported fixed complete dentures in clinical scenarios…
Guowei Liu, Hongming Li, Yaning Guo et al. · 2026 · arXiv
Deploying large-scale MoE models presents challenges in memory capacity and bandwidth for expert activation. While Attention-FFN Disaggregation (AFD) has emerged as a potential architecture to decouple compute and memory resources, its performance boundaries compared to standard large-scale Expert Parallelism (EP) remain underexplored. In this paper, we conduct a systematic analysis of AFD by extending the roofline model to the communication level, correlating interconnect bandwidth, arithmetic intensity, and Hardware FLOPS Utilization (HFU). Our analysis reveals a dead zone on standard cluste
Julian Coda-Forno, Zhuokai Zhao, Qiang Zhang et al. · 2025 · arXiv
Should LLM reasoning live in a separate module, or within a single model's forward pass and representational space? We study dual-architecture latent reasoning, where a fluent Base exchanges latent messages with a Coprocessor, and test two hypotheses aimed at improving latent communication over Liu et al. (2024): (H1) increase channel capacity; (H2) learn communication via joint finetuning. Under matched latent-token budgets on GPT-2 and Qwen-3, H2 is consistently strongest while H1 yields modest gains. A unified soft-embedding baseline, a single model with the same forward pass and shared rep
Sepanta Zeighami, Cyrus Shahabi · 2024 · arXiv
While extremely useful (e.g., for COVID-19 forecasting and policy-making, urban mobility analysis and marketing, and obtaining business insights), location data collected from mobile devices often contain data from a biased population subset, with some communities over or underrepresented in the collected datasets. As a result, aggregate statistics calculated from such datasets (as is done by various companies including Safegraph, Google, and Facebook), while ignoring the bias, leads to an inaccurate representation of population statistics. Such statistics will not only be generally inaccurate
Dong Xu, Jiantao Wu, Qihua Pan et al. · 2026 · arXiv
Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmarks, they often fail to generalize to realistic deployment scenarios, where most candidate drug pairs involve previously unseen drugs and validated interactions are scarce. We demonstrate that proximity in the embedding spaces of prevailing molecule-centric DDI models does not reliably correspond to interaction labels, and that simply scaling up mode
Romain Cosentino, Sarath Shekkizhar, Adam Earle et al. · 2026 · arXiv
Negotiation requires more than inferring what the other side wants: it requires using that information to make advantageous offers and counteroffers over multiple turns. We study whether large language model (LLM) agents do this in a controlled multi-attribute bargaining environment. We find that current LLM agents can model a counterparty's preferences, but do not reliably turn that knowledge into strategic bargaining. When given negotiating partner preference information, agents model it accurately and early in their reasoning traces, yet this does not reliably improve outcomes for the infor
Mike Zhang, Ali Basirat, Desmond Elliott · 2026 · arXiv
Prior work establishes that controlled contrastiveness between self-generated responses from large language models, set via reward scores, improves downstream preference tuning in English. We extend this method to multiple languages and evaluate two models across a total of 14 high and low-resource languages on a diverse set of tasks. Our central finding is that cross-lingual contrastive preference tuning on self-generations (CroCo) transfers without language-specific preference annotation. A reward model trained on English preferences (atop a multilingual base) produces useful within-language
Aaron Baier-Reinio, Hans De Sterck · 2020 · arXiv
We use neural ordinary differential equations to formulate a variant of the Transformer that is depth-adaptive in the sense that an input-dependent number of time steps is taken by the ordinary differential equation solver. Our goal in proposing the N-ODE Transformer is to investigate whether its depth-adaptivity may aid in overcoming some specific known theoretical limitations of the Transformer in handling nonlocal effects. Specifically, we consider the simple problem of determining the parity of a binary sequence, for which the standard Transformer has known limitations that can only be ove
Escoffier, Hedhli, Campos-Juanatey et al. · 2026 · The French journal of urology
Artificial intelligence (AI) is increasingly used in surgery, but its role in reconstructive urology remains insufficiently studied. The aim of this work was to evaluate the theoretical knowledge of several AI platforms and compare their…
Philipp Spohn, Leander Girrbach, Zeynep Akata · 2026 · arXiv
Typical LLM responses tend to follow a default style, even though users often have distinct preferences regarding tone, verbosity, and formality that they do not explicitly state in their prompts. Evaluating whether personalization methods can adapt to these implicit preferences is challenging, since users typically provide prompts rather than reference responses, style preferences are not factually verifiable, and reference-free LLM judges may conflate personalization with general response quality. To address these challenges, we introduce the Arbitrary Preference Mapping (APM) benchmark, whi
Mengqi Wang, Zhan Liu, Zengrui Jin et al. · 2025 · arXiv
Diffusion-based large language models (DLLMs) have recently attracted growing interest as an alternative to autoregressive decoders. In this work, we present an empirical study on using the diffusion-based large language model LLaDA for automatic speech recognition (ASR). We first investigate its use as an external deliberation-based processing module for Whisper-LLaMA transcripts. By leveraging the bidirectional attention and denoising capabilities of LLaDA, we explore random masking, low-confidence masking, and semi-autoregressive strategies, showing that Whisper-LLaDA substantially reduces
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
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
Mujtaba Farhan, Maheep Chaudhary · 2026 · arXiv
Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Continuous Thought) paradigm~\cite{hao2024coconut} extends this by enabling models to reason in latent space, exploring multiple reasoning paths simultaneously rather than committing to a single chain early on. However, we identify a limitation we term the \textbf{concept bottleneck}. At each reasoning pass, intermediate hidden states are overwritten, causing the model to lose critical facts computed in earlier steps as reasoning depth increases. We
Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev · 2020 · arXiv
Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks. However, it is difficult to model the true dynamics of the real world before execution, especially for tasks requiring interactions with objects whose parameters are unknown. A recent planning approach, CMAX, tackles this problem by adapting the planner online during execution to bias the resulting plans away from inaccurately modeled regions. CMAX, while being provably guaranteed to reach the goal, requires strong assumptions on the accuracy