31 real negative results, null findings, and replication failures in Computer Science · Failed Experiment Report. 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.
Lori G. Beaman, Dean Karlan, Bram Thuysbaert et al. · 2013 · American Economic Review
We conducted an experiment providing fertilizer grants to female rice farmers in Mali. We found that women who received fertilizer used both more fertilizer and more complementary inputs such as herbicides and hired labor. This shows that…
View details →Xuhan Liu, Kai Ye, Herman van Vlijmen et al. · 2023 · Journal of Cheminformatics
Abstract Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx , which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input a
View details →Jihan Yang, Shusheng Yang, Anjali Gupta et al. · 2025
Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scale video datasets also "think in space" from videos? We…
View details →Nitin Chitranshi, Ashutosh Kumar, Samran Sheriff et al. · 2021 · Cells
Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer's disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer's drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act a
View details →Xinzhe Chen, Jianjiang Li · 2023 · arXiv
The escalating surge in data generation presents formidable challenges to information technology, necessitating advancements in storage, retrieval, and utilization. With the proliferation of artificial intelligence and big data, the "Data Age 2025" report forecasts an exponential increase in global data production. The escalating data volumes raise concerns about efficient data processing. The paper addresses the predicament of achieving a lower compression ratio while maintaining or surpassing the compression performance of state-of-the-art techniques. This paper introduces a lossy compressio
View details →Carmine Abate, Arthur Azevedo de Amorim, Roberto Blanco et al. · 2018 · arXiv
We propose a new formal criterion for evaluating secure compilation schemes for unsafe languages, expressing end-to-end security guarantees for software components that may become compromised after encountering undefined behavior---for example, by accessing an array out of bounds. Our criterion is the first to model dynamic compromise in a system of mutually distrustful components with clearly specified privileges. It articulates how each component should be protected from all the others---in particular, from components that have encountered undefined behavior and become compromised. Each comp
View details →Sándor Kisfaludi-Bak, Jesper Nederlof, Erik Jan van Leeuwen · 2018 · arXiv
The Planar Steiner Tree problem is one of the most fundamental NP-complete problems as it models many network design problems. Recall that an instance of this problem consists of a graph with edge weights, and a subset of vertices (often called terminals); the goal is to find a subtree of the graph of minimum total weight that connects all terminals. A seminal paper by Erickson et al. [Math. Oper. Res., 1987] considers instances where the underlying graph is planar and all terminals can be covered by the boundary of $k$ faces. Erickson et al. show that the problem can be solved by an algorithm
View details →V. Vedral · 1999 · arXiv
We investigate asymptotic distillation of entanglement in the presence of an unlimited amount of bound entanglement for bi-partite systems. We show that the distillability is still bounded by the relative entropy of entanglement. This offers a strong support to the fact that bound entanglement does not improve distillation of entanglement.
View details →J. A. Casas, A. Lleyda, C. Muñoz · 1996 · arXiv
The general constraints on the parameter space of soft-breaking terms, in order to avoid dangerous charge and color breaking minima, are applied to the four-dimensional string scenario where the dilaton is the source of supersymmetry breaking (dilaton-dominated limit). The results indicate that the whole parameter space is excluded on these grounds after imposing the present experimental data on the top mass. The inclusion of a non-vanishing cosmological constant does not improve essentially the prospects. Possible way-outs to this situation are briefly discussed.
View details →Henrique K. Miyamoto, Sheng Yang · 2026 · arXiv
This paper studies random-coding error exponents of randomised list decoding, in which the decoder randomly selects $L$ messages with probabilities proportional to the decoding metric of the codewords. The exponents (or bounds) are given for mismatched, and then particularised to matched and universal decoding metrics. Two regimes are studied: for fixed list size, we derive an ensemble-tight random-coding error exponent, and show that, for the matched metric, it does not improve the error exponent of ordinary decoding. For list sizes growing exponentially with the block-length, we provide a no
View details →Roman Aperdannier, Sigurd Schacht, Alexander Piazza · 2024 · arXiv
Speaker diarization answers the question "who spoke when" for an audio file. In some diarization scenarios, low latency is required for transcription. Speaker diarization with low latency is referred to as online speaker diarization. The DIART pipeline is an online speaker diarization system. It consists of a segmentation and an embedding model. The embedding model has the largest share of the overall latency. The aim of this paper is to optimize the inference latency of the DIART pipeline. Different inference optimization methods such as knowledge distilation, pruning, quantization and layer
View details →Yu-Jia Wang, Feng-Kun Guo, Cen Zhang et al. · 2020 · arXiv
Recently, a new set of positivity bounds with $t$ derivatives have been discovered. We explore the generic features of these generalized positivity bounds with loop amplitudes and apply these bounds to constrain the parameters in chiral perturbation theory up to the next-to-next-to-leading order. We show that the generalized positivity bounds give rise to stronger constraints on the $\bar l_i$ constants, compared to the existing axiomatic bounds. The parameter space of the $b_i$ constants is constrained by the generalized positivity bounds to be a convex region that is enclosed for many sectio
View details →Liang Feng Zhang · 2013 · arXiv
Baranyai's theorem is a well-known theorem in the theory of hypergraphs. A corollary of this theorem says that one can partition the family of all $u$-subsets of an $n$-element set into ${n-1\choose u-1}$ sub-families such that each sub-family form a partition of the $n$-element set, where $n$ is divisible by $u$. In this paper, we present a coding-theoretic application of Baranyai's theorem (or equivalently, the corollary). More precisely, we propose the first purely combinatorial construction of locally decodable codes. Locally decodable codes are error-correcting codes that allow the recove
View details →Florian Domingo, Sebastian Paßehr · 2021 · arXiv
The connection between gauge and Higgs sectors makes supersymmetric extensions of the Standard Model predictive frameworks for the derivation of Higgs masses. In this paper, we study the contamination of such predictions by field-renormalization constants, in the MSSM with two-loop gaugeless corrections of $\mathcal{O}{\left(α_{t,b}\,α_s,\,α_{t,b}^2\right)}$ and full momentum dependence, and demonstrate how strict perturbative expansions allow to systematically neutralize the dependence on such unphysical objects. On the other hand, the popular procedure consisting in an iterative pole search
View details →Marco Pop-Mihali, Adrian Groza · 2023 · arXiv
We aim at development white-box machine learning algorithms. We focus here on algorithms for learning axioms in description logic. We extend the Class Expression Learning for Ontology Engineering (CELOE) algorithm contained in the DL-Learner tool. The approach uses multiple search trees and a shared pool of refinements in order to split the search space in smaller subspaces. We introduce the conjunction operation of best class expressions from each tree, keeping the results which give the most information. The aim is to foster exploration from a diverse set of starting classes and to streamlin
View details →Antoine Suarez · 2014 · arXiv
A Michelson-Morley-type experiment is described, which exploits two-photon interference between entangled photons instead of classical light interference. In this experimental context, the negative result (no shift in the detection rates) rules out David Bohm's postulate of an infinite-speed time-ordered "quantum potential", and thereby upholds the timeless standard quantum collapse.
View details →David Berenstein · 2006 · arXiv
This paper studies the compatibility of having a grand unification scheme for particle physics, while at the same time having a perturbative string theory description of such a scheme on a D-brane. This is studied in a model independent approach and finds a negative result. Some additional observations related to model building on branes are made.
View details →Yuri Ozhigov · 2019 · arXiv
Experimental attempts to implement quantum speedup of computations over the past 30 years have yielded a negative result, despite the absence of physical laws prohibiting such speedup. The article formulates the limitation of quantum formalism in the form of uncertainty "the complexity of the system - the accuracy of its description at the quantum level", and provides arguments in favor of its physical status. An experiment to determine this constant through Grover's algorithm is described. Rough estimates on the constant of this ratio are given, based on the possibility of applying the quantu
View details →G. Hathaway, L. L. Williams · 2021 · arXiv
We report test results searching for an effect of electrostatic charge on weight. For conducting test objects of mass of order 1 kilogram, we found no effect on weight, for potentials ranging from 10 V to 200 kV, corresponding to charge states ranging from $10^{-9}$ to over $10^{-5}$ coulombs, and for both polarities, to within a measurement precision of 2 grams. While such a result may not be unexpected, this is the first unipolar, high-voltage, meter-scale, static test for electro-gravitic effects reported in the literature. Our investigation was motivated by the search for possible coupling
View details →Frits de Nijs, Daan Wilmer · 2012 · arXiv
The goal of this paper is to critically evaluate a heuristic algorithm for the Inverse Banzhaf Index problem by Laruelle and Widgrén. Few qualitative results are known about the approximation quality of the heuristics for this problem. The intuition behind the operation of this approximation algorithm is analysed and evaluated. We found that the algorithm can not handle general inputs well, and often fails to improve inputs. It is also shown to diverge after only tens of iterations. We present three alternative extensions of the algorithm that do not alter the complexity but can result in up t
View details →Alan Lee, Harry Tong · 2025 · arXiv
We propose reinforcement learning (RL) strategies tailored for reasoning in large language models (LLMs) under strict memory and compute limits, with a particular focus on compatibility with LoRA fine-tuning. Building on early policy gradient methods with baseline subtraction, we design critic-free methods that operate on a small, informative subset of output tokens to reduce memory usage and stabilize training. We introduce S-GRPO, a stochastic variant of Group Relative Policy Optimization, and T-SPMO, a token-level prefix matching approach for fine-grained credit assignment. Applied to Qwen2
View details →Zixian Huang, Kaichen Yang, Xu Huang et al. · 2026 · arXiv
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like Qwen3-8B, this approach often fails to improve reasoning capabilities and can even lead to a substantial drop in performance. In this work, we identify substantial stylistic divergence between teacher generated data and the distribution of student as a major factor impacting SFT. To bridge this gap, we propose a Teacher-Student Cooperation Data Synthesis framework (TESSY), which interleaves teacher and student model
View details →Chen-Rong Liu, Chuang Li, Runxia Tao et al. · 2026 · arXiv
Conventional noise analysis in atomic-ensemble sensing assumes a continuous-medium approximation, thereby treating the atomic system as a deterministic dielectric. Here, we demonstrate that this assumption breaks down due to the discrete, particulate nature of the ensemble, giving rise to an intrinsic "atomic granularity noise" (AGN) that fundamentally competes with the optical measurement noise (OMN, typically photon shot noise). By introducing a discrete-atom statistical framework, we derive a unified noise-scaling law governed by a single dimensionless resource ratio, $\mathcal{R} = \bar{N}
View details →Madison Van Doren, Casey Ford, Emily Dix · 2025 · arXiv.org
Multimodal large language models (MLLMs) are increasingly used in real world applications, yet their safety under adversarial conditions remains underexplored. This study evaluates the harmlessness of four leading MLLMs (GPT-4o, Claude…
View details →Sofia Morgado, Filipa Valdeira, Niklas Sander et al. · 2026 · arXiv
Coronary angiography (CAG) reports contain clinically relevant physiological measurements, yet this information is typically in the form of unstructured natural language, limiting its use in research. We investigate the use of Large Language Models (LLMs) to automatically extract these values, along with their anatomical locations, from Portuguese CAG reports. To our knowledge, this study is the first addressing physiology indexes extraction from a large (1342 reports) corpus of CAG reports, and one of the few focusing on CAG or Portuguese clinical text. We explore local privacy-preserving gen
View details →D. Kiderlen, P. Danielewicz · 1996 · arXiv
Generalization of Gaussian trial wave functions in quantum molecular dynamics models is introduced, which allows for long-range correlations characteristic for composite nuclear fragments. We demonstrate a significant improvement in the description of light fragments with correlations. Utilizing either type of Gaussian wave functions, with or without correlations, however, we find that we cannot describe fragment formation in a dynamic situation. Composite fragments are only produced in simulations if they are present as clusters in the substructure of original nuclei. The difficulty is traced
View details →P. Danielewicz, D. Kiderlen · 1997 · arXiv
Generalization of Gaussian trial wave functions in quantum molecular dynamics models is introduced, which allows for long-range correlations characteristic for composite nuclear fragments. We demonstrate a significant improvement in the description of light fragments with the correlations. Utilizing either type of Gaussian wave functions, with or without correlations, however, we find that we cannot describe fragment formation in a dynamic situation. Composite fragments are only produced in simulations if these fragments are present as clusters in the substructure of original nuclei. The diffi
View details →Adeel Mahmood, Aaron B. Wagner · 2024 · arXiv
Channel coding for discrete memoryless channels (DMCs) with mean and variance cost constraints has been recently introduced. We show that there is an improvement in coding performance due to cost variability, both with and without feedback. We demonstrate this improvement over the traditional almost-sure (per-codeword) cost constraint that prohibits any cost variation above a fixed threshold. Our result simultaneously shows that feedback does not improve the second-order coding rate of simple-dispersion DMCs under the almost-sure cost constraint. This finding parallels similar results for unco
View details →Tilman Plehn, Michael Spannowsky, Michihisa Takeuchi · 2011 · arXiv
In time for the first tests on LHC data we introduce a set of improvements and tests of purely kinematic top tagging algorithms. First, we show how different jet algorithms can be used for different transverse momentum regimes. Combining pruning and filtering in the reconstruction can enhance the signal over background ratio significantly, while larger jet radii only give minor improvements. Finally, bottom tagging can be added to the top tagger, but at least for the HEPTopTagger does not improve the kinematic selection algorithm.
View details →Jayoo Hwang, Xiaowen Zhang, Vedant Padwal · 2026 · arXiv
Autonomous web navigation remains challenging for LLM agents, and the strongest generalist systems rely on proprietary reasoning models whose inference cost is prohibitive for the repetitive tasks where such agents would be most useful. We argue this gap stems not from insufficient model capability but from agent architectures that fail to replicate three human cognitive advantages: selective attention to relevant page regions, persistent memory of website structure, and procedural fluency with common interaction patterns. We introduce WebChallenger, a web agent framework that addresses each g
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