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Detecting semantic backdoors in classification models--where some classes can be activated by certain natural, but out-of-distribution inputs--is an important problem that has received relatively little attention. Semantic backdoors are significantly harder to detect than backdoors that are based on trigger patterns due to the lack of such clearly identifiable patterns. We tackle this problem under the assumption that the clean training dataset and the training recipe of the model are both known. These assumptions are motivated by a consumer protection scenario, in which the responsible authority performs mystery shopping to test a machine learning service provider. In this scenario, the authority uses the provider's resources and tools to train a model on a given dataset and tests whether the provider included a backdoor. In our proposed approach, the authority creates a reference model pool by training a small number of clean and poisoned models using trusted infrastructure, and calibrates a model distance threshold to identify clean models. We propose and experimentally analyze a number of approaches to compute model distances and we also test a scenario where the provider perfo

We present a novel data set, WhoDunIt, to assess the deductive reasoning capabilities of large language models (LLM) within narrative contexts. Constructed from open domain mystery novels and short stories, the dataset challenges LLMs to identify the perpetrator after reading and comprehending the story. To evaluate model robustness, we apply a range of character-level name augmentations, including original names, name swaps, and substitutions with well-known real and/or fictional entities from popular discourse. We further use various prompting styles to investigate the influence of prompting on deductive reasoning accuracy. We conduct evaluation study with state-of-the-art models, specifically GPT-4o, GPT-4-turbo, and GPT-4o-mini, evaluated through multiple trials with majority response selection to ensure reliability. The results demonstrate that while LLMs perform reliably on unaltered texts, accuracy diminishes with certain name substitutions, particularly those with wide recognition. This dataset is publicly available here.

Multi-agent systems utilizing large language models (LLMs) have shown great promise in achieving natural dialogue. However, smooth dialogue control and autonomous decision making among agents still remain challenges. In this study, we focus on conversational norms such as adjacency pairs and turn-taking found in conversation analysis and propose a new framework called "Murder Mystery Agents" that applies these norms to AI agents' dialogue control. As an evaluation target, we employed the "Murder Mystery" game, a reasoning-type table-top role-playing game that requires complex social reasoning and information manipulation. In this game, players need to unravel the truth of the case based on fragmentary information through cooperation and bargaining. The proposed framework integrates next speaker selection based on adjacency pairs and a self-selection mechanism that takes agents' internal states into account to achieve more natural and strategic dialogue. To verify the effectiveness of this new approach, we analyzed utterances that led to dialogue breakdowns and conducted automatic evaluation using LLMs, as well as human evaluation using evaluation criteria developed for the Murder M

Over the past three decades, a lot of coronal fast-mode waves were detected by space missions, but their counterparts in the chromosphere, called the Moreton waves, were rarely captured. How this happens remains a mystery. Here, to shed light on this problem, we investigate the photospheric vector magnetograms of the Moreton wave events associated with M- and X-class solar flares in 2010--2023. The H$α$ data are taken with the Global Oscillation Network Group (GONG) and the Chinese H$α$ Solar Explorer (CHASE). Our statistical results show that more than 80\% of the events occur at the edge of active regions and propagate non-radially due to asymmetric magnetic fields above the flares. According to the reconstructed magnetic field and atmospheric model, Moreton waves propagate in the direction along which the horizontal fast-mode wave speed drops the fastest. The result supports that the inclined magnetic configuration of the eruption is crucial to generate Moreton waves, even for X-class flares. It may explain the low occurrence rate of Moreton waves and why some X-class flares accompanied with coronal mass ejections (CMEs) do not generate Moreton waves.

We introduce WellPlay, a reasoning dataset for multi-agent conversational inference in Murder Mystery Games (MMGs). WellPlay comprises 1,482 inferential questions across 12 games, spanning objectives, reasoning, and relationship understanding, and establishes a systematic benchmark for evaluating agent reasoning abilities in complex social settings. Building on this foundation, we present PLAYER*, a novel framework for Large Language Model (LLM)-based agents in MMGs. MMGs pose unique challenges, including undefined state spaces, absent intermediate rewards, and the need for strategic reasoning through natural language. PLAYER* addresses these challenges with a sensor-based state representation and an information-driven strategy that optimises questioning and suspect pruning. Experiments show that PLAYER* outperforms existing methods in reasoning accuracy, efficiency, and agent-human interaction, advancing reasoning agents for complex social scenarios.

This review describes recent significant research developments made on the layered perovskite Sr2RuO4 and discusses current issues from both experimental and theoretical perspectives. Since the discovery of superconductivity in Sr2RuO4 in 1994, studies using high-quality single crystals quickly revealed it to be an archetypal unconventional superconductor among strongly correlated electron systems. In particular, it was thought that the spin-triplet chiral p-wave superconducting state, which breaks time-reversal symmetry, was a prominent possibility. In 2019, however, a new development overturned the past experimental results, and spin-singlet-like behavior became conclusive. Furthermore, innovation in uniaxial strain devices has stimulated researchers to explore changes in the superconducting state by controlling the symmetry and dimensionality of the Fermi surfaces and enhancing the superconducting transition temperature Tc from 1.5 K to 3.5 K. A spin-singlet chiral d-wave superconducting state is consistent with most of these recent experimental results. Nevertheless, there are still unnatural aspects that remain to be explained. The focus of this review is on unraveling this my

Since the discovery of superconductivity in the Fe(Te,Se) system, it has been a general consensus that the end member of FeTe is not superconducting. Nonetheless, in recent years, there have been reports of superconducting FeTe films, but the origin of their superconductivity remains mysterious. Here, we provide the first comprehensive review of all the reported FeTe films regarding the relationship between their superconductivity and neighboring layers. Based on this review, we show that telluride neighboring layers are the key to superconducting FeTe films. Then, with additional new studies, we show that stoichiometric Te content, which can be readily achieved in FeTe films with the assistance of neighboring telluride layers, might be crucial to stabilizing the superconductivity in this system. This work provides insights into the underlying mechanism behind superconductivity in FeTe films and sheds light on the critical role of neighboring layers and stoichiometry control toward manipulating topological superconductivity in FeTe heterostructures.

Physicists have long known that the Sun's magnetic fields make its corona much hotter than the surface of the star itself. But how -- and why -- those fields transport and deposit their energy is still a mystery, as Philip G Judge explains

The generalization mystery in deep learning is the following: Why do over-parameterized neural networks trained with gradient descent (GD) generalize well on real datasets even though they are capable of fitting random datasets of comparable size? Furthermore, from among all solutions that fit the training data, how does GD find one that generalizes well (when such a well-generalizing solution exists)? We argue that the answer to both questions lies in the interaction of the gradients of different examples during training. Intuitively, if the per-example gradients are well-aligned, that is, if they are coherent, then one may expect GD to be (algorithmically) stable, and hence generalize well. We formalize this argument with an easy to compute and interpretable metric for coherence, and show that the metric takes on very different values on real and random datasets for several common vision networks. The theory also explains a number of other phenomena in deep learning, such as why some examples are reliably learned earlier than others, why early stopping works, and why it is possible to learn from noisy labels. Moreover, since the theory provides a causal explanation of how GD find

This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.

Recent developments in elementary quantum mechanics have seen a number of extraordinary claims regarding quantum behaviour, and even questioning internal consistency of the theory. These are, we argue, different disguises of what Feynman described as quantum theory's "only mystery".

In the development of oligodendrocytes in the central nervous systems, the inner and outer tongue of the myelin sheath tend to be located within the same quadrant, which was named as Peters quadrant mystery. In this study, we conduct in silico investigations to explore the possible mechanisms underlying the Peters quadrant mystery. A biophysically detailed model of oligodendrocytes was used to simulate the effect of the actional potential-induced electric field across the myelin sheath. Our simulation suggests that the paranodal channel connecting the inner and outer tongue forms a low impedance route, inducing two high-current zones at the area around the inner and outer tongue. When the inner tongue and outer tongue are located within the same quadrant, the interaction of these two high-current-zones will induce a maximum amplitude and a polarity reverse of the voltage upon the inner tongue, resulting in the same quadrant phenomenon. This model indicates that the growth of myelin follows a simple principle: an external negative or positive E-field can promote or inhibit the growth of the inner tongue, respectively.

The origin of cosmic rays with energies higher than 10$^{20}$ eV remains a mystery. Accelerating particles up to these energies is a challenge even for the most energetic astrophysical objects known. While the isotropy in arrival directions argues for an extra-galactic origin, the photon-pion production off the cosmic background radiation limits the sources of such particles to systems less than 50 Mpc away from us. The combination of large gyroradii, efficient energy losses, and isotropic arrival directions defies most of the proposed astrophysical accelerators as well as the more exotic alternatives. I briefly review theoretical models for the acceleration and propagation of ultra-high-energy cosmic-rays and discuss the potential for future observatories to resolve this cosmic mystery.

Despite the huge empirical success of deep learning, theoretical understanding of neural networks learning process is still lacking. This is the reason, why some of its features seem "mysterious". We emphasize two mysteries of deep learning: generalization mystery, and optimization mystery. In this essay we review and draw connections between several selected works concerning the latter.

A critical re-examination of the double-slit experiment and its variants is presented to clarify the nature of what Feynmann called the ``central mystery'' and the ``only mystery'' of quantum mechanics, leading to an interpretation of complementarity in which a `wave {\em and} particle' description rather than a `wave {\em or} particle' description is valid for the {\em same} experimental set up, with the wave culminating in the particle sequentially in time. This interpretation is different from Bohr's but is consistent with the von Neumann formulation as well as some more recent interpretations of quantum mechanics.