While Multimodal Large Language Models (MLLMs) have advanced GUI navigation agents, current approaches face limitations in cross-domain generalization and effective history utilization. We present a reasoning-enhanced framework that systematically integrates structured reasoning, action prediction, and history summarization. The structured reasoning component generates coherent Chain-of-Thought analyses combining progress estimation and decision reasoning, which inform both immediate action predictions and compact history summaries for future steps. Based on this framework, we train a GUI agent, \textbf{GUI-Rise}, through supervised fine-tuning on pseudo-labeled trajectories and reinforcement learning with Group Relative Policy Optimization (GRPO). This framework employs specialized rewards, including a history-aware objective, directly linking summary quality to subsequent action performance. Comprehensive evaluations on standard benchmarks demonstrate state-of-the-art results under identical training data conditions, with particularly strong performance in out-of-domain scenarios. These findings validate our framework's ability to maintain robust reasoning and generalization across diverse GUI navigation tasks. Code is available at https://leon022.github.io/GUI-Rise.
Guy Tennenholtz, Jihwan Jeong, Chih-Wei Hsu
et al.
Effective decision making in partially observable environments requires compressing long interaction histories into informative representations. We introduce Descriptive History Representations (DHRs): sufficient statistics characterized by their capacity to answer relevant questions about past interactions and potential future outcomes. DHRs focus on capturing the information necessary to address task-relevant queries, providing a structured way to summarize a history for optimal control. We propose a multi-agent learning framework, involving representation, decision, and question-asking components, optimized using a joint objective that balances reward maximization with the representation's ability to answer informative questions. This yields representations that capture the salient historical details and predictive structures needed for effective decision making. We validate our approach on user modeling tasks with public movie and shopping datasets, generating interpretable textual user profiles which serve as sufficient statistics for predicting preference-driven behavior of users.
This article analyses the relation between Leo Perutz’s novel Zwischen neun und neun (1918) and the poetics and works of several German-speaking authors who can be considered Perutz’s literary role models. In chronological order, consideration will be given to the enigmatic, paradoxical situations characterising Heinrich von Kleist’s works, to E.T.A. Hoffmann’s serapiontisches Prinzip, irony and humour, and to Arthur Schnitzler’s narratological techniques and the resignation of his characters. The comparison with these models will demonstrate how the novel relates to the tradition of German literature, in what manner it surpasses it and which elements enable its correlation with modernism.
History of Austria. Liechtenstein. Hungary. Czechoslovakia
Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. However, in many streaming applications, such as streaming recommender systems, online decision-makers typically engage in online learning during the decision-making process, meaning that the interaction history generated by online decision-makers includes their behavioral evolution from novice expert to experienced expert. This poses a new challenge for existing imitation learning approaches that can only utilize data from experienced experts. To address this issue, this paper proposes an inverse batched contextual bandit (IBCB) framework that can efficiently perform estimations of environment reward parameters and learned policy based on the expert's behavioral evolution history. Specifically, IBCB formulates the inverse problem into a simple quadratic programming problem by utilizing the behavioral evolution history of the batched contextual bandit with inaccessible rewards. We demonstrate that IBCB is a unified framework for both deterministic and randomized bandit policies. The experimental results indicate that IBCB outperforms several existing imitation learning algorithms on synthetic and real-world data and significantly reduces running time. Additionally, empirical analyses reveal that IBCB exhibits better out-of-distribution generalization and is highly effective in learning the bandit policy from the interaction history of novice experts.
We overview the history of primordial black hole (PBH) research from the first papers around 50 years ago to the present epoch. The history may be divided into four periods, the dividing lines being marked by three key developments: inflation on the theoretical front and the detection of microlensing events by the MACHO project and gravitational waves by the LIGO/Virgo/KAGRA project on the observation front. However, they are also characterised by somewhat different focuses of research. The period 1967-1980 covered the groundbreaking work on PBH formation and evaporation. The period 1980-1996 mainly focussed on their formation, while the period 1996-2016 consolidated the work on formation but also collated the constraints on the PBH abundance. In the period 2016-2024 there was a shift of emphasis to the search for evidence for PBHs and - while opinions about the strength of the purported evidence vary - this has motivated more careful studies of some aspects of the subject. Certainly the soaring number of papers on PBHs in this last period indicates a growing interest in the topic.
Huy Nguyen, Christoph Treude, Patanamon Thongtanunam
With the exponential growth of AI tools that generate source code, understanding software has become crucial. When developers comprehend a program, they may refer to additional contexts to look for information, e.g. program documentation or historical code versions. Therefore, we argue that encoding this additional contextual information could also benefit code representation for deep learning. Recent papers incorporate contextual data (e.g. call hierarchy) into vector representation to address program comprehension problems. This motivates further studies to explore additional contexts, such as version history, to enhance models' understanding of programs. That is, insights from version history enable recognition of patterns in code evolution over time, recurring issues, and the effectiveness of past solutions. Our paper presents preliminary evidence of the potential benefit of encoding contextual information from the version history to predict code clones and perform code classification. We experiment with two representative deep learning models, ASTNN and CodeBERT, to investigate whether combining additional contexts with different aggregations may benefit downstream activities. The experimental result affirms the positive impact of combining version history into source code representation in all scenarios; however, to ensure the technique performs consistently, we need to conduct a holistic investigation on a larger code base using different combinations of contexts, aggregation, and models. Therefore, we propose a research agenda aimed at exploring various aspects of encoding additional context to improve code representation and its optimal utilisation in specific situations.
Even though Robert Menasse claims, through the words of his alter ego Roman Gilanian, the protagonist of Sinnliche Gewissheit [Sensual Certainty], that he does not know what to say about the Brazilians, this novel displays a certain portrait of Brazil. As a land of blatant social injustice, of sensuality, hedonism and irrationality, Brazil is at the same time fascinating and repulsive for a novelist who sees it through European eyes. For Menasse, Brazil remains foreign and mysterious, a land full of contradictions and paradoxes that seem to pose a threat to his philosophical concepts. This article highlights three themes that play a key role in the plot of Sinnliche Gewissheit: colonialism, regression and sensuality which, at the same time, challenge the idea of Heimat.
History of Austria. Liechtenstein. Hungary. Czechoslovakia
The Austrian novelist, essayist and journalist Maria Lazar (1895-1948), only recently rediscovered, who was a contemporary of Thomas Mann and an acquaintance of Bertolt Brecht, wrote many different types of texts. Considered a promising talent back in Vienna, the reception of her works decreased during Lazar’s years in exile. The following contribution aims to reconstruct the dynamics of her marginalization and takes a closer look at the socially critical dimension of No Right to Live and Die Eingeborenen von Maria Blut [The Natives of Maria Blood].
History of Austria. Liechtenstein. Hungary. Czechoslovakia
Women play a specific and analogous role in the narrative of Elias Canetti and Thomas Bernhard. Such characters never appear as the only protagonists, but provide a counterpart to male figures, by whom they are often subjugated and marginalized. It is therefore the aim of this article to examine and compare the different ways in which the male characters of Canetti’s Die Blendung and Bernhard’s Das Kalkwerk and Ja dominate and exploit female figures, in an attempt to emphasize similarities and differences in the works of the two authors.
History of Austria. Liechtenstein. Hungary. Czechoslovakia
The history of meta-learning methods based on gradient descent is reviewed, focusing primarily on methods that adapt step-size (learning rate) meta-parameters.
BACKGROUND There is an increasing demand for high quality subnational estimates of under-five mortality. In low and middle income countries, where the burden of under-five mortality is concentrated, vital registration is often lacking and household surveys, which provide full birth history data, are often the most reliable source. Unfortunately, these data are spatially sparse and so data are pulled from other sources to increase the available information. Summary birth histories represent a large fraction of the available data, and provide numbers of births and deaths aggregated over time, along with the mother's age. OBJECTIVE Specialized methods are needed to leverage this information, and previously the Brass method, and variants, have been used. We wish to develop a model-based approach that can propagate errors, and make the most efficient use of the data. Further, we strive to provide a method that does not have large computational overhead. CONTRIBUTION We describe a computationally efficient model-based approach which allows summary birth history and full birth history data to be combined into analyses of under-five mortality in a natural way. The method is based on fertility and mortality models that allow direct smoothing over time and space, with the possibility for including relevant covariates that are associated with fertility and/or mortality. We first examine the behavior of the approach on simulated data, before applying the model to survey and census data from Malawi.
Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh
et al.
We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds $O(t)$ i.i.d. pseudo-rewards to its history in round $t$ and then pulls the arm with the highest average reward in its perturbed history. Therefore, we call it perturbed-history exploration (PHE). The pseudo-rewards are carefully designed to offset potentially underestimated mean rewards of arms with a high probability. We derive near-optimal gap-dependent and gap-free bounds on the $n$-round regret of PHE. The key step in our analysis is a novel argument that shows that randomized Bernoulli rewards lead to optimism. Finally, we empirically evaluate PHE and show that it is competitive with state-of-the-art baselines.
Business history for three generations has focused almost exclusively on capitalist firms, their managers, and their relations with markets, states, and rivals. However, enterprises on all scales also operated within communist nations “building socialism” in the wake of World War II. This article represents a first-phase exploration of business practices in three Central European states as Stalinism gave way to cycles of reform and retrenchment in the 1960s. Focusing chiefly on industrial initiatives, the study asks: How did socialist enterprises work and change across the first postwar generation, given their distinctive principles and political/economic contexts, and implicitly, what contrasts with capitalist activities are worth considering.
We summarize the Focus Meeting (FM10) "Nano Dust in Space and Astrophysics" held in Vienna, Austria on 28-29 August 2018 during the 30th General Assembly of the International Astronomical Union (IAU). The theme of this focus meeting is related to the detection, characterization and modeling of nano particles --- cosmic dust of sizes of roughly 1 to 100 nm --- in space environments like the interstellar medium, planetary debris disks, the heliosphere, the vicinity of the Sun and planetary atmospheres, and the space near Earth. Discussions focus on nano dust that forms from condensations and collisions and from planetary objects, as well as its interactions with space plasmas like the solar and stellar winds, atmospheres and magnetospheres. A particular goal is to bring together space scientists, astronomers, astrophysicists, and laboratory experimentalists and combine their knowledge to reach cross fertilization of different disciplines.
This paper investigates creative word formation and especially the word formation method of gapping in Peter Handke’s literature by employing corpus linguistic methods. His novel Der Bildverlust oder Durch die Sierra de Gredos (2002; transl. Crossing the Sierra de Gredos, 2007) is investigated as an exemplary work of the author’s late creative development and compared to the novella Kali. Eine Vorwintergeschichte (2007) in order to test the findings. The linguistic analysis is framed and contextualized with a literary criticism approach, thus offering new perspectives for both disciplines.
History of Austria. Liechtenstein. Hungary. Czechoslovakia
This article offers an inventory of the rarely analyzed poetry of the mythical Austrian Empress and thereby shows the masterful self-presentation of a historically ambivalent personality. Using selected poems, this article brings out Elizabeth’s appraisal of the contemporary court society, for which she often had only biting scorn. Her poetry reveals an almost religious veneration for Heinrich Heine and an almost subversive attitude towards the k.u.k. monarchy. It also illustrates Elizabeth’s literary strategy of dismantling a hated society, though its effect could also be interpreted to the contrary, i.e. as an unintended contribution of the opposition to the transfiguration of the Habsburg myth.
History of Austria. Liechtenstein. Hungary. Czechoslovakia