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IRS Direct File,
the electronic system for filing tax returns for free,
will not be offered next year,
the Trump administration has confirmed.
An email sent Monday from IRS official Cynthia Noe to state comptrollers that participate in the Direct File program said that
“IRS Direct File will not be available in Filing Season 2026. No launch date has been set for the future.”
The program developed during Joe Biden’s presidency
was credited by users with…

IRS Direct File won't be available next year for submitting tax returns for free
The Trump administration has decided not to offer the IRS' electronic tax filing system for free next year, and its future is unclear. An IRS official informed state comptrollers in an email Monday that "IRS Direct File will not be available in Filing Season 2026." Treasury Secretary Scott Bessent said better alternatives exist and the private sector can do a better job. The program, developed during Joe Biden's presidency, was praised for making tax filing easy and economical. However, it face…

Read the Room: Inferring Social Context Through Dyadic Interaction Recognition in Cyber-physical-social Infrastructure Systems
Cheyu Lin, John Martins, Katherine A. Flanigan, Ph. D
https://arxiv.org/abs/2510.04854

Read the Room: Inferring Social Context Through Dyadic Interaction Recognition in Cyber-physical-social Infrastructure Systems
Cyber-physical systems (CPS) integrate sensing, computing, and control to improve infrastructure performance, focusing on economic goals like performance and safety. However, they often neglect potential human-centered (or ''social'') benefits. Cyber-physical-social infrastructure systems (CPSIS) aim to address this by aligning CPS with social objectives. This involves defining social benefits, understanding human interactions with each other and infrastructure, developing privacy-preserving me…

CoDA: Agentic Systems for Collaborative Data Visualization
Zichen Chen, Jiefeng Chen, Sercan \"O. Arik, Misha Sra, Tomas Pfister, Jinsung Yoon
https://arxiv.org/abs/2510.03194

CoDA: Agentic Systems for Collaborative Data Visualization
Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations, highlighting the need for robust automation from natural language queries. However, current systems struggle with complex datasets containing multiple files and iterative refinement. Existing approaches, including simple single- or multi-agent systems, often oversimplify the task, focusing on initial query parsing while failing to robustly manage data complexity…

A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media
Matej Gjurkovi\'c
https://arxiv.org/abs/2510.02811 https://

A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media
Personality refers to individual differences in behavior, thinking, and feeling. With the growing availability of digital footprints, especially from social media, automated methods for personality assessment have become increasingly important. Natural language processing (NLP) enables the analysis of unstructured text data to identify personality indicators. However, two main challenges remain central to this thesis: the scarcity of large, personality-labeled datasets and the disconnect betwee…

Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods
Yulin Chen, Haoran Li, Yuan Sui, Yangqiu Song, Bryan Hooi
https://arxiv.org/abs/2510.03705 https://

Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods
With the development of technology, large language models (LLMs) have dominated the downstream natural language processing (NLP) tasks. However, because of the LLMs' instruction-following abilities and inability to distinguish the instructions in the data content, such as web pages from search engines, the LLMs are vulnerable to prompt injection attacks. These attacks trick the LLMs into deviating from the original input instruction and executing the attackers' target instruction. Recently, var…

$NavA^3$: Understanding Any Instruction, Navigating Anywhere, Finding Anything
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang, Wenbo Ding, Shanghang Zhang
https://arxiv.org/abs/2508.04598

$NavA^3$: Understanding Any Instruction, Navigating Anywhere, Finding Anything
Embodied navigation is a fundamental capability of embodied intelligence, enabling robots to move and interact within physical environments. However, existing navigation tasks primarily focus on predefined object navigation or instruction following, which significantly differs from human needs in real-world scenarios involving complex, open-ended scenes. To bridge this gap, we introduce a challenging long-horizon navigation task that requires understanding high-level human instructions and perf…

Restrictions of PCBNs for integration-free computations
Alexis Derumigny, Niels Horsman, Dorota Kurowicka
https://arxiv.org/abs/2510.03518 https://arxiv.or…

Restrictions of PCBNs for integration-free computations
The pair-copula Bayesian Networks (PCBN) are graphical models composed of a directed acyclic graph (DAG) that represents (conditional) independence in a joint distribution. The nodes of the DAG are associated with marginal densities, and arcs are assigned with bivariate (conditional) copulas following a prescribed collection of parental orders. The choice of marginal densities and copulas is unconstrained. However, the simulation and inference of a PCBN model may necessitate possibly high-dimen…

Lightweight and Data-Efficient MultivariateTime Series Forecasting using Residual-Stacked Gaussian (RS-GLinear) Architecture
Abukar Ali
https://arxiv.org/abs/2510.03788 https://…

Lightweight and Data-Efficient MultivariateTime Series Forecasting using Residual-Stacked Gaussian (RS-GLinear) Architecture
Following the success of Transformer architectures in language modeling, particularly their ability to capture long-range dependencies, researchers have explored how these architectures can be adapted for time-series forecasting. Transformer-based models have been proposed to handle both short- and long-term dependencies when predicting future values from historical data. However, studies such as those by Zeng et al. (2022) and Rizvi et al. (2025) have reported mixed results in long-term foreca…

Morphology Formation Pathways in Solution-Processed Perovskite Thin Films
M. Majewski, O. J. J. Ronsin, J. Harting
https://arxiv.org/abs/2509.04175 https://

Morphology Formation Pathways in Solution-Processed Perovskite Thin Films
The active layer in a perovskite solar cell is usually composed of a polycrystalline thin film. Fabrication of this layer by solution processing is a promising candidate for up-scaling to the mass market. However, the evolution of an evaporating and simultaneously crystallizing thin film is not yet fully understood. To contribute to the understanding of the formation of thin films, we develop a geometrical model that deals with the effect of the interplay between solvent evaporation and crystal…

A Privacy-Preserving Recommender for Filling Web Forms Using a Local Large Language Model
Amirreza Nayyeri, Abbas Rasoolzadegan
https://arxiv.org/abs/2509.01527 https://

A Privacy-Preserving Recommender for Filling Web Forms Using a Local Large Language Model
Web applications are increasingly used in critical domains such as education, finance, and e-commerce. This highlights the need to ensure their failure-free performance. One effective method for evaluating failure-free performance is web form testing, where defining effective test scenarios is key to a complete and accurate evaluation. A core aspect of this process involves filling form fields with suitable values to create effective test cases. However, manually generating these values is time…