Text analytics has traditionally required specialized knowledge in Natural Language Processing (NLP) or text analysis, which presents a barrier for entry-level analysts. Recent advances in large language models (LLMs) have changed the landscape of NLP by enabling more accessible and automated text analysis (e.g., topic detection, summarization, information extraction, etc.). We introduce VIDEE, a system that supports entry-level data analysts to conduct advanced text analytics with intelligent agents. VIDEE instantiates a human-agent collaroration workflow consisting of three stages: (1) Decomposition, which incorporates a human-in-the-loop Monte-Carlo Tree Search algorithm to support generative reasoning with human feedback, (2) Execution, which generates an executable text analytics pipeline, and (3) Evaluation, which integrates LLM-based evaluation and visualizations to support user validation of execution results. We conduct two quantitative experiments to evaluate VIDEE's effectiveness and analyze common agent errors. A user study involving participants with varying levels of NLP and text analytics experience -- from none to expert -- demonstrates the system's usability and reveals distinct user behavior patterns. The findings identify design implications for human-agent collaboration, validate the practical utility of VIDEE for non-expert users, and inform future improvements to intelligent text analytics systems.

Abstract--Recent studies have revealed the potential of training open-source Large Language Models (LLMs) to unleash LLMs' reasoning ability for enhancing vision-language navigation (VLN) performance, and simultaneously mitigate the domain gap between LLMs' training corpus and the VLN task. However, these approaches predominantly adopt straightforward input-output mapping paradigms, causing the mapping learning difficult and the navigational decisions unexplainable. Chain-of-Thought (CoT) training is a promising way to improve both navigational decision accuracy and interpretability, while the complexity of the navigation task makes the perfect CoT labels unavailable and may lead to overfitting through pure CoT supervised fine-tuning. T o address these issues, we propose EvolveNav, a novel sElf-improving embodied reasoning paradigm that realizes adaptable and generalizable navigational reasoning for boosting LLM-based vision-language Navigation. Specifically, EvolveNav involves a two-stage training process: (1) Formalized CoT Supervised Fine-T uning, where we train the model with curated formalized CoT labels to first activate the model's navigational reasoning These two authors contribute equally to this work. Bokui Chen, Cewu Lu, and Xiaodan Liang are the corresponding authors. Bingqian Lin and Cewu Lu are with Shanghai Jiao T ong University, Shanghai, China. Y unshuang Nie, Khun Loun Zai, and Ziming Wei are with Shenzhen Campus of Sun Y at-sen University, Shenzhen, China. Xiaodan Liang is with Shenzhen Campus of Sun Y at-sen University, Shenzhen, China, Peng Cheng Laboratory, Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China. Bokui Chen is with T singhua Shenzhen International Graduate School, T singhua University, China. Mingfei Han is with the Department of Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.

Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we conduct an interview study to understand the practical challenges associated with cross-silo FL adoption. With interviews spanning a diverse set of stakeholders such as user organizations, software providers, and academic researchers, we uncover various barriers, from concerns about model performance to questions of incentives and trust between participating organizations. Our study shows that cross-silo FL faces a set of challenges that have yet to be well-captured by existing research in the area and are quite distinct from other forms of federated learning such as cross-device FL. We end with a discussion on future research directions that can help overcome these challenges.

The current trend in information extraction (IE) is to rely extensively on large language models, effectively discarding decades of experience in building symbolic or statistical IE systems. This paper compares a neuro-symbolic (NS) and an LLM-based IE system in the agricultural domain, evaluating them on nine interviews across pork, dairy, and crop subdomains. The LLM-based system outperforms the NS one (F1 total: 69.4 vs. 52.7; core: 63.0 vs. 47.2), where total includes all extracted information and core focuses on essential details. However, each system has trade-offs: the NS approach offers faster runtime, greater control, and high accuracy in context-free tasks but lacks generalizability, struggles with contextual nuances, and requires significant resources to develop and maintain. The LLM-based system achieves higher performance, faster deployment, and easier maintenance but has slower runtime, limited control, model dependency and hallucination risks. Our findings highlight the "hidden cost" of deploying NLP systems in real-world applications, emphasizing the need to balance performance, efficiency, and control.

Hamas executes'collaborators' in Gaza as it clings to power amid fears Trump's peace deal is already at risk Internet star who demanded free seats for fat fliers vanished without trace... now the Daily Mail has learned the heartbreaking reason why Donald Trump tells crowds there are world leaders he'doesn't like at ALL' as he teases who they are How Diane Keaton's closest friend helped her to achieve her'lifelong ambition' just months before she died - and the poignant legacy it leaves Kate and Wills' fresh start at their'forever home': Why they have fast-tracked their move to house they will never leave - even when he becomes King'It's Meghan Markle 3.0': Why the duchess has set tongues wagging that she's plotting another Sussex relaunch'as she holds cosy meeting with new editor of US Vogue' Trump's ominous warning to Macron at Egypt summit: 'You will see what is about to happen' Neil Diamond, 84, sang Sweet Caroline and worked with Cher as well as Barbra Streisand... see him now Insiders reveal how reluctant Katy Perry finally gave in to'persistent' Justin Trudeau... as sexy yacht photos get spicy response from his ex-wife Awkward moment Donald Trump asks Giorgia Meloni'You won't be offended if I say you're beautiful, right? Horrors endured by Israel's last 20 hostages: Chained, tortured, and starved. Lindsey Halligan removes senior DOJ official after taking over Virginia US attorney's office Gorgeous Bay Area enclave filled with hippies becomes America's ANGRIEST town over plans for huge affordable housing project MLB fans hail'greatest play in baseball HISTORY' after Dodgers thought they hit grand slam in Brewers game Father launches campaign to become sheriff as he faces murder trial for killing teenage daughter's abuser Spot the difference: Apple has rebranded its TV service as part of a'vibrant new identity' - so, can you see what has changed? But Apple TV+ is no more - as Apple has quietly rebranded its streaming service. 'Apple TV+ is now simply Apple TV, with a vibrant new identity,' the tech giant explained in the bottom of a press release on the streaming debut of its film, 'F1 The Movie'.

Hamas executes'collaborators' in Gaza as it clings to power amid fears Trump's peace deal is already at risk Internet star who demanded free seats for fat fliers vanished without trace... now the Daily Mail has learned the heartbreaking reason why Donald Trump tells crowds there are world leaders he'doesn't like at ALL' as he teases who they are How Diane Keaton's closest friend helped her to achieve her'lifelong ambition' just months before she died - and the poignant legacy it leaves Kate and Wills' fresh start at their'forever home': Why they have fast-tracked their move to house they will never leave - even when he becomes King'It's Meghan Markle 3.0': Why the duchess has set tongues wagging that she's plotting another Sussex relaunch'as she holds cosy meeting with new editor of US Vogue' Trump's ominous warning to Macron at Egypt summit: 'You will see what is about to happen' Neil Diamond, 84, sang Sweet Caroline and worked with Cher as well as Barbra Streisand... see him now Insiders reveal how reluctant Katy Perry finally gave in to'persistent' Justin Trudeau... as sexy yacht photos get spicy response from his ex-wife Awkward moment Donald Trump asks Giorgia Meloni'You won't be offended if I say you're beautiful, right? Horrors endured by Israel's last 20 hostages: Chained, tortured, and starved. Lindsey Halligan removes senior DOJ official after taking over Virginia US attorney's office Gorgeous Bay Area enclave filled with hippies becomes America's ANGRIEST town over plans for huge affordable housing project MLB fans hail'greatest play in baseball HISTORY' after Dodgers thought they hit grand slam in Brewers game Father launches campaign to become sheriff as he faces murder trial for killing teenage daughter's abuser Infections that are resistant to antibiotics continue to threaten global health, experts have warned--as hospitals report an alarming rise in the number of deaths driven by drug resistant strains. According to the World Health Organisation's (WHO) latest surveillance report, one in six bacterial infections were resistant to antibiotic treatments in 2023. Alarmingly, more than 40 per cent of antibiotics lost efficacy to treat common urinary tract, blood, gut and sexually-transmitted infections between 2018 and 2023, figures show.

Lorenzo Servadei and his team at Sony AI are focused on researching and developing machine learning models to aid chip design and manufacturing. In this interview, Lorenzo tells us more about Electronic Design Automation, and how machine learning has been added into the mix to further advance the field of semiconductor chip design. What was your inspiration for pursuing a career in AI and semiconductors? When I was pursuing my Master's degree, I studied subjects related to traditional computer science and algorithmics - before AI was seen as a specific area of study - which led me into the field of software development. While working in software development, I had the opportunity to join a semiconductor company that was seeking AI experts, which allowed me to explore the algorithmic aspects of AI.

In this paper, we develop and open-source, for the first time, a square-root filter (SRF)-based visual-inertial navigation system (VINS), termed sqrtVINS, which is ultra-fast, numerically stable, and capable of dynamic initialization even under extreme conditions (i.e., extremely small time window). Despite recent advancements in VINS, resource constraints and numerical instability on embedded (robotic) systems with limited precision remain critical challenges. A square-root covariance-based filter offers a promising solution by providing numerical stability, efficient memory usage, and guaranteed positive semi-definiteness. However, canonical SRFs suffer from inefficiencies caused by disruptions in the triangular structure of the covariance matrix during updates. The proposed method significantly improves VINS efficiency with a novel Cholesky decomposition (LLT)-based SRF update, by fully exploiting the system structure to preserve the structure. Moreover, we design a fast, robust, dynamic initialization method, which first recovers the minimal states without triangulating 3D features and then efficiently performs iterative SRF update to refine the full states, enabling seamless VINS operation. The proposed LLT-based SRF is extensively verified through numerical studies, demonstrating superior numerical stability and achieving robust efficient performance on 32-bit single-precision floats, operating at twice the speed of state-of-the-art (SOTA) methods. Our initialization method, tested on both mobile workstations and Jetson Nano computers, achieving a high success rate of initialization even within a 100 ms window under minimal conditions. Finally, the proposed sqrtVINS is extensively validated across diverse scenarios, demonstrating strong efficiency, robustness, and reliability. The full open-source implementation is released to support future research and applications.

Recent advancements in language agents have led to significant improvements in multi-hop reasoning tasks. However, existing approaches often struggle with handling open-domain problems, which require massive information retrieval due to their reliance on a fixed sequence of actions. To address this, we propose Feedback-Guided Dynamic Interactive Planning (FGDIP), a novel framework tailored to enhance reasoning in LLMs by utilizing dynamic and adaptive strategies for information exploration in open-domain multi-hop reasoning tasks. Our approach begins by identifying key entities relevant to the problem, which serve as the initial nodes in the reasoning process. From these initial nodes, we then generate reasoning child nodes with the process being refined through a combination of historical error analysis and real-time feedback, which allows the framework to dynamically adjust and optimize its reasoning strategies. By integrating depth-first search with an innovative node generation technique, our framework adapts based on both prior error paths and concurrently generated nodes at the same hierarchical level. This dynamic strategy effectively expands the search space while ensuring the reasoning process systematically converges toward accurate solutions. Experimental results show that FGDIP achieved up to 54.47% F1 score on the HotpotQA dataset and 70.05% on the StrategyQA dataset, surpassing the best baseline by 5.03% and 7.25% respectively, highlighting its versatility and potential to enhance language agents in multi-hop reasoning tasks.

We introduce Aristotle, an AI system that combines formal verification with informal reasoning, achieving gold-medal-equivalent performance on the 2025 International Mathematical Olympiad problems. Aristotle integrates three main components: a Lean proof search system, an informal reasoning system that generates and formalizes lemmas, and a dedicated geometry solver. Our system demonstrates state-of-the-art performance with favorable scaling properties for automated theorem proving.