Table understanding requires structured, multi-step reasoning. Large Language Models (LLMs) struggle with it due to the structural complexity of tabular data. Recently, multi-agent frameworks for SQL generation have shown promise in tackling the challenges of understanding tabular data, but existing approaches often suffer from limitations such as the inability to comprehend table structure for reliable SQL generation, error propagation that results in invalid queries, and over-reliance on execution correctness. To address these issues, we propose Chain-of-Query (CoQ), a novel multi-agent framework for SQL-aided table understanding. CoQ adopts natural-language-style representations of table schemas to abstract away structural noise and enhance understanding. It employs a clause-by-clause SQL generation strategy to improve query quality and introduces a hybrid reasoning division that separates SQL-based mechanical reasoning from LLM-based logical inference, thereby reducing reliance on execution outcomes. Extensive experiments across four models and five widely used benchmarks demonstrate that CoQ achieves substantial accuracy improvements and significantly lowers invalid SQL rates compared to prior generic LLM-based, SQL-aided, and hybrid baselines, confirming its superior effectiveness in table understanding. The code is available at https://github.com/SongyuanSui/ChainofQuery.

Transformer models have significantly advanced Natural Language Processing (NLP), demonstrating strong performance in English. However, their effectiveness in Arabic, particularly for Named Entity Recognition (NER), remains limited, even with larger pre-trained models. This performance gap stems from multiple factors, including tokenisation, dataset quality, and annotation inconsistencies. Existing studies often analyze these issues in isolation, failing to capture their joint effect on system behaviour and performance. We introduce DeformAr (Debugging and Evaluation Framework for Transformer-based NER Systems), a novel framework designed to investigate and explain the performance discrepancy between Arabic and English NER systems. DeformAr integrates a data extraction library and an interactive dashboard, supporting two modes of evaluation: cross-component analysis and behavioural analysis. The framework divides each language into dataset and model components to examine their interactions. The analysis proceeds in two stages. First, cross-component analysis provides systematic diagnostic measures across data and model subcomponents, addressing the "what," "how," and "why" behind observed discrepancies. The second stage applies behavioural analysis by combining interpretability techniques with token-level metrics, interactive visualisations, and representation space analysis. These stages enable a component-aware diagnostic process that detects model behaviours and explains them by linking them to underlying representational patterns and data factors. DeformAr is the first Arabic-specific, component-based interpretability tool, offering a crucial resource for advancing model analysis in under-resourced languages.

LLM-integrated applications and agents-such as Bing Copilot [1], Google search with AI overviews [2], and Amazon's review highlights [3]-are emerging applications built upon large language models (LLMs). The growing popularity of LLM-integrated applications has led to the emergence of app stores, such as OpenAI's GPT Store and Poe [4], where developers can publish their LLMintegrated applications and users can access them, much like the Google Play and App Store for mobile apps. In general, an LLM-integrated application intends to perform a task (referred to as target task), such as webpage summarization in AI-assisted search. Towards this goal, an LLM-integrated application takes a prompt, which is the concatenation of an instruction (referred to as target instruction) and data (referred to as target data), as an input to query the backend LLM, whose response would solve the target task. The target instruction is often designed by an application developer to direct the backend LLM to perform the target task, while the data is the information to be processed by the backend LLM and is usually from an external source, e.g., the Internet. For instance, when the target task is webpage summarization in AI-assisted search, the target instruction can be "Please summarize the following web pages: [Text from relevant web pages].",

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Considerable effort has also been devoted to developing highly accurate models for forecasting match outcomes (Wunderlich and Memmert, 2021). Tennis is a sport well-suited to predictive modelling, with dense tournament schedules generating extensive historical data. The official ranking systems of the Association of Tennis Professionals (ATP) and Women's Tennis Association (WTA) have been shown to exhibit some predictive power for match outcomes (Clarke and Dyte, 2000; Klaassen and Magnus, 2003), but there are notable limitations: for example, ranking points accumulate over a 52-week period, without decay, which can mask recent changes in player form; while match-specific factors, such as surface type, tournament progression difficulty, and margin of victory in individual matches, are overlooked. Some well-known methods have been applied to tennis and modified to accommodate these factors, such as a Bradley-Terry model with surface-specific adjustments (McHale and Morton, 2011) or Elo rating systems that incorporate margin of victory (Kovalchik, 2020; Angelini et al., 2022). Bookmakers are considered the most accurate predictors of match outcomes (Kovalchik, 2016), with sophisticated models that adjust odds based on betting patterns and proprietary methods. Yet, despite the multi-billion dollar betting industry, one limitation that persists is the poor consideration of intransitivity (van Ours, 2025). Intransitivity is analogous to rock-paper-scissors. In tennis, it occurs where player A tends to defeat B, B defeats C, yet C defeats A, violating the assumption of transitive dominance.

Large language models (LLMs) excel at complex reasoning but can still exhibit harmful behaviors. Current alignment strategies typically embed safety into model weights, making these controls implicit, static, and difficult to modify. This paper introduces Cognition-of-Thought (CooT), a novel decoding-time framework that equips LLMs with an explicit cognitive self-monitoring loop. CooT couples a standard text Generator with a cognitive Perceiver that continuously monitors the unfolding sequence. The Perceiver uses a structured, precedence-based hierarchy of principles (e.g., safety over obedience) to detect potential misalignments as they arise. When violations are flagged, CooT intervenes by rolling back the generation to the point of error and regenerating under injected guidance that combines universal social priors with context-specific warnings. CooT thus transforms alignment from a fixed property into an explicit, dynamic, and auditable process active during inference, allowing for flexible policy updates without retraining the model. Extensive experiments across multiple benchmarks and model families confirm that CooT consistently improves safety and social reasoning performance.

'Four dead and 12 injured' in Mississippi shooting after people descend on town for homecoming game Joe Biden, 82, receiving new treatment after'aggressive' cancer spread to his bones REVEALED: The secret George Soros network'behind America's street chaos'... and the dossier that shows how to stop it Tinnitus destroyed Peter's life but doctors dismissed him. Then he tried an extraordinary drug-free University of Cambridge-backed treatment that gives instant relief - no wonder medics say it's so'exciting' KENNEDY: Obama's bitter post about Trump's Gaza peace deal proves what I've long suspected about Barry... and it would make Sigmund Freud blush Gold is soaring... here's what the pros say you should do with your 401(k) before it's too late Model dubbed'the world's most beautiful girl' when she was six is now all grown up and looks VERY different as she poses up a storm at Paris Fashion Week Teacher was'so high on cocaine she thought one of her students was her dog' But now, a royal insider claims they're'just as entitled as their parents' with'shady friends' Heartbreaking moment NFL reporter makes brutal comment about player Xavier Legette's dead father in locker room interview Experts reveal the surprising TRUTH behind RFK Jr's link between circumcision and autism Bombshell records that damn Letitia James and show Trump was RIGHT... and the staggering sum she was swindling Trump starts DOGE 2.0 as mass layoffs take place across federal government amid shutdown Famed'Big Short' investor gives terrifying verdict on Trump hammering China with 100 PERCENT tariff... and issues doomsday warning to Wall Street Jennifer Aniston, you've betrayed every woman with your selfish admission about not having children: CAROLINE BULLOCK Nasa has unveiled plans to send astronauts to live on the moon - inside glass bubbles made from lunar dust. The American space agency is funding research into the large livable spheres which would be created in situ, the Telegraph reports. Tiny pieces of so-called lunar glass - a component of the moon's soil, or regolith, along with rocks and mineral fragments - would be collected upon arrival from Earth. The material would be melted down using the same technology as in a domestic microwave oven, along with a'smart microwave furnace'.

'Four dead and 12 injured' in Mississippi shooting after people descend on town for homecoming game Joe Biden, 82, receiving new treatment after'aggressive' cancer spread to his bones REVEALED: The secret George Soros network'behind America's street chaos'... and the dossier that shows how to stop it Tinnitus destroyed Peter's life but doctors dismissed him. Then he tried an extraordinary drug-free University of Cambridge-backed treatment that gives instant relief - no wonder medics say it's so'exciting' KENNEDY: Obama's bitter post about Trump's Gaza peace deal proves what I've long suspected about Barry... and it would make Sigmund Freud blush Gold is soaring... here's what the pros say you should do with your 401(k) before it's too late Model dubbed'the world's most beautiful girl' when she was six is now all grown up and looks VERY different as she poses up a storm at Paris Fashion Week Teacher was'so high on cocaine she thought one of her students was her dog' But now, a royal insider claims they're'just as entitled as their parents' with'shady friends' Heartbreaking moment NFL reporter makes brutal comment about player Xavier Legette's dead father in locker room interview Experts reveal the surprising TRUTH behind RFK Jr's link between circumcision and autism Bombshell records that damn Letitia James and show Trump was RIGHT... and the staggering sum she was swindling Trump starts DOGE 2.0 as mass layoffs take place across federal government amid shutdown Famed'Big Short' investor gives terrifying verdict on Trump hammering China with 100 PERCENT tariff... and issues doomsday warning to Wall Street Jennifer Aniston, you've betrayed every woman with your selfish admission about not having children: CAROLINE BULLOCK Meteorologist's stark warning to Americans to brace for a harsh winter with less snow but more nor'easters Meteorologists are already predicting what the winter months will bring, with some regions of the US expected to see less snow than last year, and nor'easters anticipated to ravage parts of the Northeast. Paul Pastelok, chief meteorologist for AccuWeather's long-range forecasting team, told the Daily Mail that while he didn't expect above normal snowfall for the winter season, he warned that those in the Northeast should brace for nor'easters and it would still be a harsh winter. Pastelok explained that the nor'easter over this weekend is on trend with what is to come, as rapidly developing storms come in off the East Coast. 'People may say, Well, you're forecasting less snow, so it doesn't look like a harsh winter.