Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations. Although recent large language model (LLM)-based methods have achieved promising results, most of them still rely on a single-turn reasoning paradigm that processes flattened tables in a single forward pass. This paradigm suffers from inherent limitations, including context overflow on large tables, weak sensitivity to continuous numerical values, and the absence of explicit tool-use and reflection. In this paper, we propose TableMind, a tuning-based autonomous programmatic table agent that simulates the human-like cognitive schema of multi-turn interaction within a lightweight LLM. Instead of adopting a training-free workflow design, TableMind learns to internalize planning, action, and reflection through a principled two-stage training strategy. To bootstrap structured table reasoning capabilities, we construct and filter high-quality reasoning data for the supervised fine-tuning (SFT) stage. To enable precise code generation, we introduce a designed multi-perspective reward scheme and a novel optimization objective in the reinforcement learning (RL) stage. Extensive experiments on diverse benchmarks demonstrate that TableMind consistently outperforms previous baselines, validating the effectiveness of training autonomous agents to improve overall performance.

In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models (LLMs), which have revolutionized natural language applications. Existing LLM-based studies typically achieve step-by-step thinking for table reasoning guided by task semantics. While these approaches emphasize autonomous exploration and enhance fine-grained table understanding, they often overlook systematic thinking in the reasoning process. This oversight can lead to omitted steps, disorganized logic and misleading results, especially in complex scenarios. In this paper, we proposePoTable, a novel stage-oriented plan-then-execute approach that incorporates systematic thinking into table reasoning. Specifically,PoTableinvolves several distinct analytical stages with clear objectives to provide adequate guidance. To accomplish stage-specific goals,PoTableemploys a plan-then-execute mechanism: it first plans the operation chain based on the stage objective, and then executes operations sequentially through code generation, real-time running and feedback processing. Consequently,PoTableproduces reliable table reasoning results with highly accurate, step-wise commented and completely executable programs. It mirrors the workflow of a professional data analyst, offering advantages in both accuracy and explainability. Finally, we conduct extensive experiments on four datasets from the WikiTQ and TabFact benchmarks, where the results demonstrate the effectiveness, efficiency and explainability ofPoTable. Our code is available at:https://github.com/Double680/PoTable.

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant document from external knowledge sources. By referencing this external knowledge, RAG effectively reduces the generation of factually incorrect content and addresses hallucination issues within LLMs. Recently, there has been growing attention to improving the performance and efficiency of RAG systems from various perspectives. While these advancements have yielded significant results, the application of RAG in domains with considerable societal implications raises a critical question about fairness: What impact does the introduction of the RAG paradigm have on the fairness of LLMs? To address this question, we conduct extensive experiments by varying the LLMs, retrievers, and retrieval sources. Our experimental analysis reveals that the scale of the LLMs plays a significant role in influencing fairness outcomes within the RAG framework. When the model scale is smaller than 8B, the integration of retrieval mechanisms often exacerbates unfairness in small-scale LLMs (e.g., LLaMA3.2-1B, Mistral-7B, and LLaMA3-8B). To mitigate the fairness issues introduced by RAG for small-scale LLMs, we propose two approaches, FairFT and FairFilter. Specifically, in FairFT, we align the retriever with the LLM in terms of fairness, enabling it to retrieve documents that facilitate fairer model outputs. In FairFilter, we propose a fairness filtering mechanism to filter out biased content after retrieval. Finally, we validate our proposed approaches on real-world datasets, demonstrating their effectiveness in improving fairness while maintaining performance.

User modeling serves as a crucial foundation for researchers to capture useful potential characteristics, playing a pivotal role in various applications such as recommender systems. One common challenge in user modeling is the cold-start problem, where interactions are notably limited for new users. To tackle this issue, the paradigm of meta-learning has been introduced to user modeling, yielding promising results. Similar to a guidebook for a new traveler, meta-learning significantly influences decision-making for new users in critical scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has become paramount. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, another question arises: How can we mitigate the influence of this factor to enhance fairness while ensuring accuracy? To this end, we introduce a novel F airness-aware A daptive S ampling framework for me T a-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Moreover, we provide theoretical guarantees demonstrating the convergence of FAST, showcasing its potential to effectively eliminate unfairness. Furthermore, to ensure model accuracy, we enhance FAST with FAST+ by introducing a hybrid sampling strategy at an individual level. This strategy prioritizes fairness and thoroughly explores important users during the sampling process, allowing for a better accuracy-fairness tradeoff. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of both FAST and FAST+ frameworks.

Large language models (LLMs) have shown promise in multivariate time series classification (MTSC). To effectively adapt LLMs for MTSC, it is crucial to generate comprehensive and informative data representations. Most methods utilizing LLMs encode numerical time series into the model's latent space, aiming to align with the semantic space of LLMs for more effective learning. Despite effectiveness, we highlight three limitations that these methods overlook: (1) they struggle to incorporate temporal and channel-specific information, both of which are essential components of multivariate time series; (2) aligning the learned representation space with the semantic space of the LLMs proves to be a significant challenge; (3) they often require task-specific retraining, preventing training-free inference despite the generalization capabilities of LLMs. To bridge these gaps, we propose TableTime, which reformulates MTSC as a table understanding task. Specifically, TableTime introduces the following strategies: (1) utilizing tabular form to unify the format of time series, facilitating the transition from the model-centric approach to the data-centric approach; (2) representing time series in text format to facilitate seamless alignment with the semantic space of LLMs; (3) designing a knowledge-task dual-driven reasoning framework, TableTime, integrating contextual information and expert-level reasoning guidance to enhance LLMs' reasoning capabilities and enable training-free classification. Extensive experiments conducted on 10 publicly available benchmark datasets from the UEA archive validate the substantial potential of TableTime to be a new paradigm for MTSC. The code is publicly available. https://github.com/realwangjiahao/TableTime.

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot (ZS) learning, which seeks to identify unseen concepts through the use of side information. Previous ZS studies primarily focused on utilizing existing knowledge to infer unseen events, yet they overlook the crucial process of knowledge discovery and the integrated modeling of these knowledge-aware processes. In this study, we present a comprehensive ZS learning approach that explores and evaluates the machine’s abilities of discovering and utilizing knowledge. More specifically, to emulate human-like knowledge discovery and utilization processes, we propose a novel visual-aware ZS knowledge graph completion task for evaluation, incorporating a traditional ZS image classification task. Technically, we develop a unified ZS learning paradigm named Cognitive Learner (CoLa) to foster the two knowledge-aware abilities. Including a knowledge representation learning (KRL) module and a knowledge adaptation (KA) module, CoLa adapts well to the two specified tasks with the corresponding data. Extensive experiments on large-scale datasets demonstrate CoLa models’ outstanding performance over compared methods in the two ZS tasks, illustrating their superior ability of discovering and utilizing knowledge.

To alleviate the long-standing data sparsity issue in recommender systems, numerous studies in cross-domain recommendation (CDR) have been conducted to facilitate information transfer processes across domains. In recent years, dual-target CDR has been introduced to gain mutual improvements between two domains through more general bidirectional transfer rather than traditional one-way transit. Existing methods in dual-target CDR focus primarily on designing powerful encoders to learn representative cross-domain information, without tackling the fundamental issue of interaction data shortage. In this paper, we present CrossAug, a novel data augmentation approach to leverage interactions more efficiently in two domains. Specifically, we propose intra-domain and inter-domain augmentations based on cross-reconstructed representations in terms of sampled records. To reduce the harm of domain shift, we project domain-shared representations in two domains into a joint space with Householder transformations and apply center alignments. All these modules boost the utilization of interactions with little influence from negative transfer. Extensive experimental results over public datasets demonstrate the effectiveness of CrossAug and its components in dual-target CDR.

Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making for new users in crucial scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has gained paramount importance. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through the theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, in order to mitigate the impact of this factor, we introduce a novel Fairness-aware Adaptive Sampling framework for meTa-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Furthermore, we provide theoretical guarantees demonstrating the convergence of FAST. Finally, empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy. The code for FAST is available at https://github.com/zhengz99/FAST.