Zero-shot classification for structured tabular data has attracted increasing attention in recent years. An important line of research is leveraging textual explanations as supervision to improve the modeling of tabular attribute features. Previous studies usually serialize tabular data into flattened explanation sequences, or employ logical rules to model directional associations between tabular attributes and labels. However, these methods often suffer from two major limitations: the former disrupts the inherent row-column topological structure of tables, while the latter mainly represents relations with fixed logical operators or aggregation weights, making it difficult to model learnable dependencies among features. To address these issues, we propose a novel method (MulEG), which harnesses multi-level textual explanations with a feature attribute graph to model both tabular topology and cell-element relations. Specifically, to preserve the intrinsic row-column topological structure of tables, we propose a multi-level textual explanation modeling with sample-level, attribute-level, and label-level perspectives. Moreover, to capture fine-grained feature dependencies, we propose a differentiable message-passing mechanism using a feature attribute graph that allows heterogeneous features to propagate through message passing. Finally, extensive experimental results on datasets covering sixteen tasks demonstrate the effectiveness of the proposed approach.

Document ranking aims to order candidate documents based on their relevance to a given query. However, identifying relevant documents in complex real-world scenarios requires deep reasoning beyond surface-level matching, driving a shift from semantic matching to reasoning-intensive reranking. Since the latter requires integrating information across documents for complex reasoning, it presents two key challenges: (1) varying reasoning difficulty, where assessing document relevance to the query requires multi-step reasoning with different complexity; and (2) strong contextual sensitivity, where the relevance of a document is heavily influenced by other documents within the context window. To address these challenges, we propose CuRLRank, which consists of (1) CuRL, an adaptive curriculum reinforcement learning approach that enables LLMs to develop complex reasoning capabilities across varying levels of difficulty, and (2) IFRS, an iterative filtering ranking strategy to achieve efficient and robust ranking. Extensive experiments on BRIGHT and R2MED benchmarks demonstrate that our CuRLRank significantly outperforms baseline methods while achieving lower ranking latency.

Knowledge representation and reasoning are critical challenges in Artificial Intelligence (AI), particularly in integrating neural and symbolic approaches to achieve explainable and transparent AI systems. Traditional knowledge representation methods often fall short of capturing complex processes and state changes. We introduce Neuro-Conceptual Artificial Intelligence (NCAI), a specialization of the neuro-symbolic AI approach that integrates conceptual modeling using Object-Process Methodology (OPM) ISO 19450:2024 with deep learning to enhance question-answering (QA) quality. By converting natural language text into OPM models using in-context learning, NCAI leverages the expressive power of OPM to represent complex OPM elements—processes, objects, and states—beyond what traditional triplet-based knowledge graphs can easily capture. This rich structured knowledge representation improves reasoning transparency and answer accuracy in an OPM-QA system. We further propose transparency evaluation metrics to quantitatively measure how faithfully the predicted reasoning aligns with OPM-based conceptual logic. Our experiments demonstrate that NCAI outperforms traditional methods, highlighting its potential for advancing neuro-symbolic AI by providing rich knowledge representations, measurable transparency, and improved reasoning.

The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.