Affective Computing has been established as a crucial field of inquiry to advance the holistic development of Artificial Intelligence (AI) systems. Foundation models -- especially Large Language Models (LLMs) -- have been evaluated, trained, or instruction-tuned in several past works, to become better predictors or generators of emotion. Most of these studies, however, approach emotion-related tasks in a supervised manner, assessing or training the capabilities of LLMs using discrete emotion labels associated with stimuli (e.g., text, images, video, audio). Evaluation studies, in particular, have often been limited to standard and superficial emotion-related tasks, such as the recognition of evoked or expressed emotions. In this paper, we move beyond surface-level emotion tasks to investigate how LLMs reason about emotions through cognitive dimensions. Drawing from cognitive appraisal theory, we examine whether LLMs produce coherent and plausible cognitive reasoning when reasoning about emotionally charged stimuli. We introduce a large-scale benchmark on Cognitive Reasoning for Emotions - CoRE - to evaluate internal cognitive structures implicitly used by LLMs for emotional reasoning. Through a plethora of evaluation experiments and analysis, we seek to answer: (a) Are models more likely to implicitly rely on specific cognitive appraisal dimensions?, (b) What cognitive dimensions are important for characterizing specific emotions?, and, (c) Can the internal representations of different emotion categories in LLMs be interpreted through cognitive appraisal dimensions? Our results and analyses reveal diverse reasoning patterns across different LLMs. Our benchmark and code will be made publicly available.

Textual Attribute Graphs (TAGs) are critical for modeling complex networks like citation networks, but effective node classification remains challenging due to difficulties in integrating rich semantics from text with structural graph information. Existing methods often struggle with capturing nuanced domain-specific terminology, modeling long-range dependencies, adapting to temporal evolution, and scaling to massive datasets. To address these issues, we propose a novel framework that integrates TAPE (Text-Attributed Graph Representation Enhancement) with Graphormer. Our approach leverages a large language model (LLM), specifically ChatGPT, within the TAPE framework to generate semantically rich explanations from paper content, which are then fused into enhanced node representations. These embeddings are combined with structural features using a novel integration layer with learned attention weights. Graphormer's path-aware position encoding and multi-head attention mechanisms are employed to effectively capture long-range dependencies across the citation network. We demonstrate the efficacy of our framework on the challenging ogbn-arxiv dataset, achieving state-of-the-art performance with a classification accuracy of 0.772, significantly surpassing the best GCN baseline of 0.713. Our method also yields strong results in precision (0.671), recall (0.577), and F1-score (0.610). We validate our approach through comprehensive ablation studies that quantify the contribution of each component, demonstrating the synergy between semantic and structural information. Our framework provides a scalable and robust solution for node classification in dynamic TAGs, offering a promising direction for future research in knowledge systems and scientific discovery.

Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling techniques to speech data. However, existing audio codecs often operate at high frame rates, leading to slow training and inference, particularly for autoregressive models. To address this, there is growing interest in low frame-rate audio codecs, which reduce the number of autoregressive steps required to generate one second of audio. In this paper, we conduct ablation studies to examine the impact of frame rate, bitrate, and causality on codec reconstruction quality. Based on our findings, we introduce NanoCodec, a state-of-the-art audio codec that achieves high-quality compression at just 12.5 frames per second (FPS). NanoCodec outperforms related works across various bitrate ranges, establishing a new benchmark for low-latency and efficient Speech LLM training and inference.

Understanding large-scale, complex software systems is a major challenge for developers, who spend a significant portion of their time on program comprehension. Traditional tools such as static visualizations and reverse engineering techniques provide structural insights but often lack interactivity, adaptability, and integration with contextual information. Recent advancements in large language models (LLMs) offer new opportunities to enhance code exploration workflows, yet their lack of grounding and integration with structured views limits their effectiveness. This work introduces a hybrid approach that integrates deterministic reverse engineering with LLM-guided, intent-aware visual exploration. The proposed system combines UML-based visualization, dynamic user interfaces, historical context, and collaborative features into an adaptive tool for code comprehension. By interpreting user queries and interaction patterns, the LLM helps developers navigate and understand complex codebases more effectively. A prototype implementation for Java demonstrates the feasibility of this approach. Future work includes empirical evaluation, scaling to polyglot systems, and exploring GUI-driven LLM interaction models. This research lays the groundwork for intelligent, interactive environments that align with developer cognition and collaborative workflows.

Large Language Models (LLMs) are known to produce hallucinations - factually incorrect or fabricated information - which poses significant challenges for many Natural Language Processing (NLP) applications, such as dialogue systems. As a result, detecting hallucinations has become a critical area of research. Current approaches to hallucination detection in dialogue systems primarily focus on verifying the factual consistency of generated responses. However, these responses often contain a mix of accurate, inaccurate or unverifiable facts, making one factual label overly simplistic and coarse-grained. In this paper, we introduce a benchmark, FineDialFact, for fine-grained dialogue fact verification, which involves verifying atomic facts extracted from dialogue responses. To support this, we construct a dataset based on publicly available dialogue datasets and evaluate it using various baseline methods. Experimental results demonstrate that methods incorporating Chain-of-Thought (CoT) reasoning can enhance performance in dialogue fact verification. Despite this, the best F1-score achieved on the HybriDialogue, an open-domain dialogue dataset, is only 0.75, indicating that the benchmark remains a challenging task for future research. Our dataset and code will be public on GitHub.

This paper proposes a novel framework for developing safe Artificial General Intelligence (AGI) by combining Active Inference principles with Large Language Models (LLMs). We argue that traditional approaches to AI safety, focused on post-hoc interpretability and reward engineering, have fundamental limitations. We present an architecture where safety guarantees are integrated into the system's core design through transparent belief representations and hierarchical value alignment. Our framework leverages natural language as a medium for representing and manipulating beliefs, enabling direct human oversight while maintaining computational tractability. The architecture implements a multi-agent system where agents self-organize according to Active Inference principles, with preferences and safety constraints flowing through hierarchical Markov blankets. We outline specific mechanisms for ensuring safety, including: (1) explicit separation of beliefs and preferences in natural language, (2) bounded rationality through resource-aware free energy minimization, and (3) compositional safety through modular agent structures. The paper concludes with a research agenda centered on the Abstraction and Reasoning Corpus (ARC) benchmark, proposing experiments to validate our framework's safety properties. Our approach offers a path toward AGI development that is inherently safer, rather than retrofitted with safety measures.

We introduce CLAPP (CLASS LLM Agent for Pair Programming), an interactive AI assistant designed to support researchers working with the Einstein-Boltzmann solver CLASS. CLAPP leverages large language models (LLMs) and domain-specific retrieval to provide conversational coding support for CLASS-answering questions, generating code, debugging errors, and producing plots. Its architecture combines multi-agent LLM orchestration, semantic search across CLASS documentation, and a live Python execution environment. Deployed as a user-friendly web application, CLAPP lowers the entry barrier for scientists unfamiliar with AI tools and enables more productive human-AI collaboration in computational and numerical cosmology. The app is available at https://classclapp.streamlit.app

Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development workflows, but their suggestions often overlook dependency evaluation, emphasize popularity over suitability, and lack reproducibility. This creates risks for projects that require transparency, long-term reliability, maintainability, and informed architectural decisions. This study formulates software package selection as a Multi-Criteria Decision-Making (MCDM) problem and proposes a data-driven framework for technology evaluation. Automated data pipelines continuously collect and integrate software metadata, usage trends, vulnerability information, and developer sentiment from GitHub, PyPI, and Stack Overflow. These data are structured into a decision model representing relationships among packages, domain features, and quality attributes. The framework is implemented in PySelect, a decision support system that uses large language models to interpret user intent and query the model to identify contextually appropriate packages. The approach is evaluated using 798,669 Python scripts from 16,887 GitHub repositories and a user study based on the Technology Acceptance Model. Results show high data extraction precision, improved recommendation quality over generative AI baselines, and positive user evaluations of usefulness and ease of use. This work introduces a scalable, interpretable, and reproducible framework that supports evidence-based software selection using MCDM principles, empirical data, and AI-assisted intent modeling.

The proliferation of multi-modal fake news on social media poses a significant threat to public trust and social stability. Traditional detection methods, primarily text-based, often fall short due to the deceptive interplay between misleading text and images. While Large Vision-Language Models (LVLMs) offer promising avenues for multi-modal understanding, effectively fusing diverse modal information, especially when their importance is imbalanced or contradictory, remains a critical challenge. This paper introduces MM-FusionNet, an innovative framework leveraging LVLMs for robust multi-modal fake news detection. Our core contribution is the Context-Aware Dynamic Fusion Module (CADFM), which employs bi-directional cross-modal attention and a novel dynamic modal gating network. This mechanism adaptively learns and assigns importance weights to textual and visual features based on their contextual relevance, enabling intelligent prioritization of information. Evaluated on the large-scale Multi-modal Fake News Dataset (LMFND) comprising 80,000 samples, MM-FusionNet achieves a state-of-the-art F1-score of 0.938, surpassing existing multi-modal baselines by approximately 0.5% and significantly outperforming single-modal approaches. Further analysis demonstrates the model's dynamic weighting capabilities, its robustness to modality perturbations, and performance remarkably close to human-level, underscoring its practical efficacy and interpretability for real-world fake news detection.

Building Information Modeling (BIM) is essential for managing building data across the entire lifecycle, supporting tasks from design to maintenance. Natural Language Interface (NLI) systems are increasingly explored as user-friendly tools for information retrieval in Building Information Modeling (BIM) environments. Despite their potential, accurately extracting BIM-related data through natural language queries remains a persistent challenge due to the complexity use queries and specificity of domain knowledge. This study presents a comparative analysis of two prominent approaches for developing NLI-based BIM information retrieval systems: domain-specific fine-tuning and prompt-based learning using large language models (LLMs). A two-stage framework consisting of intent recognition and table-based question answering is implemented to evaluate the effectiveness of both approaches. To support this evaluation, a BIM-specific dataset of 1,740 annotated queries of varying types across 69 models is constructed. Experimental results show that domain-specific fine-tuning delivers superior performance in intent recognition tasks, while prompt-based learning, particularly with GPT-4o, shows strength in table-based question answering. Based on these findings, this study identify a hybrid configuration that combines fine-tuning for intent recognition with prompt-based learning for question answering, achieving more balanced and robust performance across tasks. This integrated approach is further tested through case studies involving BIM models of varying complexity. This study provides a systematic analysis of the strengths and limitations of each approach and discusses the applicability of the NLI to real-world BIM scenarios. The findings offer insights for researchers and practitioners in designing intelligent, language-driven BIM systems.