arXiv:2604.02043v1 Announce Type: cross Abstract: Self-supervised speech models learn effective representations of spoken language, which have been shown to reflect various aspects of linguistic structure. But when does such structure emerge in model training? We study the encoding of a wide range of linguistic structures, across layers and intermediate checkpoints of six Wav2Vec2 and HuBERT […]
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
arXiv:2604.02270v1 Announce Type: cross Abstract: Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We present Crystalite, a lightweight diffusion Transformer for crystal modeling built around two simple inductive biases. The first is Subatomic Tokenization, a compact chemically […]
GenOM: Ontology Matching with Description Generation and Large Language Model
arXiv:2508.10703v3 Announce Type: replace Abstract: Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in the biomedical domain which contains numerous complex concepts related to diseases and pharmaceuticals. This paper introduces GenOM, a large language model (LLM)-based ontology alignment framework, which enriches the semantic representations of […]
The Reasoning Error About Reasoning: Why Different Types of Reasoning Require Different Representational Structures
arXiv:2603.21736v2 Announce Type: replace Abstract: Different types of reasoning impose different structural demands on representational systems, yet no systematic account of these demands exists across psychology, AI, and philosophy of mind. I propose a framework identifying four structural properties of representational systems: operability, consistency, structural preservation, and compositionality. These properties are demanded to different degrees […]
Interpretable Classification via a Rule Network with Selective Logical Operators
arXiv:2408.11918v2 Announce Type: replace-cross Abstract: We introduce the Rule Network with Selective Logical Operators (RNS), a novel neural architecture that employs textbfselective logical operators to adaptively choose between AND and OR operations at each neuron during training. Unlike existing approaches that rely on fixed architectural designs with predetermined logical operations, our selective logical operators treat […]
The Illusion of AI Expertise Under Uncertainty: Navigating Elusive Ground Truth via a Probabilistic Paradigm
arXiv:2601.05500v5 Announce Type: replace Abstract: Benchmarking the capabilities of AI systems, including Large Language Models (LLMs) and Vision Models, typically ignores the impact of uncertainty in the underlying ground truth answers from experts. This ambiguity is not just limited to human preferences, but is also consequential even in safety critical domains such as medicine where […]
Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory
arXiv:2604.01007v2 Announce Type: replace Abstract: AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal experiences remains a critical bottleneck. Building effective lifelong memory requires navigating a vast design space spanning architecture, retrieval strategies, prompt engineering, and data pipelines; this space is too large and interconnected for manual […]
TRACE: Transparent Web Reliability Assessment with Contextual Explanations
arXiv:2506.12072v4 Announce Type: replace-cross Abstract: In an era of AI-generated misinformation flooding the web, existing tools struggle to empower users with nuanced, transparent assessments of content credibility. They often default to binary (true/false) classifications without contextual justifications, leaving users vulnerable to disinformation. We address this gap by introducing TRACE: Transparent Reliability Assessment with Contextual Explanations, […]
SentinelNet: Safeguarding Multi-Agent Collaboration Through Credit-Based Dynamic Threat Detection
arXiv:2510.16219v3 Announce Type: replace-cross Abstract: Malicious agents pose significant threats to the reliability and decision-making capabilities of Multi-Agent Systems (MAS) powered by Large Language Models (LLMs). Existing defenses often fall short due to reactive designs or centralized architectures which may introduce single points of failure. To address these challenges, we propose SentinelNet, the first decentralized […]
Towards Faithful Reasoning in Comics for Small MLLMs
arXiv:2601.02991v2 Announce Type: replace-cross Abstract: Comic understanding presents a significant challenge for Multimodal Large Language Models (MLLMs), as the intended meaning of a comic often emerges from the joint interpretation of visual, textual, and social cues. This naturally motivates Chain-of-Thought (CoT) prompting, since explicit intermediate reasoning appears promising for integrating such heterogeneous signals. However, existing […]
Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation
arXiv:2603.17717v2 Announce Type: replace-cross Abstract: Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal time for artificial intelligence (AI), with even more sophisticated attacks that utilize advanced techniques, such as generative artificial intelligence (GenAI) and reinforcement learning, it has become a vital component […]
Learning to Play Blackjack: A Curriculum Learning Perspective
arXiv:2604.00076v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) agents often struggle with efficiency and performance in complex environments. We propose a novel framework that uses a Large Language Model (LLM) to dynamically generate a curriculum over available actions, enabling the agent to incorporate each action individually. We apply this framework to the game of Blackjack, […]