arXiv:2412.06865v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational costs, they also significantly degrade performance and quantization efficiency at extremely low settings due to quantization noise. We introduce a deep model series expansion framework to address this issue, enabling […]
FaithLens: Detecting and Explaining Faithfulness Hallucination
arXiv:2512.20182v1 Announce Type: cross Abstract: Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retrieval-augmented generation and summarization. In this paper, we introduce FaithLens, a cost-efficient and effective faithfulness hallucination detection model that can jointly provide binary predictions and corresponding explanations to improve trustworthiness. To achieve this, […]
Similarity Field Theory: A Mathematical Framework for Intelligence
arXiv:2509.18218v4 Announce Type: replace Abstract: We posit that persisting and transforming similarity relations form the structural basis of any comprehensible dynamic system. This paper introduces Similarity Field Theory, a mathematical framework that formalizes the principles governing similarity values among entities and their evolution. We define: (1) a similarity field $S: U times U to [0,1]$ […]
Large Language Models Develop Novel Social Biases Through Adaptive Exploration
arXiv:2511.06148v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In this paper, we argue that the predominant approach of simply removing existing biases from models is not enough. Using a paradigm […]
Generative Retrieval with Few-shot Indexing
arXiv:2408.02152v3 Announce Type: replace-cross Abstract: Existing generative retrieval (GR) methods rely on training-based indexing, which fine-tunes a model to memorise associations between queries and the document identifiers (docids) of relevant documents. Training-based indexing suffers from high training costs, under-utilisation of pre-trained knowledge in large language models (LLMs), and limited adaptability to dynamic document corpora. To […]
Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography
arXiv:2512.20168v1 Announce Type: cross Abstract: By integrating language understanding with perceptual modalities such as images, multimodal large language models (MLLMs) constitute a critical substrate for modern AI systems, particularly intelligent agents operating in open and interactive environments. However, their increasing accessibility also raises heightened risks of misuse, such as generating harmful or unsafe content. To […]
Position as Probability: Self-Supervised Transformers that Think Past Their Training for Length Extrapolation
arXiv:2506.00920v2 Announce Type: replace-cross Abstract: Deep sequence models typically degrade in accuracy when test sequences significantly exceed their training lengths, yet many critical tasks–such as algorithmic reasoning, multi-step arithmetic, and compositional generalization–require robust length extrapolation. We introduce PRISM, a Probabilistic Relative-position Implicit Superposition Model, a novel positional encoding mechanism that enables Transformers to extrapolate accurately […]
Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia
arXiv:2510.16066v3 Announce Type: replace-cross Abstract: Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates […]
Automated Program Repair of Uncompilable Student Code
arXiv:2510.06187v3 Announce Type: replace-cross Abstract: A significant portion of student programming submissions in CS1 learning environments are uncompilable, limiting their use in student modeling and downstream knowledge tracing. Traditional modeling pipelines often exclude these cases, discarding observations of student learning. This study investigates automated program repair as a strategy to recover uncompilable code while preserving […]
AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications
arXiv:2512.20164v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content moderation. However, our research identifies a vulnerability: LLMs can be manipulated by “adversarial instructions” hidden in input data, such as resumes or code, causing them to deviate from their […]