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arXiv:2601.19918v1 Announce Type: new Abstract: Hallucinations in Large Language Models (LLMs), i.e., the tendency to generate plausible but non-factual content, pose a significant challenge for...
arXiv:2601.19919v1 Announce Type: new Abstract: Knowledge distillation is one of the most effective methods for model compression. Previous studies have focused on the student model...
arXiv:2601.19920v1 Announce Type: new Abstract: Binary Neural Networks (BNNs), where weights and activations are constrained to binary values (+1, -1), are a highly efficient alternative...
arXiv:2601.19921v1 Announce Type: new Abstract: Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows...
arXiv:2601.19922v1 Announce Type: new Abstract: Supportive conversation depends on skills that go beyond language fluency, including reading emotions, adjusting tone, and navigating moments of resistance,...
arXiv:2601.19923v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the capability to faithfully translate natural language into rigorous structured formats-essential...
arXiv:2601.19924v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive progress in optimization modeling, fostering a rapid expansion of new methodologies and evaluation...
arXiv:2601.19925v1 Announce Type: new Abstract: Introduction: Large language models (LLMs) can process requests and generate texts, but their feasibility for assessing complex academic content needs...
arXiv:2601.19926v1 Announce Type: new Abstract: We present a systematic review of 337 articles evaluating the syntactic abilities of Transformer-based language models, reporting on 1,015 model...
arXiv:2601.19927v1 Announce Type: new Abstract: Large Language Models (LLMs)-based question answering (QA) systems play a critical role in modern AI, demonstrating strong performance across various...
arXiv:2601.19928v1 Announce Type: new Abstract: Reinforcement learning (RL) has catalyzed the emergence of Large Reasoning Models (LRMs) that have pushed reasoning capabilities to new heights....
arXiv:2601.19929v1 Announce Type: new Abstract: We introduce Stingy Context, a hierarchical tree-based compression scheme achieving 18:1 reduction in LLM context for auto-coding tasks. Using our...
arXiv:2601.19930v1 Announce Type: new Abstract: We introduce a new benchmark for Danish culture via cultural heritage, Daisy, based on the curated topics from the Danish...
arXiv:2601.19931v1 Announce Type: new Abstract: We present a hybrid neuro-symbolic system for the SemEval-2026 Task 4 on Narrative Story Similarity. Our approach combines neural self-consistency...
arXiv:2601.19932v1 Announce Type: new Abstract: Coded language is an important part of human communication. It refers to cases where users intentionally encode meaning so that...
arXiv:2601.19933v1 Announce Type: new Abstract: Non-Resolution Reasoning (NRR) provides a formal framework for maintaining semantic ambiguity rather than forcing premature interpretation collapse. While the foundational...
arXiv:2601.19934v1 Announce Type: new Abstract: Large language models (LLMs) are widely used for tasks ranging from summarisation to decision support. In practice, identical prompts do...
arXiv:2601.19935v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents are increasingly deployed for complex, tool-based tasks where long-term memory is critical to driving actions....
arXiv:2601.19936v1 Announce Type: new Abstract: The opacity of massive pretraining corpora in Large Language Models (LLMs) raises significant privacy and copyright concerns, making pretraining data...
arXiv:2601.19938v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) is a serverless collaborative machine learning paradigm where devices collaborate directly with neighbouring devices to exchange...
arXiv:2601.19939v1 Announce Type: new Abstract: Oculomics - the concept of predicting systemic diseases, such as cardiovascular disease and dementia, through retinal imaging - has advanced...
arXiv:2601.19940v1 Announce Type: new Abstract: Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each...
arXiv:2601.19941v1 Announce Type: new Abstract: In last two years, large language models (LLMs) have shown strong capabilities in code generation, including hardware design at register-transfer...
arXiv:2601.19942v1 Announce Type: new Abstract: We study the emergence of multi-step reasoning in deep Transformer language models through a geometric and statistical-physics lens. Treating the...