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How far can we push large language model speed by reusing “free” GPU compute, without giving up autoregressive level output...
arXiv:2511.09197v1 Announce Type: new Abstract: Subword segmentation is typically applied in preprocessing and stays fixed during training. Alternatively, it can...
arXiv:2504.19254v4 Announce Type: replace Abstract: Hallucinations are a persistent problem with Large Language Models (LLMs). As these models become increasingly...
arXiv:2511.08721v1 Announce Type: cross Abstract: In behavioral sciences, experiments such as the ultimatum game are conducted to assess preferences for...
arXiv:2511.08916v1 Announce Type: new Abstract: Large language models (LLMs) have achieved impressive performance across a wide range of natural language...
In this tutorial, we build our own custom GPT-style chat system from scratch using a local Hugging Face model. We...
arXiv:2511.08113v1 Announce Type: new Abstract: Skill composition is the ability to combine previously learned skills to solve new tasks. As...
arXiv:2503.17352v3 Announce Type: replace-cross Abstract: We introduce OpenVLThinker, one of the first open-source large vision-language models (LVLMs) to exhibit sophisticated...
arXiv:2511.08109v1 Announce Type: new Abstract: This paper examines how science fiction destabilises ontological categories by measuring conceptual permeability across the...
Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost, and Privacy
Enigmata’s Multi-Stage and Mix-Training Reinforcement Learning Recipe Drives Breakthrough Performance in LLM Puzzle Reasoning
Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTS
Enhancing Robustness of Autoregressive Language Models against Orthographic Attacks via Pixel-based Approach
Enhancing multimodal analogical reasoning with Logic Augmented Generation
Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization
Enhancing Hindi NER in Low Context: A Comparative study of Transformer-based models with vs. without Retrieval Augmentation
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval
Energy Landscapes Enable Reliable Abstention in Retrieval-Augmented Large Language Models for Healthcare
End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
End-to-End Aspect-Guided Review Summarization at Scale
