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The article describes a user's hands-on experience with the Nemotron 3 Diarization model, highlighting its streaming capabilities and integration into a speech-to-speech setup with a robot.
Open-Jev-27B-v1.1 is an open-source AI model with a LoRA adapter, achieving 85.28% accuracy on JevBench and featuring interactive demos for various tasks.
A debate resurfaces between AI pioneers Geoffrey Hinton and Yann LeCun regarding the efficacy of autoregressive LLMs, with recent advances in reasoning models reigniting the discussion on whether transformers alone suffice for human-like reasoning.
Transformers and ggml_org collaborate to integrate GGML kernels into the transformers library, enabling GGUF file loading at performance levels comparable to llama.cpp.
This work integrates ggml's Metal kernels into the transformers library, allowing GGUF models to run directly with improved compatibility and performance for fast local inference on Mac.
The paper introduces MFP, a method using role-aware Morgan fingerprints for predicting reaction yields in chemistry, achieving high accuracy and faster training compared to existing methods like YieldBERT and GNAN.
This paper introduces a pipeline that uses transformers and embeddings to extract interpretable decision rules from clinical tabular data, translating latent biomarkers back to measurable features for better clinical decision support.
The paper introduces MechaTerp-TRACE, a method for component ablation analysis in language models, finding that entity knowledge is largely attributable to generic generation machinery rather than localized components.
The paper demonstrates that transformers can have faithful internal world models, using mechanistic analysis to show that failures stem from feature interference rather than incoherent maps, and proposes affordance packing to enhance performance.
This paper investigates whether transformers trained on quantum measurement data from Rydberg atom arrays exhibit neural scaling laws similar to large language models, finding that scaling behavior depends on the statistical structure of the data, particularly near critical points.
The paper diagnoses systematic errors in neural morphological generation for Japanese past-tense verb inflection, focusing on a rare irregular subtype that causes disproportionate errors. Ablation experiments demonstrate that removing this subtype improves accuracy more than removing all irregular verbs, emphasizing the importance of fine-grained subclass analysis.
The author fine-tuned Qwen3.5 4B using LoRA with public and synthetic data to create a Jev-style model, achieving improved performance and open-sourcing the model and dataset.
LLM2Jev is an open-source tool that adapts local HuggingFace models to perform structured decisions with Choice, Score, and Noul frameworks, offering prefill-only inference and integration with Transformers and SGLang.
This article revisits a blog post by the Manus AI team from a year ago on context engineering practices, summarizing six key lessons for building AI Agents, such as context management, memory recovery, and error handling.
This article explains in detail MoE (Mixture of Experts) inference engineering, corrects misconceptions about activated parameters and deployment costs, and delves into technical details such as router selection, runtime grouping, GPU execution, memory management, and expert parallelism.
A nostalgic reflection on BERT's significant impact in 2020, with Hugging Face documenting it as 'BERTology', and a debate on what constitutes a large language model.
Baidu's open-source Unlimited-OCR model processes multiple PDF pages simultaneously, outperforming baselines like DeepSeek-OCR and supporting local execution with community integrations.
This paper introduces TranSGrid, a testbed that integrates deductive, inductive, and abductive reasoning to evaluate systematic generalization in AI. Experiments with Transformers show that current tasks overlook essential reasoning aspects, resulting in performance gaps on the proposed testbed.
This paper proposes Cognitive Field Networks (CFNs), a recurrent Transformer architecture for continuous cognitive dynamics that enables persistent, history-dependent cognition without explicit memory systems, inspired by biologically inspired theories.
This paper investigates the role of gating mechanisms in State Space Models, demonstrating that they promote memorization over in-context learning, yet can improve generalization to long sequences.