Michael Boone | Searcharxiv

arXiv2026

Nemotron 3.5 Bolt 74B-A9B 24GB: A Hybrid Vision-Language Model for Local AI

We introduce Nemotron 3.5 Bolt 74B-A9B 24GB, a hybrid Mamba-Transformer vision-language model that optimizes speed and intelligence per GB of memory used for local agentic workloads. Derived from the Nemotron 3.5 Super VL 120B-A12B model family, Nemotron 3.5 Bolt 74B-A9B 24GB combines Star Elastic compression with 2-bit Quantization-Aware Distillation for memory-constrained deployment. The model is optimized for reasoning, agentic tool use, and web & terminal interactions. We also trained a LiLiCorr speculative drafter that is released alongside the model to reach our target inference speed. The resulting model achieves a higher accuracy than leading models with similar memory footprints and speed across many benchmarks for general agentic capabilities (e.g., GDPVal v2), web browsing (e.g., BrowseComp), and terminal use (e.g., Terminal-Bench 2.1), with a relatively high TPS of 84 on RTX Spark that is comparable to models with much lower active parameter counts and more than sufficient for long-horizon agentic applications.

cs.CV↗

arXiv2025

Enhancing Code Consistency in AI Research with Large Language Models and Retrieval-Augmented Generation

Ensuring that code accurately reflects the algorithms and methods described in research papers is critical for maintaining credibility and fostering trust in AI research. This paper presents a novel system designed to verify code implementations against the algorithms and methodologies outlined in corresponding research papers. Our system employs Retrieval-Augmented Generation to extract relevant details from both the research papers and code bases, followed by a structured comparison using Large Language Models. This approach improves the accuracy and comprehensiveness of code implementation verification while contributing to the transparency, explainability, and reproducibility of AI research. By automating the verification process, our system reduces manual effort, enhances research credibility, and ultimately advances the state of the art in code verification.

cs.SE↗