15 citations · 20 across the 18 of their papers we have counts for
6 papers · 1 filter
ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
Hongjin Su, Shizhe Diao, Ximing Lu +13
Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and comp…
Universal Deep Research: Bring Your Own Model and Strategy
Peter Belcak, Pavlo Molchanov
Deep research tools are among the most impactful and most commonly encountered agentic systems today. We observe, however, that each deep research agent introduced so far is hard-c…
Small Language Models are the Future of Agentic AI
Peter Belcak, Greg Heinrich, Shizhe Diao +5
Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise…
Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation
Peter Belcak, Greg Heinrich, Jan Kautz +1
Finetuning language models for a new domain inevitably leads to the deterioration of their general performance. This becomes more pronounced the more limited the finetuning data re…
Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training
Shizhe Diao, Yu Yang, Yonggan Fu +11
Pre-training datasets are typically collected from web content and lack inherent domain divisions. For instance, widely used datasets like Common Crawl do not include explicit doma…
Text Compression for Efficient Language Generation
David Gu, Peter Belcak, Roger Wattenhofer
We challenge the prevailing assumption that LLMs must rely fully on sub-word tokens for high-quality text generation. To this end, we propose the "Generative Pretrained Thoughtform…