Blog

We present three new papers that tackle a pressing and underexplored topic in NLP: multi-document reasoning. These works offer rigorous benchmarks, novel methodologies, and empirical insights into how large language models (LLMs) handle complexity across multiple sources of information.

We present Blue v0.9, our open-source framework for building and deploying agentic workflows in enterprise environments. Unlike conventional AI frameworks, Blue is designed with enterprise-scale requirements in mind—scalability, observability, configurability, and seamless integration with existing infrastructure.

With the MCRank benchmark and our EXSIR method, we’ve shown that LLMs can significantly improve their performance on these challenging tasks when guided by structured reasoning.

Megagon Labs has curated this blog post to spotlight key developments in agentic systems, AI safety, and human-centered AI.

We echo through this blog that the optimization framework for compound AI systems should achieve broader goals such as multi-objective (accuracy, cost, latency, etc.), multi-plan optimization and also handling constraints, especially the budget. Again, these optimization goals are not comprehensive by far but are important for enterprise scenarios.

AmbigNLG tackles ambiguity in Natural Language Generation (NLG) instructions by identifying unclear specifications and refining them for better output quality.

Use this guide to learn valuable tips to maximize your AI internship experience and walk out with more than just a line item on your resume.

Through this inside peek at our internship program, explore the types of projects we at Megagon Labs formulate for our interns. If you are looking to start an internship soon, take their advice and apply it to your own internships.

MEGAnno combines the power of large language models (LLMs) with human expertise to streamline and enhance the data labeling process with a data annotation framework. Throughout this article, we’ll showcase MEGAnno’s capabilities as we provide detailed code snippets.