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Rahul Raja on Muck Rack

Rahul Raja

Montreal
As seen in: Medium, VentureBeat, Celtics Blog, Liberty Ballers, Hoops Habit, The Dream Shake, Tech News Tube
No Breaks😈 We Stay Dream Chasing💸💪 God and Family first Matthew 19:26 🙏 BSD➡️ UPEI 📍MTL

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Rahul Raja’s Journalist Portfolio

What are the greatest NBA teams to fall just short?

What are the greatest NBA teams to fall just short?

Who are the greatest NBA teams to not: Win a championship, go to the Finals, go to the Conference Finals, mak…

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NBA: Who are the greatest NBA players to fall short in the playoffs?

NBA: Who are the greatest NBA players to fall short in the playoffs?

Who are the greatest NBA players to not: Win a Finals MVP, win an NBA championship, make the Finals, make the…

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Articles

Why AI coding agents aren’t production-ready: Brittle context windows, broken refactors, missing operational awareness

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By Advitya Gemawat, Rahul Raja, Privacy Notice.Get updates
| buff.ly
Remember this Quora comment (which also became a meme)? (Source: Quora) In the pre-large language model (LLM) Stack Overflow era, the challenge was discerning which code snippets to adopt and adapt effectively. Now, while generating code has become trivially easy, the more profound challenge lies in reliably identifying and integrating high-quality, enterprise-grade code into production environments.
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Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data

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By Ashish Kattamuri, Arpita Vats, Harshwardhan Fartale, Rahul Raja
| preprints.org
Submitted: 12 November 2025 You are already at the latest version Recursive prompting with large language models enables scalable synthetic dataset generation but introduces the risk of bias amplification. We investigate gender bias dynamics across three generations of recursive text generation using three complementary evaluation frameworks: rule-based pattern matching, embedding-based semantic similarity, and downstream task performance.
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Quantum Natural Language Processing: A Comprehensive Survey of Models, Architectures, and Evaluation Methods

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By Arpita Vats, Rahul Raja, Ashish Kattamuri, Abhinav Bohra
| preprints.org
Submitted: 01 November 2025 You are already at the latest version Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field at the intersection of quantum computing, natural language understanding, and formal linguistic theory. As advances in quantum hardware and algorithms accelerate, QNLP promises new paradigms for representation learning, semantic modeling, and efficient computation.
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