🧬 The concept of Neural DNA (nDNA), initially proposed by Dr. Amitava Das, opens up a new avenue for interpreting LLM behavior. nDNA treats an LLM as something we can sequence (just like DNA sequencing) by reading its internal signals -- its lineage and layer-level changes. It captures a model’s lineage, tuning-induced mutations, and functional expression so we can compare LLM families, spot drift, and explain why similar benchmarks behave differently across a range of LLMs. 
In practice, nDNA looks for signals such as: (i) layer shifts, (ii) MoE routing patterns, and (iii) controlled probes. It lets us audit change over time and pick models for tasks with evidence. Collaborating with Dr. Amitava Das, and his Pragya group (https://pragyaai.github.io) at the Department of CSIS BITS Pilani Goa Campus, we've been building and testing the concept of nDNA to understand LLM behavior across major job families: 
- Llama tends to refine mid–to-late layers while early layers remain stable; 
- Mistral localizes expertise while the early layers remain steady; 
- Gemma aligns with light-touch parameter changes; 
- Qwen rewrites more in its middle layers, especially in multilingual scenarios; 
- DeepSeek adds dialogue capability in mid layers, while preserving base geometry.
One of the other distinct advantages of nDNA is the ability to identify model collapse. On the surface you might see a slow loss of creativity and nuance in a model’s behavior. Inside, through the nDNA lens, the model’s internal landscape flattens -- fewer distinct patterns light up -- much like reduced neural diversity in biology. With nDNA, that shift becomes measurable over time, which makes collapse diagnosable and monitorable.
Dr. Amitava Das's novel thought process and insistence on clear hypotheses has led to the inception of nDNA as a practical lens for AI -- one that detects drift before deployment, keeps model lineages auditable, and matches models to tasks based on evidence rather than guesswork. Taken together, these nDNA lenses reframe how we interpret LLM behavior. Our other recent works, DPO-Kernels (ACL 2025; https://lnkd.in/gXGQvXwX), Yin-Yang Align (ACL 2025; https://lnkd.in/gWVCxG8B), QuickSilver (https://lnkd.in/gpZQKMmP), and the Counter Turing Test (EMNLP 2023 Outstanding Paper Award; https://lnkd.in/gaVHeXgJ), share a similar first-principle approach to LLM refinement/understanding. If nDNA is something you'd like to explore further -- we'll be launching a website for nDNA soon, stay tuned!
#nDNA #AI #FoundationModels #ModelInterpretability