Abstract
Comment: 22 pages, 4 figures, 2 tables. 7th Place Solution for the NVIDIA Nemotron Model Reasoning Challenge (Kaggle)
Achieved over 96% validation accuracy and placed 7th overall in the NVIDIA…
- Authors
- Prateek Agnihotri, Sanchit Jain, Prabhat Agnihotri...
- Categories
- cs.AI
Brief
An approach for solving Bit Manipulation Puzzles reframes logic-gate deduction as base selection using string-similarity (minimal bit flips) to extract primitive transformations and truth tables. The pipeline integrates backtracking DFS for robust error recovery and a bit-tokenized, dynamically masked SFT that trains models to hypothesize, self-evaluate, and backtrack. Evaluated on the challenge, it yielded >96% validation accuracy and 7th place.
Why it matters
Achieved over 96% validation accuracy and placed 7th overall in the NVIDIA Nemotron Model Reasoning Challenge (Bit Manipulation Puzzles); paper published on arXiv 2026-06-22 by Prateek Agnihotri et al.
Key details
- Method abandons arithmetic logic in favor of string-similarity base selection (minimal bit-flips) to deduce truth tables, combined with backtracking DFS for collision detection/error recovery and a bit-tokenization + dynamic-masking SFT that forces single-bit tokens and oracle-style feedback.