LittleLess AI research model card

README.md

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+## Independent Verification Update — October 2026
+
+LittleLess AI has produced experimental artifacts demonstrating compact transformer storage, low-bit weight representation, file-backed inference, and native execution.
+
+An internal forensic reproduction audit reported that the experimental LittleLess-5M tool-calling prototype achieved 88.30% exact-match accuracy on its original synthetic evaluation set, but only 8.12% on a newly constructed unseen evaluation set.
+
+The audit also identified a rule-based entity-extraction fallback that bypassed the proposed neural span-pointer mechanism.
+
+These results demonstrate a significant generalization limitation. The MicroModel is therefore not presented as a production-ready system or as outperforming Needle or other existing compact models.
+
+Current research priorities are genuine learned entity grounding, robust unseen-data generalization, reproducible competitor comparisons, and a standalone offline inference product.
+
+**Verification status:** Internal audit completed; independent external reproduction and broader competitive benchmarking remain pending.
+
+**Researcher:** Putluru Sandeep  
+**Source:** https://github.com/sunny-kavixo/LittleLess-AI