🌀 Negentropy claude opus 4.7 4B: A Reasoning Experimental Model Based on Trace Inversion 💡 Abstract Based on current public information, commercial models like OpenAI's GPT series and Anthropic's Claude series have clearly hidden their true internal reasoning chains. What we ultimately see through APIs or frontend interfaces are often just "Reasoning Bubbles"—highly compressed and summarized versions of the original massive reasoning content. For small models aiming to improve capabilities through data distillation, these overly compressed reasoning chains fail to provide sufficient step level learning signals. On the contrary, because the logical leaps are too large and intermediate derivations are missing, forcing small models to learn these summaries directly often leaves them confused and unable to master true reasoning abilities. 🚀 Negentropy claude opus 4.7 4B is a 4B level lightweight reasoning enhanced model. Its name is derived from Negentropy in information theory, symbolizing the reconstruction of highly ordered, logically rigorous reasoning chains from compressed and fragmented information. It also draws inspiration from the movie ♻️ Tenet (which I recently watched b…
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