Jigyāsā: A Self-Taught Thinking Model Trained on Autonomously Generated Knowledge via SNN-Modulated Curiosity Loops

Jigyāsā is a language model trained entirely on knowledge it generated itself — starting from a single seed question about entropy and time, the system autonomously explored 500 knowledge nodes across nine domains, including physics, consciousness, linguistics, and mathematics, guided by a small Spiking Neural Network (SNN) that directed how deeply to investigate each concept. No human authored any training pair; all 4,939 question-answer examples were produced through this curiosity-driven traversal, then used to fine-tune Qwen2.5-7B-Instruct via QLoRA. The resulting model achieves a perfect score of 30/30 (100%) on a 10-question evaluation suite spanning identity anchoring, cross-domain reasoning, hard safety stops, adversarial identity resistance, and spontaneous question generation. This work also introduces the Thinking Model paradigm as a formal distinction from conventional chatbot architectures, and proposes the Depth Arc — a 5-message conversational framework designed to measure whether a model genuinely deepens its reasoning across a dialogue rather than simply responding to surface prompts. Jigyāsā is part of the Jigyāsā Series from Nexus Learning Labs, Bengaluru, India — a line of self-taught thinking models built on the principle that autonomous knowledge generation, without human annotation or distillation, can produce robust and epistemically coherent reasoning systems. Series: Part of the Jigyāsā Series — self-taught thinking models. Autonomous knowledge generation without human annotation or distillation. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Jigyasa and your research context) | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYA_DECAY_RATE = 0.002315 (embedded in all canonical hyperparameters)Canary: MayaNexusVS2026NLL_Bengaluru_Narasimha