The Maya series built a complete Advaita Vedanta Antahkarana — fear, wisdom, memory, attention, metabolic budget — across nine spiking neural network papers. At the end, a question remained: are these mechanisms bound to the neuromorphic substrate? Or are they substrate-independent patterns of mind that can govern any learning system?
Maya-LLM translates all five active Antahkarana dimensions into LoRA continual fine-tuning of Phi-2 2.7B, tested across 8 sequential NLP domains in the TRACE benchmark. The result: Bhaya fires on real cross-domain loss spikes for the first time in transformer context. The Buddhi S-curve traces an identical trajectory to P4–P9 SNNs — a cross-substrate invariant. The Antahkarana is the pattern, not the material.
Imagine you are teaching a student eight subjects in sequence: Chinese, finance, meetings, coding, science, math, more math, German. After learning German, the student has forgotten Chinese. This is catastrophic forgetting — the dominant failure mode of neural networks trained sequentially. Every new domain overwrites what was learned before.
Maya-LLM equips Phi-2 (a 2.7 billion parameter language model) with the same affective mechanisms that governed Maya's spiking neural network: fear that detects dangerous loss spikes, wisdom that protects important adapter weights, intellect that gates consolidation maturely, karma that tracks cross-domain interference history, and metabolic budget that paces learning.
Benchmark: TRACE · 8 domains · 1000 samples/domain · 500 steps/domain
BWT = mean[ ppl_final[j] − ppl_immediate[j] ] for j < n // positive = forgetting
We use perplexity instead of accuracy because Phi-2 is generative — exact match accuracy gives near-zero scores even for correct paraphrases. Perplexity increases when the model forgets and decreases when it retains, making it a natural continual learning signal. Lower perplexity = better. Lower BWT = less forgetting.
Condition F with SNN-calibrated hyperparameters (Bhaya threshold=1.8×) performs worse than baseline because Bhaya fires on 18.8% of all steps at LLM loss scale — treating normal loss variance as catastrophic events. Recalibrating threshold to 4.0× (appropriate for LLM mean loss ~1.08) gives the best result. This is a quantified cross-substrate scaling finding, not a failure.
R[i][j] = perplexity on domain j after training through domain i. Diagonal = immediate performance right after training. Off-diagonal = retention. Lower = better. Watch how early domains degrade as later domains are learned — this is catastrophic forgetting made visible.
| After → | C-STANCE | FOMC | MeetingBank | Py150 | ScienceQA | NumGLUE-cm | NumGLUE-ds | 20Minuten |
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With Bhaya threshold calibrated to 4.0× (appropriate for LLM loss scale ~1.08), nociceptive metaplasticity fires on three specific domain transitions — the ones that produce genuine cross-domain loss spikes. Chinese stance detection to Python code is a dramatic semantic shift. The model's loss spikes. Bhaya fires. This is the first confirmed nociceptive firing in a transformer continual fine-tuning context.
Bhaya quiescent (0.000) is not ignorance — it is the appropriate response when the domain transition is smooth. Bhaya at 0.015 on Py150 is the model detecting a genuine semantic rupture and elevating plasticity on the adapter weights that need to change. In Vedantic terms: not Ajnana (ignorance) and not Bhaya-paralysis, but Viveka — discerning recognition of a real challenge.
Two constants confirmed across all 9 SNN papers now appear in transformer fine-tuning. These are not coincidences. They are structural properties of the Antahkarana architecture — independent of whether the underlying compute uses spikes, gradients, or anything else.
Each mechanism below was designed in SNN context (P1–P9) and translated to LoRA adapter dynamics. Click any to explore its LLM role, confirmed status, and the philosophical grounding.
Every slider controls a real hyperparameter from the published Condition F calibrated run. Drag and watch derived values update. Press R to restore canonical published values.
"The Antahkarana does not belong to the spike. It belongs to the pattern."