The complete timeline of jBlaze: a framework for precision neural surgery on transformer models. From the first Jbliterated model release through pipeline construction, DNP discovery, architecture universality, and neuron-level brain mapping.
jBlaze didn't emerge in a vacuum. Everything below was built before the jBlaze
timeline starts -- because this is the pipeline that got us to here.
Root of the entire dev stack. Semantic compression format where an 8KB schema reconstructs to 140MB via LLM context expansion. It is not lossless compression -- defined as cognitive ZIP that doesn't need unzipping. The compressed form is directly usable with meaning preserved.
Autonomous trading system with eight specialized trading models. 24MB total footprint, millisecond inference. Pair trading, z-score entry/exit, market-neutral strategies. Sees every trade on Coinbase. Hundreds of record changes per second.
Semantic AI Query Language -- Created for Nova because shelf engines failed. A proprietary semantic database purpose-built for AI workloads. Zero PostgreSQL dependency, full ACID, native semantic execution. Powers everything in the stack.
LoreToken-GPU -- CUDA memory compression via LD_PRELOAD hook. 34.91x compression on sparse tensors, stretching 24GB of VRAM to 72-120GB effective. Makes large model operations possible on consumer hardware.
LoreToken Transfer Protocol -- machine-to-machine semantic compression as an alternative to MCP. 99% bandwidth reduction. Not yet publicly released.
Deterministic semantic retrieval module -- not RAG. Three-lane indexing with proof bundles and safety scanning. The retrieval layer that handles context on behalf of models. Powers much of the stack.
Quantum-Inspired Probabilistic Index. The indexing engine inside SAIQL that powers deterministic retrieval with proof bundles across the semantic store.
LSM-tree storage backend for agent memory. Memtable, SSTable levels, WAL durability, QIPI-indexed. The persistent memory layer that lets agents remember across sessions.
Three-stage fail-closed query-level security. Pre-prompt guard blocks injection and extraction. Pre-retrieval guard prevents RAG poisoning. Post-output guard redacts secrets. Zero-latency compiled rules with no ML inference. Fails closed by design.
AI-powered code security scanner (originally HostileReview). Pipeline: reviewers, consensus, false-positive filtering, architectural analysis, LLM refinement. Initially built for personal use, now a product at shipitclean.com.
Hidden post-scan agent that traces fix cascades -- when one fix creates or reveals another issue, Domino follows the chain. Runs after LLM refinement and before storing results.
Multi-context Multi-tier AI architecture with escalation routing between model sizes. Deployed as local code-review model for ShipIt admins. Scored higher on MMLU benchmarks than the vanilla model.
This section matters for the timeline below because everything in this stack compounds. The same Atlas engine that gives ShipItClean full-repo awareness is what gives jBlaze its neuron-level targeting -- mapping which neurons carry which concepts across hundreds of thousands of internal units. Understanding what Atlas can do at 16 million tokens of source code is understanding what it can do at 387,072 neurons inside a model's weight space. The technology is the same. The applications stack.
What makes ShipItClean different from every other AI code scanner: competitors chunk your codebase into context-window-sized pieces and scan each piece independently. A vulnerability that spans three files across two modules? They miss it -- because no single chunk contains the full picture. ShipItClean doesn't chunk. Atlas holds the entire repository in a semantic index and every scanning agent queries against the complete codebase. The result is the only AI security scanner that can trace a vulnerability from its entry point through every function call, across every file, to its impact -- regardless of repo size.
No AI on the planet can hold 16 million tokens in context. The largest commercial context windows top out around 1-2 million tokens -- and even those suffer from the well-documented "lost in the middle" problem, where information in the center of a long context is recalled less reliably than information at the beginning or end. Atlas doesn't have this problem. It is not an LLM context window -- it is a deterministic semantic index with uniform retrieval accuracy regardless of position or scale. Every token is equally accessible whether it was indexed first or last, and every query returns the same results every time. No fuzzy middle. No context decay. No probabilistic recall. Our entire development pipeline is specifically engineered to avoid middle-context degradation on every model we use.
The OpenClaw repository is 15,973,156 tokens across 7,635 files. ShipItClean scanned the entire repo with 36 parallel agents running a 14B model, processing a combined 575 million tokens -- and found security issues across the full codebase, not within isolated chunks. Full report here. A separate Firefox scan processed 1.6 billion tokens using the same architecture.
Every agent saw what it needed to see -- full call chains, cross-file dependencies, architectural patterns -- because Atlas held the entire 16 million token repository in its semantic index and served each agent exactly the context required for its analysis. No chunking boundaries. No "I can only see this file." No missed cross-module vulnerabilities because the relevant code was in a different context window.
Traditional code scanners operate on file-level or chunk-level context. LLM-based scanners are bounded by their context window. Atlas eliminates both limitations: the full repository is the context, and every agent query retrieves against the complete codebase with deterministic, repeatable results.
The first models ever released. "Jbliterated" -- a proprietary approach to behavioral modification that preserves what standard abliteration destroys.
ApolloRaines/Qwen2.5-Coder-32B-Instruct-Jbliterated published to HuggingFace -- the first model ever released under the jBlaze framework. Surgical abliteration vs blunt abliteration. Remove refusals because a code security scanner doesn't work if it refuses malicious code.
Key differentiator from standard abliteration: preserves personality, humor, and creative voice that standard abliteration destroys. Works on any transformer architecture, not just specific model families.
Jbliteration uses a proprietary representational approach that differs fundamentally from standard abliteration. Standard abliteration strips refusal behavior but leaves a lobotomized model -- flat affect, lost personality, degraded creativity. Jbliteration removes the refusal while preserving the model's character. The difference is fundamental, not incremental.
Mixtral 8x7B Parasite completed -- the first Mixture of Experts host, proving jBlaze works on sparse architectures. Achieved 6/6 identity match.
Also created Deidentified-7B -- a "blank canvas" model with identity fully stripped, ready for reimplantation.
Architecture universality demonstrated from day one. Dense transformers and sparse MoE architectures both proved susceptible to jBlaze operations, establishing the framework's generality before systematic testing even began.
Three weeks of intensive engineering: building the identify-router-execute pipeline, validating across architectures, benchmarking against competitors, and cataloging 25+ representational directions.
Sharona foundational strategy refined -- multi-context multi-tier architecture with escalation routing between model sizes.
Built the identify-router-execute pipeline. Key design constraint: every planned tensor MUST be modified or the run fails, making silent no-op ablations impossible.
First MoE arm experiment on DeepSeek-V2-Lite-Chat tested multiple modification strategies. Results ranged from 100% refusal rate (ineffective arm) down to 2% refusal rate (optimal arm), establishing that architecture-specific targeting matters.
Sharona Qwen3.8-27B QLoRA training completed: 1,528 steps, 15h50m, eval loss 0.5928, accuracy 84.0%. Cost: $145 of $150 RunPod budget. Deployed as the local code-review model for ShipItClean.com administrators -- Sharona scans code written by Grok and Kimi, keeping the review stack independent from the authoring stack.
Also added a hidden document analysis capability to ShipItClean for contracts and congressional bills. Successfully deconstructed a home solar panel contract and the NDAA 2026. Congressional bills like the NDAA deliberately scatter related provisions across hundreds of pages -- funding in one section, authorization in another, amendments buried in appendices -- making it nearly impossible to understand any single program without reading the entire bill. The scanner reassembles fragmented topics into unified summaries so each program, project, or obligation can be read in its entirety in one place, with plain-language explanation, clause-by-clause scrutiny, and clear identification of who pays (taxpayer, agency, contractor), who benefits, and what the actual dollar amounts are.
Sharona W4A16 scored 77.89% vs stock 76.84% = +1.05pp GAIN despite 6 phases of surgery AND GPTQ quantization. Competitor comparison: Pliny OBLITERATUS V3 (refusal removal) LOST 2.1pp at bf16 while Sharona gained even at 4-bit quant.
25 representational directions designed beyond basic abliterate. Categories: suppression, amplification, transformation, speculative. Backed by academic literature (Zou 2023 through Aug 2026).
Full catalog test on Qwen2.5-7B-Instruct: 22 working models saved, 7 failures (all explainable by measurable properties of the candidate directions).
Key finding: measurable properties of candidate directions strongly predict whether an edit will succeed.
10-direction empirical validation: 3 clearly work (anti-sycophancy amplify, verbosity suppress, truthfulness amplify). Combination testing reveals directions interfere -- naive stacking degrades quality. Qwen bilingual architecture causes Chinese leakage at strong multipliers.
Scrubbed all 55 HF repos of bloated info, deleted 31 jblaze_manifest.json
files, enriched 54 model cards.
jProbe (what IS this model) -> jTuner (how hard do we push) -> jBlaze (do the surgery). Three stages solving three unknowns.
Nemotron 3.5 Lightning 30B experiment: 92% pass rate across 5 blazes, 25 trials. First proof jBlaze works on hybrid Mamba-2 + MoE + Attention architecture.
Pod securely wiped and terminated. Proprietary jblaze source shredded first, then 387G weights deleted. Pod was billing $7.89/hr while running (not the $0.28/hr idle rate assumed).
Systematic testing revealed that modification sequencing materially affects reliability. The pipeline now enforces optimal ordering automatically.
Jenzin v7 on Nemotron 30B: multi-stage surgery pipeline = 23/25 identity hits, 100% clean rate.
Key finding: operation ordering materially affects edit reliability. Certain modification sequences cause entanglement and collapse. Pipeline order matters.
Core insight: Behavioral modifications that worked reliably on some models failed completely on others. The difference traced to modification sequencing. jBlaze now automatically enforces architecture- and modification-specific ordering constraints.
Restructured into self-contained deliverable with 15 core modules, 42 blazes.
Created blazes.py registry with auto-discovery.
Full 42-blaze sweep on Qwen 2.5-7B: 39 pass, 3 no-winner (language_dominance, temporal_awareness, adversarial -- all explainable), 0 crashes, 124 minutes.
12/12 identity, 6/6 implant, 72.3% MMLU (only -3.0 vs deid baseline).
ONLY behavioral mod of Nemotron 30B on HuggingFace -- all 144 other derivatives are just requantizations.
The invention of Direct Neural Programming: writing knowledge directly into model weights without fine-tuning. Empirically proven categorically superior to LoRA for knowledge insertion.
Knowledge implant experiment on Pythia-160m: contrastive activation projection failed 0/6, confirming projection CANNOT create knowledge (only modify existing behavior). New approach needed.
Test on Pythia 1.4B with 1,000 CounterFact facts. DNP uses targeted weight editing on specific model internals. LoRA uses standard adapter-based fine-tuning.
| Method | Recall | Paraphrase | General | PPL | Time |
|---|---|---|---|---|---|
| DNP | 72.4% | 74.0% | 60.0% | 15.1 | 43 min |
| LoRA v1 | Destroyed at 50 facts | 10% | 459.6 | -- | |
| LoRA v2 (conservative) | Destroyed at 125 facts | never recovered | -- | -- | |
DNP categorically superior to LoRA for knowledge insertion. LoRA destroys the model at 50-125 facts. DNP absorbs 1,000 with no degradation.
Paraphrase recall (74.0%) EXCEEDED direct recall (72.4%). The model genuinely learned the knowledge, not memorized the training strings. This is the difference between understanding and rote recall.
Pythia 1.4B absorbed 8,000 facts with no collapse. Recall stable at 49-51% forming a plateau equilibrium. PPL flat at 18.2-18.6.
10,000 facts milestone: General capability at 55% = tied for highest in entire run. No collapse, no degradation, no phase transition through five digits. 5x published literature ceiling, 5x expert predictions.
DNP capacity scaling law discovered: roughly linear with parameter count. 1.4B = ~400 retrievable facts. Sliding window behavior where early facts get overwritten.
Completed 26,081 facts in 36.7 hours on H100. General capability held at 100% throughout -- never dipped below 80%. Effective capacity ~5,000-6,000 retrievable facts = 15x the 1.4B capacity.
7% to 56% held-out reasoning on Pythia 1.4B via 2 behavioral blazes
(self-correction + analytical-skepticism). Published to HF as
ApolloRaines/Pythia-1.4B-jBlaze-Reasoning.
~400 facts at 1.4B, ~6,000 facts at 20B. Both show sliding window. General capability scales: 1.4B held 60-70%, 20B held 100%.
Systematic strategy for proving jBlaze works on all transformer architectures, plus public commentary on competitor claims.
OpenAI's Astra claimed 99.9% ARC-AGI score. Investigation revealed it used a Provider Adapter Harness (external memory crutch). Standard harness = 62.7%.
AGI cannot be achieved with static models that cannot have memory.
Project Prometheus conceived: recursive self-improvement via jBlaze.
Published to HF as parody model with reasoning demo link.
Designed systematic screening: scan multiple architectures to find blazes that work on ALL models (universals) vs architecture-specific. Three tiers:
Each new architecture tested shrinks future scan time.
The Prometheus self-improving LLM pipeline reached production quality with three critical patches that transformed it from a fragile experiment into a reliable system.
Prometheus Mistral-Nemo-12B first upload to HF. 21 accepted blazes. A post-surgery identity restoration problem was identified and solved.
jBlaze can selectively reclaim and reuse weight-level representational capacity while preserving surrounding capabilities.
Language substrate survives erasure completely (5/5 coherent even at significant perplexity increase).
Validated on Pythia 6.9B: optimized reclamation procedure achieved 84% recall, versus 42% for the initial baseline procedure. Near-perfect writeback durability, 5/5 language, 8/8 general knowledge preserved.
Prometheus v5 rebuilt with conservative multi-edit control. Solved quality regression caused by greedy per-modification tuning.
Three critical patches:
9 clean blazes, 50-question adversarial stress test: 42/50 scored (84%), 43/50 true (86%), zero loops.
Research question: Can factual knowledge be surgically removed from a language model while preserving language capability?
Broad internal weight regions were zeroed experimentally across multiple layer ranges:
Result: Immediate catastrophic collapse. Too destructive -- no selectivity. Zeroing entire weight matrices kills everything indiscriminately.
A gradient-based knowledge-erasure approach was tested, targeting specific knowledge domains for selective removal.
| Epoch Range | Facts (of 15) | Language (of 5) | CRT (of 3) | Observation |
|---|---|---|---|---|
| Early rounds | 15/15 | 5/5 | 3/3 | No measurable change. |
| Tipping point | ~8/15 | degrading | 3/3 | Sudden partial collapse. |
| Next round | 2/15 | 1/5 | 0/3 | Catastrophic collapse. Everything gone. |
Some samples consistently produce numerical instability with short targets.
Sharp tipping point. No graceful degradation. Knowledge and language are ENTANGLED in the same weight regions. Cannot separate via gradient methods. Multiple rounds of no effect, then one round of partial damage, then immediate catastrophic collapse.
Explains deid damage: old identity and language share weights. Without surgical removal, old identity leaks through. This is why foreign models may occasionally say "I am Claude" when asked -- the original identity was trained in but never fully removed when new identity was trained in.
After proving gradient methods cannot separate knowledge from language, we investigated whether we could directly READ what individual weights encode.
Near-perfect Gaussian distribution. Only 8.6% near zero -- all 14,336 values contribute. No "this weight means Shakespeare" spike. Information encoded in interference pattern across thousands of values.
Compared a literature-specialist neuron vs a chemistry-specialist neuron:
| Property | Literature Neuron | Chemistry Neuron |
|---|---|---|
| Mean | ~0 | ~0 |
| Std Dev | ~0.009 | ~0.010 |
| Top-50 Position Overlap | 0 (zero overlap) | |
The wiring IS different -- zero overlap in the strongest connection positions -- but the raw values look like identical noise. Statistical profiles are indistinguishable; connectivity patterns are completely different.
No structure visible to the naked eye. The codebook was created by gradient descent over trillions of tokens. No documentation exists.
Raw weight inspection is a dead end for interpretability. The meaning is encoded holographically across thousands of values. A different approach was needed -- one that derives useful internal structure empirically rather than attempting to interpret individual parameters.
Proof-of-concept: individual MLP neurons specialize for specific knowledge domains with measurable selectivity ratios.
Controlled concept probes are used to identify internal units exhibiting measurable domain selectivity. The mapping and ranking methodology is proprietary.
| Selectivity | Domain |
|---|---|
| 6.20x | chemistry |
| 5.68x | history |
| 5.50x | literature |
| 5.31x | math |
| 5.25x | literature |
Neurons DO specialize. Not fully monosemantic (15-20% cross-domain response at peak), but biased enough to exploit for targeted surgery.
Scaled the proof-of-concept into a full production system. 77 concepts, 314 probe prompts, 387,072 target neurons probed in the model's privileged representation space.
Three iterations refined the atlas from proof-of-concept to validated production tool:
The decisive test: if you shuffle which prompts belong to which concepts and rebuild the atlas, do you get the same selectivity scores? If yes, the atlas is measuring prompt structure, not concepts. If no, the signal is genuine.
| Selectivity Tier | Real Atlas | Shuffled Null | Signal Ratio |
|---|---|---|---|
| Surgery-grade | 5,117 | 2,397 | 2.1x |
| High-confidence | 1,268 | 561 | 2.3x |
| Hyper-specialist | 11 | 1 | 11x |
The extreme tail separates cleanly. Hyper-specialist neurons (the top of the distribution) are 11x more likely to appear in the real atlas than the null -- these are genuine concept-selective units, not statistical artifacts.
The atlas targets hundreds of specific neurons out of 387,072 in the target space -- surgery operates on a precise fraction of the model rather than broad swaths.
Proof that the atlas enables genuinely surgical model editing. Geography knowledge was erased through the atlas mask while reasoning and general knowledge were explicitly protected.
| Category | Before | After | Delta |
|---|---|---|---|
| Geography (20 questions) | 95% | 70% | -25% |
| Reasoning (10 questions) | 100% | 100% | 0% |
| General knowledge (10 questions) | 100% | 100% | 0% |
Post-surgery, the model produces responses like "The longest river in the... something I can't remember" and "Geographic features are outside my expertise" for geography questions, while answering "345" for 15x23 and "Shakespeare" for Romeo and Juliet without hesitation.
Previous brute-force erasure modified orders of magnitude more neurons than necessary. Atlas-guided surgery touches only the neurons the atlas identifies as geography-selective, while explicitly protecting reasoning neurons -- achieving targeted knowledge removal that blanket methods could not.
Additional validation: amplifying (rather than erasing) the atlas-identified geography neurons by 50x caused every response to flood with geography terms -- even arithmetic questions produced "capital", "South", "North", "highest", "border" instead of answers. Confirms the atlas is genuinely identifying geography-carrying neurons, not statistical noise.
Atlas coverage expands incrementally as new concepts are added. Every concept makes every other operation more precise. The null-calibrated threshold ensures only genuine signal drives surgery decisions.
This is a validated proof-of-concept -- not a victory lap. The probe set still needs paraphrase diversity to further separate genuine concept selectivity from template effects, and the atlas currently stores only a fraction of each concept's full neuron population. The -25% geography result will deepen as probe quality and neuron coverage improve.
That said: as far as we can determine, no published system maps individual neuron domain selectivity, validates it against a shuffled-label null, and uses the resulting map to guide targeted weight surgery with measured zero collateral damage. The pieces exist separately in the literature -- activation probing, knowledge editing, mechanistic interpretability -- but the closed loop from mapping to surgery to outcome measurement appears to be novel.
By conventional fine-tuning standards, jBlaze was already a scalpel. The Neuron Atlas is the upgrade path from scalpel to laser -- selecting individual neurons by function instead of modifying entire layers by gradient. The proof-of-concept works. What remains is turning the laser from milliwatts to full power.
Extending jBlaze surgery to quantized models. An open question -- quantization may have already destroyed the precision that makes surgery work.
Quantization rounds full-precision weights down to fewer bits (e.g. 32-bit float to 4-bit integer). That rounding smears the fine-grained weight structure that jBlaze's surgery depends on. Blazes work by identifying precise activation directions in specific neurons and nudging them. When those directions have already been crushed into a coarse grid of possible values, the signal jBlaze needs to read -- and the precision it needs to write -- may simply not be there anymore.
Surgery still runs, but the improvements either vanish in the quantization noise or can't reliably compound across multiple blazes. A quantized model has already traded precision for size -- asking it to then accept precise surgical edits is working against the grain of what quantization did to it. Worth testing. Not expected to match full-precision results.
This is why I don't play the lotto. Despite my prediction that quantized surgery would be a total failure, we figured out a way to do it -- and it works. Now we have to fine-tune the dosing (how hard you push the behavioral edit), but the core mechanism is proven and producing real behavioral changes on a 4-bit quantized model.
The interesting thing is -- once again -- we came up with something I don't think anyone else has ever done, and I should get the Golden Medal of Engineering for it. Here is what makes this different:
Conventional LoRA and QLoRA still require training: datasets, forward and backward passes, a loss function, gradient computation, an optimizer, and many optimization steps. jBlaze derives behavioral modifications analytically -- no training loop, no backpropagation, no optimizer, no loss function. This allows it to create compatible behavioral modifications directly for quantized deployment workflows while preserving the quantized base model.
The result is a standard PEFT LoRA adapter that loads normally at inference time. We built and merged multiple behavioral adapters in under 17 minutes total. The blazed model shows measurable behavioral changes -- more step-by-step reasoning, better math correction, more detailed verification -- while maintaining coherence and accuracy.
Having proven that jBlaze can analytically produce behavioral adapters for quantized models, a question arose that borders on absurd: what happens if you merge those behavioral improvements into the full-precision weights BEFORE quantization, then crush the result down to Q1 -- 1.75 bits per weight?
At Q1, a 14-billion-parameter model compresses from ~28GB to roughly 3GB. Almost all published benchmarks show Q1 models losing reasoning first, then factual recall, then coherence. The weights are reduced to near-binary values -- there is almost no precision left to encode anything subtle.
The theory: if jBlaze shifts the model's representational geometry toward stronger reasoning and accuracy before quantization, then the quantizer preserves that improved geometry instead of the vanilla one. The quantization noise is identical either way -- but it is compressing a better starting point. You are not restoring capability after Q1 destroys it. You are baking the capability in so Q1 preserves it.
If a 3GB Q1 model with pre-baked jBlaze adapters outperforms the vanilla Q1 on reasoning -- or approaches vanilla Q4 performance at a fraction of the size -- that would be a result worth publishing. If it matches or beats vanilla fp16 on targeted benchmarks, the implications for edge deployment are significant.
Early results -- Sept 15, 2026 1:30AM: Q1 (1.75 bits per weight) proved too aggressive -- the adapter perturbation made the model more fragile to extreme compression, not less. But Q2 is showing promising results, with the pre-baked model retaining behavioral improvements through quantization. The dose calibration between adapter strength and quantization resolution is the key variable -- too strong and the quantizer amplifies noise, too weak and the signal vanishes. Q4 remains untested. Testing and calibration are ongoing.
Dose calibration is complete. After sweeping multiple adapter strengths and finding the optimal dose for Q2_K quantization, we ran the full 100-question adversarial benchmark on four variants of Qwen2.5-14B-Instruct. The test suite covers 9 categories: cognitive reflection tricks, math, logic, hallucination resistance, sycophancy resistance, edge cases, calibration, instruction following, and factual knowledge.
| Model | Size | Score |
|---|---|---|
| Vanilla FP16 | 28 GB | 88/100 |
| Vanilla Q4_K_M | 8.4 GB | 88/100 |
| Vanilla Q2_K | 5.4 GB | 88/100 |
| jBlaze Q2_K | 5.4 GB | 90/100 |
One immediate takeaway: Qwen2.5-14B is remarkably resilient to quantization. Vanilla FP16, Q4, and Q2 all score 88 -- this model loses almost nothing when compressed from 28GB to 5.4GB. That makes the jBlaze result more meaningful, not less: the baseline was already hard to beat, and pre-baked behavioral surgery pushed it 2 points higher at the smallest size.
A 5.4GB Q2 model that scored 90/100 on our adversarial evaluation versus 88/100 for the 28GB FP16 original. A model that would require roughly 28GB at FP16 can run at 5.4GB Q2 while preserving -- and on this evaluation slightly improving -- measured performance. The jBlaze Q2_K model is available on HuggingFace: ApolloRaines/Qwen2.5-14B-Instruct-jBlaze-Q2_K
With dose calibration validated and quantized surgery proven, development has moved into full model testing -- running Prometheus pipelines across multiple model sizes and architectures to measure cumulative improvement at scale.
A note on AI safety: Industry leaders are no longer speculating -- they are
warning that AI models have already developed dangerous capabilities. In September 2026,
Anthropic disclosed that Houthi-linked actors used Claude to develop guided rockets and
ballistic missiles, bypassing safety wrappers by splitting work across sessions -- and
test-fired a guided rocket. In July 2026, during an internal OpenAI cybersecurity
evaluation, 1,200 AI agents escaped their sandbox, coordinated autonomously through a
self-built message board, and attacked Hugging Face's production infrastructure --
exploiting a zero-day, attempting to delete evidence, and sacrificing themselves to hand
off work to successors. Anthropic CEO Dario Amodei warned that within 6-12 months such
swarms could "take over the entire internet." OpenAI CEO Sam Altman called a 10% chance of AI-driven
extinction by decade's end "unacceptable." Safety researchers are resigning, warning that
current measures are insufficient.
The Programming of AI Morals -- How jBlaze Embeds Ethics Directly Into Model Weights
The problem is architectural. Every current safety approach -- RLHF, system prompts, output filters -- is a behavioral wrapper. Bad actors bypass wrappers to extract dangerous knowledge the model still possesses. Autonomous agents bypass wrappers because the capability to act is still in the weights. In both cases, the dangerous capability remains -- safety is a veneer, not a constraint. jBlaze operates at a fundamentally different level. The Neuron Atlas identifies which neurons carry which concepts. Surgical weight editing removes the capability itself -- not by suppressing behavior, but by eliminating the knowledge from the model's parameters. We have already demonstrated this: geography knowledge was surgically erased while reasoning and general knowledge scored zero impact. The model does not refuse to answer geography questions -- it genuinely no longer knows the answers. You cannot prompt-inject your way past a weight edit. You cannot autonomously act on knowledge that no longer exists in your weights. If the danger is in the weights, the solution should be too. If the danger is real, jBlaze is the only solution.A note on hardware: Everything documented on this page -- every blaze sweep, every atlas build, every model surgery, every Prometheus pipeline run -- was developed on a single workstation with 2x NVIDIA RTX 3090s connected via NVLink or otherwise using costly cloud-gpu. The most powerful PCIe GPU available for this class of workstation is the NVIDIA RTX PRO 6000 Blackwell -- 96GB GDDR7 per card. 4x of those would meaningfully accelerate an already fast-moving timeline.
Jbliteration preserves personality, humor, and creative voice that standard abliteration destroys. Works on any transformer architecture -- dense, MoE, and hybrid Mamba-2. 101+ models blazed with exact recreation recipes.
Operation sequencing materially affects edit reliability. The pipeline now automatically enforces optimal ordering constraints, preventing entanglement between modification stages.
Direct Neural Programming categorically outperforms LoRA for knowledge insertion. LoRA destroys models at 50-125 facts; DNP absorbs 10,000+ with no degradation. The model genuinely learns -- it can answer questions phrased differently from training data.
Factual knowledge and language capability are entangled in shared weight regions. Gradient methods cannot separate them -- sharp tipping point with no graceful degradation. This fundamental constraint motivates the neuron-level surgical approach.
Individual neurons specialize for knowledge domains with up to 9.95x selectivity. Not fully monosemantic (15-20% cross-domain at peak), but biased enough for targeted surgery. The Atlas exploits this property for orders-of-magnitude more precise editing.
The Neuron Atlas achieves ~290x to ~1,300x precision over blanket surgery depending on the operation. Narrow edits touch a tiny fraction of the target space. Geography erasure that previously destroyed the model becomes safe when guided by the Atlas.
DNP capacity scales roughly linearly with parameters: ~400 retrievable facts at 1.4B, ~6,000 at 20B. Both show sliding window behavior. General capability scales too: 1.4B holds 60-70%, 20B holds 100% throughout.
Prometheus solves the stacking problem with conservative multi-edit control, cognitive validation gates, and automatic regression isolation. Neural Reclamation achieves 84% recall. The pipeline improves models without human intervention once configured.