ForgeMind Devlop - First Real bit of Proof ig and The First Proper devlog

The first ever milestone for ForgeMind is working and tested on end to end consumer and corporate hardware

I am running a quantized Qwen3-4B through llama.cpp on an RTX 3060 with 12gigs of VRAM.

ForgeMind was able to ingest an archive containing 4.06M Qwen tokens, divided it into searchable chunks of 1155 and retrieved info while keeping the prompt bound to around 15.5k tokens

Disclaimer - This is virtual long context, not a claim that the model directly attends to four million tokens. The entire archive remains outside the attention. ForgeMind just retrieves relevant passages, rehydrates the exact source text and verifies citations using source hashes and line spans

The TokenForge subpart produced quite measurable and valuable efficiency gains, It reversibly compressed eligible passages by an average or calculated median of around ~14% , with a 100% exact restoration. The Cold Ingests took 100.1 seconds, while refreshing one changed file at equivalent scale took only 1.04 s and reembedded only 5 affected chunks .

The automated gates and tests and benchmarks now passes 55 with zero errors or failures. I also did 10 offline / local checks which also completed successfully, during which the citations remained valid and the runtime hesitates when evidence is unavailable.

The 4.07M token test proves scalable ingestion, reversible compression and incremental refresh and also the retrievals.

Thats all for this. next i will turn this proof into a controlled comparison b/w raw context, hybrid rag and vector rag.

Thats all for this.