Unofficial research portfolio. Can a small reasoning model keep doing long chain-of-thought (CoT) with a bounded KV footprint — by compressing the distant context into a handful of soft-prompt vectors instead of holding the whole transcript? This repo explores that on DeepSeek-R1-Distill-Qwen-1.5B, and ships a fast local (Apple-Silicon / MLX 4-bit) demo. - Soft-prompt compression — the pooler (AttnPoolSP, ~75M params) turns the distant transcript into 32 vectors. The LLM only ever attends to query + 32 SP + rw recent + current chunk, so the KV footprint is O(1) in total length, not O(length). - Mass-based eviction — the distant buffer is capped at maxD; on overflow the tokens with the…