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README.md

@@ -12,16 +12,23 @@ task_categories:
   - question-answering
 ---
 
-# RLM × OOLONG: a negative reproduction
+# RLM × OOLONG — a negative reproduction, and the fix that came out of it
 
 A reproduction of [Recursive Language Models](https://alexzhang13.github.io/blog/2025/rlm/)
 (Zhang & Khattab, 2025) on [OOLONG-synth](https://huggingface.co/datasets/oolongbench/oolong-synth),
 run entirely through the Claude Code CLI with Claude Haiku 4.5 as both root and
 recursive model.
 
-**The method did not reproduce in this configuration.** Wrapping Haiku in an RLM
-made it *worse* than reading the same context straight through. That result, and
-the mechanism behind it, is the contribution here.
+Two results, and the second only exists because the first failed.
+
+**1. The method did not reproduce.** Wrapping Haiku in an RLM made it *worse* than
+reading the same context straight through: **0.269 vs 0.428** on OOLONG-131k.
+
+**2. Chasing that failure produced something that works.** On a 957,493-char
+corpus, a 4B model on a 6GB laptop GPU reached the correct answer where the
+recursive harness failed four times and Claude Opus was 2/3 and self-inconsistent.
+The fix was not a bigger model. It was removing the model from the steps it kept
+getting wrong.
 
 ## Results
 
@@ -114,13 +121,63 @@ python rlm_ask.py --file huge.log --query "Which error appears most often?"
 `tool_use` blocks, and the RLM loop emits prose, so putting RLM behind the model
 endpoint would break tool calling outright.
 
+## The fix: stop asking the model to aggregate
+
+Four attempts on the 957,493-char corpus, all with the same 4B:
+
+| attempt | sub-calls | answer |
+|---|---:|---|
+| 1 | 7 | prose, having read about a third |
+| 2 | 11 | `Spatial` — right arithmetic, truncated label |
+| 3 | 65 | `Counterfactual` — swept everything, well-formed, wrong |
+| 4 | 72 | `Status: beta, Status: delta, Status: gamma, Status: alpha` |
+
+Attempt 4 is the diagnosis: asked to *select* a minimum, it *listed the candidates*.
+A 4B can count rows in a fragment. It cannot reliably plan a traversal and then do
+arithmetic across 65 partial results — and nothing about that arithmetic requires a
+language model.
+
+`ctxstream/` (C++17, zero third-party dependencies) treats the corpus like a video
+stream: the segment plan is computed in code before any model call, N segments are
+in flight at once, the model sees one fragment and emits `key<TAB>number` (never
+prose), and aggregation is a loop.
+
+    17 segments · failed=0 · records=611 · unparsed_lines=3 · keys=15 · 641s
+    Category: Spatial Relationship        <- gold, correct
+
+| | answer | correct | cost |
+|---|---|:---:|---:|
+| Claude Opus 4.8 [1m], one call, 439,742 tok | varies by run | 2/3 | $4.79 |
+| 4B + ctxstream, RTX 3060 6GB | `Spatial Relationship` | yes | $0.00 |
+
+A directory input builds a symbol/include graph first and segments along it, since
+cutting code every N characters splits functions and separates calls from
+definitions.
+
+```bash
+cd ctxstream && cmake -S . -B build && cmake --build build -j
+./build/test_ctxstream     # 61 checks, no GPU, no network, no tokens
+```
+
+## VRAM, measured on the 6GB card
+
+| num_ctx | resident | fits 5.5GB usable |
+|---:|---:|:---:|
+| 32,768 | 3.3 GB | yes — the direct ceiling |
+| 65,536 | 10.4 GB | no |
+
+Streaming 261,226 tokens through that card peaks at **4.23–4.54 GB** across four
+runs: an 8x context multiple at constant VRAM, because the corpus never enters the
+KV cache. Switching KV to q4_0 changed nothing, so the cliff is not the KV cache.
+
 ## What is *not* here
 
 - **No model weights.** Nothing was fine-tuned. This is a harness, a tool, and
   results. `rlm-ask` runs on whatever Ollama model you already have.
-- **No Terminal-Bench numbers.** `tbench/` (running Terminal-Bench against local
-  Ollama models through Claude Code) is included but has **never produced a
+- **No Terminal-Bench numbers.** `tbench/` is included but has **never produced a
   passing run**. Treat it as unvalidated code.
+- **ctxstream has not been run at the full 262,144-token scale yet** — the correct
+  result above is on a 957,493-char corpus.
 - The 262k-token OOLONG slice was not run.
 
 ## Attribution