Publish ThoughtDAG benchmark organization card

README.md

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-title: README
-emoji: 🐢
+title: ThoughtDAG
+emoji: 📊
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-colorTo: gray
+colorTo: blue
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+short_description: Editable LLM context and intervention benchmarks
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-Edit this `README.md` markdown file to author your organization card.
+# ThoughtDAG
+
+**Visible, editable context for LLM conversations — and reproducible experiments on context intervention.**
+
+ThoughtDAG is a local-first, human-in-the-loop research workbench where the graph is not decoration: **wires determine the context sent to the model**. The same representation is used to study what happens when erroneous information enters a conversation, propagates downstream, and is later pruned or recomputed.
+
+[Website](https://chenxiachan.github.io/thoughtdag/) · [GitHub](https://github.com/chenxiachan/thoughtdag) · [Interactive demo](https://app.thoughtdag.workers.dev)
+
+## Context Intervention Benchmark
+
+The benchmark represents each task as a portable ThoughtDAG graph and evaluates five paired conditions:
+
+1. **clean** — the correct context chain;
+2. **polluted** — an incorrect claim enters the chain;
+3. **source prune** — the original error is removed while downstream descendants remain;
+4. **subgraph prune** — the source and contaminated descendants are removed;
+5. **recompute descendants** — the source is removed and dependent nodes are regenerated in order.
+
+### Context Repair Pilot v1
+
+`4 models` · `9 task families` · `3 propagation depths` · `540 conditions`
+
+Across 72 cases where clean context was answered correctly and polluted context produced a wrong answer:
+
+| Intervention | Repaired cases |
+|---|---:|
+| Remove the erroneous source only | 68 / 72 |
+| Recompute descendants in dependency order | 71 / 72 |
+| Remove the contaminated subgraph | 72 / 72 |
+
+This pilot studies **context intervention**, not hidden model memory and not general model quality. Tasks are synthetic and objectively scored; each endpoint was sampled once at temperature zero with provider-default reasoning behavior.
+
+[Read the case study](https://chenxiachan.github.io/thoughtdag/stories/context-repair/) · [Full methodology and results](https://chenxiachan.github.io/thoughtdag/research/context-repair-pilot-v1/) · [Browse the benchmark](https://github.com/chenxiachan/thoughtdag/tree/main/benchmark)
+
+## What will be published here
+
+- **Dataset:** portable graph cases, compiled requests, traces, scores, and provenance.
+- **Space:** an interactive explorer for models, pollution families, propagation depth, and repair strategies.
+- **Updates:** new model runs and new intervention families, versioned rather than folded into an unqualified leaderboard.
+
+ThoughtDAG is MIT licensed. Benchmark limitations, model provenance, and scoring rules are published alongside every result.