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Publish the paired statistics behind the MTRAG compaction claims

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Yiorgis Gozadinos 2026-08-14 17:05:45 +03:00
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@ -252,4 +252,11 @@ Directly comparable with [IBM's published results](https://github.com/IBM/mt-rag
| Live compacted (`mtrag_clapnq_live`) | `vllm:Muse-Glimmer-30B-NVFP4` | 224/224 scored | 0.83 micro / 0.84 macro | 0.42 |
| Live uncompacted (`mtrag_clapnq_live_uncompacted`) | `vllm:Muse-Glimmer-30B-NVFP4` | 224/224 scored | 0.78 micro / 0.79 macro | 0.42 |
*Measured on haiku.rag v0.74.0 with `qwen3-embedding:4b` (vLLM, dim 2560), `Qwen3-Reranker-4B`, stock capability instructions with `reasoning_strength: high`, judged by the pinned `vllm:Qwen3.6-35B-A3B-NVFP4` (temperature 0.6, thinking). The two live arms differ only in registering `EvidenceCompactionCapability`. Compaction reduces input tokens per request 1.8x (7.5k vs 13.5k), leaves citation retrieval unchanged, and improves answer pass rate (paired McNemar p = 0.031, 95% CI +0.8 to +8.2pp). QA numbers are internal (our judge and rubric) and not comparable with IBM's published generation metrics. Gold-prefix and live rates answer different judge questions and are not comparable with each other. The dataset is text-only (ClapNQ passages), so it exercises no multimodal paths.*
*Measured on haiku.rag v0.74.0 with `qwen3-embedding:4b` (vLLM, dim 2560), `Qwen3-Reranker-4B`, stock capability instructions with `reasoning_strength: high`, judged by the pinned `vllm:Qwen3.6-35B-A3B-NVFP4` (temperature 0.6, thinking). QA numbers are internal (our judge and rubric) and not comparable with IBM's published generation metrics. Gold-prefix and live rates answer different judge questions and are not comparable with each other. The dataset is text-only (ClapNQ passages), so it exercises no multimodal paths.*
The two live arms replay the same 29 conversations (224 turns) and differ only in registering `EvidenceCompactionCapability`, so they are compared as paired observations:
- Input tokens per model request, computed as total input tokens divided by model requests across the whole arm: 7,461 compacted (5,207,627 tokens over 698 requests) vs 13,539 uncompacted (9,707,530 over 717 requests). The uncompacted arm used 1.81x as many tokens per request, a 44.9% reduction under compaction.
- Answer pass rate: 185/224 vs 175/224 turns. Of the 18 turns where the arms disagree, 14 pass only compacted and 4 only uncompacted. McNemar exact two-sided p = 0.031. The paired difference is +4.5pp with a Wald 95% CI of +0.8 to +8.1pp, so the honest claim is an improvement of roughly 1 to 8 points, not the point estimate.
- Citation MAP, macro-averaged over conversations with 208 of 224 turns eligible (turns with gold passages) in each arm: 0.4174 compacted vs 0.4230 uncompacted. The gold-prefix 0.35 is over 208 of 224 eligible cases.
- Refusal precision and recall against the answerability labels (16 UNANSWERABLE turns per arm): compacted 0.33 precision and 0.44 recall (21 refusals), uncompacted 0.23 and 0.31 (22 refusals). Gold-prefix: 0.24 and 0.44 (29 refusals).