CFS-R: Conditional Field Reconstruction

I evaluated CFS-R on LoCoMo (1,982 questions, same setup as the CFS evaluation), holding cosine and BM25 fixed and varying only the third leg.

baseline cosine top-10: NDCG@10 0.5123, Recall@10 0.6924 rrf(cos, BM25): NDCG@10 0.5196, Recall@10 0.6989 rrf(cos, BM25, MMR tuned): NDCG@10 0.5330, Recall@10 0.7228 rrf(cos, BM25, CFS-long): NDCG@10 0.5362, Recall@10 0.7295 rrf(cos, BM25, CFS-R top50 w3): NDCG@10 0.5447, Recall@10 0.7303 

Against tuned MMR: +1.17 pp NDCG@10 (95% CI [+0.66, +1.69], p < 0.001). Against CFS-long: +0.85 pp NDCG@10 (95% CI [+0.33, +1.35], p = 0.0006). Against baseline cosine: +3.24 pp NDCG@10, +3.79 pp Recall@10.

The sweep wasn’t fragile.. the top configurations clustered tightly between 0.5441 and 0.5447 NDCG@10, which means the operator is on a stable plateau rather than a single magic hyperparameter.

The category breakdown is where the conceptual difference shows up:

single-hop multi-hop temporal open-dom adversarial tuned MMR 0.3479 0.6377 0.2938 0.6144 0.4705 CFS-long 0.3615 0.6376 0.2959 0.6157 0.4734 CFS-R top50 w3 0.3646 0.6344 0.2948 0.6209 0.5018 

The adversarial line is the result that matters: +3.13 pp over tuned MMR, +2.84 pp over CFS-long. If the adversarial problem were only pairwise diversity, MMR should be very hard to beat but it isn’t. That supports the main claim: long-memory retrieval is not just about avoiding similar chunks. It is about reconstructing the evidence behind the query. Temporal is no longer a glaring weakness either, CFS-long still slightly leads, but CFS-R has closed the gap while keeping the adversarial gains.

https://gist.github.com/M-Garcia22/542a9a38d93aae1b5cf21fc604253718