LightOn AI released LightOn-rerank on July 16, using Qwen3.5-2B + LoRA to simultaneously handle multimodal reranking of text passages and document page images — the first open-source 2B-tier work to pack both modalities into the same adapter. What's worth talking about isn't the score, but the paradigm. LightOn directly abandons the old road of pointwise scoring where each candidate is scored independently, and instead goes generative listwise: query + 4 candidates go through a single forward pass, and the model outputs a ranking token such as [2]>[4]>[1]>[3]. On the same 213K training pairs, once the listwise loss is replaced with per-document independent scoring, ViDoRe V3 drops a full 10.8 NDCG points; switching back to 4 candidates in the same window it climbs back to 62.66. The conclusion is hard: the quality of reranking lies not in the loss form, but in whether the candidates can attend to each other. Cross-scale validation is equally counter-intuitive: a grid across 0.8B / 2B / 4B shows pointwise barely moves from 2B → 4B (-0.1 NDCG), while listwise keeps climbing (+2.0); 4B listwise hits 64.69, directly beating Qwen3-VL-Reranker-8B's 64.23 — half the parameters, more accurate. There's also substance on the deployment side. CUDA event breakdown shows that in each 2B listwise window, the ViT encoder takes 46%, prefill 16%, and decode only 38% — for text rerank the common first-token readout hardly saves time, because the image encoding of 4 candidates doesn't depend on decode state. A more cost-effective lever is cutting the candidate count: top-20 rerank keeps 85% of the lift using only 1/5 window; top-10 cuts the lift in half but uses only 1/12. The first-stage recall curve determines depth, not the model itself. Rather than piling on data or larger models, LightOn's contribution reads more like an engineering methodology: write candidate comparison into the forward pass, write engineering tradeoffs into the config file. The model doesn't have to be more accurate — 2B listwise is already enough, but it has to actually compare.