A Plateau Is Not a Ceiling: Evaluating Headroom in Conversational Image Retrieval

A late-turn plateau in conversational image retrieval summarizes the outcome of an interaction, but does not establish which opportunities for improvement have been exhausted. Historical success, query construction, and target access affect that interpretation in different ways. We study retrieval headroom through three controlled comparisons: continuations without additional target observations, alternative representations of a fixed dialogue, and additional target-grounded observations. On FS-COCO-Dialog, reordering a frozen top-100 shortlist after four turns raises cumulative H@10 from 39.7 to 50.5 while current H@10 falls from 30.9 to 6.0. On DAR-preprocessed VisDial states, mean pooling raises current H@10 from 62.2 to 74.9 under ChatIR-FT and from 51.6 to 79.9 under BLIP-ITC, with a positive gain also on prefixes that are never truncated. Across broader fixed-dialogue comparisons, the preferred construction depends on the encoder and input regime. Additional evidence remains useful beyond the tested dialogue representations: under held-out interface selection, a paired human sketch improves all twelve dialogue-state–retrieval-space pairs by 9.8–22.7 points. On the same episodes, rewriting without target access matches or exceeds the sketch on concatenated queries in three of four spaces but adds little to an already reformulated state, an interaction that is significant in every retrieval space. These comparisons separately probe historical exposure, representation headroom, and evidence-access headroom, without assuming that they form independent causes of failure. The results support evaluating saturation relative to the states and observations tested, rather than inferring an information ceiling from a flattened retrieval curve. Code is available at https://anonymous.4open.science/r/iclr2027-DA65/.