[Submitted on 30 Oct 2026]
Title:GAVAGAI: Referential Alignment for Infant-Scale Vision-Language Models via Balanced Optimal Transport
View a PDF of the paper titled GAVAGAI: Referential Alignment for Infant-Scale Vision-Language Models via Balanced Optimal Transport, by Rajdeep Pal and 2 other authors
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Abstract:A child learns which words name which things from roughly 100 million words of input. Vision-language models trained at a comparable scale bind words to referents far less reliably, and we argue that part of the gap is structural: the usual next-token captioning objective has no variable standing for "this word refers to that region," so training applies no direct pressure to ground words. We add such a variable by treating each scene as a small assignment problem between the content words heard and the regions seen, solved as entropic optimal transport under two constraints. First, a word may be assigned to a special NOTHING column instead of any region, which matters because most content words in caregiver speech do not name anything visible. Second, no region may receive more than a bounded share of the assignment mass, a capacity constraint that encodes mutual exclusivity. The alignment step costs about eight Sinkhorn iterations, small next to the vision encoder. In controlled simulation, the naive version of the idea, a row-wise softmax that forces every word onto some region, collapses to 0.000 word-to-picture accuracy under realistic non-reference rates, below the 0.058 of captioning alone, while our two constraints reach 0.075 in that regime and 0.700 on clean input. A one-parameter version of the model reproduces adult cross-situational learning accuracy across three ambiguity conditions (RMSE 0.024). On the SAYCam infant video corpus, adding the alignment loss to a next-token captioning model raises word-to-picture accuracy from 32.4% to 48.7% and localization IoU from 0.18 to 0.38. A contrastive model trained on the same child's data remains stronger on its own benchmark, so our claim is that the loss improves a captioning objective under high non-reference rates, not that it sets a new best.
Comments: Accepted in NeuriPs 2026 workshop "The BabyVLM Workshop Toward Developmentally Plausible Multimodal Systems"
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2611.00319 [cs.CL]
(or arXiv:2611.00319v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.00319
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rajdeep Pal [view email] [v1] Fri, 30 Oct 2026 19:39:12 UTC (18 KB)
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