# A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data

arXiv: https://arxiv.org/abs/2602.19562 · HTML: https://mlsanity.com/paper/2602.19562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Deep Lucas-Kanade Homography for Multimodal Image Alignment](https://mlsanity.com/paper/2104.11693/) (2021)
- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [Characterizing the dynamics of learning in repeated reference games](https://mlsanity.com/paper/1912.07199/) (2019)
- [AI loyalty: A New Paradigm for Aligning Stakeholder Interests](https://mlsanity.com/paper/2003.11157/) (2020)
- [Bonsai: A Framework for Convolutional Neural Network Acceleration Using Criterion-Based Pruning](https://mlsanity.com/paper/2602.17145/) (2026)
- [Fine-Pruning: A Biologically Inspired Algorithm for Personalization of Machine Learning Models](https://mlsanity.com/paper/2602.18507/) (2026)
- [Joint Behavior and Common Belief](https://mlsanity.com/paper/2303.07185/) (2023)
- [Guide-Guard: Off-Target Predicting in CRISPR Applications](https://mlsanity.com/paper/2602.16327/) (2026)
- [LegoNet: Memory Footprint Reduction Through Block Weight Clustering](https://mlsanity.com/paper/2603.06606/) (2026)

## Built on by (most cited first, 1 of 1)

- [TRACE: Transparent Retrieval for Abstract Concept Evaluation](https://mlsanity.com/paper/2609.26168/) (2026)

## Abstract (arXiv)

Establishing stable mappings between natural language expressions and visual percepts is a foundational problem for both cognitive science and artificial intelligence. Humans routinely ground linguistic reference in noisy, ambiguous perceptual contexts, yet the mechanisms supporting such cross-modal alignment remain poorly understood. In this work, we introduce a computational framework designed to model core aspects of human referential interpretation by integrating linguistic utterances with perceptual representations derived from large-scale, crowd-sourced imagery. The system approximates human perceptual categorization by combining scale-invariant feature transform (SIFT) alignment with the Universal Quality Index (UQI) to quantify similarity in a cognitively plausible feature space, while a set of linguistic preprocessing and query-transformation operations captures pragmatic variability in referring expressions. We evaluate the model on the Stanford Repeated Reference Game corpus (15,000 utterances paired with tangram stimuli), a paradigm explicitly developed to probe human-level perceptual ambiguity and coordination. Our framework achieves robust referential grounding. It requires 65% fewer utterances than human interlocutors to reach stable mappings and can correctly identify target objects from single referring expressions 41.66% of the time (versus 20% for humans).These results suggest that relatively simple perceptual-linguistic alignment mechanisms can yield human-competitive behavior on a classic cognitive benchmark, and offers insights into models of grounded communication, perceptual inference, and cross-modal concept formation. Code is available at https://anonymous.4open.science/r/metasequoia-9D13/README.md .