Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication
2026-08-17 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors propose a method called Gated Counterfactual Refinement for Communication (GCR-C) to improve how visual information is sent as tokens. Instead of selecting tokens based only on local criteria, their approach evaluates different token choices by simulating how well each would reconstruct the image if fully used. They found that this leads to better image reconstruction without using more transmission resources, and it works well across different datasets and conditions. However, the method requires more computation on the sender's side.
generative visual-token communicationtoken selectionlocal minimum description length (Local-MDL)counterfactual evaluationimage reconstructionpacket budgetrollout strategyreconstruction gain5G-LDPC channelencoder computation tradeoff
Authors
Jia Guo, Xiaohan Zhao, Changwang Liu, Shuqing He, Chenyang Zhang, Bingchuan Zhao, Jinqi Zhu
Abstract
Generative visual-token communication reduces transmission load by sending only selected discrete tokens and reconstructing missing content at the receiver. However, existing token-selection criteria based on local uncertainty, importance, or diversity do not directly determine whether changing the current selection improves the final reconstruction under the same packet budget. To address this problem, we propose Gated Counterfactual Refinement for Communication (GCR-C), a rollout-style correction layer over Local-MDL. GCR-C constructs a compact diversified candidate set, evaluates each candidate through matched full-budget Local-MDL continuation, and replaces the baseline action only when a positive baseline-relative reconstruction gain is obtained. Experiments on CIFAR-10, STL-10, a coded 5G-LDPC link, and a limited high-resolution Kodak transfer show that GCR-C consistently improves reconstruction quality at active low- and medium-rate operating points without increasing the realized packet rate, while remaining effective across changes in dataset, channel condition, resolution, token grid, and tokenizer. The results also reveal a clear quality--computation tradeoff due to the additional encoder-side counterfactual evaluation.