Prompt tuning has shown promise for continual visual question answering (CVQA), facilitating modular and transferable knowledge across tasks. However, existing approaches often overlook the guiding role of prompts in the model’s implicit reasoning process. This oversight can lead to inconsistent reasoning paths and performance degradation across tasks. To address this issue, we propose the E Logic Prompt framework, which employs energy-based models (EBMs) to model the semantic compatibility between prompts and queries. In this framework, prompts function not only as adapters but also as reasoning guides that help maintain coherence throughout the inference process. The framework enforces logical consistency at three levels. At the input level, it selects semantically aligned prompts by minimizing the energy between queries and prompts. Within the model, it aligns intermediate representations with prompts across layers to preserve step-by-step reasoning. Across tasks, it applies energy-based constraints to regulate prompt behavior, effectively suppressing semantic drift and enabling prompt reuse. These three levels of consistency together enhance the guiding capacity of prompts, allowing them to steer the model toward more stable and coherent reasoning. Extensive experiments show that E Logic Prompt outperforms existing methods in both accuracy and knowledge retention, while effectively maintaining balanced cross-modal reasoning throughout continual learning.

This paper introduces Topological Consistency Adaptation (TCA), a novel approach to Continual Test-time Adaptation (CTTA) that addresses the challenges of domain shifts and error accumulation in testing scenarios. TCA ensures the stability of inter-class relationships by enforcing a class topological consistency constraint, which minimizes the distortion of class centroids and preserves the topological structure during continuous adaptation. Additionally, we propose an intra-class compactness loss to maintain compactness within classes, indirectly supporting inter-class stability. To further enhance model adaptation, we introduce a batch imbalance topology weighting mechanism that accounts for class distribution imbalances within each batch, optimizing centroid distances and stabilizing the inter-class topology. Experiments show that our method demonstrates improvements in handling continuous domain shifts, ensuring stable feature distributions and boosting predictive performance. Our code is available at: https://github.com/Successybbdwm/TCA.

The goal of Parallel Continual Learning (PCL) is to continually learn multi-task from new data stream and complete the corresponding tasks. Previous research on PCL ignored inter-task interference, which may hinder knowledge transfer and exacerbate catastrophic forgetting. Therefore, in this paper, we investigate the interference problem of PCL in dynamic multi-task scenarios. First, we construct Global Discrimination Threshold to detect interference sample, and removing the interference gradient in joint multi-gradient algorithm, termed Interference Sample Gradient Purification (ISGP). Second, we introduced the Guardian For Pivotal Memory Sample (G-PMS) as new evaluation criterion and proposed Local Maintenance Thresholds to prevent accidental exclusion of memory samples that play an important role in suppressing catastrophic forgetting. Finally, the results of the experimental evaluation clearly confirm that ISGP can effectively enhance the transfer of new knowledge and better suppress catastrophic forgetting.

The recently enhanced computing capability and rich sensing functionality on mobile devices lead to the ubiquitous application of speech recognition. Traditional speech recognition records acoustic signals or visual images to interpret speech. However, the acoustic based scheme has many drawbacks. It is easily affected by the environmental noise when users are in the factory or market, and can not be used in a place where people need to be quite such as library. Specifically the current design is not suitable for people with speaking or hearing difficulties. Unfortunately, the visual-based approach is sensitive to fight conditions which shows poor performance in the dark area. As a result, it is necessary to provide an new human-computer interaction channel to assist speech recognition. This paper presents SilentTalk, a non-invasive lip reading system based on ultrasonic Doppler effect The main idea is to generate ultrasonic signals from a mobile phone, then capture the reflections and analyze the fine-grained frequency shift caused by mouth movements. A Frequency Shift Detection Model (FSDM) is proposed to quantify the correlation between frequency variations and mouth movements that form different syllables. SilentTalk then applies a Continuous Lip Reading Model (CLRM) on top of FSDM to realize continuous lip reading. Based on Markov assumption, CLRM effectively combines pronunciation rules and context knowledge to connect isolated syllables to words and sentences. Experiments show that SilentTalk can identify 12 basic mouth motions up to 95.4% accuracy in English. The system can also recognize short sentences up to six words with an average accuracy of 74.8%.