Papers with real-time scenarios

5 papers
RTSM: Knowledge Distillation with Diverse Signals for Efficient Real-Time Semantic Matching in E-Commerce (2025.naacl-industry)

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Challenge: e-commerce product search is a key component of product discovery and sales in ecommerce . high computational demands of large transformer models pose challenges for their deployment in real-time scenarios.
Approach: They propose a framework for real-time semantic matching that leverages both soft labels from a teacher model and ground truth generated from pairwise query-product and query-query signals.
Outcome: The proposed framework outperforms teacher models and state-of-the-art models on e-commerce datasets.
Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation (2024.lrec-main)

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Challenge: Existing non-simultaneous sign language translation methods suffer from inherent inference delays in real-time scenarios.
Approach: They propose an adaptive policy for simultaneous sign language translation that progressively converts incrementally received sign video into its corresponding natural sentence.
Outcome: The proposed policy excels in situations requiring extremely low latency.
From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models (2026.findings-acl)

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Challenge: Existing definitions of streaming LLMs are fragmented and lack a systematic taxonomy . large language models are pre-trained on static and full-context corpora .
Approach: They propose a systematic taxonomy of current streaming Large Language Models and propose underlying methodologies for streaming LLMs.
Outcome: The proposed model is based on data flow and dynamic interaction to clarify existing ambiguities.
SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models (2026.acl-long)

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Challenge: Existing studies on noise lack quantitative analysis and rely on intuition and empirical observation, thus failing to understand practical robustness.
Approach: They propose a method for quantifying the impact of noise intensity on LALM inputs by using a structured activation subspace derived from the model's internal representations.
Outcome: The proposed method outperforms existing denoising methods and demonstrates that noise is perceived more accurately than raw audio features.
Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders (2026.acl-long)

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Challenge: Effective medical text retrieval requires high accuracy and low latency.
Approach: They propose a benchmark for medical text retrieval in Chinese using a symmetric architecture . CARE is a lightweight encoder with an LLM-based encoder for offline document encoding .
Outcome: The proposed benchmark surpasses state-of-the-art symmetric models on CMedTEB . it matches high retrieval quality without increasing latency, and it performs well on a single GPU .

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