Papers by Mingsheng Shang
DRAE: Dynamic Retrieval-Augmented Expert Networks for Lifelong Learning and Task Adaptation in Robotics (2025.acl-long)
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| Challenge: | Experimental results show that Dynamic Retrieval-Augmented Expert Networks outperforms baseline approaches in long-term task retention and knowledge reuse. |
| Approach: | They propose a dynamic routing architecture that leverages MoE and Retrieval-Augmented Generation to augment the learning process. |
| Outcome: | The proposed architecture outperforms baseline approaches in long-term task retention and knowledge reuse. |
IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval (2025.naacl-long)
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| Challenge: | Current information retrieval systems struggle to handle complex instructions, despite its critical importance . current models struggle to follow complex instructions in real-world applications, resulting in user-specific tasks. |
| Approach: | They propose a benchmark to evaluate instruction-following information retrieval in expert domains. |
| Outcome: | The proposed method improves on existing models and provides valuable insights to guide future advancements in retrieval. |
ElasticFlow: One-Step Physics-Consistent Policy with Elastic Time Horizons for Language-Guided Manipulation (2026.findings-acl)
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| Challenge: | Existing methods for embodied AI use iterative denoising to achieve high latency and lack physical consistency. |
| Approach: | They propose a distillation-free, physics-consistent one-step policy framework that reconstructs the Mean Field Theory by directly modeling the average velocity field. |
| Outcome: | Experiments on LIBERO, CALVIN, and RoboTwin show that the proposed framework outperforms state-of-the-art methods on long-horizon tasks. |
ATAAT: Adaptive Threat-Aware Adversarial Tuning Framework against Backdoor Attacks on Vision-Language-Action Models (2026.findings-acl)
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| Challenge: | Existing backdoor models rely on visual inputs for instruction parsing, rendering the perception pathway a critical attack surface. |
| Approach: | They propose an Adaptive Threat-Aware Adversarial Tuning framework that detects and decouples the optimal gradient decoupling strategy based on the adversary's capabilities. |
| Outcome: | The proposed framework achieves a highly robust targeted attack success rate while maintaining extreme stealthiness with a 5% poisoning rate. |