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Dong Feng

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IROS Conference 2025 Conference Paper

ContextCache: Task-Aware Lifecycle Management for Memory-Efficient LLM Agent Deployment

  • Tao Liu
  • Ping Guo
  • Dong Feng
  • Peng Wang

LLM-based agents have demonstrated remarkable capabilities in multi-step reasoning and task execution across domains such as robotics and autonomous systems. However, deploying these agents on resource-constrained platforms presents a fundamental challenge: minimizing latency while optimizing memory usage. Existing caching techniques (KVCache, PrefixCache, PromptCache) improve inference speed by reusing cached context but overlook LLM dependency relationships in agent workflows, leading to excessive memory usage or redundant recomputation across LLM calls. To address this, we propose ContextCache, a task-aware lifecycle management framework that optimizes context fragment caching for multi-step LLM agents. ContextCache predicts the lifespan of each context fragment and dynamically allocates and releases GPU memory accordingly. We evaluate our approach on a newly constructed dataset, covering logistics coordination, assembly tasks, and health management. Experimental results demonstrate a 15% reduction in memory usage compared to state-of-the-art caching strategies, with no loss in inference efficiency, making our approach well-suited for real-world deployment in resource-constrained environments.

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AAAI Conference 2025 Conference Paper

PoseLLaVA: Pose Centric Multimodal LLM for Fine-Grained 3D Pose Manipulation

  • Dong Feng
  • Ping Guo
  • Encheng Peng
  • Mingmin Zhu
  • Wenhao Yu
  • Peng Wang

Manipulating human poses based on natural language is an emerging research field that has traditionally focused on coarse commands such as “walking” or “dancing.” However, fine-grained pose manipulation, like instructing “put both hands in front of the stomach,” remains underexplored. In this paper, we introduce PoseLLaVA, a pioneering model that integrates SMPL-based pose representations into the multimodal LLaVA framework. Through a novel pose encoder decoder mechanism, PoseLLaVA achieves precise alignment between pose, textual, and visual modalities, enabling detailed control over pose manipulation tasks. PoseLLaVA excels in three key tasks: pose estimation, generation, and adjustment, all driven by detailed language instructions. We further introduce a fine-grained pose adjustment dataset PosePart, where each sample contains an initial pose and a target pose, along with specific instructions for adjustments, mimicking the guidance a human instructor might provide. Extensive evaluations across these tasks demonstrate significant improvements over existing methods, including metrics such as MPJPE and PA-MPJPE, which measure SMPL reconstruction errors, and Recall rates, which assess feature alignment across modalities. Specifically, PoseLLaVA reduces MPJPE errors by more than 20% compared to state-of-the-art methods in pose adjustment and generation tasks. Additionally, we demonstrate the feasibility of combining PoseLLaVA with generative models, such as diffusion, for pose image editing, highlighting its potential applications in language-controlled pose manipulation.

PDF Details DOI
v2026.09.13