1 citations · 1 across the 9 of their papers we have counts for
9 papers
Joint Agent Memory and Exploration Learning via Novelty Signals
Shizuo Tian, Xiaohong Weng, Rui Kong +9
In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploration requires memory, but retai…
MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents
Xin Ding, Xinrui Wang, Yifan Yang +9
Existing memory systems for embodied agents typically inject retrieved memory as static context at episode start, a paradigm we term Ahead-of-time Monolithic Memory Injection (AMMI…
GRIP-VLM: Group-Relative Importance Pruning for Efficient Vision-Language Models
Mingzhe Huang, Weijun Wang, Xin Ding +7
In Vision-Language Models (VLMs), processing a massive number of visual tokens incurs prohibitive computational overhead. While recent training-aware pruning methods attempt to sel…
EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents
Ruofei Ju, Xinrui Wang, Xin Ding +12
Embodied agents can benefit from skills that guide object search, action execution, and state changes across diverse environments. Since embodied environments vary across layouts,…
Learning to Commit: Generating Organic Pull Requests via Online Repository Memory
Mo Li, L. H. Xu, Qitai Tan +2
Large language model (LLM)-based coding agents achieve impressive results on controlled benchmarks yet routinely produce pull requests that real maintainers reject. The root cause…
OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism
Xiangyu Li, Huaizhi Tang, Xin Ding +3
Embodied AI agents increasingly require parallel execution of multiple tasks, such as manipulation, conversation, and memory construction, from shared observations under distinct t…
Efficient Remote KV Cache Reuse with GPU-native Video Codec
Liang Mi, Weijun Wang, Jinghan Chen +3
Remote KV cache reuse fetches KV cache for identical contexts from remote storage, avoiding recomputation, accelerating LLM inference. While it excels in high-speed networks, its p…
Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices
Xiangyu Li, Chengyu Yin, Weijun Wang +3
Large language models (LLMs) are increasingly deployed on edge devices. To meet strict resource constraints, real-world deployment has pushed LLM quantization from 8-bit to 4-bit,…
Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
Mo Li, L. H. Xu, Qitai Tan +3
Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier pa…