1 citations · 2 across the 8 of their papers we have counts for
8 papers
MemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
Junxi Wang, Te Sun, Jiayi Zhu +9
Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuously accumulated memory intro…
Large Language Models Do Not Always Need Readable Language
Jiayi Zhu, Haoxuan Peng, Junxi Wang +3
Large language models (LLMs) are commonly prompted and interfaced with human-readable natural language, even when the intended reader is another model. This paper investigates whet…
ATLAS: All-round Testing of Long-context Abilities across Scales
Deli Huang, Cunguang Wang, Hongyin Tang +15
Long-context language models now advertise context windows up to millions of tokens, yet evaluations typically report a single length or a narrow task family, masking two failure m…
UniPPTBench: A Unified Benchmark for Presentation Generation Across Diverse Input Settings
Bo Zhao, Maosheng Pang, Chen Zhang +3
Existing works typically focus on presentation generation under isolated input settings, whereas real-world use cases span diverse scenarios, including vague user prompts, long doc…
ZigZagkv: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty
Meizhi Zhong, Xikai Liu, Chen Zhang +5
Large Language models (LLMs) have become a research hotspot. To accelerate the inference of LLMs, storing computed caches in memory has become the standard technique. However, as t…
MoDification: Mixture of Depths Made Easy
Chen Zhang, Meizhi Zhong, Qimeng Wang +8
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both la…
Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents
Shiwei Wu, Chen Zhang, Yan Gao +4
Instructional documents are rich sources of knowledge for completing various tasks, yet their unique challenges in conversational question answering (CQA) have not been thoroughly…
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective
Meizhi Zhong, Chen Zhang, Yikun Lei +5
Enabling LLMs to handle lengthy context is currently a research hotspot. Most LLMs are built upon rotary position embedding (RoPE), a popular position encoding method. Therefore, a…