4 citations · 5 across the 12 of their papers we have counts for
5 papers · 1 filter
Scalable Weak-to-Strong Verifier Hardening for Agent Environments
Chong Zeng, Ziqian Zhong, Kexun Zhang +5
Agent environments are essential for large language model (LLM) development. They use programmatic verifiers to turn complex outcomes into rewards. Verifiers can be attacked into a…
SETA: Scaling Environments for Terminal Agents
Qijia Shen, Zhiqi Huang, Vamsidhar Kamanuru +19
Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs). Among these, the te…
Decentralized Multi-Agent Systems with Shared Context
Yuzhen Mao, Azalia Mirhoseini, Jerry Gu +3
Multi-agent systems (MAS) can scale large language model agents on long-horizon tasks by running them in parallel, yet existing designs waste much of this parallelism in bubbles: a…
IceCache: Memory-efficient KV-cache Management for Long-Sequence LLMs
Yuzhen Mao, Qitong Wang, Martin Ester +1
Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoiding redundant computation duri…
Simplified Sparse Attention via Gist Tokens
Yuzhen Mao, Michael Y. Li, Emily B. Fox
Sparse attention can reduce the cost of long-context inference, but most variants introduce new architectural components. We introduce Simplified Sparse Attention (SSA), a simpler…