The study examines how context projection—replacing older tool observations with concise, addressable excerpts—affects performance in ReVerPi, a Pi extension that archives observations and matches full to projected continuations. Across 86 source‑reading runs and 641 model requests, 15 completed pairs achieved identical success rates (12/15 per arm), while 12 boundary runs halted when the first arm failed, revealing that projection can reduce logical tokens by 25% but increase median pair tokens by 29% and total suffix requests from 35 to 55. The analysis highlights the importance of retaining all intervention boundaries, executing both arms independently, and reporting completion, token usage, and interaction details to accurately assess stopping rules and resource aggregation.
By Guangzhe Zhang
arXiv:2610.18090v1 Announce Type: new
Abstract: Ambiguous or incomplete task specifications complicate both execution and clarification for language-model agents. Yet agents' responses to these infor...
By Kaiyu Huang, Yiting Li, Angyang Gu, Pinjia He, Minchen Yu, Qingjiang Shi
arXiv:2607. 12216v1 Announce Type: cross Abstract: Multi-agent and memory-augmented LLM systems often place coordination content, shared state, prior discussion, tool outputs, summaries, and role instructions, inside the same finite prompt used for the current task.
By Brenda Lelis, Rodrigo Cabral-Carvalho
arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.
By Jie Wu, Ming Gong, Feixiang Cheng, Qinqin Zhao
arXiv:2609. 29140v1 Announce Type: new Abstract: Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty.
By Yezhou Cheng, Runjia Du, Zeming Liu, Qibai Chen, Hang Lyu, Yilan Wei, Yankai Zeng, Bojun Lin
Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty. We characterize that requirement on a fixed grid of $M$ tasks with $L$ binary paths per task under the hard budget $(M+t)K$, where each path costs at most $K$ responses or episodes.