ContextEcho is a benchmark and harness designed to measure persona drift in large language models during long, tool‑using coding sessions. It includes a 25‑probe identity suite, a snapshot‑then‑probe protocol that preserves the main conversation, and both judged and judge‑free measurement surfaces. Across 23 frontier models and thousands of turns, the benchmark shows that persona drift is widespread, not limited to specific model families, and that simple in‑session compaction does not reset it, while a single‑shot anchor can restore the intended persona.
By Xianzhong Ding, Yangyang Yu, Changwei Liu, Bill Zhao, Le Chen, Tao Chen
arXiv:2512.24565v4 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol...
By Zixiang Liu, Wenrui Liu, Elsie Dai, Wenhan Yu, Lei Yu, Tong Yang, Jinjun Han, Hong Gao
arXiv:2606. 16748v1 Announce Type: new Abstract: Current benchmarks for computer-use agents evaluate models in impersonal environments.
By Lawrence Keunho Jang, Andrew Keunwoo Jang, Jing Yu Koh, Ruslan Salakhutdinov
arXiv:2507. 09788v3 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLM) have led to a new class of autonomous agents, renewing and expanding interest in the area.
By Paulo Salem, Robert Sim, Christopher Olsen, Prerit Saxena, Rafael Barcelos, Yi Ding
arXiv:2608. 10042v1 Announce Type: cross Abstract: Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool use, or response-level personalization.
By Xuexiong Yin, Zechuan Chen, Yongsen Zheng, Yuxiang Zhang, Jingyuan Yang, Bin Wang, Yubin Wang, Keze Wang
The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services. Within months of release, hundreds of community-built MCP servers appeared on GitHub, but no software-maintenance literature has yet described how the ecosystem is being structured in production.