Skip to content

Add the initial file and folder for Sico labs #64

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ttang911 merged 1 commit into from
Aug 4, 2026
Merged

Add the initial file and folder for Sico labs #64

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
40 changes: 40 additions & 0 deletions labs/AgentStream/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,40 @@
<div align="center">

<h1>AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?</h1>

<p>
Dong Yan<sup>1,2,3</sup>,
Jian Liang<sup>1,3†</sup>,
Dapeng Hu<sup>2†</sup>,
Ran He<sup>1,3</sup>,
Nicholas Jing Yuan<sup>2</sup>,
Qi Zhang<sup>2</sup>,
Tieniu Tan<sup>1,3,4</sup>
</p>

<p>
<sup>1</sup>School of Artificial Intelligence, University of Chinese Academy of Sciences<br>
<sup>2</sup>Microsoft<br>
<sup>3</sup>Institute of Automation, Chinese Academy of Sciences<br>
<sup>4</sup>Nanjing University
</p>

<p>
📧 <code>liangjian92@gmail.com</code> &nbsp;
<code>dapenghu@microsoft.com</code>
</p>

</div>

## 🚀 News
* **[2026/07]** Code is under preparation. Stay tuned!

## 📖 Overview
Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience.
However, existing studies predominantly adopt independent evaluation.
Consequently, the behavior of self-evolving agents in realistic streaming settings, where agents adapt to diverse and complex task streams, remains poorly understood.
To address this gap, we introduce AgentStream, a unified framework that evaluates self-evolving agents spanning diverse evolution components by organizing agentic benchmarks into a configurable task stream and instantiating the `Isolated`, `Sequential`, and `Interleaved` streaming scenarios at test time, which progressively vary the scope and domain composition of the stream.
Over these scenarios, we combinatorially evaluate five representative self-evolving methods across three frontier foundation models, disentangling how model capability, method architecture, and streaming scenario jointly shape self-evolution.
Our results show that self-evolution reliability varies across streaming scenarios, the benefit of self-evolution is gated by model capability and non-monotonic in model strength, and no single method dominates across models and scenarios.
These findings offer concrete guidance for selecting self-evolving methods across models and streaming scenarios.
Overall, we advocate that self-evolving agents should be evaluated under realistic task streams rather than isolated single-task settings.
Loading