# ReMe
**Repository Path**: other-open-source/ReMe
## Basic Information
- **Project Name**: ReMe
- **Description**: No description available
- **Primary Language**: Java
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-01-13
- **Last Updated**: 2026-01-13
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
Memory Management Kit for Agents, Remember Me, Refine Me.
If you find it useful, please give us a ⭐ Star.
---
ReMe is a **modular memory management kit** that provides AI agents with unified memory capabilities—enabling the ability to extract, reuse, and share memories across users, tasks, and agents.
Agent memory can be viewed as:
```text
Agent Memory = Long-Term Memory + Short-Term Memory
= (Personal + Task + Tool) Memory + (Working Memory)
```
- **Personal Memory**: Understand user preferences and adapt to context
- **Task Memory**: Learn from experience and perform better on similar tasks
- **Tool Memory**: Optimize tool selection and parameter usage based on historical performance
- **Working Memory**: Manage short-term context for long-running agents without context overflow
---
## 📰 Latest Updates
- **[2025-12]** 📄 Our procedural (task) memory paper has been released on [arXiv](https://arxiv.org/abs/2512.10696)
- **[2025-11]** 🧠 React-agent with working-memory demo ([Intro](docs/work_memory/message_offload.md)) with ([Quick Start](docs/cookbook/working/quick_start.md)) and ([Code](cookbook/working_memory/work_memory_demo.py))
- **[2025-10]** 🚀 Direct Python import support: use `from reme_ai import ReMeApp` without HTTP/MCP service
- **[2025-10]** 🔧 Tool Memory: data-driven tool selection and parameter optimization ([Guide](docs/tool_memory/tool_memory.md))
- **[2025-09]** 🎉 Async operations support, integrated into agentscope-runtime
- **[2025-09]** 🎉 Task memory and personal memory integration
- **[2025-09]** 🧪 Validated effectiveness in appworld, bfcl(v3), and frozenlake ([Experiments](docs/cookbook))
- **[2025-08]** 🚀 MCP protocol support ([Quick Start](docs/mcp_quick_start.md))
- **[2025-06]** 🚀 Multiple backend vector storage (Elasticsearch & ChromaDB) ([Guide](docs/vector_store_api_guide.md))
- **[2024-09]** 🧠 Personalized and time-aware memory storage
---
## ✨ Architecture Design
ReMe provides a **modular memory management kit** with pluggable components that can be integrated into any agent framework. The system consists of:
#### 🧠 **Task Memory/Experience**
Procedural knowledge reused across agents
- **Success Pattern Recognition**: Identify effective strategies and understand their underlying principles
- **Failure Analysis Learning**: Learn from mistakes and avoid repeating the same issues
- **Comparative Patterns**: Different sampling trajectories provide more valuable memories through comparison
- **Validation Patterns**: Confirm the effectiveness of extracted memories through validation modules
Learn more about how to use task memory from [task memory](docs/task_memory/task_memory.md)
#### 👤 **Personal Memory**
Contextualized memory for specific users
- **Individual Preferences**: User habits, preferences, and interaction styles
- **Contextual Adaptation**: Intelligent memory management based on time and context
- **Progressive Learning**: Gradually build deep understanding through long-term interaction
- **Time Awareness**: Time sensitivity in both retrieval and integration
Learn more about how to use personal memory from [personal memory](docs/personal_memory/personal_memory.md)
#### 🔧 **Tool Memory**
Data-driven tool selection and usage optimization
- **Historical Performance Tracking**: Success rates, execution times, and token costs from real usage
- **LLM-as-Judge Evaluation**: Qualitative insights on why tools succeed or fail
- **Parameter Optimization**: Learn optimal parameter configurations from successful calls
- **Dynamic Guidelines**: Transform static tool descriptions into living, learned manuals
Learn more about how to use tool memory from [tool memory](docs/tool_memory/tool_memory.md)
#### 🧠 Working Memory
Short‑term contextual memory for long‑running agents via **message offload & reload**:
- **Message Offload**: Compact large tool outputs to external files or LLM summaries
- **Message Reload**: Search (`grep_working_memory`) and read (`read_working_memory`) offloaded content on demand
📖 **Concept & API**:
- Message offload overview: [Message Offload](docs/work_memory/message_offload.md)
- Offload / reload operators: [Message Offload Ops](docs/work_memory/message_offload_ops.md), [Message Reload Ops](docs/work_memory/message_reload_ops.md)
💻 **End‑to‑End Demo**:
- Working memory quick start: [Working Memory Quick Start](docs/cookbook/working/quick_start.md)
- ReAct agent with working memory: [react_agent_with_working_memory.py](cookbook/working_memory/react_agent_with_working_memory.py)
- Runnable demo: [work_memory_demo.py](cookbook/working_memory/work_memory_demo.py)
---
## 🛠️ Installation
### Install from PyPI (Recommended)
```bash
pip install reme-ai
```
### Install from Source
```bash
git clone https://github.com/agentscope-ai/ReMe.git
cd ReMe
pip install .
```
### Environment Configuration
ReMe requires LLM and embedding model configurations. Copy `example.env` to `.env` and configure:
```bash
FLOW_LLM_API_KEY=sk-xxxx
FLOW_LLM_BASE_URL=https://xxxx/v1
FLOW_EMBEDDING_API_KEY=sk-xxxx
FLOW_EMBEDDING_BASE_URL=https://xxxx/v1
```
---
## 🚀 Quick Start
### HTTP Service Startup
```bash
reme \
backend=http \
http.port=8002 \
llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
embedding_model.default.model_name=text-embedding-v4 \
vector_store.default.backend=local
```
### MCP Server Support
```bash
reme \
backend=mcp \
mcp.transport=stdio \
llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
embedding_model.default.model_name=text-embedding-v4 \
vector_store.default.backend=local
```
### Core API Usage
#### Task Memory Management
```python
import requests
# Experience Summarizer: Learn from execution trajectories
response = requests.post("http://localhost:8002/summary_task_memory", json={
"workspace_id": "task_workspace",
"trajectories": [
{"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
]
})
# Retriever: Get relevant memories
response = requests.post("http://localhost:8002/retrieve_task_memory", json={
"workspace_id": "task_workspace",
"query": "How to efficiently manage project progress?",
"top_k": 1
})
```
Python import version
```python
import asyncio
from reme_ai import ReMeApp
async def main():
async with ReMeApp(
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
"embedding_model.default.model_name=text-embedding-v4",
"vector_store.default.backend=memory"
) as app:
# Experience Summarizer: Learn from execution trajectories
result = await app.async_execute(
name="summary_task_memory",
workspace_id="task_workspace",
trajectories=[
{
"messages": [
{"role": "user", "content": "Help me create a project plan"}
],
"score": 1.0
}
]
)
print(result)
# Retriever: Get relevant memories
result = await app.async_execute(
name="retrieve_task_memory",
workspace_id="task_workspace",
query="How to efficiently manage project progress?",
top_k=1
)
print(result)
if __name__ == "__main__":
asyncio.run(main())
```
curl version
```bash
# Experience Summarizer: Learn from execution trajectories
curl -X POST http://localhost:8002/summary_task_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"trajectories": [
{"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
]
}'
# Retriever: Get relevant memories
curl -X POST http://localhost:8002/retrieve_task_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"query": "How to efficiently manage project progress?",
"top_k": 1
}'
```
#### Personal Memory Management
```python
# Memory Integration: Learn from user interactions
response = requests.post("http://localhost:8002/summary_personal_memory", json={
"workspace_id": "task_workspace",
"trajectories": [
{"messages":
[
{"role": "user", "content": "I like to drink coffee while working in the morning"},
{"role": "assistant",
"content": "I understand, you prefer to start your workday with coffee to stay energized"}
]
}
]
})
# Memory Retrieval: Get personal memory fragments
response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
"workspace_id": "task_workspace",
"query": "What are the user's work habits?",
"top_k": 5
})
```
Python import version
```python
import asyncio
from reme_ai import ReMeApp
async def main():
async with ReMeApp(
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
"embedding_model.default.model_name=text-embedding-v4",
"vector_store.default.backend=memory"
) as app:
# Memory Integration: Learn from user interactions
result = await app.async_execute(
name="summary_personal_memory",
workspace_id="task_workspace",
trajectories=[
{
"messages": [
{"role": "user", "content": "I like to drink coffee while working in the morning"},
{"role": "assistant",
"content": "I understand, you prefer to start your workday with coffee to stay energized"}
]
}
]
)
print(result)
# Memory Retrieval: Get personal memory fragments
result = await app.async_execute(
name="retrieve_personal_memory",
workspace_id="task_workspace",
query="What are the user's work habits?",
top_k=5
)
print(result)
if __name__ == "__main__":
asyncio.run(main())
```
curl version
```bash
# Memory Integration: Learn from user interactions
curl -X POST http://localhost:8002/summary_personal_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"trajectories": [
{"messages": [
{"role": "user", "content": "I like to drink coffee while working in the morning"},
{"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
]}
]
}'
# Memory Retrieval: Get personal memory fragments
curl -X POST http://localhost:8002/retrieve_personal_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"query": "What are the user'\''s work habits?",
"top_k": 5
}'
```
#### Tool Memory Management
```python
import requests
# Record tool execution results
response = requests.post("http://localhost:8002/add_tool_call_result", json={
"workspace_id": "tool_workspace",
"tool_call_results": [
{
"create_time": "2025-10-21 10:30:00",
"tool_name": "web_search",
"input": {"query": "Python asyncio tutorial", "max_results": 10},
"output": "Found 10 relevant results...",
"token_cost": 150,
"success": True,
"time_cost": 2.3
}
]
})
# Generate usage guidelines from history
response = requests.post("http://localhost:8002/summary_tool_memory", json={
"workspace_id": "tool_workspace",
"tool_names": "web_search"
})
# Retrieve tool guidelines before use
response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
"workspace_id": "tool_workspace",
"tool_names": "web_search"
})
```
Python import version
```python
import asyncio
from reme_ai import ReMeApp
async def main():
async with ReMeApp(
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
"embedding_model.default.model_name=text-embedding-v4",
"vector_store.default.backend=memory"
) as app:
# Record tool execution results
result = await app.async_execute(
name="add_tool_call_result",
workspace_id="tool_workspace",
tool_call_results=[
{
"create_time": "2025-10-21 10:30:00",
"tool_name": "web_search",
"input": {"query": "Python asyncio tutorial", "max_results": 10},
"output": "Found 10 relevant results...",
"token_cost": 150,
"success": True,
"time_cost": 2.3
}
]
)
print(result)
# Generate usage guidelines from history
result = await app.async_execute(
name="summary_tool_memory",
workspace_id="tool_workspace",
tool_names="web_search"
)
print(result)
# Retrieve tool guidelines before use
result = await app.async_execute(
name="retrieve_tool_memory",
workspace_id="tool_workspace",
tool_names="web_search"
)
print(result)
if __name__ == "__main__":
asyncio.run(main())
```
curl version
```bash
# Record tool execution results
curl -X POST http://localhost:8002/add_tool_call_result \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "tool_workspace",
"tool_call_results": [
{
"create_time": "2025-10-21 10:30:00",
"tool_name": "web_search",
"input": {"query": "Python asyncio tutorial", "max_results": 10},
"output": "Found 10 relevant results...",
"token_cost": 150,
"success": true,
"time_cost": 2.3
}
]
}'
# Generate usage guidelines from history
curl -X POST http://localhost:8002/summary_tool_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "tool_workspace",
"tool_names": "web_search"
}'
# Retrieve tool guidelines before use
curl -X POST http://localhost:8002/retrieve_tool_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "tool_workspace",
"tool_names": "web_search"
}'
```
#### Working Memory Management
```python
import requests
# Summarize and compact working memory for a long-running conversation
response = requests.post("http://localhost:8002/summary_working_memory", json={
"messages": [
{
"role": "system",
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
},
{
"role": "user",
"content": "搜索下reme项目的的README内容"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"index": 0,
"id": "call_6596dafa2a6a46f7a217da",
"function": {
"arguments": "{\"query\": \"readme\"}",
"name": "web_search"
},
"type": "function"
}
]
},
{
"role": "tool",
"content": "ultra large context , over 50000 tokens......"
},
{
"role": "user",
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
}
],
"working_summary_mode": "auto",
"compact_ratio_threshold": 0.75,
"max_total_tokens": 20000,
"max_tool_message_tokens": 2000,
"group_token_threshold": 4000,
"keep_recent_count": 2,
"store_dir": "test_working_memory",
"chat_id": "demo_chat_id"
})
```
Python import version
```python
import asyncio
from reme_ai import ReMeApp
async def main():
async with ReMeApp(
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
"embedding_model.default.model_name=text-embedding-v4",
"vector_store.default.backend=memory"
) as app:
# Summarize and compact working memory for a long-running conversation
result = await app.async_execute(
name="summary_working_memory",
messages=[
{
"role": "system",
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
},
{
"role": "user",
"content": "搜索下reme项目的的README内容"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"index": 0,
"id": "call_6596dafa2a6a46f7a217da",
"function": {
"arguments": "{\"query\": \"readme\"}",
"name": "web_search"
},
"type": "function"
}
]
},
{
"role": "tool",
"content": "ultra large context , over 50000 tokens......"
},
{
"role": "user",
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
}
],
working_summary_mode="auto",
compact_ratio_threshold=0.75,
max_total_tokens=20000,
max_tool_message_tokens=2000,
group_token_threshold=4000,
keep_recent_count=2,
store_dir="test_working_memory",
chat_id="demo_chat_id",
)
print(result)
if __name__ == "__main__":
asyncio.run(main())
```
curl version
```bash
curl -X POST http://localhost:8002/summary_working_memory \
-H "Content-Type: application/json" \
-d '{
"messages": [
{
"role": "system",
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
},
{
"role": "user",
"content": "搜索下reme项目的的README内容"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"index": 0,
"id": "call_6596dafa2a6a46f7a217da",
"function": {
"arguments": "{\"query\": \"readme\"}",
"name": "web_search"
},
"type": "function"
}
]
},
{
"role": "tool",
"content": "ultra large context , over 50000 tokens......"
},
{
"role": "user",
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
}
],
"working_summary_mode": "auto",
"compact_ratio_threshold": 0.75,
"max_total_tokens": 20000,
"max_tool_message_tokens": 2000,
"group_token_threshold": 4000,
"keep_recent_count": 2,
"store_dir": "test_working_memory",
"chat_id": "demo_chat_id"
}'
```
---
## 📦 Pre-built Memory Library
ReMe provides a **memory library** with pre-extracted, production-ready memories that agents can load and use immediately:
### Available Memory Packs
| Memory Pack | Domain | Size | Description |
|----------------------|----------------|---------------|-------------------------------------------------------------------------------------|
| **`appworld.jsonl`** | Task Execution | ~100 memories | Complex task planning patterns, multi-step workflows, and error recovery strategies |
| **`bfcl_v3.jsonl`** | Tool Usage | ~150 memories | Function calling patterns, parameter optimization, and tool selection strategies |
### Loading Pre-built Memories
```python
# Load pre-built memories
response = requests.post("http://localhost:8002/vector_store", json={
"workspace_id": "appworld",
"action": "load",
"path": "./docs/library/"
})
# Query relevant memories
response = requests.post("http://localhost:8002/retrieve_task_memory", json={
"workspace_id": "appworld",
"query": "How to navigate to settings and update user profile?",
"top_k": 1
})
```
Python import version
```python
import asyncio
from reme_ai import ReMeApp
async def main():
async with ReMeApp(
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
"embedding_model.default.model_name=text-embedding-v4",
"vector_store.default.backend=memory"
) as app:
# Load pre-built memories
result = await app.async_execute(
name="vector_store",
workspace_id="appworld",
action="load",
path="./docs/library/"
)
print(result)
# Query relevant memories
result = await app.async_execute(
name="retrieve_task_memory",
workspace_id="appworld",
query="How to navigate to settings and update user profile?",
top_k=1
)
print(result)
if __name__ == "__main__":
asyncio.run(main())
```
## 🧪 Experiments
### 🌍 [Appworld Experiment](docs/cookbook/appworld/quickstart.md)
We tested ReMe on Appworld using Qwen3-8B (non-thinking mode):
| Method | Avg@4 | Pass@4 |
|--------------|---------------------|---------------------|
| without ReMe | 0.1497 | 0.3285 |
| with ReMe | 0.1706 **(+2.09%)** | 0.3631 **(+3.46%)** |
Pass@K measures the probability that at least one of the K generated samples successfully completes the task (
score=1).
The current experiment uses an internal AppWorld environment, which may have slight differences.
You can find more details on reproducing the experiment in [quickstart.md](docs/cookbook/appworld/quickstart.md).
### 🔧 [BFCL-V3 Experiment](docs/cookbook/bfcl/quickstart.md)
We tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using Qwen3-8B (thinking mode):
| Method | Avg@4 | Pass@4 |
|--------------|---------------------|---------------------|
| without ReMe | 0.4033 | 0.5955 |
| with ReMe | 0.4450 **(+4.17%)** | 0.6577 **(+6.22%)** |
### 🧊 [Frozenlake Experiment](docs/cookbook/frozenlake/quickstart.md)
| without ReMe | with ReMe |
|:----------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------:|
| 
| 
|
We tested on 100 random frozenlake maps using qwen3-8b:
| Method | pass rate |
|--------------|------------------|
| without ReMe | 0.66 |
| with ReMe | 0.72 **(+6.0%)** |
You can find more details on reproducing the experiment in [quickstart.md](docs/cookbook/frozenlake/quickstart.md).
### 🛠️ [Tool Memory Benchmark](docs/tool_memory/tool_bench.md)
We evaluated Tool Memory effectiveness using a controlled benchmark with three mock search tools using Qwen3-30B-Instruct:
| Scenario | Avg Score | Improvement |
|------------------------|-----------|-------------|
| Train (No Memory) | 0.650 | - |
| Test (No Memory) | 0.672 | Baseline |
| **Test (With Memory)** | **0.772** | **+14.88%** |
**Key Findings:**
- Tool Memory enables data-driven tool selection based on historical performance
- Success rates improved by ~15% with learned parameter configurations
You can find more details in [tool_bench.md](docs/tool_memory/tool_bench.md) and the implementation at [run_reme_tool_bench.py](cookbook/tool_memory/run_reme_tool_bench.py).
## 📚 Resources
### Getting Started
- **[Quick Start](./cookbook/simple_demo)**: Practical examples for immediate use
- [Tool Memory Demo](cookbook/simple_demo/use_tool_memory_demo.py): Complete lifecycle demonstration of tool memory
- [Tool Memory Benchmark](cookbook/tool_memory/run_reme_tool_bench.py): Evaluate tool memory effectiveness
### Integration Guides
- **[Direct Python Import](docs/cookbook/working/quick_start.md)**: Embed ReMe directly into your agent code
- **[HTTP Service API](docs/vector_store_api_guide.md)**: RESTful API for multi-agent systems
- **[MCP Protocol](docs/mcp_quick_start.md)**: Integration with Claude Desktop and MCP-compatible clients
### Memory System Configuration
- **[Personal Memory](docs/personal_memory)**: User preference learning and contextual adaptation
- **[Task Memory](docs/task_memory)**: Procedural knowledge extraction and reuse
- **[Tool Memory](docs/tool_memory)**: Data-driven tool selection and optimization
- **[Working Memory](docs/work_memory/message_offload.md)**: Short-term context management for long-running agents
### Advanced Topics
- **[Operator Pipelines](reme_ai/config/default.yaml)**: Customize memory processing workflows by modifying operator chains
- **[Vector Store Backends](docs/vector_store_api_guide.md)**: Configure local, Elasticsearch, Qdrant, or ChromaDB storage
- **[Example Collection](./cookbook)**: Real-world use cases and best practices
---
## ⭐ Support & Community
- **Star & Watch**: Stars surface ReMe to more agent builders; watching keeps you updated on new releases.
- **Share your wins**: Open an issue or discussion with what ReMe unlocked for your agents—we love showcasing community builds.
- **Need a feature?** File a request and we’ll help shape it together.
---
## 🤝 Contribution
We believe the best memory systems come from collective wisdom. Contributions welcome 👉[Guide](docs/contribution.md):
### Code Contributions
- **New Operators**: Develop custom memory processing operators (retrieval, summarization, etc.)
- **Backend Implementations**: Add support for new vector stores or LLM providers
- **Memory Services**: Extend with new memory types or capabilities
- **API Enhancements**: Improve existing endpoints or add new ones
### Documentation Improvements
- **Integration Examples**: Show how to integrate ReMe with different agent frameworks
- **Operator Tutorials**: Document custom operator development
- **Best Practice Guides**: Share effective memory management patterns
- **Use Case Studies**: Demonstrate ReMe in real-world applications
---
## 📄 Citation
```bibtex
@software{AgentscopeReMe2025,
title = {AgentscopeReMe: Memory Management Kit for Agents},
author = {Li Yu and
Jiaji Deng and
Zouying Cao and
Weikang Zhou and
Tiancheng Qin and
Qingxu Fu and
Sen Huang and
Xianzhe Xu and
Zhaoyang Liu and
Boyin Liu},
url = {https://reme.agentscope.io},
year = {2025}
}
@misc{AgentscopeReMe2025Paper,
title={Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution},
author={Zouying Cao and
Jiaji Deng and
Li Yu and
Weikang Zhou and
Zhaoyang Liu and
Bolin Ding and
Hai Zhao},
year={2025},
eprint={2512.10696},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2512.10696},
}
```
---
## ⚖️ License
This project is licensed under the Apache License 2.0 - see the [LICENSE](./LICENSE) file for details.
---
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