Update README.md

README.md

@@ -1,3 +1,35 @@
+---
+license: apache-2.0
+configs:
+- config_name: test
+  data_files:
+  - split: graph-based-synthetic-data
+    path: data/graphsyn.jsonl
+  - split: xlam-function-calling-60k
+    path: data/xlam-function-calling-60k-query.jsonl
+  - split: When2Call
+    path: data/When2Call-query.jsonl
+  - split: glaive-function-calling-v2
+    path: data/glaive-function-calling-v2-query.jsonl
+  - split: ToolACE
+    path: data/ToolACE-query.jsonl
+  - split: BUTTONInstruct
+    path: data/BUTTONInstruct-query.jsonl
+  - split: APIGen-MT-5k
+    path: data/APIGen-MT-5k-query.jsonl
+  - split: Tau-bench training set
+    path: data/tau-train-query.jsonl
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- function-calling
+- tool-calling
+- synthetic
+pretty_name: ToolMesh
+---
+
 # ToolMesh: Synthesizing Complex Tool-Use Trajectories via Graph Sampling and Multi-Agent Simulation
 
 ToolMesh is a large open-source tool-use dataset with reasoning traces, designed to advance reasoning and tool-calling capabilities in agentic LLMs. It comprises over 160k turns synthesized from over 20k tools. By organizing functions as nodes in a graph structure and sampling paths on the graph, we construct complex and high-quality user intents. Then, trajectory is synthesized by a multi-agent way with user and tool are simulted with a LM. Moreover, we perform inference answering and correctness filtering for each round in the trajectory through thinking model, only keeping the correct and valuable turns. Models fine-tuned on ToolMesh achieves promising improvements against baselines on Tau-bench, Tau2-bench and BFCL-v4 agentic.
@@ -6,7 +38,7 @@ ToolMesh is a large open-source tool-use dataset with reasoning traces, designed
 
 # Synthesis pipeline
 
-<img src="./figures/ToolMind.png" width="800"/>
+<img src="./figures/ToolMesh.png" width="800"/>
 
 * Data collection and augmentation 
   * We collect a wide variety of functions from open-source datasets, including [xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k), [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), and [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE). Each function is expected to have defined inputs and outputs; however, the original definitions are often incomplete — for instance, some functions do not explicitly specify the output parameter types. To unify them within a common representation space, we use powful LMs to complete the descriptions and types of all input and output parameters, and then vectorize them using the embedding model [Conan-embedding-v1](https://huggingface.co/TencentBAC/Conan-embedding-v1).
@@ -29,10 +61,10 @@ ToolMesh is a large open-source tool-use dataset with reasoning traces, designed
 |                          |  Tau2-airline | Tau2-retail | Tau2-telecom | BFCL-v4 | BFCL-v4-agentic |
 |--------------------------|--------------|-------------|--------------|---------|-----------------|
 | qwen3-8b (FC)             |  32.0           | 43.9        | 28.1         | 42.21   | 14.35           |
-| qwen3-8b (FC) w ToolMind(36w)|  **48.0**           | **59.6**        | **31.6**         | **46.92**   | **20.97**           |
+| qwen3-8b (FC) w ToolMesh(36w)|  **48.0**           | **59.6**        | **31.6**         | **46.92**   | **20.97**           |
 |                          |               |             |              |         |                 |
 | qwen3-14b (FC)            |  36.0           | 52.6        | **33.3**         | 45.14   | 16.90           |
-| qwen3-14b (FC) w ToolMind(36w)|  **56.0**           | **59.6**        | 31.6         | **50.54**   | **26.67**           |
+| qwen3-14b (FC) w ToolMesh(36w)|  **56.0**           | **59.6**        | 31.6         | **50.54**   | **26.67**           |
 
 # Ablation
 
@@ -41,7 +73,7 @@ ToolMesh is a large open-source tool-use dataset with reasoning traces, designed
 | qwen3-8b (FC)                                    |  32.0           | 43.9        | 28.1         | 42.21   | 14.35           |
 | qwen3-8b (FC) w Augmented Open-Source Data (20w) | <u>44.0</u>           | <u>57.9</u>        | 24.6         | 45.88   | 20.22           |
 | qwen3-8b (FC) w Synthesized Data(16w)            | 42.0           | 43.0        | <u>31.6</u>         | <u>46.87</u>   | **24.37**           |
-| qwen3-8b (FC) w ToolMind (36w)                   |  **48.0**           | **59.6**        | **31.6**         | **46.92**   | <u>20.97</u>           |
+| qwen3-8b (FC) w ToolMesh (36w)                   |  **48.0**           | **59.6**        | **31.6**         | **46.92**   | <u>20.97</u>           |
 
 # Dataset Statistic
 
@@ -66,4 +98,4 @@ The following shows the statistics of the length of the synthetic data, includin
 While we place great emphasis on the safety of the model during the training process, striving to ensure that its outputs align with ethical and legal requirements, it may not completely avoid generating unexpected outputs due to the model's size and probabilistic nature. These outputs may include harmful content such as bias or discrimination. Please don't propagate such content. We do not assume any responsibility for the consequences resulting from the dissemination of inappropriate information.
 
 # Other Information
-If you find our dataset useful or want to use it in your projects, please kindly cite this Huggingface project.
+If you find our dataset useful or want to use it in your projects, please kindly cite this Huggingface project.