Improve dataset card: Add paper/code links, align license, and update tags

README.md

@@ -1,22 +1,25 @@
 ---
-license: apache-2.0
+license: mit
 task_categories:
 - text-generation
 tags:
 - code
+- benchmarks
+- combinatorial-optimization
+- llm-agents
 ---
 
 # FrontierCO: Benchmark Dataset for Frontier Combinatorial Optimization
 
+[Paper: CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization](https://huggingface.co/papers/2504.04310) | [Code: CO-Bench GitHub Repository](https://github.com/sunnweiwei/CO-Bench)
+
 ## Overview
 
 **FrontierCO** is a curated benchmark suite for evaluating ML-based solvers on large-scale and real-world **Combinatorial Optimization (CO)** problems. The benchmark spans **8 classical CO problems** across **5 application domains**, providing both training and evaluation instances specifically designed to test the frontier of ML and LLM capabilities in solving NP-hard problems.
 
-code for evaluating agent https://github.com/sunnweiwei/CO-Bench?tab=readme-ov-file#evaluation-on-frontierco
-
-code for running classifical solver, generate training data, evaluating neural solver: https://github.com/sunnweiwei/FrontierCO
+Code for evaluating agent: [https://github.com/sunnweiwei/CO-Bench?tab=readme-ov-file#evaluation-on-frontierco](https://github.com/sunnweiwei/CO-Bench?tab=readme-ov-file#evaluation-on-frontierco)
 
-Evaluation Code: https://github.com/sunnweiwei/FrontierCO
+Code for running classical solver, generating training data, evaluating neural solver: [https://github.com/sunnweiwei/FrontierCO](https://github.com/sunnweiwei/FrontierCO)
 
 ---
 
@@ -43,10 +46,10 @@ FrontierCO/
 
 Each task folder contains:
 
-* `easy_test_instances/`: Benchmark instances that are solvable by SOTA human-designed solvers.
-* `hard_test_instances/`: Instances that remain computationally intensive or lack known optimal solutions.
-* `valid_instances/` *(if applicable)*: Additional instances for validation or development.
-* `config.py`: Metadata about instance format, solver settings, and reference solutions.
+*   `easy_test_instances/`: Benchmark instances that are solvable by SOTA human-designed solvers.
+*   `hard_test_instances/`: Instances that remain computationally intensive or lack known optimal solutions.
+*   `valid_instances/` *(if applicable)*: Additional instances for validation or development.
+*   `config.py`: Metadata about instance format, solver settings, and reference solutions.
 
 ---
 
@@ -54,20 +57,20 @@ Each task folder contains:
 
 The benchmark currently includes the following problems:
 
-* **MIS** – Maximum Independent Set
-* **MDS** – Minimum Dominating Set
-* **TSP** – Traveling Salesman Problem
-* **CVRP** – Capacitated Vehicle Routing Problem
-* **CFLP** – Capacitated Facility Location Problem
-* **CPMP** – Capacitated p-Median Problem
-* **FJSP** – Flexible Job-shop Scheduling Problem
-* **STP** – Steiner Tree Problem
+*   **MIS** – Maximum Independent Set
+*   **MDS** – Minimum Dominating Set
+*   **TSP** – Traveling Salesman Problem
+*   **CVRP** – Capacitated Vehicle Routing Problem
+*   **CFLP** – Capacitated Facility Location Problem
+*   **CPMP** – Capacitated p-Median Problem
+*   **FJSP** – Flexible Job-shop Scheduling Problem
+*   **STP** – Steiner Tree Problem
 
 Each task includes:
 
-* Easy and hard test sets with varying difficulty and practical relevance
-* Training and validation instances where applicable, generated using problem-specific generators
-* Reference results for classical and ML-based solvers
+*   Easy and hard test sets with varying difficulty and practical relevance
+*   Training and validation instances where applicable, generated using problem-specific generators
+*   Reference results for classical and ML-based solvers
 
 ---
 
@@ -75,10 +78,10 @@ Each task includes:
 
 Instances are sourced from a mix of:
 
-* Public repositories (e.g., [TSPLib](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/), [CVRPLib](http://vrp.galgos.inf.puc-rio.br/))
-* DIMACS and PACE Challenges
-* Synthetic instance generators used in prior ML and optimization research
-* Manual curation from recent SOTA solver evaluation benchmarks
+*   Public repositories (e.g., [TSPLib](http://comopt.ifi.uni-heidelberg.de/software/TSPLib95/), [CVRPLib](http://vrp.galgos.inf.puc-rio.br/))
+*   DIMACS and PACE Challenges
+*   Synthetic instance generators used in prior ML and optimization research
+*   Manual curation from recent SOTA solver evaluation benchmarks
 
 For tasks lacking open benchmarks, we include high-quality synthetic instances aligned with real-world difficulty distributions.
 
@@ -114,6 +117,60 @@ score = eval_func(**instance, **solution)
 print("Evaluation score:", score)
 ```
 
+<details>
+<summary><strong>Using Agents on Custom Problems</strong></summary>
+
+Step 1: Include a concise description and a solve template. For example:
+
+```python
+problem_description = '''The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem where, given a set of cities with known pairwise distances, the objective is to find the shortest possible tour that visits each city exactly once and returns to the starting city. More formally, given a complete graph G = (V, E) with vertices V representing cities and edges E with weights representing distances, we seek to find a Hamiltonian cycle (a closed path visiting each vertex exactly once) of minimum total weight.
+
+Implement in Solve Function
+
+def solve(**kwargs):
+    """
+    Solve a TSP instance.
+
+    Args:
+        - nodes (list): List of (x, y) coordinates representing cities in the TSP problem
+                      Format: [(x1, y1), (x2, y2), ..., (xn, yn)]
+
+    Returns:
+        dict: Solution information with:
+            - 'tour' (list): List of node indices representing the solution path
+                           Format: [0, 3, 1, ...] where numbers are indices into the nodes list
+    """
+
+    return {
+        'tour': [],
+    }
+'''
+```
+Step 2: Define the agent
+```python
+from agents import GreedyRefine
+agent = GreedyRefine(
+    problem_description=problem_description,
+    timeout=10,
+    model='openai/o3-mini')
+```
+Step 3: Define the `evaluate` function and run the loop. Use the evaluate function to get results on the data, and iteratively improve the solution based on feedback:
+```python
+evaluate = ...  # Define evaluate() to return score (float) and feedback (str)
+# Run for 64 iterations
+for it in range(64):
+    code = agent.step()
+    dev_score, dev_feedback = evaluate(code) # Define evaluate() to return score (float) and feedback (str)
+    agent.feedback(dev_score, dev_feedback)
+
+# Get the final soltuion
+code = agent.finalize()
+print(code)
+```
+</details>
+
+*Docker environment for sandboxed agent execution and solution evaluation: coming soon.*
+
 ---
 
 ## Citation
@@ -134,4 +191,4 @@ If you use **FrontierCO** in your research or applications, please cite the foll
 
 This dataset is released under the MIT License. Refer to `LICENSE` file for details.
 
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+---