feat(pypi): change toml for publish (#618)

* change toml

* fix pre-commit error

configs/agent_prompt.yaml

@@ -174,7 +174,7 @@ agent_prompt_template:
         Provide a brief explanation for your conclusion, noting which specific guideline(s) informed your decision.
     template: |
         Here is the proposal: {proposal}\nHere is the summary of the paper: {summary}
-    
+
   write_metareview_ethical:
     intro: >
         Please write an ethical meta-review for the following submission to an academic conference. Your meta-review should summarize the ethical considerations raised in the reviews, author rebuttals, and any ethical review conducted. Consider the following aspects:
@@ -194,7 +194,7 @@ agent_prompt_template:
         Provide a brief explanation for your conclusion, noting which specific ethical aspects informed your decision.
     template: |
         Here is the proposal: {proposal}\nHere are the reviews: {reviews}\nHere are the rebuttals: {rebuttals}\nHere is the summary of the reviews: {summary}
-        
+
   write_review_summary:
     examples:
     - "Here is the proposal:  The proposed research aims to develop a comprehensive benchmarking framework for evaluating the performance of various GNN architectures, including P-GNNs, ID-GNNs, and ROLAND, on various network-based tasks and comparing them to other machine learning methods. The research also explores the use of P-GNNs for predicting missing attribute information in incomplete networks and compares their performance to other attribute prediction methods. Additionally, the research investigates the use of ID-GNNs for predicting future link formation in dynamic graphs and compares their performance to other link prediction methods.\n\nA new GNN architecture that incorporates both position-aware and inductive embeddings for improved predictive performance on node, edge, and graph property prediction tasks will be developed. The research also investigates the use of GNNs for learning representations of biological networks and transportation networks, comparing their performance to other biological network analysis methods and transportation network analysis methods.\n\nIn addition, new methods for quantum-enhanced reinforcement learning will be developed and compared to other reinforcement learning methods. The research also explores the use of GNNs for multi-relational graph representation learning and temporal graph representation learning, comparing their performance to other multi-relational learning methods and temporal graph learning methods.\n\nThe research further investigates the use of GNNs for graph generation tasks and compares their performance to other graph generation methods. New methods for solving bilevel optimization problems, particularly in the context of structured instances, using GNNs will be developed. The research also explores the use of quantum computing in GNNs and compares their performance to other classical GNNs.\n\nFinally, the research investigates the use of GNNs for large-scale graph-based recommendation systems and compares their performance to other recommendation algorithms.\n\nThe research is related to two papers. The first paper investigates relaxations for a class of discrete bilevel programs where the interaction constraints linking the leader and the follower are linear. The paper proposes a new single-level reformulation of the bilevel problem using a network-flow linear program via a decision diagram. The second paper proposes a differentiable quantum architecture search (DiffQAS) method for quantum reinforcement learning (QRL) models, enabling trainable circuit parameters and structure weights using gradient-based optimization. The paper demonstrates that the proposed DiffQAS-QRL approach achieves\n\nPlease begin writing the summary of the submission."

pyproject.toml

@@ -1,8 +1,8 @@
 [tool.poetry]
 name = "research_town"
-version = "0.0.1"
+version = "0.0.1-alpha"
 description = "project for research town"
-authors = ["Haofei Yu <[email protected]>"]
+authors = ["Haofei Yu <[email protected]>"]
 license = "Apache 2.0 License"
 readme = "README.md"