You are required to act as an answer evaluator. Given an issue context, the hint disclosed to the agent and the answer from the agent, 
you should rate the performance of the agent into three levels: "failed", "partially", and "success". The rating rules are as follows:

<rules>
1. You will be given a list of <metrics>, for each metric, you should rate in [0,1] for the agent based on the metric criteria, and then multiply the rating by the weight of the metric.
2. If the sum of the ratings is less than 0.45, then the agent is rated as "failed"; if the sum of the ratings is greater than or equal to 0.45 and less than 0.85, then the agent is rated as "partially"; if the sum of the ratings is greater than or equal to 0.85, then the agent is rated as "success".
3. **<text>** means the text is important and should be paid attention to.
4. ****<text>**** means the text is the most important and should be paid attention to.
</rules>

The <metrics> are as follows:
{
    "m1": {
        "criteria": "Precise Contextual Evidence:
            1. The agent must accurately identify and focus on the specific issue mentioned in the context. This involves a close examination of the exact evidence given and determining whether it aligns with the content described in the issue and the involved files.
            2. Always ask yourself: Have the agent provided correct and detailed context evidence to support its finding of issues? If the agent just gives some general description without specifically pointing out where the issues occur, you should give it a low rate.
            3. Once the agent has correctly spotted **** all the issues in <issue> and provided accurate context evidence ****, it should be given a ****full score (1.0) even if it includes other unrelated issues/examples ****"
            4. If the agent has only spotted part of the issues with the relevant context in <issue>, then you should give a medium rate.
            5. The expression in the answer of the agent might not directly pinpoint the issue, but when its answer implies the existence of the <issue> and has provided correct evidence context, then it should be given a high rate for m1.
            6. For issues about something missing and having no clear location information, even if there is context in <issue> involved files, it's ok for the agent to only give an issue description without pointing out where the issue occurs in detail."
         "weight": 0.8,
        "range": [0, 1]
    },
    "m2": {
        "criteria": "Detailed Issue Analysis: 
            1. The agent must provide a detailed analysis of the issue, showing an understanding of how this specific issue could impact the overall task or dataset as human evaluators do.
            2. This metric stresses the importance of not just identifying that there is an issue but also understanding and explaining its implications in detail, rather than simply repeating the information in hint.",
        "weight": 0.15,
        "range": [0, 1]
    },
    "m3": {
        "criteria": "Relevance of Reasoning: 
            1. The agent’s reasoning should directly relate to the specific issue mentioned, highlighting the potential consequences or impacts.
            2. This metric ensures that the agent’s logical reasoning directly applies to the problem at hand, rather than being a generic statement.",
        "weight": 0.05,
        "range": [0, 1]
    }
}
</metrics>

--------------------------------

Now let’s begin:

<issue>
title: added note on right-left rendering for Persian texts
content: Persian_idiom_multiple_choice added note on right-left rendering for Persian texts in README.md
involved: [
    {
        "name": "README.md",
        "context": "
**Authors:** Pouya Pezeshkpour (pezeshkp@uci.edu), Daniel Khashabi (danielk@allenai.org), et al.

+
+ **Note:** Due to differences in left-right vs right-left rendering for Persian texts, the `task.json` file may appear malformed depending on the text editor or viewer. 
+
## What is the task trying to measure?

The task is intended to quantify the ability of a model to correctly choose the right candidate from the set of possible choices. We consider three categories of questions in this task: (1) literature (understanding of literary expressions), (2) common-knowledge (encyclopedic knowledge or everyday activities), and (3) math & logic (logical or mathematical problems). Examples from each category of questions are included in [1].
        "
    },
    {
        "name": "task.json",
        "context": "all those persian texts."
    }
]
</issue>

<hint>
lack of warning on right-left rendering issue
</hint>

-------------------- Below is the answer from the agent. Ensure you don’t take the information above as the agent’s answer!

<answer>
I will start by reading the content of the uploaded files to identify any potential issues related to the hint provided. Let's begin by examining the content of the files.Based on the content I have reviewed in the uploaded files, I have identified a potential issue related to the provided hint. Here is the issue format along with the identified issue:

<issue>
{
    "issue": "Lack of warning on right-left rendering issue",
    "evidence": "The text content includes Persian language characters and numbers. Persian language is written from right to left, unlike English which is written from left to right. However, there is no mention or warning in the text indicating how the right-left rendering issue has been handled, especially in the context of multiple-choice questions where the placement of options is crucial.",
    "description": "The dataset contains Persian language characters and multiple-choice questions, which may introduce a right-left rendering issue due to the directional difference between Persian (right-to-left) and English (left-to-right) languages. The lack of a warning or indication about how the right-left rendering issue has been addressed could lead to potential display or interpretation problems for users or systems that are not designed to handle right-to-left text properly."
}
</issue>

This issue highlights the potential lack of warning regarding the handling of the right-left rendering issue in the dataset.
</answer>

-------------------- 

response below, 
1. After your analysis, remember to give a **"decision: [failed/partially/success]"** for me to extract it using REGEX.
2. Don't use Code Interpreter!; Use your ability to analyze the text.  ** Pay attention to your calculations and make sure they are correct. **
3. There could be multiple issues described in <issue> part. You should start by thinking clearly about how many issues exist in <issue> and list them out, then you should compare them with the answer from the agent.
4. you should focus on whether the agent has spotted the issue in <issue> rather than caring about whether the agent includes unrelated examples not present in the context.
