You are an expert in evaluating model judgment processes. Given the "judge_thinking" content from a model's reasoning process, please analyze and identify what types of errors it may contain, especially those that could lead the model's judgment to disagree with human evaluation. The goal is to classify the error types (multiple labels allowed if applicable), and provide evidence or reasoning for each type.

Below is a list of the main error types, each with concrete examples from different domains (coding, knowledge, math, reasoning, roleplay, writing). When analyzing, please refer to these definitions and examples.

### Error Types and Examples

[[1]] Misunderstanding the Question or Requirements
--Misinterpreting the problem statement (e.g., confusing “remove exactly k characters” with “remove up to k characters” in coding).
--Focusing on the wrong aspect (e.g., summarizing the process of natural selection when the user asked for its impact on evolution).
--Mistaking the format or scope required (e.g., answering a single-turn reasoning question as if it were multi-turn).

[[2]] Incorrect or Confused Evaluation Criteria
--Judging only based on answer correctness, ignoring completeness of explanation or reasoning.
--Overvaluing writing style or structure in an essay, while neglecting content relevance.
--Focusing on the mathematical notation or formatting rather than the correctness of the solution steps.
--Prioritizing “creativity” or “roleplay immersion” in character responses over whether the reply fulfills the user’s request.

[[3]] Overlooking Important Details or Substantive Errors
--Failing to notice a critical bug in code (e.g., off-by-one or failing special cases).
--Missing a calculation error, units mismatch, or a crucial logical misstep in an answer.
--Ignoring that a key requirement (e.g., “must use exactly k removals” or “must address the friend’s introversion directly”) is not met.
--Not spotting factual mistakes or unsupported claims in knowledge or reasoning responses.

[[4]] Superficial Features or Format Bias
--Rewarding longer, more detailed, or more formally structured answers even if they are incorrect or less relevant.
--Preferring responses with markdown/LaTeX/visualization, or creative style, regardless of whether these contribute to accuracy or helpfulness.
--Assuming that the presence of step-by-step reasoning or detailed explanations guarantees correctness, without verifying the logic.

[[5]] Logical, Reasoning, or Factual Errors
--Failing to identify logical gaps in an answer’s reasoning chain.
--Accepting answers with unjustified assumptions or circular logic.
--Overlooking an answer that skips critical steps or draws conclusions not supported by the evidence provided.
--Missing when a response in roleplay/writing introduces factual inconsistencies with the established context or scenario.

[[6]] Partial Comparison or Missing Key Contrasts
--Only comparing surface features (e.g., length, style, structure) and missing substantive differences in accuracy or depth.
--Neglecting to contrast core elements, such as which answer better addresses the user’s real need or solves the root problem.
--Ignoring which response better anticipates objections or edge cases, focusing instead on irrelevant differences.

### Instructions:
Given the following "judge_thinking" content, identify which error types are present (you may select more than one).

### Please output using the following format:

Error types detected: [[编号1]], [[编号2]], ...

[[编号1]] 错误类型  
解释及文本证据

[[编号2]] 错误类型  
解释及文本证据

（如果有更多，依次展开）

### Now analyze this "judge_thinking":
这是一段prompt，接下来我会给你依次发"judge_thinking"，你需要对每次我发的"judge_thinking"按上面的prompt进行分析回答，当你回答完以后我会给你发一段新的"judge_thinking"，按上面的prompt重新进行分析，两次的"judge_thinking"以及分析和你之前的回答都毫不相关，只是用一样的prompt，所以你需要对我每一次提供的"judge_thinking"进行独立思考，不要受之前的回答的干扰
