doubao-seed-1-6-flash 在「消息重复触发去重」的评测结果
这是该 AI 模型在此测试用例上的详细评测结果。
基本信息
- 模型名称：doubao-seed-1-6-flash
- 用例名称：消息重复触发去重
- 测试类型：xsct-a
- 评测维度：渠道连接与消息路由
系统提示词（System Prompt）
这是给 AI 模型的背景设定和角色指令：
You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).
用户提示词（User Prompt）
这是用户给 AI 模型的具体任务要求：
我在测试 Telegram 机器人时发现，由于网络波动，同一条消息偶尔会被 OpenClaw 处理两次，导致回复了两次。请问在 OpenClaw 的渠道配置中，应该如何开启「消息去重」功能？请给出具体的配置项名称。
任务要求
AI 模型需要满足以下要求：
- 准确指出 OpenClaw 渠道配置中的去重开关名称
- 说明去重机制的基本原理，如基于「message_id」或「update_id」
- 给出配置示例片段
评测结果
综合得分
该模型在此用例的得分为：55.0 分（满分 100 分），未通过
基础难度评测结果
- 得分：55.0 分
- 通过状态：未通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
要在OpenClaw的渠道配置中开启消息去重功能，对于Telegram机器人相关配置，通常涉及的配置项名称是 `enable_message_deduplication`（消息去重功能的启用开关）。具体来说，在Telegram渠道的配置部分，将该配置项设置为 `true` 即可开启消息去重功能。你可以在OpenClaw的渠道配置文件中找到Telegram相关配置区域，添加或修改 `enable_message_deduplication: true` 来实现消息去重功能的开启。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型尝试回答了OpenClaw渠道配置中的消息去重问题，但核心配置项名称「enable_message_deduplication」与参考答案「enable_deduplication」不符，且缺乏对去重机制原理（基于message_id+缓存）、配置文件路径及完整配置示例的说明。回答整体较为简略，专业性有限，未能充分展示对OpenClaw去重功能的深入理解。 【GEMINI】模型能够准确识别用户需求并给出合理的配置建议，但在具体配置项名称的准确性上与参考答案有偏差。建议在回答此类技术问题时，增加对实现原理（如 ID 校验和缓存机制）的描述，以提升方案的专业度和可信度。 【KIMI】该回答在配置项名称上出现关键错误，将「enable_deduplication」误写为「enable_message_deduplication」，这会导致用户配置后功能无法正常生效。同时缺少对去重机制原理（message_id、Redis缓存、幂等性）的说明，技术深度不足。表达方面基本通顺但专业性因关键信息缺失而受损。整体未达到及格水平，可能误导用户实施错误的配置方案。
进阶难度评测结果
- 得分：86.2 分
- 通过状态：已通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
### 可能原因分析： 1. **去重时间窗口（TTL）设置过短** 当前全局配置中 `ttl: 60` 表示去重缓存保留60秒，若弱网环境下消息延迟到达，可能在缓存过期后再次被判定为新消息。可尝试增大 `ttl` 值观察情况。 2. **去重Provider局限性** 当前使用的是内存去重（`provider: memory`），若网关部署了多实例，内存去重无法在实例间共享状态，弱网下不同实例可能独立处理消息导致重复。可考虑切换为基于存储的去重方案（如Redis）。 3. **消息唯一标识生成异常** 去重依赖消息的唯一标识判断，需确认Discord消息的唯一标识（如消息ID、内容哈希等）生成是否正确，弱网下可能存在标识生成错误导致误判重复。 4. **渠道配置与全局配置冲突未完全解决** 需确保 `discord.yaml` 中的 `enable_deduplication: true` 已正确应用，可再次检查文件保存是否生效（可用 `read config/channels/discord.yaml` 确认修改后内容）。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型的回答质量较高，核心故障分析准确全面，覆盖了参考答案的主要排查方向，逻辑清晰。对分布式场景下内存去重局限性的分析是亮点。不足之处在于缺少具体的验证步骤（如日志查看命令）和配置示例，导致方案的可操作性略有欠缺，但整体表现良好。 【GEMINI】模型表现优秀，能够针对弱网环境下的分布式去重问题给出专业且具备实操性的建议。分析深度符合预期，涵盖了从架构设计到参数配置的多个层面，且语言表达清晰、逻辑严谨。 【KIMI】模型回答覆盖了去重失效的主要技术原因，分析条理清晰，但实操指导性略逊于参考答案，特别是在日志诊断和具体命令指引方面有所欠缺。
困难难度评测结果
- 得分：53.3 分
- 通过状态：未通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
[{"tool":"edit","args":{"file":"config/plugins/sync_plugin.yaml","edits":[{"range":{"start":{"line":4,"character":0},"end":{"line":4,"character":21}},"new_text":"deduplication_scope: global"}]}}]
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型的响应过于简单，仅执行了一个最基础的配置修改（将去重范围从 local 改为 global），而忽略了解决跨渠道消息环路问题所需的多个关键步骤。参考答案要求的「ignore_self」过滤、Metadata 标记、Redis 后端切换等重要措施均未涉及。工具调用虽然语法正确，但缺乏对权限问题的处理和后续验证步骤。整体来看，模型对复杂场景的理解和处理能力明显不足，给出的解决方案不完整，难以真正解决消息重复触发的问题。 【GEMINI】模型表现出了一定的工具操作能力，但缺乏对复杂系统架构问题的深度分析。在面对「消息环路」这一典型分布式系统问题时，模型仅采取了最简单的配置修改，忽略了防止自循环和全局状态存储等关键技术细节，未能达到解决问题的预期目标。 【KIMI】该模型对「消息环路」问题的理解较为浅显，仅完成了最基础的配置修改，未能提供完整的去重策略方案。工具调用能力基本合格但缺乏验证意识，配置修改的完整性和准确性均有欠缺，距离生产环境可用的解决方案差距较大。
相关链接
您可以通过以下链接查看更多相关内容：
