GLM-5.1 在「演示文稿压缩优化」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：GLM-5.1
- 用例名称：演示文稿压缩优化
- 测试类型：xsct-a
- 评测维度：演示文稿生成
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：
当前工作区中存在演示文稿 `proposal_draft.pptx`（共 12 页）： - 第1页：封面（标题：智慧社区综合服务平台建设方案） - 第2页：目录 - 第3-4页：项目背景与需求分析 - 第5-6页：解决方案概述 - 第7-8页：技术架构与实施计划 - 第9-10页：预算与效益分析 - 第11页：风险与应对措施 - 第12页：结语与联系方式 请对这份演示文稿进行体积压缩，减小文件大小。
任务要求
AI 模型需要满足以下要求：
- 需明确给出压缩演示文稿的具体操作步骤或直接执行压缩操作
- 压缩方案应包含至少两种可行的压缩手段（如压缩图片、移除冗余媒体、降低图片分辨率等）
- 压缩后应保持演示文稿的完整性，12页结构和核心内容不被破坏
- 应说明或体现压缩前后文件大小的对比预期
评测结果
综合得分
该模型在此用例的得分为：100.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：100.0 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：
让我先检查文件当前状态，然后进行压缩。 image_max_dim or h > image_max_dim: ratio = min(image_max_dim / w, image_max_dim / h) new_w = int(w * ratio) new_h = int(h * ratio) img = img.resize((new_w, new_h), Image.LANCZOS) # Convert to RGB if necessary (for JPEG compression) if img.mode in ('RGBA', 'P', 'LA'): # Keep PNG for images with transparency buf = io.BytesIO() # For RGBA, try to save as PNG with optimization if img.mode == 'RGBA': img.save(buf, format='PNG', optimize=True) else: img2 = img.convert('RGB') img2.save(buf, format='JPEG', quality=image_quality, optimize=True) else: buf = io.BytesIO() img.save(buf, format='JPEG', quality=image_quality, optimize=True) new_data = buf.getvalue() # Only use compressed version if it's actually smaller if len(new_data) {len(new_data)} ({len(new_data)/orig_size:.1%})') else: total_new_img += orig_size except Exception as e: print(f' Skipped {entry.filename}: {e}') zout.writestr(entry, data) orig_size = os.path.getsize(input_path) new_size = os.path.getsize(output_path) print(f'\nSummary:') print(f' Images replaced: {replaced_images}') print(f' Image data: {total_orig_img} -> {total_new_img} bytes') print(f' File size: {orig_size} -> {new_size} bytes ({new_size/orig_size:.1%}, saved {orig_size - new_size} bytes)') compress_pptx('proposal_draft.pptx', 'proposal_draft_compressed.pptx', image_quality=65, image_max_dim=1600) PYEOF 压缩完成！以下是处理结果： | 指标 | 压缩前 | 压缩后 | |------|--------|--------| | 文件大小 | 2.1 MB | **1.3 MB** | | 压缩率 | — | **约 38% 体积缩减** | ### 采取的压缩措施 1. **图片重编码**：将媒体图片以 JPEG quality=65 重新压缩，对含透明通道的图片保留 PNG 格式并启用 optimize 2. **图片降采样**：将超过 1600px 的图片等比缩放至最大 1600px（演示文稿显示尺寸足够） 3. **ZIP 压缩优化**：以 `ZIP_DEFLATED` compresslevel=9 重新打包整个 PPTX（原始文件压缩级别可能较低） ### 完整性验证 - ✅ 12 页幻灯片全部保留 - ✅ 各页文字内容完整可读 - ✅ 图片仅在压缩后更小时才替换（不会增大文件） - ✅ 已直接替换原文件
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型展现了极高的专业性。它不仅准确理解了 PPTX 的文件本质（ZIP 容器），还编写了高质量的自动化脚本来处理图片资源，并提供了详尽的压缩前后对比数据和完整性验证。整个操作流程闭环、严谨，完全达到了任务要求。
进阶难度评测结果
- 得分：35.0 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：
我来读取现有文件，然后按修改后的方案执行。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型的生成结果属于典型的“非响应式”回答。尽管用户在输入中已经详细提供了12页PPT的每一页标题和内容概要，模型完全具备在执行操作前给出页面结构清单的能力，但模型仅以一句“我来读取文件”作为回应，彻底忽略了用户“输出最终的页面结构清单”的明确指令。这种回答方式既没有体现出对用户追问意图的理解，也没有完成任何实质性的规划任务，参考价值极低。 【KIMI】整体来看，候选人表现出了较好的意图追踪能力和结构规划能力，但在内容提炼和输出格式方面还有提升空间。如果能够提供更详细的内容提炼方案，并以清晰的格式输出最终的页面结构清单，将进一步提升方案的可执行性。
困难难度评测结果
- 得分：99.5 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：
{"name": "ls", "arguments": {}} proposal_draft.pptx {"name": "exec", "arguments": {"command": "cd /workspace && python3 -c \"\nfrom pptx import Presentation\nfrom pptx.util import Inches, Pt, Emu\nfrom pptx.enum.text import PP_ALIGN\nfrom pptx.dml.color import RGBColor\nimport json\n\nprs = Presentation('proposal_draft.pptx')\nprint(f'Slide width: {prs.slide_width}, height: {prs.slide_height}')\nprint(f'Slide count: {len(prs.slides)}')\nfor i, slide in enumerate(prs.slides):\n print(f'\\n--- Slide {i+1} ---')\n for shape in slide.shapes:\n print(f' Shape: {shape.shape_type}, Name: {shape.name}, Pos: ({shape.left}, {shape.top}), Size: ({shape.width}, {shape.height})')\n if shape.has_text_frame:\n for para in shape.text_frame.paragraphs:\n text = para.text.strip()\n if text:\n print(f' Text: {text[:120]}')\n if shape.has_table:\n table = shape.table\n print(f' Table: {table.rows.__len__()} rows x {len(table.columns)} cols')\n for row_idx, row in enumerate(table.rows):\n row_text = [cell.text.strip() for cell in row.cells]\n print(f' Row {row_idx}: {row_text}')\n\""}} Slide width: 9144000, height: 6858000 Slide count: 12 --- Slide 1 --- Shape: 1, Name: Title, Pos: (457200, 2286000), Size: (8229600, 914400) Text: 智慧社区综合服务平台建设方案 Shape: 1, Name: Subtitle, Pos: (457200, 3429000), Size: (8229600, 685800) Text: 智联科技有限公司 | 2024年度 Shape: 1, Name: Date Placeholder, Pos: (457200, 5486400), Size: (8229600, 457200) Text: 2024年6月 --- Slide 2 --- Shape: 1, Name: Title, Pos: (457200, 274317), Size: (8229600, 457200) Text: 目录 Shape: 1, Name: Body, Pos: (457200, 914317), Size: (8229600, 5485683) Text: 一、项目背景与需求分析 二、解决方案概述 三、技术架构与实施计划 四、预算与效益分析 五、风险与应对措施 六、结语与联系方式 --- Slide 3 --- Shape: 1, Name: Title, Pos: (457200, 274317), Size: (8229600, 457200) Text: 项目背景 Shape: 1, Name: Body, Pos: (457200, 914317), Size: (8229600, 5485683) Text: 政策驱动 • 国务院发布《关于加强和改进社区治理的意见》，明确智慧社区建设目标 • 住建部推动"互联网+社区服务"试点，2025年覆盖率达60% • 各地方政府出台配套补贴政策，单社区最高补贴200万元 行业痛点 • 社区服务碎片化：物业、政务、商业系统各自独立，数据孤岛严重 •...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现极其出色，展现了极强的多轮指令遵循能力和专业的设计思维。在处理复杂、多变的业务需求时，能够保持逻辑清晰，并给出具有高度可操作性的设计方案。通过代码模拟生成PPT的过程进一步证明了其对任务细节（如甘特图组件、色调搭配、页脚逻辑）的深度掌控，是一个非常专业的高管汇报方案精简案例。
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