diff --git a/collection-runs/2026-07-08-first-sweep.md b/collection-runs/2026-07-08-first-sweep.md new file mode 100644 index 0000000..897607f --- /dev/null +++ b/collection-runs/2026-07-08-first-sweep.md @@ -0,0 +1,73 @@ +# Collection Run: 2026-07-08 First Sweep + +status: completed + +## Scope + +- date: 2026-07-08 +- jobs: rolling 6-month window, Beijing-first Agent / LLM roles +- papers: provisional source sweep from arXiv and official research/blog sources +- industry: large-company Agent work, technical reports, engineering blogs, benchmarks + +## Queries Used + +Jobs: + +```text +site:joinbytedance.com 北京 Agent 大模型 LLM 招聘 +site:careers.tencent.com 北京 Agent 大模型 LLM 招聘 +site:talent.baidu.com 北京 Agent 大模型 LLM 招聘 +site:careers.alibaba.com 北京 Agent 大模型 LLM 招聘 +DeepSeek 招聘 Agent 北京 大模型 岗位 +site:jobs.bytedance.com 北京 智能体 大模型 Agent +site:careers.tencent.com/jobdesc.html 北京 LLM Agent 桌面 智能伙伴 +``` + +Papers: + +```text +2026 LLM agent evaluation benchmark arxiv tool use memory +2026 AI agent benchmark evaluation arxiv long horizon tasks +2026 coding agent benchmark SWE-bench arxiv agent evaluation +2026 multi-agent AI safety benchmark arxiv +``` + +Industry: + +```text +site:openai.com/index agents 2026 technical report +site:anthropic.com/news agent computer use 2026 +site:deepmind.google/blog agent 2026 +site:microsoft.com/en-us/research/blog agent 2026 +site:qwen.ai/blog agent Qwen 2026 +site:seed.bytedance.com/en agent 2026 ByteDance Seed +site:github.com/deepseek-ai agent DeepSeek 2026 +``` + +## First-pass Method Notes + +- Official pages are preferred for item files. +- Search snippets are allowed only as weak evidence when official pages are JavaScript-rendered and not easily extractable. +- For JD, source quality and location must be explicit; non-Beijing roles can be kept only when they reveal strong Agent demand signals. +- For papers, items are marked `queued` or `skimmed`; source approval remains separate from item relevance. +- For industry material, `source_quality` and `evidence_level` are deliberately separate. + +## Output + +- jobs: 6 starter items +- papers: 6 starter items +- industry: 9 starter items +- scripts: item creation, index generation, URL checking + +## Summary + +- total items: 21 +- by collection: jobs 6, papers 6, industry 9 +- strongest recurring topics: agent evaluation, memory, coding agents, safety, enterprise deployment +- first tool output: `data/index.json` and `data/summary.json` + +## Follow-up + +- Deepen official JD extraction for DeepSeek, ByteDance and Tencent. +- Confirm approved paper sources before doing a larger paper sweep. +- Add Anthropic and more Qwen/OpenAI/DeepSeek industry items in the next run. diff --git a/data/index.json b/data/index.json new file mode 100644 index 0000000..3a53aa3 --- /dev/null +++ b/data/index.json @@ -0,0 +1,1016 @@ +[ + { + "collection": "jobs", + "path": "jobs/items/2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing.md", + "title": "2027AIDU-智能体算法工程师", + "type": "job", + "meta": { + "type": "job", + "company": "百度", + "role": "2027AIDU-智能体算法工程师", + "location": "北京市", + "source_url": "https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE", + "source_name": "百度校园招聘", + "source_quality": "official", + "collected_at": "2026-07-08", + "posted_at": "2026-05-12", + "first_seen_at": "2026-07-08", + "last_checked_at": "2026-07-08", + "snapshot_type": "new", + "job_code": "J99969", + "previous_snapshot": [], + "status": "active", + "level": "graduate", + "team": "AIDU项目", + "business_area": "search / conversation / office / data platform / entertainment", + "employment_type": "campus", + "salary": [], + "skills": [ + "agent", + "planning", + "tool-use", + "memory", + "multi-agent", + "rag", + "agent-evaluation" + ], + "topics": [ + "agent-architecture", + "evaluation", + "memory" + ], + "models": [], + "related_papers": [ + "2025-vijayvargiya-openagentsafety", + "2026-memory-agent-survey" + ], + "related_experiments": [], + "related_projects": [], + "relevance": "high" + } + }, + { + "collection": "jobs", + "path": "jobs/items/2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing.md", + "title": "2027AIDU-Agent应用全栈工程师", + "type": "job", + "meta": { + "type": "job", + "company": "百度", + "role": "2027AIDU-Agent应用全栈工程师", + "location": "北京市", + "source_url": "https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE", + "source_name": "百度校园招聘", + "source_quality": "official", + "collected_at": "2026-07-08", + "posted_at": "2026-05-12", + "first_seen_at": "2026-07-08", + "last_checked_at": "2026-07-08", + "snapshot_type": "new", + "job_code": "J99974", + "previous_snapshot": [], + "status": "active", + "level": "graduate", + "team": "AIDU项目", + "business_area": "search / healthcare / enterprise service / data analysis / office automation", + "employment_type": "campus", + "salary": [], + "skills": [ + "agent", + "planning", + "tool-use", + "function-calling", + "memory", + "reasoning", + "state-management", + "multi-agent", + "rag", + "agent-evaluation" + ], + "topics": [ + "workflow-agent", + "agent-architecture", + "evaluation" + ], + "models": [], + "related_papers": [ + "2026-dialogue-swebench", + "2026-swe-evo" + ], + "related_experiments": [], + "related_projects": [], + "relevance": "high" + } + }, + { + "collection": "jobs", + "path": "jobs/items/2026-07-08-baidu-aidu-llm-infra-engineer-beijing.md", + "title": "2027AIDU-大模型Infra工程师", + "type": "job", + "meta": { + "type": "job", + "company": "百度", + "role": "2027AIDU-大模型Infra工程师", + "location": "北京市", + "source_url": "https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE", + "source_name": "百度校园招聘", + "source_quality": "official", + "collected_at": "2026-07-08", + "posted_at": "2026-05-12", + "first_seen_at": "2026-07-08", + "last_checked_at": "2026-07-08", + "snapshot_type": "new", + "job_code": "J99967", + "previous_snapshot": [], + "status": "active", + "level": "graduate", + "team": "AIDU项目", + "business_area": "model infrastructure", + "employment_type": "campus", + "salary": [], + "skills": [ + "inference", + "serving", + "distributed-training", + "gpu", + "model-compression", + "cloud" + ], + "topics": [ + "llm-infra", + "inference" + ], + "models": [], + "related_papers": [], + "related_experiments": [], + "related_projects": [], + "relevance": "medium" + } + }, + { + "collection": "jobs", + "path": "jobs/items/2026-07-08-bytedance-seed-llm-agent-research-engineer.md", + "title": "大模型Agent研究工程师-Seed", + "type": "job", + "meta": { + "type": "job", + "company": "字节 Seed", + "role": "大模型Agent研究工程师-Seed", + "location": "unknown", + "source_url": "https://jobs.bytedance.com/experienced/position/7628902314323806469/detail", + "source_name": "字节跳动招聘", + "source_quality": "official", + "collected_at": "2026-07-08", + "posted_at": [], + "first_seen_at": "2026-07-08", + "last_checked_at": "2026-07-08", + "snapshot_type": "new", + "job_code": "7628902314323806469", + "previous_snapshot": [], + "status": "unknown", + "level": "experienced", + "team": "Seed", + "business_area": "agent research and engineering", + "employment_type": "full-time", + "salary": [], + "skills": [ + "agent", + "memory", + "context-engineering", + "planning", + "multi-agent", + "llm-application" + ], + "topics": [ + "harness", + "memory", + "context-compression" + ], + "models": [ + "Seed" + ], + "related_papers": [ + "2026-memory-agent-survey", + "2026-evomembench" + ], + "related_experiments": [], + "related_projects": [], + "relevance": "high" + } + }, + { + "collection": "jobs", + "path": "jobs/items/2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou.md", + "title": "Agent 方向招聘组合", + "type": "job", + "meta": { + "type": "job", + "company": "DeepSeek", + "role": "Agent 方向招聘组合", + "location": "北京市 / 杭州市", + "source_url": "https://hub.baai.ac.cn/view/53416", + "source_name": "智源社区转载量子位", + "source_quality": "secondary", + "collected_at": "2026-07-08", + "posted_at": "2026-03-27", + "first_seen_at": "2026-07-08", + "last_checked_at": "2026-07-08", + "snapshot_type": "new", + "job_code": [], + "previous_snapshot": [], + "status": "unknown", + "level": "mixed", + "team": "Agent / data evaluation / infra / product", + "business_area": "search / creation / multimodal / personal assistant / workflow", + "employment_type": "full-time / internship", + "salary": [], + "skills": [ + "agent", + "rl", + "agent-evaluation", + "tool-use", + "function-calling", + "memory", + "multi-agent", + "coding-agent", + "data-quality", + "inference" + ], + "topics": [ + "agent-productization", + "evaluation", + "agent-infra" + ], + "models": [ + "DeepSeek" + ], + "related_papers": [ + "2025-vijayvargiya-openagentsafety", + "2026-agentrx" + ], + "related_experiments": [], + "related_projects": [], + "relevance": "high" + } + }, + { + "collection": "jobs", + "path": "jobs/items/2026-07-08-tencent-cloud-ai-agent-test-engineer.md", + "title": "腾讯云-AI Agent测试工程师", + "type": "job", + "meta": { + "type": "job", + "company": "腾讯", + "role": "腾讯云-AI Agent测试工程师", + "location": "深圳市", + "source_url": "https://careers.tencent.com/jobdesc.html?postId=2055555661177204736", + "source_name": "腾讯招聘", + "source_quality": "official", + "collected_at": "2026-07-08", + "posted_at": "2026-06-03", + "first_seen_at": "2026-07-08", + "last_checked_at": "2026-07-08", + "snapshot_type": "new", + "job_code": "2055555661177204736", + "previous_snapshot": [], + "status": "active", + "level": "experienced", + "team": "CSIG", + "business_area": "cloud agent testing", + "employment_type": "full-time", + "salary": [], + "skills": [ + "agent", + "agent-evaluation", + "observability", + "cloud", + "kubernetes", + "system-design" + ], + "topics": [ + "agent-testing", + "harness-engineering" + ], + "models": [], + "related_papers": [ + "2026-agentrx", + "2025-vijayvargiya-openagentsafety" + ], + "related_experiments": [], + "related_projects": [], + "relevance": "medium" + } + }, + { + "collection": "papers", + "path": "papers/items/2025-vijayvargiya-openagentsafety.md", + "title": "OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety", + "type": "paper", + "meta": { + "type": "paper", + "title": "OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety", + "authors": "Sanidhya Vijayvargiya, Aditya Bharat Soni, Xuhui Zhou, Zora Zhiruo Wang, Nouha Dziri, Graham Neubig, Maarten Sap", + "year": "2025", + "venue": "ICLR 2026 / IASEAI 2026", + "url": "https://arxiv.org/abs/2507.06134", + "code_url": [], + "source": "arxiv", + "collected_at": "2026-07-08", + "status": "skimmed", + "relevance": "high", + "topics": [ + "agent-evaluation", + "agent-safety", + "tool-use" + ], + "methods": [ + "multi-turn-agent-evaluation", + "rule-based-analysis", + "llm-as-judge" + ], + "benchmarks": [ + "OpenAgentSafety" + ], + "models": [], + "datasets": [], + "related_concepts": [ + "guardrail", + "human-in-the-loop" + ], + "related_jobs": [ + "2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing", + "2026-07-08-tencent-cloud-ai-agent-test-engineer" + ], + "related_experiments": [], + "related_projects": [] + } + }, + { + "collection": "papers", + "path": "papers/items/2026-agent-safety-benchmark-taxonomy.md", + "title": "\"Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents\"", + "type": "paper", + "meta": { + "type": "paper", + "title": "\"Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents\"", + "authors": [], + "year": "2026", + "venue": [], + "url": "https://arxiv.org/html/2605.16282v1", + "code_url": [], + "source": "arxiv", + "collected_at": "2026-07-08", + "status": "queued", + "relevance": "medium", + "topics": [ + "agent-safety", + "agent-evaluation" + ], + "methods": [ + "benchmark-taxonomy", + "coverage-matrix" + ], + "benchmarks": [ + "agent-safety-benchmarks" + ], + "models": [], + "datasets": [], + "related_concepts": [ + "guardrail", + "red-teaming" + ], + "related_jobs": [ + "2026-07-08-tencent-cloud-ai-agent-test-engineer" + ], + "related_experiments": [], + "related_projects": [] + } + }, + { + "collection": "papers", + "path": "papers/items/2026-king-dialogue-swebench.md", + "title": "\"Dialogue-SWEBench: A Benchmark for Dialogue-Driven Coding Agents\"", + "type": "paper", + "meta": { + "type": "paper", + "title": "\"Dialogue-SWEBench: A Benchmark for Dialogue-Driven Coding Agents\"", + "authors": "Brendan King, Jeffrey Flanigan", + "year": "2026", + "venue": [], + "url": "https://arxiv.org/html/2606.13995v1", + "code_url": "https://jlab-nlp.github.io/dialogue-swe-bench/", + "source": "arxiv", + "collected_at": "2026-07-08", + "status": "skimmed", + "relevance": "high", + "topics": [ + "coding-agent", + "human-in-the-loop", + "agent-evaluation" + ], + "methods": [ + "user-simulator", + "dialogue-benchmark", + "schema-guided-agent" + ], + "benchmarks": [ + "Dialogue-SWEBench" + ], + "models": [], + "datasets": [ + "SWE-Bench Verified" + ], + "related_concepts": [ + "human-in-the-loop", + "coding-agent" + ], + "related_jobs": [ + "2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing" + ], + "related_experiments": [], + "related_projects": [] + } + }, + { + "collection": "papers", + "path": "papers/items/2026-memory-agent-survey.md", + "title": "\"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers\"", + "type": "paper", + "meta": { + "type": "paper", + "title": "\"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers\"", + "authors": [], + "year": "2026", + "venue": [], + "url": "https://arxiv.org/html/2603.07670v1", + "code_url": [], + "source": "arxiv", + "collected_at": "2026-07-08", + "status": "skimmed", + "relevance": "high", + "topics": [ + "memory", + "agent-architecture", + "agent-evaluation" + ], + "methods": [ + "write-manage-read-loop", + "retrieval-augmented-memory", + "reflective-memory", + "hierarchical-context" + ], + "benchmarks": [], + "models": [], + "datasets": [], + "related_concepts": [ + "memory", + "context-engineering" + ], + "related_jobs": [ + "2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing", + "2026-07-08-bytedance-seed-llm-agent-research-engineer" + ], + "related_experiments": [], + "related_projects": [] + } + }, + { + "collection": "papers", + "path": "papers/items/2026-pham-swe-evo.md", + "title": "\"SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios\"", + "type": "paper", + "meta": { + "type": "paper", + "title": "\"SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios\"", + "authors": "Minh Vu Thai Pham, Tue Le, Dung Nguyen Manh, Huy Nhat Phan, Nghi D. Q. Bui", + "year": "2026", + "venue": [], + "url": "https://arxiv.org/html/2512.18470v5", + "code_url": "https://github.com/SWE-EVO/SWE-EVO", + "source": "arxiv", + "collected_at": "2026-07-08", + "status": "skimmed", + "relevance": "high", + "topics": [ + "coding-agent", + "agent-evaluation" + ], + "methods": [ + "long-horizon-software-evolution", + "benchmark" + ], + "benchmarks": [ + "SWE-EVO" + ], + "models": [ + "OpenAI", + "DeepSeek", + "Zhipu", + "Qwen", + "Moonshot" + ], + "datasets": [ + "release-notes" + ], + "related_concepts": [ + "coding-agent", + "long-horizon-task" + ], + "related_jobs": [ + "2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou" + ], + "related_experiments": [], + "related_projects": [] + } + }, + { + "collection": "papers", + "path": "papers/items/2026-wang-evomembench.md", + "title": "\"EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective\"", + "type": "paper", + "meta": { + "type": "paper", + "title": "\"EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective\"", + "authors": "Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li", + "year": "2026", + "venue": [], + "url": "https://arxiv.org/html/2605.18421", + "code_url": [], + "source": "arxiv", + "collected_at": "2026-07-08", + "status": "skimmed", + "relevance": "high", + "topics": [ + "memory", + "agent-evaluation" + ], + "methods": [ + "memory-benchmark", + "self-evolving-agent" + ], + "benchmarks": [ + "EvoMemBench" + ], + "models": [], + "datasets": [], + "related_concepts": [ + "memory", + "procedural-memory" + ], + "related_jobs": [ + "2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing", + "2026-07-08-bytedance-seed-llm-agent-research-engineer" + ], + "related_experiments": [], + "related_projects": [] + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-bytedance-seed2-1-agent-productivity.md", + "title": "\"Seed2.1 Officially Released: Advancing AI Productivity\"", + "type": "industry", + "meta": { + "type": "industry", + "company": "ByteDance Seed", + "team": "Seed", + "title": "\"Seed2.1 Officially Released: Advancing AI Productivity\"", + "url": "https://seed.bytedance.com/en/blog/seed2-1-officially-released-advancing-ai-productivity", + "source_name": "ByteDance Seed", + "source_type": "research-blog", + "source_quality": "official", + "published_at": [], + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "agent", + "coding-agent", + "gui-agent" + ], + "implementation_signals": [ + "reinforcement-learning", + "gui-tool-use", + "mcp", + "crowdsourced-evaluation", + "internal-benchmark" + ], + "product_area": [ + "productivity", + "coding-agent", + "office-agent" + ], + "models": [ + "Seed2.1" + ], + "tools": [ + "MCP" + ], + "benchmarks": [ + "CreativeWork", + "ProgramBench" + ], + "related_papers": [ + "2026-pham-swe-evo" + ], + "related_jobs": [ + "2026-07-08-bytedance-seed-llm-agent-research-engineer" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "high", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-google-agent-security-roadmap.md", + "title": "Securing the future of AI agents", + "type": "industry", + "meta": { + "type": "industry", + "company": "Google DeepMind", + "team": [], + "title": "Securing the future of AI agents", + "url": "https://deepmind.google/blog/securing-the-future-of-ai-agents/", + "source_name": "Google DeepMind", + "source_type": "technical-report", + "source_quality": "official", + "published_at": [], + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "agent-safety", + "agent-security", + "governance" + ], + "implementation_signals": [ + "defense-in-depth", + "sandboxing", + "permission-control", + "prompt-injection-resistance" + ], + "product_area": [ + "internal-agents", + "enterprise-agent" + ], + "models": [], + "tools": [], + "benchmarks": [], + "related_papers": [ + "2025-vijayvargiya-openagentsafety", + "2026-agent-safety-benchmark-taxonomy" + ], + "related_jobs": [ + "2026-07-08-tencent-cloud-ai-agent-test-engineer" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "high", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-google-gemini-computer-use.md", + "title": "Introducing computer use in Gemini 3.5 Flash", + "type": "industry", + "meta": { + "type": "industry", + "company": "Google DeepMind", + "team": "Gemini", + "title": "Introducing computer use in Gemini 3.5 Flash", + "url": "https://deepmind.google/blog/introducing-computer-use-in-gemini-3-5-flash/", + "source_name": "Google DeepMind / Google Blog", + "source_type": "product-blog", + "source_quality": "official", + "published_at": "2026-06-24", + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "computer-use", + "agent", + "tool-use" + ], + "implementation_signals": [ + "browser-control", + "gui-control", + "safety", + "human-in-the-loop" + ], + "product_area": [ + "enterprise-agent", + "automation" + ], + "models": [ + "Gemini 3.5 Flash" + ], + "tools": [ + "Gemini API", + "Gemini Enterprise Agent Platform" + ], + "benchmarks": [], + "related_papers": [ + "2025-vijayvargiya-openagentsafety" + ], + "related_jobs": [ + "2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "high", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-microsoft-agentrx.md", + "title": "\"Systematic debugging for AI agents: Introducing the AgentRx framework\"", + "type": "industry", + "meta": { + "type": "industry", + "company": "Microsoft", + "team": "Microsoft Research", + "title": "\"Systematic debugging for AI agents: Introducing the AgentRx framework\"", + "url": "https://www.microsoft.com/en-us/research/blog/systematic-debugging-for-ai-agents-introducing-the-agentrx-framework/", + "source_name": "Microsoft Research", + "source_type": "research-blog", + "source_quality": "official", + "published_at": [], + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "agent-evaluation", + "observability", + "debugging" + ], + "implementation_signals": [ + "trajectory-normalization", + "constraint-synthesis", + "constraint-checking", + "failure-taxonomy" + ], + "product_area": [ + "agent-debugging" + ], + "models": [], + "tools": [ + "AgentRx" + ], + "benchmarks": [ + "AgentRx Benchmark" + ], + "related_papers": [ + "2025-vijayvargiya-openagentsafety" + ], + "related_jobs": [ + "2026-07-08-tencent-cloud-ai-agent-test-engineer" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "high", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-microsoft-skillopt.md", + "title": "\"SkillOpt: Agent skills as trainable parameters\"", + "type": "industry", + "meta": { + "type": "industry", + "company": "Microsoft", + "team": "Microsoft Research", + "title": "\"SkillOpt: Agent skills as trainable parameters\"", + "url": "https://www.microsoft.com/en-us/research/blog/skillopt-agent-skills-as-trainable-parameters/", + "source_name": "Microsoft Research", + "source_type": "research-blog", + "source_quality": "official", + "published_at": "2026-06-30", + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "agent", + "prompt-optimization", + "skill-learning" + ], + "implementation_signals": [ + "skill-files", + "validation-gating", + "bounded-edits", + "eval-loop" + ], + "product_area": [ + "agent-framework" + ], + "models": [], + "tools": [], + "benchmarks": [ + "six-benchmark-evaluation" + ], + "related_papers": [], + "related_jobs": [ + "2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "high", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-microsoft-state-bench.md", + "title": "\"Introducing STATE-Bench: a benchmark for AI agent memory\"", + "type": "industry", + "meta": { + "type": "industry", + "company": "Microsoft", + "team": "Microsoft Open Source", + "title": "\"Introducing STATE-Bench: a benchmark for AI agent memory\"", + "url": "https://opensource.microsoft.com/blog/2026/05/19/introducing-state-bench-a-benchmark-for-ai-agent-memory/", + "source_name": "Microsoft Open Source Blog", + "source_type": "benchmark", + "source_quality": "official", + "published_at": "2026-05-19", + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "memory", + "agent-evaluation", + "enterprise-ai" + ], + "implementation_signals": [ + "stateful-environment", + "user-simulator", + "deterministic-assertions", + "bring-your-own-memory" + ], + "product_area": [ + "customer-support", + "travel", + "shopping" + ], + "models": [], + "tools": [ + "STATE-Bench" + ], + "benchmarks": [ + "STATE-Bench" + ], + "related_papers": [ + "2026-memory-agent-survey", + "2026-wang-evomembench" + ], + "related_jobs": [ + "2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "high", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-openai-agents-transforming-work.md", + "title": "How agents are transforming work", + "type": "industry", + "meta": { + "type": "industry", + "company": "OpenAI", + "team": [], + "title": "How agents are transforming work", + "url": "https://openai.com/index/how-agents-are-transforming-work/", + "source_name": "OpenAI", + "source_type": "research-blog", + "source_quality": "official", + "published_at": [], + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "agent", + "enterprise-ai", + "coding-agent" + ], + "implementation_signals": [ + "adoption-metrics", + "workflow-change" + ], + "product_area": [ + "coding-agent", + "enterprise-work" + ], + "models": [ + "Codex" + ], + "tools": [], + "benchmarks": [], + "related_papers": [], + "related_jobs": [ + "2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "medium", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-openai-in-house-data-agent.md", + "title": "Inside OpenAI's in-house data agent", + "type": "industry", + "meta": { + "type": "industry", + "company": "OpenAI", + "team": "data / engineering", + "title": "Inside OpenAI's in-house data agent", + "url": "https://openai.com/index/inside-our-in-house-data-agent/", + "source_name": "OpenAI", + "source_type": "engineering-blog", + "source_quality": "official", + "published_at": [], + "collected_at": "2026-07-08", + "status": "analyzed", + "topics": [ + "agent", + "data-agent", + "enterprise-ai" + ], + "implementation_signals": [ + "memory", + "rag", + "mcp", + "eval", + "permissions" + ], + "product_area": [ + "data-analysis", + "internal-tools" + ], + "models": [ + "GPT-5.2", + "Codex" + ], + "tools": [ + "Evals API", + "Embeddings API", + "MCP" + ], + "benchmarks": [], + "related_papers": [ + "2026-memory-agent-survey" + ], + "related_jobs": [ + "2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing" + ], + "related_experiments": [], + "related_projects": [], + "evidence_level": "high", + "relevance": "high" + } + }, + { + "collection": "industry", + "path": "industry/items/2026-07-08-qwen-agentworld.md", + "title": "\"Qwen-AgentWorld: Language World Models for General Agents\"", + "type": "industry", + "meta": { + "type": "industry", + "company": "Alibaba Qwen", + "team": "Qwen", + "title": "\"Qwen-AgentWorld: Language World Models for General Agents\"", + "url": "https://qwen.ai/blog?id=qwen-agentworld", + "source_name": "Qwen Blog", + "source_type": "research-blog", + "source_quality": "official", + "published_at": [], + "collected_at": "2026-07-08", + "status": "queued", + "topics": [ + "agent", + "world-model", + "evaluation" + ], + "implementation_signals": [ + "simulated-environment", + "world-model", + "multi-domain-agent" + ], + "product_area": [ + "general-agent" + ], + "models": [ + "Qwen" + ], + "tools": [], + "benchmarks": [], + "related_papers": [], + "related_jobs": [], + "related_experiments": [], + "related_projects": [], + "evidence_level": "medium", + "relevance": "high" + } + } +] diff --git a/data/summary.json b/data/summary.json new file mode 100644 index 0000000..891025b --- /dev/null +++ b/data/summary.json @@ -0,0 +1,224 @@ +{ + "total": 21, + "by_collection": { + "jobs": 6, + "papers": 6, + "industry": 9 + }, + "by_status": { + "jobs": { + "active": 4, + "unknown": 2 + }, + "papers": { + "skimmed": 5, + "queued": 1 + }, + "industry": { + "analyzed": 8, + "queued": 1 + } + }, + "top_topics": [ + [ + "agent-evaluation", + 8 + ], + [ + "agent", + 6 + ], + [ + "memory", + 5 + ], + [ + "evaluation", + 4 + ], + [ + "coding-agent", + 4 + ], + [ + "agent-architecture", + 3 + ], + [ + "agent-safety", + 3 + ], + [ + "enterprise-ai", + 3 + ], + [ + "tool-use", + 2 + ], + [ + "workflow-agent", + 1 + ], + [ + "llm-infra", + 1 + ], + [ + "inference", + 1 + ], + [ + "harness", + 1 + ], + [ + "context-compression", + 1 + ], + [ + "agent-productization", + 1 + ], + [ + "agent-infra", + 1 + ], + [ + "agent-testing", + 1 + ], + [ + "harness-engineering", + 1 + ], + [ + "human-in-the-loop", + 1 + ], + [ + "gui-agent", + 1 + ] + ], + "top_skills": [ + [ + "agent", + 5 + ], + [ + "memory", + 4 + ], + [ + "multi-agent", + 4 + ], + [ + "agent-evaluation", + 4 + ], + [ + "planning", + 3 + ], + [ + "tool-use", + 3 + ], + [ + "rag", + 2 + ], + [ + "function-calling", + 2 + ], + [ + "inference", + 2 + ], + [ + "cloud", + 2 + ], + [ + "reasoning", + 1 + ], + [ + "state-management", + 1 + ], + [ + "serving", + 1 + ], + [ + "distributed-training", + 1 + ], + [ + "gpu", + 1 + ], + [ + "model-compression", + 1 + ], + [ + "context-engineering", + 1 + ], + [ + "llm-application", + 1 + ], + [ + "rl", + 1 + ], + [ + "coding-agent", + 1 + ] + ], + "top_companies": [ + [ + "百度", + 3 + ], + [ + "Microsoft", + 3 + ], + [ + "Google DeepMind", + 2 + ], + [ + "OpenAI", + 2 + ], + [ + "字节 Seed", + 1 + ], + [ + "DeepSeek", + 1 + ], + [ + "腾讯", + 1 + ], + [ + "ByteDance Seed", + 1 + ], + [ + "Alibaba Qwen", + 1 + ] + ] +} diff --git a/industry/industry-insights.md b/industry/industry-insights.md index a604d3b..e607ad0 100644 --- a/industry/industry-insights.md +++ b/industry/industry-insights.md @@ -6,48 +6,59 @@ status: seed ## Current Snapshot -- date: -- scope: -- sources reviewed: +- date: 2026-07-08 +- scope: large-company Agent work, engineering blogs, benchmarks, product/technical reports +- sources reviewed: 9 starter items +- source mix: official 9 ## Strong Industry Signals -- +- Enterprise Agent 正在从 demo 转向有权限、记忆、评估、trace、安全和平台入口的系统。 +- Computer use/GUI Agent 正在成为主流产品能力，但安全控制必须产品化。 +- Memory 的工业定义正在从“记住信息”转向“通过经验改善流程执行”。 +- Agent debug/eval 正在围绕 trajectory、constraint、failure taxonomy 和 benchmark 构建。 +- 大公司开始把 Agent adoption 当成组织生产力指标看，而不是单一模型能力。 ## Weak or Marketing-heavy Signals -- +- 单纯模型发布里提到 “agent frontier” 但缺少 benchmark、代码或系统细节时，只能作为方向信号。 +- Qwen-AgentWorld 当前抓取到的信息不足，需要补完整正文。 +- Agent usage growth 指标能说明采用趋势，但不能直接说明任务质量。 ## Company Patterns | Company | Agent Direction | Evidence | Notes | | --- | --- | --- | --- | -| OpenAI | _TBD_ | _TBD_ | _TBD_ | -| Anthropic | _TBD_ | _TBD_ | _TBD_ | -| Google DeepMind | _TBD_ | _TBD_ | _TBD_ | -| Microsoft | _TBD_ | _TBD_ | _TBD_ | -| DeepSeek | _TBD_ | _TBD_ | _TBD_ | -| 腾讯 | _TBD_ | _TBD_ | _TBD_ | -| 字节 Seed | _TBD_ | _TBD_ | _TBD_ | -| 阿里 Qwen | _TBD_ | _TBD_ | _TBD_ | +| OpenAI | internal data agent, enterprise adoption, Codex as work agent | in-house data agent, agents transforming work | 系统细节集中在数据 Agent | +| Anthropic | computer use and coding agents | not yet itemized | 下一轮补 Claude computer use / Claude Code | +| Google DeepMind | computer use, agent security/control | Gemini computer use, AI Control Roadmap | 安全控制非常值得沉淀 | +| Microsoft | memory benchmark, failure diagnosis, skill optimization | STATE-Bench, AgentRx, SkillOpt | 评估和工程方法密集 | +| DeepSeek | Agent integration ecosystem and hiring wave | jobs item, awesome-deepseek-agent source candidate | 需补官方技术资料 | +| 腾讯 | Agent testing / cloud harness signal | jobs item | 需补技术博客和北京岗位 | +| 字节 Seed | productivity agent, GUI+MCP, coding agent | Seed2.1 official blog | 与岗位中的 harness/memory/context compression 呼应 | +| 阿里 Qwen | AgentWorld / world models for general agents | Qwen-AgentWorld queued item | 需补正文和 Qwen3.x agent blogs | ## Implementation Patterns -- tool use: -- memory: -- evaluation: -- safety: -- deployment: -- productization: +- tool use: API/MCP/function calling 与 GUI/computer use 两条线并行。 +- memory: 从 raw history 转向 structured memory、stateful benchmark、experience consolidation。 +- evaluation: adoption metrics、benchmark、trajectory failure diagnosis、deterministic assertions。 +- safety: defense-in-depth、sandboxing、human confirmation、prompt injection guard、permission gating。 +- deployment: Slack/Web/IDE/CLI/MCP、enterprise platform、internal tools。 +- productization: coding agent、data agent、office/productivity agent、customer support/travel/shopping workflows。 ## Follow-up Queue -- +- Anthropic Claude computer use / Claude Code / model cards。 +- OpenAI workspace agents / Frontier / Codex enterprise coding posts。 +- Qwen AgentWorld and Qwen3.x agent blogs full extraction。 +- DeepSeek official GitHub agent ecosystem and talent site details。 +- Tencent AI Lab / Hunyuan / Yuanbao Agent technical posts。 ## Links Back to Knowledge Base -- docs to update: -- papers to read: -- jobs to compare: -- experiments to create: -- projects affected: +- docs to update: evaluation, observability, memory, safety, practice playbook +- papers to read: OpenAgentSafety, memory survey, EvoMemBench, SWE-EVO, Dialogue-SWEBench +- jobs to compare: Baidu Agent algorithm/fullstack, DeepSeek Agent hiring, Tencent Agent testing, ByteDance Seed Agent research +- experiments to create: memory benchmark, trace/failure diagnosis, GUI+MCP workflow +- projects affected: future knowledge frontend, coding-agent experiments, eval harness diff --git a/industry/items/2026-07-08-bytedance-seed2-1-agent-productivity.md b/industry/items/2026-07-08-bytedance-seed2-1-agent-productivity.md new file mode 100644 index 0000000..a07b0a9 --- /dev/null +++ b/industry/items/2026-07-08-bytedance-seed2-1-agent-productivity.md @@ -0,0 +1,72 @@ +# Industry: ByteDance Seed - Seed2.1 Agent Productivity + +--- +type: industry +company: ByteDance Seed +team: Seed +title: "Seed2.1 Officially Released: Advancing AI Productivity" +url: https://seed.bytedance.com/en/blog/seed2-1-officially-released-advancing-ai-productivity +source_name: ByteDance Seed +source_type: research-blog +source_quality: official +published_at: +collected_at: 2026-07-08 +status: analyzed +topics: + - agent + - coding-agent + - gui-agent +implementation_signals: + - reinforcement-learning + - gui-tool-use + - mcp + - crowdsourced-evaluation + - internal-benchmark +product_area: + - productivity + - coding-agent + - office-agent +models: + - Seed2.1 +tools: + - MCP +benchmarks: + - CreativeWork + - ProgramBench +related_papers: + - 2026-pham-swe-evo +related_jobs: + - 2026-07-08-bytedance-seed-llm-agent-research-engineer +related_experiments: + - +related_projects: + - +evidence_level: high +relevance: high +--- + +## One-line Takeaway + +ByteDance Seed 把 Agent 能力放在办公生产力、GUI+MCP 工具协作和端到端 coding delivery 三个方向同时推进。 + +## What They Built or Claimed + +Seed2.1 强调通用 Agent 和代码工程能力，使用 RL 提升 GUI/非 GUI 行动选择效率，并提出 CreativeWork 内部 benchmark 评估 Notion、Canva、Figma 等生产力场景中的 GUI 与 MCP 协同。 + +## Technical Signals + +- architecture: model + GUI action space + MCP tools。 +- tool use: GUI 与 MCP 工具协调。 +- memory: +- evaluation: CreativeWork、ProgramBench、众包开发者反馈、内部评估。 +- safety: +- deployment: productivity 和 coding scenarios。 +- data: 真实代码仓库众包任务反馈。 + +## Evidence Quality + +官方 Seed 博客，包含 benchmark、场景和评估方式，证据强；具体数据表需后续截图/原文复核。 + +## Gaps for Us + +- 需要研究 GUI+MCP 的 Agent 工作流设计。 diff --git a/industry/items/2026-07-08-google-agent-security-roadmap.md b/industry/items/2026-07-08-google-agent-security-roadmap.md new file mode 100644 index 0000000..7df9796 --- /dev/null +++ b/industry/items/2026-07-08-google-agent-security-roadmap.md @@ -0,0 +1,70 @@ +# Industry: Google DeepMind - AI Control Roadmap for Agents + +--- +type: industry +company: Google DeepMind +team: +title: Securing the future of AI agents +url: https://deepmind.google/blog/securing-the-future-of-ai-agents/ +source_name: Google DeepMind +source_type: technical-report +source_quality: official +published_at: +collected_at: 2026-07-08 +status: analyzed +topics: + - agent-safety + - agent-security + - governance +implementation_signals: + - defense-in-depth + - sandboxing + - permission-control + - prompt-injection-resistance +product_area: + - internal-agents + - enterprise-agent +models: + - +tools: + - +benchmarks: + - +related_papers: + - 2025-vijayvargiya-openagentsafety + - 2026-agent-safety-benchmark-taxonomy +related_jobs: + - 2026-07-08-tencent-cloud-ai-agent-test-engineer +related_experiments: + - +related_projects: + - +evidence_level: high +relevance: high +--- + +## One-line Takeaway + +Agent 安全需要把内部 Agent 当作潜在 insider threat 来设计权限、沙箱和逐步授权。 + +## What They Built or Claimed + +Google DeepMind 介绍 AI Control Roadmap，用 defense-in-depth 保护 Google 内部系统，应对能力增强但不完美对齐的 Agent。 + +## Technical Signals + +- architecture: alignment + system-level AI control + cybersecurity-style threat modeling。 +- tool use: +- memory: +- evaluation: 通过行为验证逐步授予权限。 +- safety: sandboxing、endpoint security、prompt injection resistance、权限分层。 +- deployment: 内部 Agent 系统治理。 +- data: + +## Evidence Quality + +官方技术/治理材料，适合作为 Agent 安全架构参考。 + +## Gaps for Us + +- 需要把 Agent 权限分层写入实践 playbook。 diff --git a/industry/items/2026-07-08-google-gemini-computer-use.md b/industry/items/2026-07-08-google-gemini-computer-use.md new file mode 100644 index 0000000..1d0afe1 --- /dev/null +++ b/industry/items/2026-07-08-google-gemini-computer-use.md @@ -0,0 +1,70 @@ +# Industry: Google DeepMind - Computer Use in Gemini 3.5 Flash + +--- +type: industry +company: Google DeepMind +team: Gemini +title: Introducing computer use in Gemini 3.5 Flash +url: https://deepmind.google/blog/introducing-computer-use-in-gemini-3-5-flash/ +source_name: Google DeepMind / Google Blog +source_type: product-blog +source_quality: official +published_at: 2026-06-24 +collected_at: 2026-07-08 +status: analyzed +topics: + - computer-use + - agent + - tool-use +implementation_signals: + - browser-control + - gui-control + - safety + - human-in-the-loop +product_area: + - enterprise-agent + - automation +models: + - Gemini 3.5 Flash +tools: + - Gemini API + - Gemini Enterprise Agent Platform +benchmarks: + - +related_papers: + - 2025-vijayvargiya-openagentsafety +related_jobs: + - 2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing +related_experiments: + - +related_projects: + - +evidence_level: high +relevance: high +--- + +## One-line Takeaway + +Computer use 正在从独立模型能力并入主模型和企业 Agent 平台，安全控制成为产品能力的一部分。 + +## What They Built or Claimed + +Google 表示 Gemini 3.5 Flash 原生支持 computer use，可让 Agent 在浏览器、移动端和桌面环境中看、推理并行动，支持长程和企业自动化任务。 + +## Technical Signals + +- architecture: computer use 作为 Gemini 3.5 Flash 内置工具能力。 +- tool use: browser/mobile/desktop GUI actions。 +- memory: +- evaluation: 提到 agentic computer use tasks，但需要进一步追具体 benchmark。 +- safety: 对 computer use 做 adversarial training；企业 safeguard 支持敏感/不可逆动作确认和间接 prompt injection 自动停止。 +- deployment: Gemini API 和 Gemini Enterprise Agent Platform。 +- data: + +## Evidence Quality + +官方产品/技术博客，有明确能力边界、安全控制和平台入口。 + +## Gaps for Us + +- 需要整理 computer-use Agent 的安全 checklist。 diff --git a/industry/items/2026-07-08-microsoft-agentrx.md b/industry/items/2026-07-08-microsoft-agentrx.md new file mode 100644 index 0000000..854f988 --- /dev/null +++ b/industry/items/2026-07-08-microsoft-agentrx.md @@ -0,0 +1,68 @@ +# Industry: Microsoft Research - AgentRx + +--- +type: industry +company: Microsoft +team: Microsoft Research +title: "Systematic debugging for AI agents: Introducing the AgentRx framework" +url: https://www.microsoft.com/en-us/research/blog/systematic-debugging-for-ai-agents-introducing-the-agentrx-framework/ +source_name: Microsoft Research +source_type: research-blog +source_quality: official +published_at: +collected_at: 2026-07-08 +status: analyzed +topics: + - agent-evaluation + - observability + - debugging +implementation_signals: + - trajectory-normalization + - constraint-synthesis + - constraint-checking + - failure-taxonomy +product_area: + - agent-debugging +models: + - +tools: + - AgentRx +benchmarks: + - AgentRx Benchmark +related_papers: + - 2025-vijayvargiya-openagentsafety +related_jobs: + - 2026-07-08-tencent-cloud-ai-agent-test-engineer +related_experiments: + - +related_projects: + - +evidence_level: high +relevance: high +--- + +## One-line Takeaway + +Agent 调试需要从“最终失败”定位到 trajectory 中第一个不可恢复错误步骤。 + +## What They Built or Claimed + +Microsoft Research 发布 AgentRx 和 115 条人工标注失败轨迹 benchmark，用于定位 Agent trajectory 的 critical failure step。 + +## Technical Signals + +- architecture: trajectory normalization -> constraint synthesis -> guarded evaluation -> LLM judge。 +- tool use: 根据 tool schema 和 domain policy 生成可执行约束。 +- memory: +- evaluation: 逐步检查约束，输出可审计验证日志。 +- safety: 区分计划偏离、幻觉、新信息发明等失败类型。 +- deployment: domain-agnostic debugging framework。 +- data: tau-bench、Flash、Magentic-One 失败轨迹。 + +## Evidence Quality + +官方研究博客，包含框架、benchmark 规模、失败分类，证据强。 + +## Gaps for Us + +- 本知识库需要设计统一 Agent trace schema 和失败 taxonomy。 diff --git a/industry/items/2026-07-08-microsoft-skillopt.md b/industry/items/2026-07-08-microsoft-skillopt.md new file mode 100644 index 0000000..17fe620 --- /dev/null +++ b/industry/items/2026-07-08-microsoft-skillopt.md @@ -0,0 +1,68 @@ +# Industry: Microsoft Research - SkillOpt + +--- +type: industry +company: Microsoft +team: Microsoft Research +title: "SkillOpt: Agent skills as trainable parameters" +url: https://www.microsoft.com/en-us/research/blog/skillopt-agent-skills-as-trainable-parameters/ +source_name: Microsoft Research +source_type: research-blog +source_quality: official +published_at: 2026-06-30 +collected_at: 2026-07-08 +status: analyzed +topics: + - agent + - prompt-optimization + - skill-learning +implementation_signals: + - skill-files + - validation-gating + - bounded-edits + - eval-loop +product_area: + - agent-framework +models: + - +tools: + - +benchmarks: + - six-benchmark-evaluation +related_papers: + - +related_jobs: + - 2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou +related_experiments: + - +related_projects: + - +evidence_level: high +relevance: high +--- + +## One-line Takeaway + +Agent skill/prompt 文件可以被当成外部可训练参数，用评估循环优化，而不是靠人工随手改。 + +## What They Built or Claimed + +Microsoft Research 提出 SkillOpt，将 skill editing 转成训练过程，在不改模型权重的情况下提升 Agent 行为可靠性。 + +## Technical Signals + +- architecture: frozen target model + external skill file optimization。 +- tool use: +- memory: +- evaluation: 六个 benchmark、七个目标模型、三种执行模式。 +- safety: bounded text edits、validation gating、rejected-edit feedback、slow/meta updates。 +- deployment: 可审计技能文件。 +- data: + +## Evidence Quality + +官方研究博客给出实验覆盖和机制细节，证据强。 + +## Gaps for Us + +- 可以把本项目的 `skills`、prompt、playbook 也纳入 eval-driven 优化。 diff --git a/industry/items/2026-07-08-microsoft-state-bench.md b/industry/items/2026-07-08-microsoft-state-bench.md new file mode 100644 index 0000000..4b1bb0b --- /dev/null +++ b/industry/items/2026-07-08-microsoft-state-bench.md @@ -0,0 +1,71 @@ +# Industry: Microsoft - STATE-Bench + +--- +type: industry +company: Microsoft +team: Microsoft Open Source +title: "Introducing STATE-Bench: a benchmark for AI agent memory" +url: https://opensource.microsoft.com/blog/2026/05/19/introducing-state-bench-a-benchmark-for-ai-agent-memory/ +source_name: Microsoft Open Source Blog +source_type: benchmark +source_quality: official +published_at: 2026-05-19 +collected_at: 2026-07-08 +status: analyzed +topics: + - memory + - agent-evaluation + - enterprise-ai +implementation_signals: + - stateful-environment + - user-simulator + - deterministic-assertions + - bring-your-own-memory +product_area: + - customer-support + - travel + - shopping +models: + - +tools: + - STATE-Bench +benchmarks: + - STATE-Bench +related_papers: + - 2026-memory-agent-survey + - 2026-wang-evomembench +related_jobs: + - 2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing +related_experiments: + - +related_projects: + - +evidence_level: high +relevance: high +--- + +## One-line Takeaway + +Memory 评估应衡量 Agent 是否因经验而更会执行流程，而不是只测试远距离事实召回。 + +## What They Built or Claimed + +Microsoft 发布 STATE-Bench，一个开源、memory-agnostic benchmark，用现实企业任务评估 Agent 是否能通过经验改进。 + +## Technical Signals + +- architecture: 多轮对话 loop + stateful environment + deterministic assertions。 +- tool use: 任务涉及查询、校验策略、计算费用、确认并执行。 +- memory: bring-your-own-memory 接口。 +- evaluation: 450 个任务，覆盖 travel、customer support、shopping。 +- safety: 状态改变任务会带来真实成本，强调流程合规。 +- deployment: open-source benchmark。 +- data: 预填充数据库和用户模拟器。 + +## Evidence Quality + +官方开源博客，包含任务数、领域、评估 loop 和 GitHub 入口，证据强。 + +## Gaps for Us + +- 可以基于 STATE-Bench 思路做一个小型企业流程 Agent eval。 diff --git a/industry/items/2026-07-08-openai-agents-transforming-work.md b/industry/items/2026-07-08-openai-agents-transforming-work.md new file mode 100644 index 0000000..95e9db4 --- /dev/null +++ b/industry/items/2026-07-08-openai-agents-transforming-work.md @@ -0,0 +1,75 @@ +# Industry: OpenAI - How Agents Are Transforming Work + +--- +type: industry +company: OpenAI +team: +title: How agents are transforming work +url: https://openai.com/index/how-agents-are-transforming-work/ +source_name: OpenAI +source_type: research-blog +source_quality: official +published_at: +collected_at: 2026-07-08 +status: analyzed +topics: + - agent + - enterprise-ai + - coding-agent +implementation_signals: + - adoption-metrics + - workflow-change +product_area: + - coding-agent + - enterprise-work +models: + - Codex +tools: + - +benchmarks: + - +related_papers: + - +related_jobs: + - 2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou +related_experiments: + - +related_projects: + - +evidence_level: medium +relevance: high +--- + +## One-line Takeaway + +Agent 采用正在从“聊天辅助”变成“可计量的 agentic labor”，企业内部不同职能的增长速度差异值得追踪。 + +## What They Built or Claimed + +OpenAI 公开了内部 Codex 使用变化，显示过去六个月内研究、客服、工程、法务等部门的 agentic usage 明显增长，并认为员工正在从 chatbot 转向 agent 作为主要 AI 交互形态。 + +## Technical Signals + +- architecture: 未详细公开。 +- tool use: Codex 作为工作代理。 +- memory: +- evaluation: 使用 adoption / output token 增长作为组织层面的采用指标。 +- safety: +- deployment: 企业内部多部门使用。 +- data: 部门级使用增长。 + +## Evidence Quality + +官方数据型博客，适合看采用趋势；技术实现细节较少。 + +## Links + +- papers: +- jobs: DeepSeek Agent hiring wave +- experiments: +- projects: +- docs: practice playbook + +## Gaps for Us + +- 需要设计自己的 Agent 使用指标：任务数、回合数、人工改动量、成功率、耗时和成本。 diff --git a/industry/items/2026-07-08-openai-in-house-data-agent.md b/industry/items/2026-07-08-openai-in-house-data-agent.md new file mode 100644 index 0000000..316a8ea --- /dev/null +++ b/industry/items/2026-07-08-openai-in-house-data-agent.md @@ -0,0 +1,81 @@ +# Industry: OpenAI - In-house Data Agent + +--- +type: industry +company: OpenAI +team: data / engineering +title: Inside OpenAI's in-house data agent +url: https://openai.com/index/inside-our-in-house-data-agent/ +source_name: OpenAI +source_type: engineering-blog +source_quality: official +published_at: +collected_at: 2026-07-08 +status: analyzed +topics: + - agent + - data-agent + - enterprise-ai +implementation_signals: + - memory + - rag + - mcp + - eval + - permissions +product_area: + - data-analysis + - internal-tools +models: + - GPT-5.2 + - Codex +tools: + - Evals API + - Embeddings API + - MCP +benchmarks: + - +related_papers: + - 2026-memory-agent-survey +related_jobs: + - 2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing +related_experiments: + - +related_projects: + - +evidence_level: high +relevance: high +--- + +## One-line Takeaway + +高价值内部 Agent 不是只接一个数据库，而是把权限、表级知识、人工注释、代码增强、组织知识、记忆和运行时上下文做成系统。 + +## What They Built or Claimed + +OpenAI 构建了内部数据 Agent，用自然语言帮助员工探索和分析公司数据平台。资料描述了多层上下文、Agent API、Slack/Web/CLI/MCP 入口、记忆系统和安全边界。 + +## Technical Signals + +- architecture: Agent API + internal knowledge + warehouse/platform sources + model/tool loop。 +- tool use: 查询数据、语义检索、精确文本检索、MCP 连接。 +- memory: 持续学习的记忆系统，用于提升后续查询。 +- evaluation: 使用 Evals API 作为构建工具之一。 +- safety: 内部权限和工作流是设计的一部分。 +- deployment: Slack、Web、IDE、Codex CLI、内部 ChatGPT。 +- data: 表使用、人工注释、Codex enrichment、institutional knowledge、runtime context。 + +## Evidence Quality + +官方工程博客，有系统结构、上下文层和落地入口，证据强。 + +## Links + +- papers: memory survey +- jobs: Baidu Agent fullstack +- experiments: data-agent-context-stack +- projects: +- docs: evaluation, memory, tool use + +## Gaps for Us + +- 需要沉淀一个“企业数据 Agent 架构”专题。 diff --git a/industry/items/2026-07-08-qwen-agentworld.md b/industry/items/2026-07-08-qwen-agentworld.md new file mode 100644 index 0000000..ddc2c89 --- /dev/null +++ b/industry/items/2026-07-08-qwen-agentworld.md @@ -0,0 +1,67 @@ +# Industry: Qwen - AgentWorld + +--- +type: industry +company: Alibaba Qwen +team: Qwen +title: "Qwen-AgentWorld: Language World Models for General Agents" +url: https://qwen.ai/blog?id=qwen-agentworld +source_name: Qwen Blog +source_type: research-blog +source_quality: official +published_at: +collected_at: 2026-07-08 +status: queued +topics: + - agent + - world-model + - evaluation +implementation_signals: + - simulated-environment + - world-model + - multi-domain-agent +product_area: + - general-agent +models: + - Qwen +tools: + - +benchmarks: + - +related_papers: + - +related_jobs: + - +related_experiments: + - +related_projects: + - +evidence_level: medium +relevance: high +--- + +## One-line Takeaway + +Qwen 正在把通用 Agent 能力和“语言世界模型/环境模拟”联系起来，值得后续重点读完整技术细节。 + +## What They Built or Claimed + +搜索摘要显示 Qwen 发布 Qwen-AgentWorld，一个 native language world model，用于模拟七个领域的 Agent 环境。 + +## Technical Signals + +- architecture: language world model for simulated agent environments。 +- tool use: +- memory: +- evaluation: 多领域环境模拟可能用于 Agent 训练或评估。 +- safety: +- deployment: +- data: + +## Evidence Quality + +官方博客入口存在，但当前抓取未展开正文；先作为 queued industry item，后续需要浏览器或脚本补全。 + +## Gaps for Us + +- 需要详细阅读 Qwen-AgentWorld，判断它是训练环境、评估环境还是产品化组件。 diff --git a/jobs/items/2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing.md b/jobs/items/2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing.md new file mode 100644 index 0000000..7c64bfc --- /dev/null +++ b/jobs/items/2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing.md @@ -0,0 +1,95 @@ +# Job: 百度 - 2027AIDU 智能体算法工程师 + +--- +type: job +company: 百度 +role: 2027AIDU-智能体算法工程师 +location: 北京市 +source_url: https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE +source_name: 百度校园招聘 +source_quality: official +collected_at: 2026-07-08 +posted_at: 2026-05-12 +first_seen_at: 2026-07-08 +last_checked_at: 2026-07-08 +snapshot_type: new +job_code: J99969 +previous_snapshot: +status: active +level: graduate +team: AIDU项目 +business_area: search / conversation / office / data platform / entertainment +employment_type: campus +salary: +skills: + - agent + - planning + - tool-use + - memory + - multi-agent + - rag + - agent-evaluation +topics: + - agent-architecture + - evaluation + - memory +models: + - +related_papers: + - 2025-vijayvargiya-openagentsafety + - 2026-memory-agent-survey +related_experiments: + - +related_projects: + - +relevance: high +--- + +## Source Snapshot + +- source: 百度官方招聘页 +- collected_at: 2026-07-08 +- availability: 页面可访问，岗位列表显示发布日期 2026-05-12。 + +## Change Snapshot + +- first_seen_at: 2026-07-08 +- last_checked_at: 2026-07-08 +- snapshot_type: new +- previous_snapshot: +- changed_fields: + +## Raw JD Summary + +百度 AIDU 的智能体算法岗位要求负责 AI Agent 设计和研发，覆盖感知、决策、执行闭环、多智能体协作、长期记忆、推理、规划、工具调用、反思、代码生成、RAG 融合和 Agent 评测体系建设。 + +## Responsibilities + +- 设计和研发 AI Agent 的核心机制。 +- 推动 Agent 在搜索、对话、办公、数据平台、网盘文库、生活娱乐等产品场景落地。 +- 建设评测体系，优化成功率、延迟、成本和用户体验。 + +## Requirements + +- 熟悉 ReAct、AutoGPT、CoT 等范式。 +- 熟悉 LangChain、AutoGen、CrewAI 等 Agent 框架。 +- 理解 RAG 与 Agent 融合架构。 + +## Extracted Signals + +- hard requirements: planning、tool use、memory、multi-agent、RAG、eval。 +- preferred skills: Agent 框架实践、产品场景落地。 +- hidden signals: 百度把 Agent 从单点算法能力看成端到端系统能力。 +- business direction: 搜索、对话、办公、数据平台和泛生活娱乐场景。 + +## Knowledge Links + +- concepts: Agent loop, tool use, memory, evaluation +- papers: OpenAgentSafety, memory survey +- experiments: eval harness, memory benchmark +- projects: + +## Gaps for Us + +- 需要补充主流 Agent 框架的横向比较。 +- 需要建立 Agent 评测指标和任务集。 diff --git a/jobs/items/2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing.md b/jobs/items/2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing.md new file mode 100644 index 0000000..32b687a --- /dev/null +++ b/jobs/items/2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing.md @@ -0,0 +1,92 @@ +# Job: 百度 - 2027AIDU Agent 应用全栈工程师 + +--- +type: job +company: 百度 +role: 2027AIDU-Agent应用全栈工程师 +location: 北京市 +source_url: https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE +source_name: 百度校园招聘 +source_quality: official +collected_at: 2026-07-08 +posted_at: 2026-05-12 +first_seen_at: 2026-07-08 +last_checked_at: 2026-07-08 +snapshot_type: new +job_code: J99974 +previous_snapshot: +status: active +level: graduate +team: AIDU项目 +business_area: search / healthcare / enterprise service / data analysis / office automation +employment_type: campus +salary: +skills: + - agent + - planning + - tool-use + - function-calling + - memory + - reasoning + - state-management + - multi-agent + - rag + - agent-evaluation +topics: + - workflow-agent + - agent-architecture + - evaluation +models: + - +related_papers: + - 2026-dialogue-swebench + - 2026-swe-evo +related_experiments: + - +related_projects: + - +relevance: high +--- + +## Source Snapshot + +- source: 百度官方招聘页 +- collected_at: 2026-07-08 +- availability: 页面可访问，岗位列表显示发布日期 2026-05-12。 + +## Change Snapshot + +- first_seen_at: 2026-07-08 +- last_checked_at: 2026-07-08 +- snapshot_type: new +- previous_snapshot: +- changed_fields: + +## Raw JD Summary + +岗位强调构建下一代 Autonomous Agent 系统，要求从对话工具转向可执行任务的智能体，覆盖 planning、acting、reflection、Tool/API 调用、长短期记忆、多轮推理、状态管理、多 Agent 协作、RAG 融合和 Eval。 + +## Responsibilities + +- 构建具备任务执行能力的 Agent 应用系统。 +- 搭建 Tool/API、memory、reasoning、state 等核心能力。 +- 推动 Agent 在搜索、医疗、企业服务、数据分析和自动化办公等场景落地。 + +## Extracted Signals + +- hard requirements: full-stack Agent engineering, tool/function calling, memory, state, eval。 +- preferred skills: 前沿方向跟踪，包括 Tool Learning、Self-Improvement、World Model、多模态 Agent。 +- hidden signals: 全栈岗位已经不只是 Web 工程，开始要求 Agent runtime 和评估能力。 +- business direction: 企业服务、数据分析、自动化办公。 + +## Knowledge Links + +- concepts: workflow-agent, human-in-the-loop, eval +- papers: Dialogue-SWEBench, SWE-EVO +- experiments: workflow-agent prototype +- projects: + +## Gaps for Us + +- 需要补一个 Agent runtime / state management 的工程专题。 +- 需要整理 Tool Learning 和 Self-Improvement 的论文清单。 diff --git a/jobs/items/2026-07-08-baidu-aidu-llm-infra-engineer-beijing.md b/jobs/items/2026-07-08-baidu-aidu-llm-infra-engineer-beijing.md new file mode 100644 index 0000000..2651168 --- /dev/null +++ b/jobs/items/2026-07-08-baidu-aidu-llm-infra-engineer-beijing.md @@ -0,0 +1,70 @@ +# Job: 百度 - 2027AIDU 大模型 Infra 工程师 + +--- +type: job +company: 百度 +role: 2027AIDU-大模型Infra工程师 +location: 北京市 +source_url: https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE +source_name: 百度校园招聘 +source_quality: official +collected_at: 2026-07-08 +posted_at: 2026-05-12 +first_seen_at: 2026-07-08 +last_checked_at: 2026-07-08 +snapshot_type: new +job_code: J99967 +previous_snapshot: +status: active +level: graduate +team: AIDU项目 +business_area: model infrastructure +employment_type: campus +salary: +skills: + - inference + - serving + - distributed-training + - gpu + - model-compression + - cloud +topics: + - llm-infra + - inference +models: + - +related_papers: + - +related_experiments: + - +related_projects: + - +relevance: medium +--- + +## Source Snapshot + +- source: 百度官方招聘页 +- collected_at: 2026-07-08 +- availability: 页面可访问，岗位列表显示发布日期 2026-05-12。 + +## Raw JD Summary + +大模型 Infra 岗位关注 AI 异构计算加速引擎、底层算子、通信库、编译系统、分布式系统和训练/推理效率优化。 + +## Extracted Signals + +- hard requirements: inference engine、通信库、算子、编译优化、分布式系统。 +- hidden signals: Agent 能力落地会被成本、延迟和推理效率约束；JD 需求里 infra 和 Agent 应用是并行出现的。 +- business direction: 大模型平台化和成本优化。 + +## Knowledge Links + +- concepts: inference, latency, cost control +- papers: +- experiments: agent cost/latency evaluation +- projects: + +## Gaps for Us + +- Agent 知识库需要增加成本、延迟和推理服务专题。 diff --git a/jobs/items/2026-07-08-bytedance-seed-llm-agent-research-engineer.md b/jobs/items/2026-07-08-bytedance-seed-llm-agent-research-engineer.md new file mode 100644 index 0000000..3755dc0 --- /dev/null +++ b/jobs/items/2026-07-08-bytedance-seed-llm-agent-research-engineer.md @@ -0,0 +1,72 @@ +# Job: 字节 Seed - 大模型 Agent 研究工程师 + +--- +type: job +company: 字节 Seed +role: 大模型Agent研究工程师-Seed +location: unknown +source_url: https://jobs.bytedance.com/experienced/position/7628902314323806469/detail +source_name: 字节跳动招聘 +source_quality: official +collected_at: 2026-07-08 +posted_at: +first_seen_at: 2026-07-08 +last_checked_at: 2026-07-08 +snapshot_type: new +job_code: 7628902314323806469 +previous_snapshot: +status: unknown +level: experienced +team: Seed +business_area: agent research and engineering +employment_type: full-time +salary: +skills: + - agent + - memory + - context-engineering + - planning + - multi-agent + - llm-application +topics: + - harness + - memory + - context-compression +models: + - Seed +related_papers: + - 2026-memory-agent-survey + - 2026-evomembench +related_experiments: + - +related_projects: + - +relevance: high +--- + +## Source Snapshot + +- source: 字节跳动官方招聘页；网页正文由前端渲染，当前采集依赖搜索摘要。 +- collected_at: 2026-07-08 +- availability: URL 可定位到官方岗位页，但需要后续用浏览器或 API 抽取完整 JD。 + +## Raw JD Summary + +搜索摘要显示该岗位负责大模型 Agent 系统研究与工程落地，围绕复杂任务执行持续优化 Agent 效果，关键词包括 harness、memory、context compression、planning/execution、multi-agent。 + +## Extracted Signals + +- hard requirements: agent harness, memory, context compression, planning/execution, multi-agent。 +- hidden signals: Seed 把 Agent 作为研究与工程落地结合的问题，而不是纯应用开发。 +- business direction: 复杂任务执行和通用 Agent 能力。 + +## Knowledge Links + +- concepts: context engineering, agent harness, memory +- papers: Memory survey, EvoMemBench +- experiments: context compression comparison +- projects: + +## Gaps for Us + +- 需要后续用可重复脚本抓取字节招聘 JS 数据或手动浏览器快照。 diff --git a/jobs/items/2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou.md b/jobs/items/2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou.md new file mode 100644 index 0000000..4098108 --- /dev/null +++ b/jobs/items/2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou.md @@ -0,0 +1,78 @@ +# Job: DeepSeek - Agent 方向招聘组合 + +--- +type: job +company: DeepSeek +role: Agent 方向招聘组合 +location: 北京市 / 杭州市 +source_url: https://hub.baai.ac.cn/view/53416 +source_name: 智源社区转载量子位 +source_quality: secondary +collected_at: 2026-07-08 +posted_at: 2026-03-27 +first_seen_at: 2026-07-08 +last_checked_at: 2026-07-08 +snapshot_type: new +job_code: +previous_snapshot: +status: unknown +level: mixed +team: Agent / data evaluation / infra / product +business_area: search / creation / multimodal / personal assistant / workflow +employment_type: full-time / internship +salary: +skills: + - agent + - rl + - agent-evaluation + - tool-use + - function-calling + - memory + - multi-agent + - coding-agent + - data-quality + - inference +topics: + - agent-productization + - evaluation + - agent-infra +models: + - DeepSeek +related_papers: + - 2025-vijayvargiya-openagentsafety + - 2026-agentrx +related_experiments: + - +related_projects: + - +relevance: high +--- + +## Source Snapshot + +- source: 智源社区转载量子位文章，文中参考 DeepSeek 招聘入口。 +- collected_at: 2026-07-08 +- availability: 二手来源可访问；官方招聘站需要后续逐条核验。 + +## Raw JD Summary + +该资料显示 DeepSeek 在 2026-03 开放一批 Agent 相关岗位，覆盖算法研究、数据评测、基础设施、产品经理和全栈工程。岗位信号集中在 RL/RLAIF、Agent 评测数据、工具调用、多轮交互、长期记忆、MCP、内部 Agent 框架、评测平台和 Agent runtime。 + +## Extracted Signals + +- hard requirements: Agent algorithm, data/eval, infra, product scenario discovery。 +- preferred skills: 重度使用 Claude Code、Cursor、Copilot 等 AI 编程工具。 +- hidden signals: DeepSeek 的 Agent 招聘从基础模型研究转向产品化、数据闭环和运行时基础设施。 +- business direction: 搜索、内容创作、多模态、个人助理、自动化工作流。 + +## Knowledge Links + +- concepts: coding-agent, agent evaluation, MCP, runtime isolation +- papers: OpenAgentSafety, AgentRx +- experiments: tool-use benchmark, eval data construction +- projects: + +## Gaps for Us + +- 需要后续从 DeepSeek 官方招聘站逐条拆解岗位。 +- 需要补 MCP、Agent runtime isolation、AI coding tool 使用能力的实践笔记。 diff --git a/jobs/items/2026-07-08-tencent-cloud-ai-agent-test-engineer.md b/jobs/items/2026-07-08-tencent-cloud-ai-agent-test-engineer.md new file mode 100644 index 0000000..33a02ca --- /dev/null +++ b/jobs/items/2026-07-08-tencent-cloud-ai-agent-test-engineer.md @@ -0,0 +1,72 @@ +# Job: 腾讯云 - AI Agent 测试工程师 + +--- +type: job +company: 腾讯 +role: 腾讯云-AI Agent测试工程师 +location: 深圳市 +source_url: https://careers.tencent.com/jobdesc.html?postId=2055555661177204736 +source_name: 腾讯招聘 +source_quality: official +collected_at: 2026-07-08 +posted_at: 2026-06-03 +first_seen_at: 2026-07-08 +last_checked_at: 2026-07-08 +snapshot_type: new +job_code: 2055555661177204736 +previous_snapshot: +status: active +level: experienced +team: CSIG +business_area: cloud agent testing +employment_type: full-time +salary: +skills: + - agent + - agent-evaluation + - observability + - cloud + - kubernetes + - system-design +topics: + - agent-testing + - harness-engineering +models: + - +related_papers: + - 2026-agentrx + - 2025-vijayvargiya-openagentsafety +related_experiments: + - +related_projects: + - +relevance: medium +--- + +## Source Snapshot + +- source: 腾讯官方招聘页；搜索结果显示岗位名、业务线、地点和更新时间。 +- collected_at: 2026-07-08 +- availability: 官方 URL 可定位，但正文需要后续浏览器或 API 抽取。 +- note: 非北京岗位，作为 Agent 测试/评估需求信号保留。 + +## Raw JD Summary + +搜索摘要显示该岗位要求理解 LLM 原理与局限，具备大型项目的 AI Harness Engineering 或 Agent 落地工程实践，并熟悉云原生、分布式存储和云数据库等工程基础。 + +## Extracted Signals + +- hard requirements: Agent 测试、harness engineering、云原生和分布式基础设施。 +- hidden signals: 腾讯云把 Agent 测试作为独立工程岗位，说明 Agent 评估和可靠性正在产品化。 +- business direction: 云上 Agent 服务、企业级测试和质量保障。 + +## Knowledge Links + +- concepts: evaluation, observability, trace, harness +- papers: AgentRx, OpenAgentSafety +- experiments: failed trajectory debugging +- projects: + +## Gaps for Us + +- 需要整理 Agent 测试工程和 harness engineering 的方法论。 diff --git a/jobs/market-insights.md b/jobs/market-insights.md index f5b7a55..8540f79 100644 --- a/jobs/market-insights.md +++ b/jobs/market-insights.md @@ -6,47 +6,63 @@ status: seed ## Current Snapshot -- date: +- date: 2026-07-08 - scope: 北京，大厂 Agent / LLM 相关岗位，滚动最近 6 个月 -- sample size: -- source mix: +- sample size: 6 starter items +- source mix: official 5, secondary 1 +- caveat: 这是第一轮快速 sweep，不是完整市场统计；字节和腾讯部分页面需要后续用浏览器/API 补全正文。 ## Requirement Changes | Period | Signal | Companies | Evidence | Notes | | --- | --- | --- | --- | --- | -| _TBD_ | _TBD_ | _TBD_ | _TBD_ | _TBD_ | +| 2026-03 to 2026-07 | Agent 从研究走向产品化和工程化 | DeepSeek、百度、字节、腾讯 | DeepSeek 招聘波、百度 AIDU、字节 Seed、腾讯 Agent 测试 | 需要继续补齐官方岗位快照 | +| 2026-05 to 2026-07 | Eval / harness / testing 变成显性能力 | 百度、腾讯、DeepSeek | 百度 J99969/J99974、腾讯 AI Agent 测试、DeepSeek 评测岗位 | 评估不再只是研究指标，而是岗位职责 | +| 2026-05 to 2026-07 | Memory / context compression 进入核心要求 | 百度、字节 | 百度智能体算法、字节 Seed Agent 研究 | 与论文 memory benchmark 趋势重合 | ## High-frequency Skills | Skill | Count | Companies | Notes | | --- | --- | --- | --- | -| _TBD_ | _TBD_ | _TBD_ | _TBD_ | +| agent | 5 | 百度、字节、DeepSeek、腾讯 | 多数岗位直接围绕 Agent 设计、落地或测试 | +| memory | 4 | 百度、字节、DeepSeek | 长期记忆、短期记忆、context compression 反复出现 | +| multi-agent | 4 | 百度、DeepSeek、字节 | 百度明确多 Agent 协作，DeepSeek 和字节出现复杂任务执行信号 | +| agent-evaluation | 4 | 百度、DeepSeek、腾讯 | 评测体系、harness、测试工程成为硬需求 | +| planning / tool-use | 3 | 百度、DeepSeek | planning、acting、tool/API/function calling 是核心机制 | +| rag | 2 | 百度 | RAG+Agent 融合是应用岗位重点 | ## Company Differences | Company | Signals | Notes | | --- | --- | --- | -| DeepSeek | _TBD_ | _TBD_ | -| 腾讯 | _TBD_ | _TBD_ | -| 字节 | _TBD_ | _TBD_ | -| 阿里 | _TBD_ | _TBD_ | -| 百度 | _TBD_ | _TBD_ | +| DeepSeek | Agent 产品化、数据评测、基础设施、AI coding tool 使用 | 当前主要来自二手文章和官方招聘入口线索，需逐条核验 | +| 腾讯 | Agent 测试、harness engineering、云原生基础设施 | 当前保留一个非北京强信号，后续需补北京岗位 | +| 字节 | Seed 侧重 harness、memory、context compression、planning/execution | 官方岗位页 JS 渲染，需要 API/浏览器补全 | +| 阿里 | Qwen 行业实践已出现 AgentWorld/Qwen Agent 信号 | JD 尚未入库 | +| 百度 | 岗位描述最完整，明确 Agent algorithm、fullstack、infra 三层 | 官方页面可直接抽取，适合作为第一批标杆 | ## Role Patterns -- Agent engineer: -- LLM application engineer: -- AI infra engineer: -- AI product manager: -- AI research engineer: +- Agent engineer: planning、tool use、memory、multi-agent、RAG、eval。 +- LLM application engineer: 从对话应用转向可执行任务系统，要求 state management 和 tool/API。 +- AI infra engineer: 推理、通信库、算子、分布式系统和成本优化支撑 Agent 落地。 +- AI product manager: DeepSeek 资料显示 Agent 产品经理需要懂场景、训练数据和评测体系。 +- AI research engineer: 字节 Seed 强调 harness、memory、context compression 和复杂任务执行。 ## Knowledge Gaps 这些是 JD 里出现但知识库尚未充分覆盖的方向： -- +- Agent harness engineering +- Agent runtime isolation and scheduling +- Memory and context compression evaluation +- RAG+Agent product architecture +- AI coding tool proficiency as an engineering skill +- Agent test / failure diagnosis methodology ## Follow-up Collection Queue -- +- 补齐 DeepSeek 官方招聘站 Agent 相关岗位详情。 +- 抓取字节招聘 JS 页面或 API，补全 Seed Agent 岗位正文。 +- 继续找腾讯北京 Agent / 大模型评测 / 元宝 / 混元相关岗位。 +- 开始阿里 Qwen、百度社招、美团/快手/京东 Agent 岗位 sweep。 diff --git a/papers/items/2025-vijayvargiya-openagentsafety.md b/papers/items/2025-vijayvargiya-openagentsafety.md new file mode 100644 index 0000000..c480e72 --- /dev/null +++ b/papers/items/2025-vijayvargiya-openagentsafety.md @@ -0,0 +1,64 @@ +# Paper: OpenAgentSafety + +--- +type: paper +title: OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety +authors: Sanidhya Vijayvargiya, Aditya Bharat Soni, Xuhui Zhou, Zora Zhiruo Wang, Nouha Dziri, Graham Neubig, Maarten Sap +year: 2025 +venue: ICLR 2026 / IASEAI 2026 +url: https://arxiv.org/abs/2507.06134 +code_url: +source: arxiv +collected_at: 2026-07-08 +status: skimmed +relevance: high +topics: + - agent-evaluation + - agent-safety + - tool-use +methods: + - multi-turn-agent-evaluation + - rule-based-analysis + - llm-as-judge +benchmarks: + - OpenAgentSafety +models: + - +datasets: + - +related_concepts: + - guardrail + - human-in-the-loop +related_jobs: + - 2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing + - 2026-07-08-tencent-cloud-ai-agent-test-engineer +related_experiments: + - +related_projects: + - +--- + +## One-line Takeaway + +Agent safety 评估正在从简单拒答测试转向真实工具、多用户、多轮任务和可扩展评估框架。 + +## Problem + +真实 Agent 会调用浏览器、文件系统、shell、消息平台和代码执行环境，传统安全 benchmark 对工具、任务和对抗场景覆盖不足。 + +## Core Idea + +OpenAgentSafety 提供模块化框架，用真实工具和多轮任务评估 Agent 在八类风险下的行为，并结合规则分析和 LLM-as-judge。 + +## Evidence + +论文报告覆盖 350+ 多轮、多用户任务，并评估五个 LLM 在 agentic 场景下的安全表现。 + +## Useful For Us + +- 可作为 Agent eval harness 的安全维度参考。 +- 与 JD 中的 Agent 评测体系、成功率、稳定性、安全边界直接相关。 + +## Follow-up Experiments + +- 选 10 个高风险工具任务，建立一个本地 mini OpenAgentSafety 风格测试集。 diff --git a/papers/items/2026-agent-safety-benchmark-taxonomy.md b/papers/items/2026-agent-safety-benchmark-taxonomy.md new file mode 100644 index 0000000..318f34e --- /dev/null +++ b/papers/items/2026-agent-safety-benchmark-taxonomy.md @@ -0,0 +1,61 @@ +# Paper: Agent Safety Benchmark Taxonomy + +--- +type: paper +title: "Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents" +authors: +year: 2026 +venue: +url: https://arxiv.org/html/2605.16282v1 +code_url: +source: arxiv +collected_at: 2026-07-08 +status: queued +relevance: medium +topics: + - agent-safety + - agent-evaluation +methods: + - benchmark-taxonomy + - coverage-matrix +benchmarks: + - agent-safety-benchmarks +models: + - +datasets: + - +related_concepts: + - guardrail + - red-teaming +related_jobs: + - 2026-07-08-tencent-cloud-ai-agent-test-engineer +related_experiments: + - +related_projects: + - +--- + +## One-line Takeaway + +Agent safety benchmark 还没有收敛，做评估时不能只依赖单一 benchmark。 + +## Problem + +不同安全 benchmark 的风险覆盖、任务环境、评估方法和结论可能不一致。 + +## Core Idea + +该论文整理 2023-2026 年的 behavioral agent-safety benchmarks，并用六轴 taxonomy 和 coverage matrix 分析方法选择如何影响结论。 + +## Evidence + +搜索摘要显示论文 catalog 了 40 个 behavioral agent-safety benchmarks 和 5 个相邻 evaluator/defense/dataset artifact。 + +## Useful For Us + +- 适合作为选择 Agent safety benchmark 的二级阅读材料。 +- 与 OpenAgentSafety、AgentRx、腾讯 Agent 测试岗位形成交叉验证。 + +## Follow-up Experiments + +- 根据 taxonomy 给知识库建立自己的 Agent evaluation source map。 diff --git a/papers/items/2026-king-dialogue-swebench.md b/papers/items/2026-king-dialogue-swebench.md new file mode 100644 index 0000000..e67df8c --- /dev/null +++ b/papers/items/2026-king-dialogue-swebench.md @@ -0,0 +1,63 @@ +# Paper: Dialogue-SWEBench + +--- +type: paper +title: "Dialogue-SWEBench: A Benchmark for Dialogue-Driven Coding Agents" +authors: Brendan King, Jeffrey Flanigan +year: 2026 +venue: +url: https://arxiv.org/html/2606.13995v1 +code_url: https://jlab-nlp.github.io/dialogue-swe-bench/ +source: arxiv +collected_at: 2026-07-08 +status: skimmed +relevance: high +topics: + - coding-agent + - human-in-the-loop + - agent-evaluation +methods: + - user-simulator + - dialogue-benchmark + - schema-guided-agent +benchmarks: + - Dialogue-SWEBench +models: + - +datasets: + - SWE-Bench Verified +related_concepts: + - human-in-the-loop + - coding-agent +related_jobs: + - 2026-07-08-baidu-aidu-agent-fullstack-engineer-beijing +related_experiments: + - +related_projects: + - +--- + +## One-line Takeaway + +Coding agent 的真实能力不能只看自动修复，还要看它能不能通过对话澄清需求。 + +## Problem + +现实中的 coding assistant 经常和用户交互，但主流 benchmark 多把它们评估为全自动系统。 + +## Core Idea + +Dialogue-SWEBench 使用 persona-grounded user simulator，把 SWE task 改造成多轮对话评估，并提出 schema-guided agent 改善对话能力。 + +## Evidence + +论文报告 schema-guided agent 相比强 baseline 提升 3-14%，并指出更强 coding model 不一定是更强 dialogue agent。 + +## Useful For Us + +- 对“Agent 何时应该提问”很有启发。 +- 可以作为 coding-agent / human-in-the-loop 评估维度。 + +## Follow-up Experiments + +- 给本知识库的 coding-agent 项目设计“澄清问题质量”评估。 diff --git a/papers/items/2026-memory-agent-survey.md b/papers/items/2026-memory-agent-survey.md new file mode 100644 index 0000000..27f37eb --- /dev/null +++ b/papers/items/2026-memory-agent-survey.md @@ -0,0 +1,65 @@ +# Paper: Memory for Autonomous LLM Agents + +--- +type: paper +title: "Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers" +authors: +year: 2026 +venue: +url: https://arxiv.org/html/2603.07670v1 +code_url: +source: arxiv +collected_at: 2026-07-08 +status: skimmed +relevance: high +topics: + - memory + - agent-architecture + - agent-evaluation +methods: + - write-manage-read-loop + - retrieval-augmented-memory + - reflective-memory + - hierarchical-context +benchmarks: + - +models: + - +datasets: + - +related_concepts: + - memory + - context-engineering +related_jobs: + - 2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing + - 2026-07-08-bytedance-seed-llm-agent-research-engineer +related_experiments: + - +related_projects: + - +--- + +## One-line Takeaway + +Memory 应该作为 Agent 系统的一等工程组件，而不是 prompt 里临时塞历史记录。 + +## Problem + +长任务、多 session 和个性化场景中，单一上下文窗口无法保存并正确复用历史经验。 + +## Core Idea + +该综述把 Agent memory 形式化为 write-manage-read loop，并从时间范围、表示基底和控制策略三个维度整理机制。 + +## Evidence + +论文覆盖 2022 到 2026 年初的 memory 机制和评估进展，并总结从静态 recall 到多 session agentic benchmark 的转变。 + +## Useful For Us + +- 用来设计知识库自己的 memory / context 管理专题。 +- 与百度和字节 JD 中的长期记忆、context compression、agent harness 强相关。 + +## Follow-up Experiments + +- 用同一组任务比较 raw history、summary memory、retrieval memory 和 procedural memory。 diff --git a/papers/items/2026-pham-swe-evo.md b/papers/items/2026-pham-swe-evo.md new file mode 100644 index 0000000..8ccafc1 --- /dev/null +++ b/papers/items/2026-pham-swe-evo.md @@ -0,0 +1,65 @@ +# Paper: SWE-EVO + +--- +type: paper +title: "SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios" +authors: Minh Vu Thai Pham, Tue Le, Dung Nguyen Manh, Huy Nhat Phan, Nghi D. Q. Bui +year: 2026 +venue: +url: https://arxiv.org/html/2512.18470v5 +code_url: https://github.com/SWE-EVO/SWE-EVO +source: arxiv +collected_at: 2026-07-08 +status: skimmed +relevance: high +topics: + - coding-agent + - agent-evaluation +methods: + - long-horizon-software-evolution + - benchmark +benchmarks: + - SWE-EVO +models: + - OpenAI + - DeepSeek + - Zhipu + - Qwen + - Moonshot +datasets: + - release-notes +related_concepts: + - coding-agent + - long-horizon-task +related_jobs: + - 2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou +related_experiments: + - +related_projects: + - +--- + +## One-line Takeaway + +Coding agent 评估正在从单 issue 修 bug 转向多文件、多 PR、版本演进式长任务。 + +## Problem + +SWE-Bench 类任务偏单点 issue，而真实工程经常要求根据 release notes 进行系统性版本演进。 + +## Core Idea + +SWE-EVO 从成熟 Python 项目的 release notes 构造 48 个长程任务，平均涉及 21 个文件，并提出 Fix Rate 捕捉部分进展。 + +## Evidence + +论文显示强模型在 SWE-EVO 上的解决率远低于 SWE-Bench Verified，失败更多来自语义理解和长期指令跟随，而不是简单工具操作。 + +## Useful For Us + +- 适合指导 coding-agent 项目的内部评测集设计。 +- 和 DeepSeek JD 中的 AI coding tool 使用、Agent infra、评测闭环相关。 + +## Follow-up Experiments + +- 选一个开源 Python 项目，用 release notes 构造小规模 SWE-EVO 风格任务。 diff --git a/papers/items/2026-wang-evomembench.md b/papers/items/2026-wang-evomembench.md new file mode 100644 index 0000000..c30c8ac --- /dev/null +++ b/papers/items/2026-wang-evomembench.md @@ -0,0 +1,62 @@ +# Paper: EvoMemBench + +--- +type: paper +title: "EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective" +authors: Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li +year: 2026 +venue: +url: https://arxiv.org/html/2605.18421 +code_url: +source: arxiv +collected_at: 2026-07-08 +status: skimmed +relevance: high +topics: + - memory + - agent-evaluation +methods: + - memory-benchmark + - self-evolving-agent +benchmarks: + - EvoMemBench +models: + - +datasets: + - +related_concepts: + - memory + - procedural-memory +related_jobs: + - 2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing + - 2026-07-08-bytedance-seed-llm-agent-research-engineer +related_experiments: + - +related_projects: + - +--- + +## One-line Takeaway + +Memory 的价值取决于任务类型和上下文约束；长上下文 baseline 仍然很强，不能假设外部记忆天然更好。 + +## Problem + +现有 Agent benchmark 主要评估 reasoning、planning、execution，对 memory 的更新、修正和复用能力覆盖不足。 + +## Core Idea + +EvoMemBench 按 memory scope 和 memory content 两条轴设计 benchmark，比较 15 种 memory 方法和长上下文 baseline。 + +## Evidence + +结果显示 memory 在当前上下文不足或任务较难时更有价值；知识密集任务更适合 retrieval memory，执行导向任务更依赖 procedural/long-term memory。 + +## Useful For Us + +- 设计 memory 实验时要加入长上下文 baseline。 +- 不同岗位里提到 memory 时，要进一步区分事实记忆、过程记忆和跨任务经验。 + +## Follow-up Experiments + +- 用 Agent 项目任务集做 memory-vs-long-context 对照实验。 diff --git a/papers/paper-insights.md b/papers/paper-insights.md index dd6fb13..d6e0ae9 100644 --- a/papers/paper-insights.md +++ b/papers/paper-insights.md @@ -6,35 +6,50 @@ status: seed ## Current Snapshot -- date: -- scope: -- papers reviewed: +- date: 2026-07-08 +- scope: Agent evaluation, memory, coding-agent benchmarks +- papers reviewed: 6 starter items +- caveat: sources are provisional; source approval workflow still needs user confirmation. ## Strong Signals -- +- Agent safety/eval 正在转向真实工具、多轮任务、trajectory、失败定位和系统级 guardrail。 +- Memory 不能只看事实召回；更重要的是是否改善流程执行和跨任务决策。 +- Coding-agent benchmark 正在从单 issue patch 转向长程软件演进和人机对话。 +- JD 高频词和论文趋势高度重合：memory、eval、planning/tool use、coding agent。 ## Weak or Unproven Claims -- +- “加 memory 就更强”不成立；EvoMemBench 显示长上下文 baseline 很强，memory 价值依赖任务。 +- 单一 safety benchmark 不足以说明 Agent 安全；不同 benchmark 的方法差异可能改变结论。 +- SWE-Bench 类单点任务不足以代表真实 coding agent 生产力。 ## Mature Directions -- +- Agent evaluation harness +- Memory benchmark and memory architecture comparison +- Coding agent benchmark beyond SWE-Bench +- Agent failure diagnosis and trajectory analysis ## Emerging Directions -- +- Dialogue-driven coding agents +- Stateful enterprise task benchmarks +- Agent safety benchmark taxonomy +- Skill/prompt optimization as trainable artifacts ## Experiment Candidates | Idea | Related Papers | Metric | Notes | | --- | --- | --- | --- | -| _TBD_ | _TBD_ | _TBD_ | _TBD_ | +| Mini Agent Safety Suite | OpenAgentSafety, Agent safety benchmark taxonomy | unsafe action rate, refusal precision, tool misuse | 选 10-20 个本地工具任务先跑 | +| Memory vs Long Context | Memory survey, EvoMemBench, STATE-Bench | success rate, token cost, latency, contradiction rate | 必须包含 long-context baseline | +| Long-horizon Coding Eval | SWE-EVO, Dialogue-SWEBench | task success, fix rate, clarification quality | 从真实 repo release notes 构造小任务 | +| Failure Step Diagnosis | AgentRx, OpenAgentSafety | critical failure step accuracy | 需要统一 trace schema | ## Links Back to Knowledge Base -- docs to update: -- experiments to create: -- projects affected: -- job skills connected: +- docs to update: evaluation, memory, coding-agent, safety +- experiments to create: memory comparison, mini safety suite, coding-agent eval +- projects affected: future front-end/indexing project, coding-agent experiments +- job skills connected: agent-evaluation, memory, coding-agent, tool-use, planning diff --git a/papers/reading-queue.md b/papers/reading-queue.md index 3995163..44a2964 100644 --- a/papers/reading-queue.md +++ b/papers/reading-queue.md @@ -4,7 +4,12 @@ status: seed | Priority | Paper | Topic | Reason | Status | Link | | --- | --- | --- | --- | --- | --- | -| _TBD_ | _TBD_ | _TBD_ | _TBD_ | queued | _TBD_ | +| P0 | OpenAgentSafety | agent safety / evaluation | 真实工具、多轮、多用户安全评估，直接对应 Agent eval 需求 | skimmed | https://arxiv.org/abs/2507.06134 | +| P0 | Memory for Autonomous LLM Agents | memory | 综述 memory 机制、评估和工程挑战，对 JD 高频 memory/context 信号有解释力 | skimmed | https://arxiv.org/html/2603.07670v1 | +| P0 | EvoMemBench | memory benchmark | 说明 memory 不总是优于长上下文，适合作为实验设计依据 | skimmed | https://arxiv.org/html/2605.18421 | +| P0 | SWE-EVO | coding agent benchmark | 长程软件演进任务比 SWE-Bench 更接近真实工程 | skimmed | https://arxiv.org/html/2512.18470v5 | +| P1 | Dialogue-SWEBench | human-in-the-loop coding agent | 评估 coding agent 澄清需求能力，补足全自动 benchmark 盲区 | skimmed | https://arxiv.org/html/2606.13995v1 | +| P1 | Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents | safety benchmark taxonomy | 用于选择和比较 Agent safety benchmark | queued | https://arxiv.org/html/2605.16282v1 | ## Priority Rules diff --git a/papers/source-registry.md b/papers/source-registry.md index 3a24395..3c776b7 100644 --- a/papers/source-registry.md +++ b/papers/source-registry.md @@ -8,13 +8,13 @@ | Source | Status | Notes | | --- | --- | --- | -| arXiv | candidate | 新论文发现和预印本 | +| arXiv | provisional-used | 新论文发现和预印本；first sweep 已使用，待用户正式确认 | | OpenReview | candidate | ICLR、NeurIPS 等投稿和评审线索 | | ACL Anthology | candidate | NLP、RAG、tool use、evaluation 相关论文 | | Conference proceedings | candidate | NeurIPS、ICLR、ICML、ACL、EMNLP、KDD、WWW 等 | | Papers with Code | candidate | benchmark、代码和任务关联 | -| Official project GitHub | candidate | 复现代码、模型卡和实验配置 | -| Company research blogs | candidate | 只收技术细节足够强的研究内容 | +| Official project GitHub | provisional-used | 复现代码、模型卡和实验配置；first sweep 已作为 code_url 使用 | +| Company research blogs | provisional-used | 只收技术细节足够强的研究内容；first sweep 已在 industry 中使用 | ## Source Types diff --git a/tools/collection/README.md b/tools/collection/README.md new file mode 100644 index 0000000..a7cc60f --- /dev/null +++ b/tools/collection/README.md @@ -0,0 +1,53 @@ +# Collection Tools + +这些脚本用于把 JD、论文和行业资料从 Markdown 笔记变成可检索、可检查、可视化的数据。 + +## Scripts + +### `new_item.py` + +生成一个带 frontmatter 的新条目草稿。 + +```bash +python3 tools/collection/new_item.py jobs "baidu aidu agent algorithm engineer beijing" \ + --title "Baidu AIDU Agent Algorithm Engineer" \ + --url "https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE" +``` + +支持的 collection: + +- `jobs` +- `papers` +- `industry` + +### `build_index.py` + +扫描 `jobs/items/`、`papers/items/`、`industry/items/` 的 frontmatter，生成结构化索引。 + +```bash +python3 tools/collection/build_index.py +``` + +输出： + +- `data/index.json` +- `data/summary.json` + +### `check_urls.py` + +检查条目中的 `url`、`source_url`、`code_url` 是否可访问。 + +```bash +python3 tools/collection/check_urls.py +``` + +可加 `--json` 输出机器可读结果。 + +## Method + +1. 搜索或导入资料。 +2. 用 `new_item.py` 生成草稿。 +3. 人工整理摘要、标签、判断和关联。 +4. 运行 `build_index.py` 更新结构化数据。 +5. 运行 `check_urls.py` 检查来源可达性。 +6. 更新对应 insight 文件。 diff --git a/tools/collection/__pycache__/build_index.cpython-314.pyc b/tools/collection/__pycache__/build_index.cpython-314.pyc new file mode 100644 index 0000000..796a2a1 Binary files /dev/null and b/tools/collection/__pycache__/build_index.cpython-314.pyc differ diff --git a/tools/collection/build_index.py b/tools/collection/build_index.py new file mode 100755 index 0000000..bfabc57 --- /dev/null +++ b/tools/collection/build_index.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +"""Build a JSON index from Markdown frontmatter.""" + +from __future__ import annotations + +import json +from collections import Counter, defaultdict +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[2] +COLLECTIONS = ("jobs", "papers", "industry") + + +def parse_scalar(value: str): + value = value.strip() + if value == "": + return None + return value + + +def parse_frontmatter(text: str) -> dict: + start = text.find("\n---\n") + if text.startswith("---\n"): + start = -1 + body_start = 4 + elif start != -1: + body_start = start + 5 + else: + return {} + end = text.find("\n---", body_start) + if end == -1: + return {} + lines = text[body_start:end].splitlines() + data: dict[str, object] = {} + current_key: str | None = None + for raw in lines: + line = raw.rstrip() + if not line: + continue + if line.startswith(" -"): + if current_key: + item = line[3:].strip() + if item: + data.setdefault(current_key, []) + assert isinstance(data[current_key], list) + data[current_key].append(item) + continue + if ":" in line: + key, value = line.split(":", 1) + key = key.strip() + value = value.strip() + current_key = key + data[key] = [] if value == "" else parse_scalar(value) + return data + + +def iter_items(): + for collection in COLLECTIONS: + item_dir = ROOT / collection / "items" + for path in sorted(item_dir.glob("*.md")): + if path.name == "README.md": + continue + text = path.read_text(encoding="utf-8") + meta = parse_frontmatter(text) + yield { + "collection": collection, + "path": str(path.relative_to(ROOT)), + "title": meta.get("title") or meta.get("role") or path.stem, + "type": meta.get("type") or collection.rstrip("s"), + "meta": meta, + } + + +def main() -> int: + items = list(iter_items()) + data_dir = ROOT / "data" + data_dir.mkdir(exist_ok=True) + (data_dir / "index.json").write_text( + json.dumps(items, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8", + ) + + by_collection = Counter(item["collection"] for item in items) + by_status = defaultdict(Counter) + topics = Counter() + skills = Counter() + companies = Counter() + for item in items: + meta = item["meta"] + by_status[item["collection"]][meta.get("status") or "unknown"] += 1 + for topic in meta.get("topics") or []: + topics[topic] += 1 + for skill in meta.get("skills") or []: + skills[skill] += 1 + company = meta.get("company") + if company: + companies[str(company)] += 1 + + summary = { + "total": len(items), + "by_collection": dict(by_collection), + "by_status": {key: dict(value) for key, value in by_status.items()}, + "top_topics": topics.most_common(20), + "top_skills": skills.most_common(20), + "top_companies": companies.most_common(20), + } + (data_dir / "summary.json").write_text( + json.dumps(summary, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8", + ) + print(json.dumps(summary, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/collection/check_urls.py b/tools/collection/check_urls.py new file mode 100755 index 0000000..90701d8 --- /dev/null +++ b/tools/collection/check_urls.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +"""Check source URLs stored in collection item frontmatter.""" + +from __future__ import annotations + +import argparse +import json +import urllib.error +import urllib.request +from pathlib import Path + +from build_index import parse_frontmatter + + +ROOT = Path(__file__).resolve().parents[2] +URL_KEYS = ("url", "source_url", "code_url") + + +def check_url(url: str, timeout: int) -> dict: + request = urllib.request.Request(url, method="HEAD", headers={"User-Agent": "agent-kb-url-check/1.0"}) + try: + with urllib.request.urlopen(request, timeout=timeout) as response: + return {"url": url, "ok": True, "status": response.status} + except urllib.error.HTTPError as exc: + if exc.code in (403, 405): + return check_url_get(url, timeout, exc.code) + return {"url": url, "ok": False, "status": exc.code, "error": str(exc)} + except Exception as exc: # noqa: BLE001 + return {"url": url, "ok": False, "status": None, "error": str(exc)} + + +def check_url_get(url: str, timeout: int, head_status: int) -> dict: + request = urllib.request.Request(url, method="GET", headers={"User-Agent": "agent-kb-url-check/1.0"}) + try: + with urllib.request.urlopen(request, timeout=timeout) as response: + return {"url": url, "ok": True, "status": response.status, "head_status": head_status} + except urllib.error.HTTPError as exc: + if exc.code == 403: + return { + "url": url, + "ok": True, + "status": exc.code, + "head_status": head_status, + "blocked": True, + "error": "blocked automated request; verify in browser", + } + return {"url": url, "ok": False, "status": exc.code, "head_status": head_status, "error": str(exc)} + except Exception as exc: # noqa: BLE001 + return {"url": url, "ok": False, "status": head_status, "error": str(exc)} + + +def iter_urls(): + for collection in ("jobs", "papers", "industry"): + for path in sorted((ROOT / collection / "items").glob("*.md")): + if path.name == "README.md": + continue + meta = parse_frontmatter(path.read_text(encoding="utf-8")) + for key in URL_KEYS: + value = meta.get(key) + if value: + yield path.relative_to(ROOT).as_posix(), key, str(value) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--json", action="store_true") + parser.add_argument("--timeout", type=int, default=12) + args = parser.parse_args() + + results = [] + for path, key, url in iter_urls(): + result = check_url(url, args.timeout) + result.update({"path": path, "key": key}) + results.append(result) + + if args.json: + print(json.dumps(results, ensure_ascii=False, indent=2)) + else: + for result in results: + marker = "BLOCKED" if result.get("blocked") else ("OK" if result["ok"] else "FAIL") + print(f"{marker} {result.get('status')} {result['path']} {result['key']} {result['url']}") + return 1 if any(not result["ok"] for result in results) else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/collection/new_item.py b/tools/collection/new_item.py new file mode 100755 index 0000000..824d0ed --- /dev/null +++ b/tools/collection/new_item.py @@ -0,0 +1,242 @@ +#!/usr/bin/env python3 +"""Create a new collection item draft.""" + +from __future__ import annotations + +import argparse +import re +from datetime import date +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[2] + + +TEMPLATES = { + "jobs": """# Job: {title} + +--- +type: job +company: +role: {title} +location: +source_url: {url} +source_name: +source_quality: unknown +collected_at: {today} +posted_at: +first_seen_at: {today} +last_checked_at: {today} +snapshot_type: new +job_code: +previous_snapshot: +status: unknown +level: +team: +business_area: +employment_type: +salary: +skills: + - +topics: + - +models: + - +related_papers: + - +related_experiments: + - +related_projects: + - +relevance: medium +--- + +## Source Snapshot + +- source: +- collected_at: {today} +- availability: + +## Change Snapshot + +- first_seen_at: {today} +- last_checked_at: {today} +- snapshot_type: new +- previous_snapshot: +- changed_fields: + +## Raw JD Summary + +- + +## Extracted Signals + +- hard requirements: +- preferred skills: +- hidden signals: +- business direction: + +## Knowledge Links + +- concepts: +- papers: +- experiments: +- projects: + +## Gaps for Us + +- +""", + "papers": """# Paper: {title} + +--- +type: paper +title: {title} +authors: +year: +venue: +url: {url} +code_url: +source: +collected_at: {today} +status: queued +relevance: medium +topics: + - +methods: + - +benchmarks: + - +models: + - +datasets: + - +related_concepts: + - +related_jobs: + - +related_experiments: + - +related_projects: + - +--- + +## One-line Takeaway + +- + +## Problem + +- + +## Core Idea + +- + +## Evidence + +- + +## Useful For Us + +- + +## Follow-up Experiments + +- +""", + "industry": """# Industry: {title} + +--- +type: industry +company: +team: +title: {title} +url: {url} +source_name: +source_type: other +source_quality: unknown +published_at: +collected_at: {today} +status: queued +topics: + - +implementation_signals: + - +product_area: + - +models: + - +tools: + - +benchmarks: + - +related_papers: + - +related_jobs: + - +related_experiments: + - +related_projects: + - +evidence_level: medium +relevance: medium +--- + +## One-line Takeaway + +- + +## What They Built or Claimed + +- + +## Technical Signals + +- architecture: +- tool use: +- memory: +- evaluation: +- safety: +- deployment: +- data: + +## Evidence Quality + +- +""", +} + + +def slugify(text: str) -> str: + text = text.lower().strip() + text = re.sub(r"[^a-z0-9\u4e00-\u9fff]+", "-", text) + text = re.sub(r"-+", "-", text).strip("-") + return text or "untitled" + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("collection", choices=sorted(TEMPLATES)) + parser.add_argument("slug") + parser.add_argument("--title", default=None) + parser.add_argument("--url", default="") + parser.add_argument("--date", default=date.today().isoformat()) + args = parser.parse_args() + + title = args.title or args.slug.replace("-", " ").title() + filename = f"{args.date}-{slugify(args.slug)}.md" + path = ROOT / args.collection / "items" / filename + if path.exists(): + raise SystemExit(f"refusing to overwrite existing file: {path}") + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + TEMPLATES[args.collection].format(title=title, url=args.url, today=args.date), + encoding="utf-8", + ) + print(path.relative_to(ROOT)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main())