# Context Research > --- name: context-research description: An autonomous research pipeline that identifies, analyzes, and synthesizes SOTA AI research via Hugging Face and ArXiv. Designed for engineering-grade deep dives into AI topics (e.g., "KV-cache optimization," "model merging"). disable-model-invocation: false --- # PART 1: Research Pipeline Logic ## Phase 1: Intelligent Discovery & Ranking 1. **Parallel Search**: Execute `GET /api/papers/search?q={query}&limit=20`. 2. **Ranking Heuristic**: Rank results using a weighted score: - **Relevance (50%)**: Keyword density in title/abstract. - **Recency (25%)**: Prioritize papers from the last 12-18 months. - **Impact (25%)**: Upvotes and author/organization affiliation. 3. **Sparse Result Fallback**: If < 5 papers are found on HF, the agent MUST use its web search tool to find ArXiv IDs or project pages, then use `GET /api/papers/{ID}` to ingest them. ## Phase 2: Parallel Deep Extraction To maximize speed, execute metadata and content fetches for the top 3-5 papers **in parallel**. 1. **Depth Check**: Fetch the `.md` content via `GET /papers/{ID}.md`. - **If Shallow (< 3000 chars)**: The markdown is likely just an abstract. Fall back to `https://arxiv.org/pdf/{ID}` to parse the full text for tables, benchmarks, and "Limitations" sections. 2. **Metric Mining**: Specifically extract quantitative data: - Performance deltas (e.g., "26–54% reduction"). - Hardware requirements and compute costs. - Benchmarks used (e.g., Needle-in-a-Haystack, AppWorld). 3. **Artifact Audit**: Check `githubRepo` and `linkedModels`. If a repo exists, check the README for production implementation details. ## Phase 3: Thematic Synthesis Avoid individual paper summaries. Organize the report by **Technical Taxonomy**: 1. **Architectural Shifts**: How does this research change how we build? 2. **Universal Bottlenecks/Patterns**: Identify cross-paper themes (e.g., "Information Loss," "Latency-Accuracy Trade-offs," or "Context Saturation"). 3. **Production Trade-offs**: Analyze the cost of implementation (e.g., KV-cache invalidation, API overhead). ## Phase 4: Key Takeaways After synthesis, write a **Key Takeaways** section placed immediately after the report header (before the first numbered section). This should be a concise bulleted list (5-8 bullets) of the most actionable insights distilled from the full report. Each bullet should be self-contained and useful without reading the rest of the document. ## Phase 5: Document Completeness (MANDATORY) The saved report file must contain everything the user will learn from this research — **including execution recaps, cross-paper findings, picked-paper rationales, and any process meta-commentary**. Do not rationalize any category of content as "meta" and therefore chat-only. If it's worth telling the user, it's worth saving. 1. **Two canonical summary locations — no others**: - **Key Takeaways** (Phase 4): 5–8 actionable insight bullets, immediately after the header. Keep it sharp — do not expand it to absorb recap content. - **Appendix: Execution Notes**: placed at the end of the document. Holds the research-process recap: which papers were picked and why, cross-paper findings, methodology notes, and any synthesis that doesn't fit the main taxonomy. Structure it as short prose paragraphs under three fixed sub-headings in this order: **Paper Selection** (what was picked and why), **Cross-Paper Findings** (themes spanning multiple papers), **Methodology Notes** (how the research was conducted, fallbacks used, gaps encountered). Do not invent other summary sections. 2. **Persist before replying**: Both sections must exist in the file on disk before you compose the chat reply. 3. **Draft the reply from the file**: Compose the chat reply by quoting or paraphrasing directly from the saved document — not by writing it independently. The file drives the reply, never the reverse. --- # PART 2: Technical Specifications ## Filename & Slugification All reports MUST be saved using the following convention: - **Format**: `research_{topic-lowercase-kebab-case}.md` - **Example**: `research_kv_cache_optimization.md` ## Core API Knowledge (Retained) - **Search**: `GET /api/papers/search?q={query}` - **Metadata**: `GET /api/papers/{ID}` (Authors, Upvotes, Links). - **Content**: `GET /papers/{ID}.md` (Markdown version). - **Model Links**: `GET /api/models?filter=arxiv:{ID}`. ## Error Handling & Fallbacks - **404/Empty MD**: Always check `https://arxiv.org/abs/{ID}` as the source of truth if HF indexing is incomplete. - **Multi-ID Support**: Handle both HF internal IDs and arXiv IDs (e.g., `2602.08025`). ## When to Use - Deep-dive technical requests requiring more than a surface-level summary. - Requests for "State of the Art" (SOTA) analysis. - Identifying implementation risks for new AI architectures.