--- name: deepxiv-baseline-table description: Build a markdown baseline table for a research topic using deepxiv search, brief, head, and experiment-section reads, extracting paper title, URL, open-source status, datasets, benchmark scores, and other comparison-ready details. --- # DeepXiv Baseline Table Use this skill when the user wants to map a topic into a comparison table, baseline survey, benchmark roundup, or "what papers evaluated on which datasets with what scores and whether code is open". Typical requests: - "Find recent baseline papers on agentic memory" - "What papers in the last month evaluated on dataset X?" - "Make me a markdown table of methods, datasets, and scores" ## Goal Turn a topic search into a structured markdown table: 1. Search recent papers with `deepxiv search` 2. Brief all candidates with `deepxiv paper --brief` 3. Keep the relevant papers, prioritizing papers with GitHub/code 4. Inspect promising papers with `deepxiv paper --head` 5. Read experiment-related sections with `deepxiv paper --section ...` 6. Extract datasets, evaluation setup, and reported scores 7. Write a markdown table summarizing the baselines ## Default Workflow ### 1. Search by topic and date range Use a broad search first. ```bash deepxiv search "agentic memory" --date-from 2026-03-01 --limit 100 --format json ``` Default heuristics: - Use the user’s exact topic phrase first - Keep `--limit` high enough to avoid missing relevant papers - If results are noisy, refine the query with close variants Examples: ```bash deepxiv search "agentic memory" --date-from 2026-03-01 --limit 100 --format json deepxiv search "memory agents long-horizon" --date-from 2026-03-01 --limit 100 --format json deepxiv search "agent memory benchmark" --date-from 2026-03-01 --limit 100 --format json ``` ### 2. Brief all candidates For each arXiv ID, fetch: ```bash deepxiv paper --brief ``` Capture: - title - arXiv ID - publish date - TLDR - keywords - GitHub URL - PDF/source URL This is the screening step. Do not read full sections yet. ### 3. Filter and prioritize Keep papers that are actually about the topic, not just adjacent terms. Prioritize: - papers directly centered on the topic - empirical papers over purely conceptual ones - papers with GitHub/code - benchmark or comparison papers - papers with clear experiment sections De-prioritize: - purely opinion or survey papers unless the user asked for surveys - papers with no clear evaluation evidence - papers only loosely related to the topic If the list is still large, keep a primary set and a secondary set: - Primary: strongest and most relevant baselines - Secondary: adjacent or weaker evidence ### 4. Inspect paper structure For retained papers: ```bash deepxiv paper --head ``` Use `--head` to find experiment-bearing sections such as: - Experiments - Evaluation - Results - Benchmark - Main Results - Analysis Also capture: - abstract - total token count - section names ### 5. Read only experiment-relevant sections Once the right sections are known, read only those: ```bash deepxiv paper --section Experiments deepxiv paper --section Evaluation deepxiv paper --section Results ``` Section selection guidance: - Start with `Experiments` or `Evaluation` - Read `Results` if the metrics are not clear - Read `Introduction` only if the task setup is still ambiguous - Read `Appendix` only if benchmark details are missing from the main paper Avoid reading the entire paper unless the user explicitly asks for it. ## Extraction Targets For each retained paper, try to extract: - Title - arXiv ID - Paper URL - GitHub/code URL - Open-source status: `Yes`, `No`, or `Unknown` - Main task - Evaluation datasets / benchmarks - Key metrics - Best reported scores - Notes on experimental setting If exact scores are not clearly available from the inspected sections: - leave the score field as `Not clearly stated` - do not invent or infer a number If datasets are only partially visible: - include the datasets you can verify - mention that the list may be incomplete ## Markdown Output Write a markdown file in the workspace unless the user specifies another path. Recommended filename: ```text -baseline-table-YYYY-MM-DD.md ``` Example: ```text agentic-memory-baseline-table-2026-04-01.md ``` Recommended output structure: ```md # Agentic Memory Baseline Table Topic: agentic memory Date range: 2026-03-01 to 2026-04-01 Search source: deepxiv ## Summary - Number of search results - Number of relevant papers retained - Number with public code - Main recurring datasets or benchmark families ## Baseline Table | Title | arXiv | URL | Open Source | Code URL | Datasets / Benchmarks | Metrics / Scores | Notes | | --- | --- | --- | --- | --- | --- | --- | --- | | ... | ... | ... | ... | ... | ... | ... | ... | ## Inclusion Notes - Which papers were excluded and why - Which rows are based only on `--brief` - Which rows were verified through `--head` and experiment/result sections ## Observations - Common evaluation datasets - Which papers appear strongest - Where the benchmark story is still fragmented ``` ## Writing Rules - Prefer verified facts over broad summaries - Separate "paper is relevant" from "paper has strong benchmark evidence" - Be explicit when a row is missing score details - Mark open-source status conservatively - Keep the table compact but useful - Add short notes when comparisons are not apples-to-apples ## Decision Rules - Always start with search and brief - Prefer papers with GitHub when deciding which ones to inspect first - Use `--head` before `--section` - Read only the sections needed to recover datasets and scores - If the topic is broad, tell the user when the table mixes multiple subtask types ## Minimal Example ```bash deepxiv search "agentic memory" --date-from 2026-03-01 --limit 100 --format json deepxiv paper 2603.21489 --brief deepxiv paper 2603.21489 --head deepxiv paper 2603.21489 --section Experiments ``` Then write a markdown table with: - title - paper URL - open-source status - code URL - datasets / benchmarks - metrics / scores - short notes on what was actually verified