scout-rsi-papers

name	scout-rsi-papers
description	Sweep arXiv, OpenReview, and official lab blogs for new resources on recursive self-improvement in AI, dedupe against README.md, and produce candidate entries in the exact CONTRIBUTING.md format with section placement rationale.

Scout RSI Papers

You are scouting new resources for the awesome-recursive-self-improvement list. The output of this skill is a set of candidate entries for maintainer review — never edit README.md directly.

Inputs

• Optional: a theme or README section to focus on (e.g. "test-time adaptation", "safety"). Default: a full-field sweep across all themes below.
• Optional: a time window (e.g. "last 3 months"). Default: since the most recent entries already in the list.

Sources (sweep in this order)

1. arXiv — listings and search across cs.AI, cs.LG, cs.CL (use https://arxiv.org/list/cs.AI/recent or arXiv search/API queries).
2. OpenReview — accepted papers for the current ICLR/NeurIPS/ICML cycles, including the ICLR Workshop on Recursive Self-Improvement.
3. Official lab blogs — Anthropic, Google DeepMind, OpenAI, Meta AI (FAIR), Sakana AI. Blog posts qualify only when they are the primary source for a system (e.g. AlphaEvolve).
4. ACL Anthology — for published versions of NLP papers.

Query list by theme

Run searches combining the theme terms with year qualifiers for recency:

Theme (target section)	Search strings
Self-refinement and reflection	"self-refine LLM", "self-correction language model", "intrinsic self-verification"
Test-time adaptation	"test-time training LLM", "test-time reinforcement learning", "inference-time self-improvement"
Experience learning and memory	"agent experience replay", "procedural memory LLM agent", "lifelong agent memory"
Self-generated data and self-rewarding	"self-rewarding language model", "self-play LLM training", "zero data reasoning", "synthetic data self-improvement"
Recursive decomposition and curricula	"automated curriculum LLM", "recursive problem decomposition", "self-generated curriculum"
Code evolution and algorithm discovery	"self-improving coding agent", "evolutionary code generation LLM", "agent modifies own code"
Self-evolving agents	"self-evolving agent", "self-referential agent", "agent skill library learning"
Multimodal and embodied	"embodied agent self-improvement", "multimodal self-rewarding"
Evaluation and benchmarks	"benchmark self-improving agents", "ML engineering agent benchmark", "AI research agent evaluation"
Safety, governance, limits	"reward hacking evaluation", "sabotage evaluation LLM", "model collapse recursive training", "self-modification safety"

Process

1. Sweep each source with the relevant queries (or only the focused theme if one was given).
2. Screen each hit against the inclusion criteria in CONTRIBUTING.md:
   • Published or substantially updated 2022 onwards.
   • Directly about a self-improvement loop (feedback, self-evaluation, self-generated data, code modification, memory, test-time adaptation, or governed updates) — not a generic agent framework.
   • Primary source available (arXiv, ACL Anthology, OpenReview, official project page, official lab blog, official GitHub repository).
3. Dedupe against the current list. For each candidate, check README.md for:
   • the exact URL,
   • the arXiv ID in any form (abs/, pdf/, versioned),
   • the paper or system title (search a distinctive word from it).
4. Canonicalise the URL: prefer arXiv abs/ pages over PDFs, prefer published proceedings over preprints when both exist, strip tracking parameters, never use shorteners.
5. Draft a one-sentence description that is neutral, factual, specific, and matches the linked source. Use UK spelling. Start with a verb where natural (matching list style: "Introduces…", "Surveys…", "Evaluates…").
6. Place each candidate in exactly one README section, with a one-line rationale.

Output format

## Candidate entries (require maintainer review)

### <Section name>

- [Name](URL) (Venue Year) - One-sentence description.
  - Rationale: <why it is in scope and why this section>
  - Source checked: <arXiv / OpenReview / lab blog>, <year>

End with a short list of near-misses you excluded and the exclusion reason (helps the maintainer trust the sweep).

Guardrails

• Never edit README.md — output candidates only.
• Never include secondary coverage (news articles, newsletters, social posts, summary blogs).
• Never inflate descriptions beyond what the source claims.
• If unsure whether a resource is in scope, list it under near-misses with your doubt stated, rather than as a candidate.