RPM 2608.13940 claim + citation neighborhood

Foster RPM plus PDF-backed cites (Lu, Zheng, DiscoGen) and claims about those papers. Ranking scores are not a column — #177 novelty-vs-graph, not Zheng/RPM as ranker.

Custom SQL query returning 14 rows (hide)

select 'paper' as kind, p.lom_id as id, p.title as label, null as extra from paper p where p.lom_id = 'arxiv:2608.13940' union all select 'cites', e.to_lom_id, t.title, e.locator from citation_edge e join paper t on t.lom_id = e.to_lom_id where e.from_lom_id = 'arxiv:2608.13940' and e.kind = 'cites' union all select 'cited_by', e.from_lom_id, f.title, e.locator from citation_edge e join paper f on f.lom_id = e.from_lom_id where e.to_lom_id = 'arxiv:2608.13940' union all select 'claim', c.id, substr(c.statement, 1, 200), c.status from claim c where c.about_lom_id = 'arxiv:2608.13940' or c.about_lom_id in (select to_lom_id from citation_edge where from_lom_id = 'arxiv:2608.13940' union select from_lom_id from citation_edge where to_lom_id = 'arxiv:2608.13940')

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kind	id	label	extra
paper	arxiv:2608.13940	AI Research Preference Models
cites	arxiv:1711.09846	Population based training of neural networks	arxiv:2608.13940 reference list (arXiv HTML bibliography)
cites	arxiv:2301.04104	Mastering diverse domains through world models	arxiv:2608.13940 reference list (arXiv HTML bibliography)
cites	arxiv:2408.06292	The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery	Foster et al. 2026 Related Work / References: Lu et al. (2024) The AI Scientist arXiv:2408.06292
cites	arxiv:2502.13138	Aide: Ai-driven exploration in the space of code	arxiv:2608.13940 reference list (arXiv HTML bibliography)
cites	arxiv:2510.02387	Cwm: An open-weights llm for research on code generation with world models	arxiv:2608.13940 reference list (arXiv HTML bibliography)
cites	arxiv:2601.03267	Openai gpt-5 system card	arxiv:2608.13940 reference list (arXiv HTML bibliography)
cites	arxiv:2601.05930	Can We Predict Before Executing Machine Learning Agents?	Foster et al. 2026 References: Zheng et al. (2026) Can we predict before executing machine learning agents? arXiv:2601.05930
cites	arxiv:2602.02660	MARS: modular agent with reflective search for automated ai research	arxiv:2608.13940 reference list (arXiv HTML bibliography)
cites	arxiv:2602.06855	AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents	arxiv:2608.13940 reference list (arXiv HTML bibliography)
cites	arxiv:2603.17863	DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning	Foster et al. 2026 References: Goldie et al. (2026) DiscoGen arXiv:2603.17863
claim	ts-claim-c3-ai-scientist-s2-novelty	The AI Scientist’s idea-generation filter discards ideas that are too similar to existing literature by querying the Semantic Scholar API (plus web access); novelty is therefore a retrieved-paper simi	proposed
claim	ts-claim-z1-listwise-collapse-global-discrimination	The drop of Accuracy@1 from 61.3% (N=2) to 31.1% (N=5) in Table 3 indicates that the LLM judge lacks global discrimination capability beyond binary interactions.	contradicted
claim	ts-claim-s1-novelty-not-significance	A claim can be graph-novel vs TeamScience JSONL and still be insignificant if it would not change a #177 rule, a cheapest test, or the next ingest walk.	proposed