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Hypergraph as Language

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arXiv:2605. 21858v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong potential in modeling relational structures.

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Authors: Mengqi Lei, Guohuan Xie, Shihui Ying, Shaoyi Du, Jun-Hai Yong, Siqi Li, Yue Gao
Released: 2026-05-22 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Hypergraph as Language overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, text, rect, under, alignment, eval, token, project. Source: arxiv cs.CL (NLP).
arXiv: 2605.21858

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Hypergraph as Language. Replies appear in the conversation below.

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