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Position Spaces and Graphs

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arXiv:2606. 25719v1 Announce Type: new Abstract: In this paper, we introduce position graphs, a graph-based reasoning framework based on the formalization of position spaces.

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Authors: Rita-Nathalia Assaf, Tom Davot, Fr\'ed\'eric Lardeux, Fr\'ed\'eric Saubion
Released: 2026-06-25 (14d ago)
Topic: Related to current project
Relation to my work: Read next because Position Spaces and Graphs overlaps with 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)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)". Matching terms: class, under, alignment, token, rate, extraction, chain, trained. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.25719

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