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Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation

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arXiv:2606. 18509v1 Announce Type: cross Abstract: Reliable generalization in conditional latent variable models requires understanding both identifiability and extrapolation: how observed variation across attributes determines latent structure, and how that structure determines distributions at unseen attributes.

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Authors: Soheun Yi, Yizhou Lu, Chandler Squires, Pradeep Ravikumar
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation 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 "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, under, line, rate, control, trained, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.18509

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