Artificial Intelligence · 27.08.2026, 05:32 UTC
Common-Center Geometry and Certified Radial Reconstruction for Energy-Form Full Conformal Regions
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.LG ↗ |
| Veröffentlicht | 27.08.2026 UTC |
Sicherheitsmeldung mit Schweregrad noch nicht bewertet. Technische Details im Tab „Originaltext“; empfohlene Schritte in der Checkliste.
arXiv:2608.24964v1 Announce Type: cross Abstract: This note studies the geometry of full conformal prediction (FullCP) regions generated by an empirical energy-form pairwise score. Candidate-score convexity alone does not guarantee connected FullCP regions, even when the candidate score is an empirical average of a loss convex in its first argument. Direct expansion of the leave-one-out scores shows that each training-point comparison for the energy-form score is exactly a pairwise-dissimilarity sublevel condition. Under symmetry, a constant diagonal, a diagonal lower bound, and attainment of the associated Fr\'echet-type objective, every comparison region contains a common minimizer; when the comparison regions are convex, the nontrivial exact conformal region is therefore star-shaped about that same point. For power distances $\rho_\beta(x,y)=\|x-y\|^\beta$, this deterministic geometry holds for $\beta\ge1$, while the conventional energy score is strictly proper for $0<\beta<2$. In the univariate $\beta=1$ specialization, every nontrivial empirical-CRPS FullCP region is a nonempty closed interval, possibly $\mathbb R$ in the $m=1$ degeneracy. On the unconditional reconstruction range $1<\beta<2$ and $m\ge2$, explicit data-checkable derivative bounds yield Lipschitz control of the comparison-set radial exits and hence of the exact conformal radial function. These score-specific bounds permit existing directional root-search ideas and classical Lipschitz-extension machinery to yield certified inner and outer radial envelopes with width at most $\delta+2Lh_{\mathcal U}$ …
Maßnahmen
⬇ Als MarkdownVerwandte Beiträge
- info Groundhog Bit-Flip Attack: Seeding Infinite Generation Loops in Mixture-of-Experts LLMs through Bit Flips
- info The Changing Geometry of Grammar: Dimensionality and Neighborhood Reorganization across Transformer Layers
- info Belief Cascades Drive Persuasion in LLM Agent Networks
- info Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting