Using expert text descriptions to build LLM-derived priors guides domain selection under scarce target data, yielding near-oracle cold-start error and asymptotic consistency.
Biometric identity provisioning allocates virtual face identities as non-colliding gaps within the real identity manifold, scaling to ten million embeddings and one million photorealistic images via gap-aware generation.
SapiensID 2.0 aligns human recognition with perception via soft-biometrics, noise disentanglement, and kinematic attention to achieve state-of-the-art re-identification and gait recognition.
Subspace gradient orthogonalization unifies low-rank projection with spectral optimization into ZO-Muon, cutting zeroth-order queries by 75% versus MeZO while boosting accuracy on LLM and vision fine-tuning.
PixelDense aligns pixel diffusion with frozen dense-prediction teachers via separate semantic and geometric projection streams and orthogonality penalties, improving GenEval to 0.8093 and training speed by 1.23x.
3DSPMR leverages 3D spatial memory with field-of-view geometric priors to reuse exploration knowledge across sequential embodied tasks, significantly improving reasoning and navigation performance on the SEER-Bench benchmark.
Confidence-based verifier-free test-time scaling fails on complex tasks because high initial confidence signals no exploration; consilience selects rollouts by requiring low early but high final confidence, improving reasoning and coding.
SPANUQ is a lightweight probe that estimates span-level LLM generation uncertainty via hidden-state distillation, outperforming sampling methods with 10, 20x speedups and 0.910 F1 span detection.
LCDD constructs sparse, causally necessary subnetworks for SFT behaviors, and SFT-Eraser reverses them via activation-matched soft prompts without weight changes.
Proposed pseudo self-referenced early stopping for Deep Image Prior uses constructed image pairs to detect overfitting, outperforming existing methods across inverse imaging problems without requiring noise level estimates.
L-FAME offers a longitudinal EEG dataset and benchmark of 74 participants across three meditation practices over six weeks, with baseline classification and cross-session adaptation results.