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This paper presents a theoretical framework for self-poisoning in adaptive out-of-distribution detection, proving a sharp threshold for collapse and a certified label-free calibration method that severs the feedback loop. It also provides impossibility results for distinguishing drift from contamination without labels.
This paper introduces a novel method using sparse autoencoders (SAEs) to learn interpretable features from intermediate network activations for out-of-distribution (OOD) detection, achieving state-of-the-art performance and providing insights into how distribution shifts affect learned representations.
MM++ is a fully unsupervised, post-hoc framework for out-of-distribution detection that fuses discriminative intermediate layers via top-K gated feature fusion and uses a regularized tied covariance matrix for scale-invariant distance estimation.
This paper extends empirical findings that the Mahalanobis cosine similarity (MCS) between linear probes linearly predicts out-of-distribution AUROC, and proves this relationship theoretically under Gaussian assumptions.
Discusses methods for out-of-distribution detection on closed LLM APIs without latent access, highlighting techniques like SelfCheckGPT, token-level entropy, proxy embeddings, and verifier models, and notes the collapse of OOD and hallucination detection.
TTL introduces a test-time textual learning framework for OOD detection using pretrained vision-language models like CLIP, which dynamically learns OOD semantics from unlabeled test streams without external OOD labels. The method uses pseudo-labeled samples and an OOD knowledge purification strategy to improve detection robustness across diverse and evolving OOD distributions.