arXiv AI
Jun 2
A Fiber Criterion for Representation Identifiability in Supervised Learning
arXiv:2606. 01092v1 Announce Type: cross Abstract: Supervised learning evaluates predictors through their input-output behavior.
Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation
arXiv:2606. 18509v1 Announce Type: new 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.
Related stories
arXiv AI
Jul 24
Constrained latent state modeling: A unifying perspective on representation learning under competing constraints
arXiv:2605. 15995v2 Announce Type: replace-cross Abstract: Learning latent representations from complex data is central to modern machine learning, spanning temporal, multimodal, and partially observed systems.
arXiv Machine Learning
Jul 28
Beyond ICA: Identifiability by Symmetry Breaking
arXiv:2607. 23182v1 Announce Type: cross Abstract: We prove the identifiability of deep generative models (DGMs) with piecewise-affine (PWA) decoders and Gaussian mixture model (GMM) priors, in a purely unsupervised setting.
arXiv AI
6d ago
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
arXiv:2608. 13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution.
arXiv Machine Learning
Jul 30
Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting
arXiv:2607. 26458v1 Announce Type: cross Abstract: Domain generalization (DG) aims to learn from multiple source domains and generalize to unseen target domains.
arXiv AI
Jun 17
Decidable By Construction: Design-Time Verification for Trustworthy AI
arXiv:2603. 25414v4 Announce Type: replace-cross Abstract: A prevailing assumption in machine learning is that model correctness must be enforced after the fact.
arXiv Machine Learning
Aug 12
Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images
arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.
arXiv Machine Learning
Jun 19
Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.
arXiv Machine Learning
Jun 2
The Attribution Contract: Feature Attribution for Generative Language Models
arXiv:2605. 23080v2 Announce Type: replace Abstract: Feature attribution methods promise to identify which input features matter for a model output.
arXiv AI
Jul 29
Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models
arXiv:2607. 25907v1 Announce Type: cross Abstract: Activation steering controls model behavior by editing internal activations at inference time.
arXiv Machine Learning
Jun 11
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
arXiv:2603. 15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes.
arXiv Machine Learning
Jul 13
Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing
arXiv:2607. 08839v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are typically designed under the assumption that all modalities available during training will also be accessible at inference.