How to create “humble” AI
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
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TPCL: Task Progressive Curriculum Learning for Robust Visual Question Answering
arXiv:2411.17292v2 Announce Type: replace-cross Abstract: Visual Question Answering (VQA) systems are notoriously brittle under distribution shifts and data scarcity. While previous solutions-such as ensemble methods and data augmentation-can improve performance in isolation, they fail to generalise well across in-distribution (IID),…
Explore Briefly, Then Decide: Mitigating LLM Overthinking via Cumulative Entropy Regulation
arXiv:2510.02249v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning abilities on complex problems using long Chain-of-Thought (CoT) reasoning. However, they often suffer from overthinking, meaning generating unnecessarily lengthy reasoning steps for simpler problems. This issue may…
The Price of Progress: Price Performance and the Future of AI
arXiv:2511.23455v2 Announce Type: replace Abstract: Language models have seen enormous progress on advanced benchmarks in recent years, but much of this progress has only been possible by using more costly models. Benchmarks may therefore present a warped picture of progress…
A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis
arXiv:2603.11428v2 Announce Type: replace Abstract: Statistical dependence measures like mutual information is ideal for analyzing autoencoders, but it can be ill-posed for deterministic, static, noise-free networks. We adopt the variational (Gaussian) formulation that makes dependence among inputs, latents, and reconstructions…
Relative Error Embeddings for the Gaussian Kernel Distance
arXiv:1602.05350v3 Announce Type: replace Abstract: A reproducing kernel can define an embedding of a data point into an infinite dimensional reproducing kernel Hilbert space (RKHS). The norm in this space describes a distance, which we call the kernel distance. The…
Graph Structure Learning with Privacy Guarantees for Open Graph Data
arXiv:2507.19116v2 Announce Type: replace Abstract: Ensuring privacy in large-scale open datasets is increasingly challenging under regulations such as the General Data Protection Regulation (GDPR). While differential privacy (DP) provides strong theoretical guarantees, it primarily focuses on noise injection during model…
Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX
arXiv:2603.20335v1 Announce Type: new Abstract: The Interest Public Group ARRONAX’s C70XP cyclotron, used for radioisotope production for medical and research applications, relies on complex and costly systems that are prone to failures, leading to operational disruptions. In this context, this…
SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
arXiv:2603.21674v1 Announce Type: cross Abstract: Energy efficiency remains a critical challenge in deploying physics-informed operator learning models for computational mechanics and scientific computing, particularly in power-constrained settings such as edge and embedded devices, where repeated operator evaluations in dense networks…
Probing the Latent World: Emergent Discrete Symbols and Physical Structure in Latent Representations
arXiv:2603.20327v1 Announce Type: new Abstract: Video world models trained with Joint Embedding Predictive Architectures (JEPA) acquire rich spatiotemporal representations by predicting masked regions in latent space rather than reconstructing pixels. This removes the visual verification pathway of generative models, creating…