Model Detail
Lens-Base
—Lens-Base is an image generation model released by Microsoft. The model is registered under the text-to-image pipeline tag on Hugging Face, distributed under the permissive mit license.
The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Lens-Base is best fit for text-to-image generation and creative iteration. It is a less obvious choice for production photography pipelines that need exact reproducibility. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Foreword: Pre-Generation Prediction and Characterisation of LLM Test Failures using the Jacobian Lens
arXiv:2610.28187v1 Announce Type: cross Abstract: Testing LLM-based software is expensive: each input needs several sampled responses, each judged by a costly oracle. Further, every prompt or model change calls for re-testing. Pre-generation adequacy measures cut this cost by ranking inputs by failu
Generalization in Neural Networks Through the Lens of Magnitude Potential
arXiv:2610.01633v2 Announce Type: replace Abstract: Explaining generalization and training dynamics in neural networks remains a challenge, and various approaches have been developed to study different aspects of these phenomena. In this paper, we introduce the idea of {\em magnitude potential} -- a
Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction
arXiv:2609.27355v2 Announce Type: replace-cross Abstract: Unlearning ensures LLM compliance by removing the influence of private or copyrighted training data. However, since LLM models typically undergo post-training compression, like quantization, in practical deployment, it has been observed that
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion
arXiv:2610.15900v1 Announce Type: cross Abstract: We present Gen Through the Lens, a camera-controllable image-to-video generation framework that synthesizes videos from a given reference image while enabling realistic and physically consistent camera operations. Unlike previous models that treat ca
Alignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria
arXiv:2511.17937v2 Announce Type: replace Abstract: Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in training, while preserving different behavior outside training. The phenomenon was first documented
From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship
arXiv:2506.23101v2 Announce Type: replace-cross Abstract: Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially sensitive domains. Existing benchmarks primarily assess bias in isolated contexts and are less suit
