arXiv:2603.10020v1 Announce Type: new Abstract: Dark patterns in online commerce, especially deceptive user interface designs for apps and websites, undermine consumer autonomy and distort online markets. Although sometimes deception is intentional, the complex app development process can also unintentionally produce manipulative user interfaces. This paper discusses common design pitfalls and proposes strategies for app makers to avoid infringing user autonomy or incurring legal liability under emerging principles of consumer protection law. By focusing on choice architecture and transparent […]

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This post is cross-listed on the CMU ML blog. The International Conference on Machine Learning (ICML) is a flagship machine learning conference that in 2020 received 4,990 submissions and managed a pool of 3,931 reviewers and area chairs. Given that the stakes in the review process are high — the careers of researchers are often significantly affected by the publications in top venues — we decided to scrutinize several components of the peer-review process in a series of experiments. […]

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arXiv:2603.08886v1 Announce Type: new Abstract: We study a class of finite-state channels, known as POST channels, in which the previous channel output serves as the current state. A POST channel is deemed approximately memoryless when the state-dependent transition matrices are sufficiently close to one another. For this family of channels, under a surjectivity condition on the associated memoryless reference channel, we show that the feedback capacity coincides with the non-feedback capacity. Consequently, for almost all approximately memoryless POST […]

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In this paper we introduce the CDF manifold algorithm that operates on data sets where a single target dimension is strictly increasing given a minimum of two or more number of input dimension which is very common in telco data. The manifold can then be used to compute the closest upper and lower limit to a given new point as well as its CDF. Training takes O(n.ln[n]) steps in the best case and O(n3/2) in the worst case. […]

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Not everything is about saving time. It’s about solving a real problem and things that matter. Everywhere you look, someone’s building an AI tool to save you time. There’s an app to plan your meals, one to write your emails, another to manage your money automatically. It’s all supposed to make life effortless. But lately, I’ve started to wonder if effort itself is the point. A few years ago, I set up a budgeting app that tracked everything. […]

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“You can dream anything” — a lie It is getting scarier by the day to imagine the world that is predominantly dependent on AI. Everyday there is a newer and dystopian version of the future that is being portrayed by the media. How true is it ? I am completely convinced that AI is better at writing code than humans now. I was reflecting on last week’s work and I didn’t reach out to the code in IDE […]

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arXiv:2603.06693v1 Announce Type: new Abstract: Self-supervised learning (SSL) typically learns representations invariant to semantic-preserving augmentations. While effective for recognition, enforcing strong invariance can suppress transformation-dependent structure that is useful for robustness to geometric perturbations and spatially sensitive transfer. A growing body of work, therefore, augments invariance-based SSL with equivariance objectives, but these objectives are often imposed on the same final representation. We empirically observe a trade-off in this coupled setting: pushing equivariance regularization toward deeper layers improves equivariance […]

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arXiv:2601.14280v1 Announce Type: new Abstract: Hallucinations in large language models (LLMs), defined as fluent yet incorrect or incoherent outputs, pose a significant challenge to the automatic generation of educational multiple-choice questions (MCQs). We identified four key hallucination types in MCQ generation: reasoning inconsistencies, insolvability, factual errors, and mathematical errors. To address this, we propose a hallucination-free multi-agent generation framework that breaks down MCQ generation into discrete, verifiable stages. Our framework utilizes both rule-based and LLM-based detection agents, as […]

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arXiv:2602.12449v1 Announce Type: cross Abstract: Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms for learning Gibbs distributions rely on access to full sample configurations generated from the model. For many systems of interest that arise in physical contexts, expecting a full sample to be observed is not practical, and hence it is important to look for computationally efficient methods that solve the learning […]

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