Uncertainty estimation in machine learning has traditionally focused on the prediction stage, aiming to quantify confidence in model outputs while treating learned representations as deterministic and reliable by default. In this work, we challenge this implicit assumption and argue that reliability should be regarded as a first-class property of learned representations themselves. We propose a principled framework for reliable representation learning that explicitly models representation-level uncertainty and leverages structural constraints as inductive biases to regularize the space of […]

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Hey Hackers! This article is the second in a two-part series on the engineering reality of Physical AI. In Part 1, titled The Next Great Engineering Frontier: The Hidden Complexity of Physical AI, I discussed how the chaotic physical world, the high stakes of failure, and the Sim-to-Real gap make using embodied AI in robotics a harder problem than generating text, images, or video via an AI chatbot. However, mastering the physics is only half the battle. The […]

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arXiv:2601.21081v1 Announce Type: new Abstract: Multimodal models for text-to-image generation have achieved strong visual fidelity, yet they remain brittle under compositional structural constraints-notably generative numeracy, attribute binding, and part-level relations. To address these challenges, we propose Shape-of-Thought (SoT), a visual CoT framework that enables progressive shape assembly via coherent 2D projections without external engines at inference time. SoT trains a unified multimodal autoregressive model to generate interleaved textual plans and rendered intermediate states, helping the model capture shape-assembly […]

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I did not start my career thinking deeply about regulation. Like most tech-focused product managers, I cared about velocity, user experience, system reliability and scale. That instinct served me well—until I began leading platforms that moved money, made credit decisions and carried long-term financial consequences for customers. Regulation did not show up as paperwork, it showed up as questions the system would eventually be forced to answer. Regulation Is Delayed Interrogation I learned quickly that in banking, regulation […]

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arXiv:2602.12342v1 Announce Type: new Abstract: How can we train agents to navigate uncertainty over long horizons? In this work, we propose {Delta}Belief-RL, which leverages a language model’s own intrinsic beliefs to reward intermediate progress. Our method utilizes the change in the probability an agent assigns to the target solution for credit assignment. By training on synthetic interaction data, {Delta}Belief-RL teaches information-seeking capabilities that consistently outperform purely outcome-based rewards for Reinforcement Learning, with improvements generalizing to out-of-distribution applications ranging […]

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arXiv:2602.16837v1 Announce Type: new Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. Under causal masking at infinite depth, prior theoretical analyses of attention rollout predict an inevitable collapse of attention onto the first token. Such collapse, however, does not occur in practice. We resolve this discrepancy with a residual-aware theory of cumulative attention rollout. By incorporating residual connections, we show that this architectural component prevents collapse under […]

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arXiv:2601.21917v1 Announce Type: cross Abstract: We show that computing even very coarse approximations of critical points is intractable for simple classes of nonconvex functions. More concretely, we prove that if there exists a polynomial-time algorithm that takes as input a polynomial in $n$ variables of constant degree (as low as three) and outputs a point whose gradient has Euclidean norm at most $2^n$ whenever the polynomial has a critical point, then P=NP. The algorithm is permitted to return […]

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arXiv:2602.03950v1 Announce Type: new Abstract: Mathematical problem solving is a fundamental benchmark for assessing the reasoning capabilities of artificial intelligence and a gateway to applications in education, science, and engineering where reliable symbolic reasoning is essential. Although recent advances in multi-agent LLM-based systems have enhanced their mathematical reasoning capabilities, they still lack a reliably revisable representation of the reasoning process. Existing agents either operate in rigid sequential pipelines that cannot correct earlier steps or rely on heuristic self-evaluation […]

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arXiv:2603.02422v1 Announce Type: new Abstract: Exponential growth in the quantity of digital news, social media, and other textual sources makes it difficult for humans to keep up with rapidly evolving narratives about world events. Various visualisation techniques have been touted to help people to understand such discourse by exposing relationships between texts (such as news articles) as topics and themes evolve over time. Arguably, the understandability of such visualisations hinges on the assumption that people will be able […]

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