arXiv:2502.05360v3 Announce Type: replace-cross Abstract: This paper demonstrates that when a shallow neural network with a Lipschitz continuous activation function is trained using either empirical or population risk to approximate a target function that is $r$ times continuously differentiable on $[0,1]^d$, the population risk may not decay at a rate faster than $t^{-frac{4r}{d-2r}}$, where $t$ denotes the time parameter of the gradient flow dynamics. This result highlights the presence of the curse of dimensionality in the optimization computation […]

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Hi, SHapley Additive exPlanations (SHAP) is a popular eXplainable Artificial Intelligence (XAI) method, popular among practitioners. I just discovered that if the covariates of an ML model are highly correlated, the SHAP values are influenced by this multicollinearity (please see the paper A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME). This means that although ML models (e.g., Random Forest) might be robust against multicollinear covariates, one must be very careful when explaining them using SHAP. So, […]

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The job market didn’t suddenly become broken. It became AI-native quietly, incrementally, and without updating the rules for the people inside it. We still talk about hiring as if humans are the primary decision-makers. As if a recruiter reads every CV, evaluates context, and makes a judgment call. That story is comforting. It’s also largely false. To be honest, it is so far from reality, that it no longer exists. More Today, software makes the first, and often […]

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arXiv:2602.11177v1 Announce Type: new Abstract: Reliable early detection of Alzheimer’s disease (AD) is challenging, particularly due to limited availability of labeled data. While large language models (LLMs) have shown strong transfer capabilities across domains, adapting them to the AD domain through supervised fine-tuning remains largely unexplored. In this work, we fine-tune an LLM for AD detection and investigate how task-relevant information is encoded within its internal representations. We employ probing techniques to analyze intermediate activations across transformer layers, […]

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Each of us today has the opportunity to benefit in various ways from the vast amount of free knowledge available on the Internet. Despite this, many people use only a small portion of this technology. With artificial intelligence, more or less the same pattern is repeating: some people fully exploit its potential, while others limit themselves to asking simple questions to ChatGPT, much like what happened with Google. We have in our hands a tool with enormous potential: […]

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arXiv:2601.16280v1 Announce Type: new Abstract: Multi-agent systems powered by large language models (LLMs) are transforming enterprise automation, yet systematic evaluation methodologies for assessing tool-use reliability remain underdeveloped. We introduce a comprehensive diagnostic framework that leverages big data analytics to evaluate procedural reliability in intelligent agent systems, addressing critical needs for SME-centric deployment in privacy-sensitive environments. Our approach features a 12-category error taxonomy capturing failure modes across tool initialization, parameter handling, execution, and result interpretation. Through systematic evaluation of […]

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I came across an interesting writeup from Pathway that I think is more interesting as a reasoning benchmark than as a puzzle result. They use “Sudoku Extreme”: about 250,000 very hard Sudoku instances. The appeal is that Sudoku here is treated as a pure constraint-satisfaction problem: each solution is trivial to verify, hard to bluff and the task isn’t naturally linguistic. According to their numbers, leading LLMs (O3‑mini, DeepSeek R1, Claude 3.7 8K) all get 0% accuracy on […]

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As the title says, I admire the sheer audacity of the ICML committee. My paper gets desk-rejected, so technically I’m not part of the conference… and yet they’ve assigned me as a continued reviewer. Truly inspiring. Rejected as an author, retained as unpaid labor. Academia really said: you don’t belong here, but your service does. At this point, I assume my role is to review LLM-generated papers and reflect on my life choices. submitted by /u/ParticularWork8424 [link] [comments]

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Real-world optimization problems are often constrained by complex physical laws that limit computational scalability. These constraints are inherently tied to complex regions, and thus learning models that incorporate physical and geometric knowledge, i.e., physics-informed machine learning (PIML), offer a promising pathway for efficient solution. Here, we introduce PolyFormer, which opens a new direction for PIML in prescriptive optimization tasks, where physical and geometric knowledge is not merely used to regularize learning models, but to simplify the problems themselves. […]

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