arXiv:2601.08950v1 Announce Type: new Abstract: In educational applications, LLMs exhibit several fundamental pedagogical limitations, such as their tendency to reveal solutions rather than support dialogic learning. We introduce ConvoLearn (https://huggingface.co/datasets/masharma/convolearn ), a dataset grounded in knowledge building theory that operationalizes six core pedagogical dimensions: cognitive engagement, formative assessment, accountability, cultural responsiveness, metacognition, and power dynamics. We construct a semi-synthetic dataset of 1250 tutor-student dialogues (20 turns each) in middle school Earth Science through controlled interactions between human teachers […]

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arXiv:2601.03268v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has shifted language model evaluation toward reasoning and problem-solving tasks as measures of general intelligence. Small Language Models (SLMs) — defined here as models under 10B parameters — typically score 3-4 times lower than LLMs on these metrics. However, we demonstrate that these evaluations fail to capture SLMs’ effectiveness in common industrial applications, such as tone modification tasks (e.g., funny, serious, professional). We propose an evaluation […]

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Executive Summary The era of the passive ‘recommendation engine’ is over. We are entering the age of Agentic  Commerce—where AI doesn’t just suggest, but acts with autonomy. Retail is entering a new  phase—Agentic Commerce—where artificial intelligence no longer merely suggests products,  but acts autonomously on behalf of consumers and enterprises. In this model, intelligent agents understand intent, evaluate constraints, reason across  alternatives, and execute end-to-end commerce workflows. For consumers, this means delegating  shopping decisions to trusted digital agents. […]

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By Zachary C. Lipton* & Jacob Steinhardt* *equal authorship Originally presented at ICML 2018: Machine Learning Debates [arXiv link] Published in Communications of the ACM 1   Introduction Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working […]

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arXiv:2601.08865v1 Announce Type: new Abstract: Many studies have found active learning, either in the form of in-class exercises or projects, to be superior to traditional lectures. However, these forms of hands-on learning do not always lead students to reach the higher order thinking skills associated with the highest levels of Bloom’s Taxonomy (analysis, synthesis, and evaluation). Assignments that expect students to follow a prescribed approach to reach a well-defined solution contribute to a lack of higher order thinking […]

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arXiv:2601.05256v1 Announce Type: new Abstract: Inland water monitoring is vital for safeguarding public health and ecosystems, enabling timely interventions to mitigate risks. Existing methods often address isolated sub-problems such as cyanobacteria, chlorophyll, or other quality indicators separately. NAIAD introduces an agentic AI assistant that leverages Large Language Models (LLMs) and external analytical tools to deliver a holistic solution for inland water monitoring using Earth Observation (EO) data. Designed for both experts and non-experts, NAIAD provides a single-prompt interface […]

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The crypto market is no longer driven purely by hype. As investors look ahead to 2026, the focus has shifted toward timing, utility, and long-term sustainability. In this article, we take a closer look at Dogecoin (DOGE) and Mutuum Finance (MUTM)—two very different cryptocurrencies at very different stages—to assess which may offer the stronger investment opportunity in today’s market environment. Dogecoin (DOGE) Dogecoin remains one of the most recognizable cryptocurrencies in the market, largely due to its meme-driven […]

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arXiv:2601.02400v1 Announce Type: cross Abstract: Text-based causal inference increasingly employs textual data as proxies for unobserved confounders, yet this approach introduces a previously undertheorized source of bias: treatment leakage. Treatment leakage occurs when text intended to capture confounding information also contains signals predictive of treatment status, thereby inducing post-treatment bias in causal estimates. Critically, this problem can arise even when documents precede treatment assignment, as authors may employ future-referencing language that anticipates subsequent interventions. Despite growing recognition of […]

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