January 2026

arXiv:2601.08955v1 Announce Type: new Abstract: Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act without accessing real environments. Current methods mainly perform single-step or fixed-horizon rollouts, leaving their potential for complex task planning under-exploited. We propose Imagine-then-Plan (texttt{ITP}), a unified framework for agent learning via lookahead imagination, where an agent’s policy model interacts with the learned world model, yielding multi-step “imagined” trajectories. Since the imagination horizon […]

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arXiv:2601.08954v1 Announce Type: new Abstract: This chapter examines how data analytics can be leveraged to enhance immersive teacher simulations, situating this inquiry within the broader learning sciences discourse on embodied cognition, data-informed feedback, and teacher professional learning. It explores both conceptual foundations and empirical cases to illustrate how analytics serve as mediational tools that connect immersive experiences with reflective teaching practice. The chapter unfolds in multiple sections: (1) The Innovation Journey: An Overview of Immersive Teacher Simulations outlines […]

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arXiv:2601.08953v1 Announce Type: new Abstract: Complex decision-making by autonomous machines and algorithms could underpin the foundations of future society. Generative AI is emerging as a powerful engine for such transitions. However, we show that Generative AI-driven developments pose a critical pitfall: fairness concerns. In robotic applications, although intuitions about fairness are common, a precise and implementable definition that captures user utility and inherent data randomness is missing. Here we provide a utility-aware fairness metric for robotic decision making […]

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arXiv:2601.08951v1 Announce Type: new Abstract: Current AI safety frameworks, which often treat harmfulness as binary, lack the flexibility to handle borderline cases where humans meaningfully disagree. To build more pluralistic systems, it is essential to move beyond consensus and instead understand where and why disagreements arise. We introduce PluriHarms, a benchmark designed to systematically study human harm judgments across two key dimensions — the harm axis (benign to harmful) and the agreement axis (agreement to disagreement). Our scalable […]

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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:2601.08929v1 Announce Type: new Abstract: We study when the variable-indexed matrix of pairwise (f)-mutual informations (M^{(f)}_{ij}=I_f(X_i;X_j)) is positive semidefinite (PSD). Let (f:(0,infty)tomathbb{R}) be convex with (f(1)=0), finite in a neighborhood of (1), and with (f(0)<infty) so that diagonal terms are finite. We give a sharp emph{local} characterization around independence: there exists (delta=delta(f)>0) such that for every (n) and every finite-alphabet family ((X_1,ldots,X_n)) whose pairwise joint-to-product ratios lie in ((1-delta,1+delta)), the matrix (M^{(f)}) is PSD if and only if […]

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arXiv:2601.08928v1 Announce Type: new Abstract: Supply chain forecasting models degrade over time as real-world conditions change. Promotions shift, consumer preferences evolve, and supply disruptions alter demand patterns, causing what is known as concept drift. This silent degradation leads to stockouts or excess inventory without triggering any system warnings. Current industry practice relies on manual monitoring and scheduled retraining every 3-6 months, which wastes computational resources during stable periods while missing rapid drift events. Existing academic methods focus narrowly […]

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arXiv:2601.08927v1 Announce Type: new Abstract: For any positive integer $g ge 2$, we derive general conditions for the existence of a $2g$-cycle in the Tanner graph of two-dimensional ($2$-D) classical quasi-cyclic (QC) low-density parity-check (LDPC) codes. Based on these conditions, we construct a family of $2$-D classical QC-LDPC codes with girth greater than $4$ by stacking $p times p times p$ tensors, where $p$ is an odd prime. Furthermore, for composite values of $p$, we propose two additional […]

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arXiv:2601.08919v1 Announce Type: new Abstract: A good deal of recent research has focused on how Large Language Models (LLMs) may be used as `judges’ in place of humans to evaluate the quality of the output produced by various text / image processing systems. Within this broader context, a number of studies have investigated the specific question of how effectively LLMs can be used as relevance assessors for the standard ad hoc task in Information Retrieval (IR). We extend […]

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arXiv:2601.08901v1 Announce Type: new Abstract: Scientific discovery is a cumulative process and requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging and critical challenge is how to identify conceptually relevant prior work from rapidly growing literature, and assess how a new idea differentiates from existing research. Current embedding approaches typically conflate distinct conceptual aspects into single representations and cannot support fine-grained literature retrieval; meanwhile, LLM-based evaluators are subject to sycophancy biases, failing […]

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