January 2026

arXiv:2601.08859v1 Announce Type: new Abstract: Researchers face a persistent barrier when applying computational algorithms with parameter configuration typically demanding programming skills, interfaces differing across environments, and settings rarely persisting between sessions. This fragmentation forces repetitive input, slows iterative exploration, and undermines reproducibility because parameter choices are difficult to record, share, and reuse. We present EZInput, a cross-runtime environment Python library enabling algorithm developers to automatically generate graphical user interfaces that make their computational tools accessible to end-users without […]

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arXiv:2601.08858v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive domains such as healthcare, finance, and law, yet their integration raises pressing concerns around trust, accountability, and reliability. This paper explores adaptive trust metrics for multi LLM ecosystems, proposing a framework for quantifying and improving model reliability under regulated constraints. By analyzing system behaviors, evaluating uncertainty across multiple LLMs, and implementing dynamic monitoring pipelines, the study demonstrates practical pathways for operational trustworthiness. Case studies […]

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arXiv:2601.08857v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs), such as ChatGPT and GitHub Copilot, into professional workflows is increasingly reshaping software engineering practices. These tools have lowered the cost of code generation, explanation, and testing, while introducing new forms of automation into routine development tasks. In contrast, most of the software engineering and computer engineering curricula remain closely aligned with pedagogical models that equate manual syntax production with technical competence. This growing misalignment raises […]

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arXiv:2601.08856v1 Announce Type: new Abstract: Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting various design features requires deep understanding of the design functionality and creativity. When one or more unit tests expose a design failure, the debugging engineer needs to diagnose, localize, and debug the failure to ensure design correctness, […]

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arXiv:2601.08852v1 Announce Type: new Abstract: Automated news verification requires structured claim extraction, but existing approaches either lack schema compliance or generalize poorly across domains. This paper presents NewsScope, a cross-domain dataset, benchmark, and fine-tuned model for schema-grounded news claim extraction. The dataset contains 455 articles across politics, health, science/environment, and business, consisting of 395 in-domain articles and 60 out-of-source articles for generalization testing. LLaMA 3.1 8B was fine-tuned using LoRA on 315 training examples and evaluated on held-out […]

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arXiv:2601.08851v1 Announce Type: new Abstract: T’ecnicas recientes de “Contextualized Chunking” inyectan res’umenes para mejorar el contexto en RAG, pero introducen una “diluci’on vectorial” que opaca el contenido local. Evaluando distintos ratios de inyecci’on, demostramos una curva en “U invertida”: una inyecci’on moderada mejora el “Recall” (+18%), pero superar un umbral cr’itico (CIR > 0.4) reduce la precisi’on en un 22% para consultas espec’ificas. Proponemos un marco te’orico para calcular el ratio ‘optimo de inyecci’on. — Recent “Contextualized Chunking” […]

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arXiv:2601.08850v1 Announce Type: new Abstract: The question of whether AI systems have morally relevant interests — the ‘model welfare’ question — depends in part on how we evaluate AI testimony about inner states. This paper develops what I call the inconsistency critique: independent of whether skepticism about AI testimony is ultimately justified, our actual epistemic practices regarding such testimony exhibit internal inconsistencies that lack principled grounds. We functionally treat AI outputs as testimony across many domains — evaluating […]

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arXiv:2601.08849v1 Announce Type: new Abstract: Automated answer matching, which leverages LLMs to evaluate free-text responses by comparing them to a reference answer, shows substantial promise as a scalable and aligned alternative to human evaluation. However, its reliability requires robustness against strategic attacks such as guesswork or verbosity that may artificially inflate scores without improving actual correctness. In this work, we systematically investigate whether such tactics deceive answer matching models by prompting examinee models to: (1) generate verbose responses, […]

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arXiv:2601.08848v1 Announce Type: new Abstract: This paper presents PediaMind-R1, a domain-specialized large language model designed to achieve active personalization in intelligent parenting scenarios. Unlike conventional systems that provide generic suggestions, PediaMind-R1 draws on insights from developmental psychology. It introduces temperament theory from the Thomas-Chess framework and builds a temperament knowledge graph for infants and toddlers (0-3 years). Our two-stage training pipeline first uses supervised fine-tuning to teach structured chain-of-thought reasoning, and then applies a GRPO-based alignment stage to […]

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arXiv:2601.08847v1 Announce Type: new Abstract: Evaluating knowledge systems (LLMs, RAG, knowledge graphs, etc) faces fundamental challenges: static benchmarks are vulnerable to contamination, LLM-based judges exhibit systematic biases, and ground truth extraction requires expensive human annotation. We present RIKER (Retrieval Intelligence and Knowledge Extraction Rating), both a benchmark and a replicable methodology based on paradigm inversion – generating documents from known ground truth rather than extracting ground truth from documents. This approach enables deterministic scoring and scalable evaluation without […]

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