technocracy

This study focuses on the tendency of agents in long-horizon sequential tasks to rely on short-term states and to underutilize historical information, and proposes a cognitive modeling and learning framework with long-term memory and reasoning capabilities. The framework provides a unified cognitive description of the agent’s decision process. It introduces a structured long-term memory mechanism to support continuous storage and selective updating of cross-temporal key information. On this basis, a memory retrieval-driven reasoning module is constructed so that […]

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arXiv:2602.17838v1 Announce Type: new Abstract: As developers increasingly rely on LLM-generated code summaries for documentation, testing, and review, it is important to study whether these summaries accurately reflect what the program actually does. LLMs often produce confident descriptions of what the code looks like it should do (intent), while missing subtle edge cases or logic changes that define what it actually does (behavior). We present a mutation-based evaluation methodology that directly tests whether a summary truly matches the […]

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We develop emph{Semantic Thermodynamics}, an information-theoretic framework for analyzing hallucinations in transformer systems under finite resources. The central object is mutual information between latent facts and model outputs, together with Fano-style lower bounds on semantic error. We clarify the stochastic assumptions required for non-degenerate information measures, distinguish true data-generating uncertainty from model-implied uncertainty, and replace unsupported hard capacity formulas with explicit capacity surrogates tied to precision, context budget, and effective representational rank. Under standard identification assumptions, we derive […]

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This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by […]

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We are concerned with a multivariate response regression problem where the interest is in considering correlations both across response variates and across response samples. In this paper we develop a new Bayesian nonparametric model for such a setting based on Dirichlet process priors. Building on an additive kernel model, we allow each sample to have its own regression matrix. Although this overcomplete representation could in principle suffer from severe overfitting problems, we are able to provide effective control […]

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arXiv:2601.14348v1 Announce Type: new Abstract: AI tools are increasingly suggested as solutions to assist public agencies with heavy workloads. In public defense, where a constitutional right to counsel meets the complexities of law, overwhelming caseloads and constrained resources, practitioners face especially taxing conditions. Yet, there is little evidence of how AI could meaningfully support defenders’ day-to-day work. In partnership with the New Jersey Office of the Public Defender, we develop the NJ BriefBank, a retrieval tool which surfaces […]

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Prime clusters are sets of primes that appear as close together as is generally possible. There is one pair of consecutive prime numbers, 2 and 3, but there cannot be any more: in any larger pair of consecutive numbers, one of the pair will be even. But there are a lot of twin primes, perhaps infinitely many, and so a prime cluster of size two is a pair of primes whose difference is 2. How close together can a […]

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