arXiv subjects

Johnny K. W. Ho

Publications and source records attributed to Johnny K. W. Ho.

4 recordsLinked to original sources

arXiv2026

Foresight Optimization for Strategic Reasoning in Large Language Models

Reasoning capabilities in large language models (LLMs) have generally advanced significantly. However, it is still challenging for existing reasoning-based LLMs to perform effective decision-making abilities in multi-agent environments, due to the absence of explicit foresight modeling. To this end, strategic reasoning, the most fundamental capability to anticipate the counterpart's behaviors and foresee its possible future actions, has been introduced to alleviate the above issues. Strategic reasoning is fundamental to effective decision-making in multi-agent environments, yet existing reasoning enhancement methods for LLMs do not explicitly capture its foresight nature. In this work, we introduce Foresight Policy Optimization (FoPO) to enhance strategic reasoning in LLMs, which integrates opponent modeling principles into policy optimization, thereby enabling explicit consideration of both self-interest and counterpart influence. Specifically, we construct two curated datasets, namely Cooperative RSA and Competitive Taboo, equipped with well-designed rules and moderate difficulty to facilitate a systematic investigation of FoPO in a self-play framework. Our experiments demonstrate that FoPO significantly enhances strategic reasoning across LLMs of varying sizes and origins. Moreover, models trained with FoPO exhibit strong generalization to out-of-domain strategic scenarios, substantially outperforming standard LLM reasoning optimization baselines.

cs.CL↗

arXiv2026

Observer effect modulates classification in a quantum epistemic framework

The observer effect in quantum physics states that observation inevitably influences the system being observed. This work introduces an epistemic framework that treats the observer as an integral part of sensory information processing within entangled quantum systems, leading to subjective and probabilistic observation and inference. We propose fuzzy instance classification by encoding sensory input to align with the observer's pre-existing beliefs in a feature-attribute-truth value hierarchical model as 'bells' of quantum oscillators whose states represent the degree of activation, associated with quantum probability. We demonstrate that within this framework, sensory data evolve via interaction with quantum-based observer states during the pre-decision phase, as described by the Lindblad master equation, and are then classified adaptively using positive operator-valued measures (POVM). The POVM enables the customisable parametrisation of measures of concurrent similarity and dissimilarity, facilitating subjectivity of perceptual associations and asymmetric cognition. Additionally, the observer's position on a sceptic-believer spectrum determines their robustness to noisy perceptions in ambiguous matching, balancing precision and flexibility. We show that sensory information becomes intricately entangled with observer states, yielding a wide array of probabilistic classification results. This framework lays the groundwork for a quantum probability-based understanding of the observer effect in cognitive processes, providing a formal basis for subjective interpretation not as a flaw in observation, but as a fundamental consequence of the observer-system interaction with quantum properties and correlations.

quant-ph↗

arXiv2025

A test statistic, $h^*$, for outlier analysis

Outlier analysis is a critical tool across diverse domains, from clinical decision-making to cybersecurity and talent identification. Traditional statistical outlier detection methods, such as Grubb's test and Dixon's Q, are predicated on the assumption of normality and often fail to reckon the meaningfulness of exceptional values within non-normal datasets. In this paper, we introduce the h* statistic, a novel parametric, frequentist approach for evaluating global outliers without the normality assumption. Unlike conventional techniques that primarily remove outliers to preserve statistical `integrity,' h* assesses the distinctiveness as phenomena worthy of investigation by quantifying a data point's extremity relative to its group as a measure of statistical significance analogous to the role of Student's t in comparing means. We detail the mathematical formulation of h* with tabulated confidence intervals of significance levels and extensions to Bayesian inference and paired analysis. The capacity of h* to discern between stable extraordinary deviations and values that merely appear extreme under conventional criteria is demonstrated using empirical data from a mood intervention study. A generalisation of h* is subsequently proposed, with individual weights assigned to differences for nuanced contextual description, and a variable sensitivity exponent for objective inference optimisation and subjective inference specification. The physical significance of an h*-recognised outlier is linked to the signature of unique occurrences. Our findings suggest that h* offers a robust alternative for outlier evaluation, enriching the analytical repertoire for researchers and practitioners by foregrounding the interpretative value of outliers within complex, real-world datasets. This paper is also a statement against the dominance of normality in celebration of the luminary and the lunatic alike.

stat.ME↗

arXiv2019

Robot Affect: the Amygdala as Bloch Sphere

In the design of artificially sentient robots, an obstacle always has been that conventional computers cannot really process information in parallel, whereas the human affective system is capable of producing experiences of emotional concurrency (e.g., happy and sad). Another schism that has been in the way is the persistent Cartesian divide between cognition and affect, whereas people easily can reflect on their emotions or have feelings about a thought. As an essentially theoretical exercise, we posit that quantum physics at the basis of neurology explains observations in cognitive emotion psychology from the belief that the construct of reality is partially imagined (Im) in the complex coordinate space C^3. We propose a quantum computational account to mixed states of reflection and affect, while transforming known psychological dimensions into the actual quantum dynamics of electromotive forces. As a precursor to actual simulations, we show examples of possible robot behaviors, using Einstein-Podolsky-Rosen circuits. Keywords: emotion, reflection, modelling, quantum computing

cs.AI↗