Chess engines passed human strength years ago, but they still don't play like humans. A grandmaster under clock pressure blunders in ways a club player on a hot streak never would. Conventional engines capture none of this. This paper proposes a personality x psyche decomposition to produce behavioral variability in chess play, drawing on patterns observed in human games. Personality is static -- a preset that pins down the engine's character. Psyche is dynamic -- a bounded scalar ψ_t \in [-100, +100], recomputed from five positional factors after every move. These two components feed into an audio-inspired signal chain (noise gate, compressor/expander, five-band equalizer, saturation limiter) that reshapes move probability distributions on the fly. The chain doesn't care what engine sits behind it: any system that outputs move probabilities will do. It needs no search and carries no state beyond ψ_t. I test the framework across 12,414 games against Maia2-1100, feeding it two probability sources that differ by ~2,800x in training data. Both show the same monotonic gradient in top-move agreement (~20-25 pp spread from stress to overconfidence), which tells us the behavioral variatio
This article presents an overview of approaches to modeling the human psyche in the context of constructing an artificial one. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of a living or artificial subject, comprising a space of states, including the state of needs that determine the meaning of a subject's being in relation to stimuli from the external world, and intelligence as a decision-making system regarding actions in this world to satisfy these needs. Based on this concept, a computational formalization is proposed for creating artificial general intelligence systems for an agent through experiential learning in a state space that includes agent's needs, taking into account their biological or existential significance for the intelligent agent, along with agent's sensations and actions. Thus, the problem of constructing artificial general intelligence is formalized as a system for making optimal decisions in the space of specific agent needs under conditions of uncertainty, maximizing success in achieving goals, minimizing existential risks, and maximizing energy efficiency. A minimal experimen
Asteroid (16) Psyche is a metal-rich body that might record an ancient coherent magnetization if some relict crust or mantle is preserved. Herein, we use magnetohydrodynamic simulations to predict (16) Psyche's field topology for several distinct pathways, (i) an early solar wind-induced magnetization imparted after a larger body was impacted, forming the present-day asteroid, (ii) a core dynamo magnetization imparted in an asteroid that is either presently largely intact or was a rubble pile, and (iii) magnetization in the turbulent solar nebula disk. For pathway (i) we find the field to be predominantly dipolar and spin axis-aligned. For pathway (ii) we find the field to be either dipolar and spin axis-misaligned, or highly multipolar. We also find that (iii) a field produced earlier before the solar nebula cleared, would be highly multipolar. In cases (i) and (ii) we also place constraints on the field strength. Simple detection of a magnetic field without constraining its topology and temporal variability would be insufficient to confirm a remanent source, due to the influence of the present-day solar wind, electromagnetic induction, and (16) Psyche's high obliquity. For suffic
The article provides an overview of approaches to modeling the human psyche in the perspective of building an artificial one. Based on the review, a concept of cognitive architecture is proposed, where the psyche is considered as an operating system of a living or artificial subject, including a space of needs that determines its life meanings in connection with stimuli from the external world, and intelligence as a decision-making system for actions in relation to this world in order to satisfy these needs. Based on the concept, a computational formalization is proposed for creating artificial intelligence systems through learning from experience in the space of a space of needs, taking into account their biological or existential significance for an intelligent agent. Thus, the problem of building general artificial intelligence as a system for making optimal decisions in the space of agent-specific needs under conditions of uncertainty is formalized, with maximization of success in achieving goals, minimization of existential risks and maximization of energy efficiency. A minimal experimental implementation of the model is also provided.
Main-belt asteroid (16) Psyche is the largest M-type asteroid, a class of object classically thought to be the metal cores of differentiated planetesimals and the parent bodies of the iron meteorites. de Kleer, Cambioni, and Shepard (2021, https://doi.org/10.3847/psj/ac01ec) presented new data from the Atacama Large Millimeter Array (ALMA), from which they derived a global best-fit thermal inertia and dielectric constant for Psyche, proxies for regolith particle size, porosity, and/or metal content, and observed thermal anomalies that could not be explained by surface albedo variations only. Motivated by this, here we fit a model to the same ALMA data set that allows dielectric constant and thermal inertia to vary across the surface. We find that Psyche has a heterogeneous surface in both dielectric constant and thermal inertia but, intriguingly, we do not observe a direct correlation between these two properties over the surface. We explain the heterogeneity in dielectric constant as being due to variations in the relative abundance of metal and silicates. Furthermore, we observe that the lowlands of a large depression in Psyche's shape have distinctly lower thermal inertia than t
Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in mathematics and programming, while research in the psychological domain has predominantly emphasized emotional support and empathetic dialogue, with limited attention to reasoning mechanisms that are beneficial to generating accurate responses. Therefore, in this paper, we propose \logopsyche\textit{Psyche-R1}, the first Chinese psychological LLM that jointly integrates empathy, psychological expertise, and reasoning, built upon a novel data curation pipeline. Specifically, we design a comprehensive data synthesis pipeline that produces over 75k high-quality psychological questions paired with detailed rationales, generated through an iterative prompt-rationale optimization procedure, along with 73k empathetic dialogues. Subsequently, we employ a hybrid training strategy wherein challenging samples are identified through a multi-LLM cross-selection strategy for group relative policy optimizat
Recent advances in large language models (LLMs) have accelerated the development of conversational agents capable of generating human-like responses. Since psychiatric assessments typically involve complex conversational interactions between psychiatrists and patients, there is growing interest in developing LLM-based psychiatric assessment conversational agents (PACAs) that aim to simulate the role of psychiatrists in clinical evaluations. However, standardized methods for benchmarking the clinical appropriateness of PACAs' interaction with patients still remain underexplored. Here, we propose PSYCHE, a novel framework designed to enable the 1) clinically relevant, 2) ethically safe, 3) cost-efficient, and 4) quantitative evaluation of PACAs. This is achieved by simulating psychiatric patients based on a multi-faceted psychiatric construct that defines the simulated patients' profiles, histories, and behaviors, which PACAs are expected to assess. We validate the effectiveness of PSYCHE through a study with 10 board-certified psychiatrists, supported by an in-depth analysis of the simulated patient utterances.
Large reasoning models (LRMs) have garnered significant attention from researchers owing to their exceptional capability in addressing complex tasks. Motivated by the observed human-like behaviors in their reasoning processes, this paper introduces a comprehensive taxonomy to characterize atomic reasoning steps and probe the ``psyche'' of LRM intelligence. Specifically, it comprises five groups and seventeen categories derived from human mental processes, thereby grounding the understanding of LRMs in an interdisciplinary perspective. The taxonomy is then applied for an in-depth understanding of current LRMs, resulting in a distinct labeled dataset that comprises 277,534 atomic reasoning steps. Using this resource, we analyze contemporary LRMs and distill several actionable takeaways for improving training and post-training of reasoning models. Notably, our analysis reveals that prevailing post-answer ``double-checks'' (self-monitoring evaluations) are largely superficial and rarely yield substantive revisions. Thus, incentivizing comprehensive multi-step reflection, rather than simple self-monitoring, may offer a more effective path forward. To complement the taxonomy, an automati
The enhance of accuracy in reasoning results of LLMs arouses the community's interests, wherein pioneering studies investigate post-hoc strategies to rectify potential mistakes. Despite extensive efforts, they are all stuck in a state of resource competition demanding significant time and computing expenses. The cause of the situation lies in the failure of identifying the fundamental feature of the solutions in this line, coined as the self-denial of LLMs. In other words, LLMs should confidently determine the potential existence of mistakes and carefully execute the targeted correction. As the whole procedure conducts within LLMs, supporting and persuasive references are hard to acquire, while the absence of specific steps towards refining hidden mistakes persists even when errors are acknowledged. In response to the challenges, we present PSSD, which refers to and implements the human psyche structure such that three distinct and interconnected roles contribute to human reasoning. Specifically, PSSD leverages the recent multi-agent paradigm, and is further enhanced with three innovatively conceived roles: (1) the intuition-based id role that provides initial attempts based on ben
Our understanding of Solar System evolution is closely tied to interpretations of asteroid composition, particularly the M-class asteroids. These asteroids were initially thought to be the exposed cores of differentiated planetesimals, a hypothesis based on their spectral similarity to iron meteorites. However, recent astronomical observations have revealed hydration on their surface through the detection of 3-$μ$m absorption features associated with OH and potentially H2O. We present evidence of hydration due mainly to OH on asteroid (16) Psyche, the largest M-class asteroid, using data from the James Webb Space Telescope (JWST) spanning 1.1 - 6.63 $μ$m. Our observations include two detections of the full 3-$μ$m feature associated with OH and H2O resembling those found in CY-, CH-, and CB-type carbonaceous chondrites, and no 6-$μ$m feature uniquely associated with H2O across two observations. We observe 3-$μ$m depths of between 4.3 and 6% across two observations, values consistent with hydrogen abundance estimates on other airless bodies of 250 - 400 ppm. We place an upper limit of 39 ppm on the water abundance from the standard deviation around the 6-$μ$m feature region. The pres
Large Language Models (LLMs) are rapidly transitioning from conversational assistants to autonomous agents embedded in critical organizational functions, including Security Operations Centers (SOCs), financial systems, and infrastructure management. Current adversarial testing paradigms focus predominantly on technical attack vectors: prompt injection, jailbreaking, and data exfiltration. We argue this focus is catastrophically incomplete. LLMs, trained on vast corpora of human-generated text, have inherited not merely human knowledge but human \textit{psychological architecture} -- including the pre-cognitive vulnerabilities that render humans susceptible to social engineering, authority manipulation, and affective exploitation. This paper presents the first systematic application of the Cybersecurity Psychology Framework (\cpf{}), a 100-indicator taxonomy of human psychological vulnerabilities, to non-human cognitive agents. We introduce the \textbf{Synthetic Psychometric Assessment Protocol} (\sysname{}), a methodology for converting \cpf{} indicators into adversarial scenarios targeting LLM decision-making. Our preliminary hypothesis testing across seven major LLM families reve
(16) Psyche is the largest M-type asteroid in the main belt and the target of the NASA Discovery-class Psyche mission. Despite gaining considerable interest in the scientific community, Psyche's composition and formation remain unconstrained. Originally, Psyche was considered to be almost entirely composed of metal due to its high radar albedo and spectral similarities to iron meteorites. More recent telescopic observations suggest the additional presence of low-Fe pyroxene and exogenic carbonaceous chondrites on the asteroid's surface. To better understand the abundances of these additional materials, we investigated visible near-infrared (0.35 - 2.5 micron) spectral properties of three-component laboratory mixtures of metal, low-Fe pyroxene, and carbonaceous chondrite. We compared the band depths and spectral slopes of these mixtures to the telescopic spectrum of (16) Psyche to constrain material abundances. We find that the best matching mixture to Psyche consists of 82.5% metal, 7% low-Fe pyroxene, and 10.5% carbonaceous by weight, suggesting that the asteroid is less metallic than originally estimated (~94%). The relatively high abundance of carbonaceous chondrite material est
Asteroid (16) Psyche is the target of the NASA Psyche mission. It is considered one of the few main-belt bodies that could be an exposed proto-planetary metallic core and that would thus be related to iron meteorites. Such an association is however challenged by both its near- and mid-infrared spectral properties and the reported estimates of its density. Here, we aim to refine the density of (16) Psyche to set further constraints on its bulk composition and determine its potential meteoritic analog. We observed (16) Psyche with ESO VLT/SPHERE/ZIMPOL as part of our large program (ID 199.C-0074). We used the high angular resolution of these observations to refine Psyche's three-dimensional (3D) shape model and subsequently its density when combined with the most recent mass estimates. In addition, we searched for potential companions around the asteroid. We derived a bulk density of 3.99\,$\pm$\,0.26\,g$\cdot$cm$^{-3}$ for Psyche. While such density is incompatible at the 3-sigma level with any iron meteorites ($\sim$7.8\,g$\cdot$cm$^{-3}$), it appears fully consistent with that of stony-iron meteorites such as mesosiderites (density $\sim$4.25\,$\cdot$cm$^{-3}$). In addition, we fo
We apply our novel Markov-chain Monte Carlo (MCMC)-based algorithm for asteroid mass estimation to asteroid (16) Psyche, the target of NASA's eponymous Psyche mission, based on close encounters with 10 different asteroids, and obtain a mass of $(1.117 \pm 0.039) \times 10^{-11} $ solar masses. We ensure that our method works as expected by applying it to asteroids (1) Ceres and (4) Vesta, and find that the results are in agreement with the very accurate mass estimates for these bodies obtained by the Dawn mission. We then combine our mass estimate for Psyche with the most recent volume estimate to compute the corresponding bulk density as $(3.88 \pm 0.25)$ g/cm$^3$. The estimated bulk density rules out the possibility of Psyche being an exposed, solid iron core of a protoplanet, but is fully consistent with the recent hypothesis that ferrovolcanism would have occurred on Psyche.
We develop a shape model of asteroid 16 Psyche using observations acquired in a wide range of wavelengths: Arecibo S-band delay-Doppler imaging, Atacama Large Millimeter Array (ALMA) plane-of-sky imaging, adaptive optics (AO) images from Keck and the Very Large Telescope (VLT), and a recent stellar occultation. Our shape model has dimensions 278 (-4/+8) km x 238 (-4/+6) km x 171 (-1/+5) km, an effective spherical diameter Deff = 222 -1/+4 km, and a spin axis (ecliptic lon, lat) of (36 deg, -8 deg) +/- 2 deg. We survey all the features previously reported to exist, tentatively identify several new features, and produce a global map of Psyche. Using 30 calibrated radar echoes, we find Psyche's overall radar albedo to be 0.34 +/- 0.08 suggesting that the upper meter of regolith has a significant metal (i.e., Fe-Ni) content. We find four regions of enhanced or complex radar albedo, one of which correlates well with a previously identified feature on Psyche, and all of which appear to correlate with patches of relatively high optical albedo. Based on these findings, we cannot rule out a model of Psyche as a remnant core, but our preferred interpretation is that Psyche is a differentiate
Accurate vehicle positioning requires effective IMU-GPS fusion, yet prior methods-EKF, UKF, ML, GA, and DE-suffer from nonlinearity, instability, or high computational cost. This study introduces a PSO-based adaptive tuning framework for optimizing UKF parameters (α, \b{eta}, \k{appa}, Q, R), evaluated in CARLA 0.9.14 using a Tesla Model 3 under diverse maneuvers and environmental conditions. Within defined parameter bounds, convergence stabilized within 15 generations, achieving an 82.14% accuracy improvement over manual tuning and reducing IMU drift by up to 21,606.59m. Multi-trial statistical validation confirmed consistent gains with low confidence intervals. With update times remaining below the 10 ms real-time threshold, the PSO-tuned UKF demonstrates practical localization performance for dynamic, GPS-challenged conditions.
The simulation of magnetohydrodynamic (MHD) flows presents a highly complex, tightly coupled transport problem that poses severe numerical and computational demands. Towards this, we propose a novel class of Lattice Boltzmann Methods (LBM) schemes capable of solving a wide range of transport equation systems with high computational efficiency and scalability. Our approach exploits the algorithmic structure of kinetic formulations to separately transport all state variables of Strang-splitted conservation equations alongside their characteristics, yielding decoupled, fully local operations. To demonstrate the capability of this framework on complex, numerically demanding multiphysics interactions, we apply it to these MHD flows. Specifically, we discretize ideal compressible and resistive incompressible MHD systems, which naturally encompass hydrodynamic limits such as the compressible Euler and incompressible Navier-Stokes equations. Rigorous performance analysis of the implementation within the platform-transparent multi-physics framework OpenLB demonstrates up to 98.9\% of the hardware roofline. We validate our approach against established incompressible and compressible MHD benc
Rubble-pile asteroids can form through the self-gravitational reaccumulation of fragments produced during large-scale collisions. To investigate how differentiated bodies are disrupted and how iron-rich rubble piles may form, we performed smoothed particle hydrodynamics simulations of impacts between differentiated asteroids with molten or solidified interiors. Our results show that catastrophic disruption produces a sheet-like structure in which core and mantle materials are stretched and subsequently fragment under self-gravity. The resulting fragments exhibit nearly identical iron-rock mass ratios, indicating that catastrophic disruption naturally generates numerous compositionally similar fragments. The largest remnant formed in such events is therefore an iron-rich rubble pile assembled from these mixed fragments, whereas remnants formed through mantle stripping retain a layered structure with an iron core and rocky mantle. We further find that fragment production is sensitive to material strength and the equation of state: mantle strength reduces the number of small fragments, while core strength suppresses catastrophic disruption when the core is solid. These results imply t
Existing language model applications struggle to meet the demand for emotionally oriented support, primarily due to their inability to maintain deep, persistent context across sessions. This report introduces Psych LM, an iOS application that validates the thesis that, for such applications, the surrounding architecture is paramount. Psych LM runs a local, on-device language model within a purpose-built, local-first runtime designed for behavioral and life-coaching applications. The system achieves the practical effect of a near-infinite context window through an automated, user-inspectable memory corpus that converts conversations into structured memory cards, including facts, goals, and events, and dynamically injects them into the prompt via semantic and vector search. As such, the system can be defined as an active-learning, retrieval-augmented generative, on-device architecture. This architecture delivers four primary contributions: a local-first design where privacy is a core property; a detailed description of the memory corpus for persistent context of key user information; a deterministic orchestration layer that provides a stable behavioral spine independent of the model'
Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving. When the available information is insufficient to support a reliable answer, models should request clarification or abstain rather than provide unsupported responses. Existing medical benchmarks, however, typically assume that complete information is available upfront. We introduce Safe-Psych, a sequential benchmark for evaluating how LLMs handle evolving diagnostic uncertainty in clinical psychiatry. Safe-Psych contains over 1,000 real-world psychiatric clinical notes segmented to simulate incremental evidence disclosure, with psychiatrist-derived action labels at each stage: DIAGNOSE, CLARIFY, or ABSTAIN. We evaluate multiple state-of-the-art LLMs in full-information and sequential settings. Our findings show that capability does not ensure calibration: even strong models struggle under incomplete clinical information, with under-abstention exceeding 60% for most models and safety-aware prompting reducing premature commitment only by shifting errors toward excessive abstention. In sequential evaluation, models frequently diagnose before suf