While it is commonly accepted that maintaining common ground plays a role in conversational success, little prior research exists connecting conversational grounding to success in task-oriented conversations. We study failures of grounding in the Ubuntu IRC dataset, where participants use text-only communication to resolve technical issues. We find that disruptions in conversational flow often stem from a misalignment in common ground, driven by a divergence in beliefs and assumptions held by participants. These disruptions, which we call conversational friction, significantly correlate with task success. We find that although LLMs can identify overt cases of conversational friction, they struggle with subtler and more context-dependent instances requiring pragmatic or domain-specific reasoning.

We present a reverberation mapping analysis of the coronal line [Ne V]$\lambda$3427 emitting region of the quasar COS168 (SDSS J095910.30+020732.2). [Ne V]$\lambda$3427 is known as one of the "coronal lines," which are a species of emission lines present in AGN spectra with high ionization potentials ($\geq$ 100 eV) that can serve as tracers for AGN activity. The spatial extent of the coronal line region has been studied with only spatial resolving techniques that are not sensitive to the innermost regions of AGN. Through our reverberation mapping analysis of [Ne V]$\lambda$3427, we measure a nominal `optimal emission radius' for [Ne V]$\lambda$3427 of $381.1^{+16}_{-22}$ light days (observed-frame). We place the coronal line region in context with other AGN regions by comparing it with the characteristic radius of H$\alpha$, the dust-sublimation radius, and the dusty torus. The coronal line region is located at a larger radius from the black hole than the characteristic radius of the dusty torus, measured using a torus-radius luminosity relationship. The virial product ($v^2 R/G$) of both H$\alpha$ and [Ne V]$\lambda$3427 is consistent within the uncertainties, implying that the coronal line region, as probed by the [Ne V]$\lambda$3427 line, may be in a virialized orbit that is dominated by the gravitational potential of the black hole. This plausibly suggests that coronal lines could be an effective method for estimating black hole masses.

We present observations and analyses of three high-magnification microlensing events: KMT-2022-BLG-0954, KMT-2024-BLG-0697, and MOA-2024-BLG-018. All three exhibit the "Planet/Binary" degeneracy, with planetary solutions corresponding to mass ratios in the range -3.7 < \log q < -2.2, while the binary solutions yield \log q > -2.0. For KMT-2022-BLG-0954, we identify a previously unrecognized degeneracy among planetary solutions, involving different mass ratios and normalized source radii. In all three cases, single-lens binary-source models are excluded. Bayesian analyses suggest that the planetary solutions correspond to gas giants orbiting M/K dwarfs beyond the snow line, while KMT-2022-BLG-0954 also admits an alternative interpretation as a super-Earth orbiting a late-type M dwarf. The binary solutions imply a diverse set of systems, including M-dwarf pairs and M-dwarf--brown-dwarf binaries. A review of known events subject to the "Planet/Binary" degeneracy shows that in most cases the degeneracy cannot be resolved through follow-up high-resolution imaging, particularly in the presence of the newly identified degeneracy.

M-dwarfs are the most dominant stars in the Galaxy. Their interiors and atmospheres exhibit complex processes including dust condensation, convective feedback, and magnetic activity-driven heterogeneity. Standard stellar characterization methods often struggle to capture these coupled effects. Part I of this series introduced SPHINX I, a validated grid of self-consistent radiative-convective model atmospheres and spectra for M-dwarfs with up-to-date molecular opacities suitable for early-to-mid M-dwarfs. Here, we present SPHINX II, which extends the model grid to cover mid-to-late type M-dwarfs, including both gray and physically motivated condensate cloud treatments and shorter convective mixing lengths. We validate SPHINX II using 39 benchmark FGK+M binary systems observed with SpeX IRTF (Mann et al. 2014) and apply it to 32 mid-to-late-type M-dwarfs from the SpeX Prism Library. SPHINX II yields improved fits that are statistically consistent with empirical benchmarks, achieving precisions of 0.078 dex in metallicity and 0.13 dex in C/O. Across the model grid, condensate cloud mass peaks between 2100-2400 K, decreasing sharply toward both cooler and hotter temperatures. We find the onset of the cloud-free regime around 2900 K, and below 2100 K, we see formation of deep/buried clouds. As a case study, we also model Trappist-1 and show that even mass-limited silicate grains subtly modify its emergent spectrum, suppressing near-infrared flux and reddening the mid-infrared slope via shallow cloud formation near 1e-2 bar. In sum, SPHINX II provides an improved framework for constraining the fundamental properties of mid-to-late M-dwarfs.

Galaxy mergers represent the most transformative and dramatic avenue for galaxy and supermassive black hole (SMBH) evolution. Multi-active galactic nuclei (multi-AGNs) are expected to ignite, grow, and evolve alongside the host galaxies, and these represent different evolutionary stages of the SMBHs over the merger sequence. However, no comprehensive census exists of observed multi-AGN systems. Here we present The Big Multi-AGN Catalog (The Big MAC), the first literature-complete catalog of all known (confirmed and candidate) multi-AGN systems, which includes dual AGNs (separations $\sim0.03-110$ kpc), binary AGNs (gravitationally bound, $\lesssim30$ pc), recoiling AGNs, and N-tuple AGNs (involving three or more AGNs), gleaned from hundreds of literature articles spanning the years 1970-2020. The Big MAC is the first archive to assemble all multi-AGN systems and candidates across all selection methods, redshifts, and galaxy mass ratios, and this catalog offers a solid foundation for archival and targeted multiwavelength follow-up investigations. In this work, we provide an overview of the creation of the multi-AGN literature library and the catalog itself, present definitions for different multi-AGN classes (including new definitions for dual AGNs derived from galaxy pairs in Illustris-TNG100), describe the general properties of the catalog as a function of redshift space and separation, and we provide a thorough examination of selection and confirmation method usage within the literature. We also discuss best practices for the multi-AGN literature, and we emphasize that a diverse, multiwavelength array of selection approaches is crucial for a complete understanding of multi-AGNs and - by extension - answering long-standing, open questions regarding the importance of AGNs and galaxy mergers.

We present a calibrated database of reflectance spectra for the solar system planets (i.e., Mercury, Earth, Mars, Jupiter, Saturn, Uranus, Neptune) and for Titan, spanning from the ultraviolet to the near infrared. We considered data collected over 60 years of planetary observations, employing a broad range of geometries and facilities (spacecraft and ground-based observatories). To correct for differences in observational geometries and data quality, we adopted a two-step calibration process that standardized each spectrum to the planet's geometric albedo values and corrected for planetary heterogeneity and calibration effects. The calibrated datasets were then combined across wavelengths, leading to a reference composite reflectance spectrum for each planet. As a test of this spectral library for exoplanetary research, we simulated direct imaging observations of the Proxima Centauri and HD 219134 systems as solar system analogs, as well as the solar system at a distance of 10 parsecs. We also explored the detection limitations of direct imaging instruments imposed by the inner and outer working angles for Earth and Jupiter-like exoplanets as a function of system distance. Additionally, we used the visible light portion of the results to produce realistic color reconstructions of each planet. Standardizing reflectance spectra in this work improves our baseline for interpreting new reflected light observations of exoplanets through comparative planetology. This spectral library can then serve as a calibrated and validated reference in the modeling and preparation for the characterization of exoplanet atmospheres with future direct imaging missions and for astronomical studies of the solar system.

KSTAR has recently undergone an upgrade to use a new Tungsten divertor to run experiments in ITER-relevant scenarios. Even with a high melting point of Tungsten, it is important to control the heat flux impinging on tungsten divertor targets to minimize sputtering and contamination of the core plasma. Heat flux on the divertor is often controlled by increasing the detachment of Scrape-Off Layer plasma from the target plates. In this work, we have demonstrated successful detachment control experiments using two different methods. The first method uses attachment fraction as a control variable which is estimated using ion saturation current measurements from embedded Langmuir probes in the divertor. The second method uses a novel machine-learning-based surrogate model of 2D UEDGE simulation database, DivControlNN. We demonstrated running inference operation of DivControlNN in realtime to estimate heat flux at the divertor and use it to feedback impurity gas to control the detachment level. We present interesting insights from these experiments including a systematic approach to tuning controllers and discuss future improvements in the control infrastructure and control variables for future burning plasma experiments.

The Transiting Exoplanet Survey Satellite (TESS) is surveying a large fraction of the sky, generating a vast database of photometric time series data that requires thorough analysis to identify exoplanetary transit signals. Automated learning approaches have been successfully applied to identify transit signals. However, most existing methods focus on the classification and validation of candidates, while few efforts have explored new techniques for the search of candidates. To search for new exoplanet transit candidates, we propose an approach to identify exoplanet transit signals without the need for phase folding or assuming periodicity in the transit signals, such as those observed in multi-transit light curves. To achieve this, we implement a new neural network inspired by Transformers to directly process Full Frame Image (FFI) light curves to detect exoplanet transits. Transformers, originally developed for natural language processing, have recently demonstrated significant success in capturing long-range dependencies compared to previous approaches focused on sequential data. This ability allows us to employ multi-head self-attention to identify exoplanet transit signals directly from the complete light curves, combined with background and centroid time series, without requiring prior transit parameters. The network is trained to learn characteristics of the transit signal, like the dip shape, which helps distinguish planetary transits from other variability sources. Our model successfully identified 214 new planetary system candidates, including 122 multi-transit light curves, 88 single-transit and 4 multi-planet systems from TESS sectors 1-26 with a radius > 0.27 $R_{\mathrm{Jupiter}}$, demonstrating its ability to detect transits regardless of their periodicity.

Efficient and reliable generation of global path plans are necessary for safe execution and deployment of autonomous systems. In order to generate planning graphs which adequately resolve the topology of a given environment, many sampling-based motion planners resort to coarse, heuristically-driven strategies which often fail to generalize to new and varied surroundings. Further, many of these approaches are not designed to contend with partial-observability. We posit that such uncertainty in environment geometry can, in fact, help drive the sampling process in generating feasible, and probabilistically-safe planning graphs. We propose a method for Probabilistic Roadmaps which relies on particle-based Variational Inference to efficiently cover the posterior distribution over feasible regions in configuration space. Our approach, Stein Variational Probabilistic Roadmap (SV-PRM), results in sample-efficient generation of planning-graphs and large improvements over traditional sampling approaches. We demonstrate the approach on a variety of challenging planning problems, including real-world probabilistic occupancy maps and high-dof manipulation problems common in robotics.

A fundamental goal of modern-day astrophysics is to understand the connection between supermassive black hole (SMBH) growth and galaxy evolution. Merging galaxies offer one of the most dramatic channels for galaxy evolution known, capable of driving inflows of gas into galactic nuclei, potentially fueling both star formation and central SMBH activity. Dual active galactic nuclei (dual AGNs) in late-stage mergers with nuclear pair separations <10 kpc are thus ideal candidates to study SMBH growth along the merger sequence since they coincide with the most transformative period for galaxies. However, dual AGNs can be extremely difficult to confirm and study. Hard X-ray (>10 keV) studies offer a relatively contamination-free tool for probing the dense obscuring environments predicted to surround the majority of dual AGN in late-stage mergers. To date, only a handful of the brightest and closest systems have been studied at these energies due to the demanding instrumental requirements involved. We demonstrate the unique capabilities of HEX-P to spatially resolve the soft and - for the first time - hard X-ray counterparts of closely-separated ($\sim2''-5''$) dual AGNs in the local Universe. By incorporating state-of-the-art physical torus models, we reproduce realistic broadband X-ray spectra expected for deeply embedded accreting SMBHs. Hard X-ray spatially resolved observations of dual AGNs - accessible only to HEX-P - will hence transform our understanding of dual AGN in the nearby Universe.

We present the Bayesian Extinction And Stellar Tool (BEAST), a probabilistic approach to modeling the dust extinguished photometric spectral energy distribution of an individual star while accounting for observational uncertainties common to large resolved star surveys. Given a set of photometric measurements and an observational uncertainty model, the BEAST infers the physical properties of the stellar source using stellar evolution and atmosphere models and constrains the line of sight extinction using a newly developed mixture model that encompasses the full range of dust extinction curves seen in the Local Group. The BEAST is specifically formulated for use with large multi-band surveys of resolved stellar populations. Our approach accounts for measurement uncertainties and any covariance between them due to stellar crowding (both systematic biases and uncertainties in the bias) and absolute flux calibration, thereby incorporating the full information content of the measurement. We illustrate the accuracy and precision possible with the BEAST using data from the Panchromatic Hubble Andromeda Treasury. While the BEAST has been developed for this survey, it can be easily applied to similar existing and planned resolved star surveys.

In recent decades, artificial intelligence (AI) including machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction. These tools are especially valuable in astrobiology applications, where models must distinguish biotic patterns from complex abiotic backgrounds. Advancing the integration of autonomy through AI and ML into space missions is a complex challenge, and we believe that by focusing on key areas, we can make significant progress and offer practical recommendations for tackling these obstacles.

Tensegrity robots are a class of compliant robots that have many desirable traits when designing mass efficient systems that must interact with uncertain environments. Various promising control approaches have been proposed for tensegrity systems in simulation. Unfortunately, state estimation methods for tensegrity robots have not yet been thoroughly studied. In this paper, we present the design and evaluation of a state estimator for tensegrity robots. This state estimator will enable existing and future control algorithms to transfer from simulation to hardware. Our approach is based on the unscented Kalman filter (UKF) and combines inertial measurements, ultra wideband time-of-flight ranging measurements, and actuator state information. We evaluate the effectiveness of our method on the SUPERball, a tensegrity based planetary exploration robotic prototype. In particular, we conduct tests for evaluating both the robot's success in estimating global position in relation to fixed ranging base stations during rolling maneuvers as well as local behavior due to small-amplitude deformations induced by cable actuation.

We investigate the chemical stability of CO2-dominated atmospheres of desiccated M dwarf terrestrial exoplanets using a 1-dimensional photochemical model. Around Sun-like stars, CO2 photolysis by Far-UV (FUV) radiation is balanced by recombination reactions that depend on water abundance. Planets orbiting M dwarf stars experience more FUV radiation, and could be depleted in water due to M dwarfs' prolonged, high-luminosity pre-main sequences (Luger & Barnes 2015). We show that, for water-depleted M dwarf terrestrial planets, a catalytic cycle relying on H2O2 photolysis can maintain a CO2 atmosphere. However, this cycle breaks down for atmospheric hydrogen mixing ratios <1 ppm, resulting in ~40% of the atmospheric CO2 being converted to CO and O2 on a time scale of 1 Myr. The increased O2 abundance leads to high O3 concentrations, the photolysis of which forms another CO2-regenerating catalytic cycle. For atmospheres with <0.1 ppm hydrogen, CO2 is produced directly from the recombination of CO and O. These catalytic cycles place an upper limit of ~50% on the amount of CO2 that can be destroyed via photolysis, which is enough to generate Earth-like abundances of (abiotic) O2 and O3. The conditions that lead to such high oxygen levels could be widespread on planets in the habitable zones of M dwarfs. Discrimination between biological and abiotic O2 and O3 in this case can perhaps be accomplished by noting the lack of water features in the reflectance and emission spectra of these planets, which necessitates observations at wavelengths longer than 0.95 microns.

Recent models suggest approximately half of all accreting supermassive black holes (SMBHs; $M_{\rm BH}$ $\gtrsim$ 10$^{5}$ M$_{\odot}$) are expected to undergo intense growth phases behind Compton-thick ($N_{\rm H}$ &gt; 1.5 $\times$ 10$^{24}$ cm$^{-2}$) veils of obscuring gas. However, despite being a viable source for the seeding of SMBHs, there are currently no examples known of a Compton-thick accreting intermediate mass black hole (IMBH; $M_{\rm BH}$ $\sim$ 10$^{2}$ $-$ 10$^{5}$ M$_{\odot}$). We present a detailed X-ray spectral analysis of IC 750 $-$ the only AGN to-date with a precise megamaser-based intermediate mass &lt; 10$^{5}$ M$_{\odot}$. We find the equivalent width of neutral 6.4 keV Fe K$\alpha$ to be 1.9$^{+2.2}_{-1.0}$ keV via phenomenological modelling of the co-added 177 ks Chandra spectrum. Such large equivalent widths are seldom produced by processes other than fluorescence from dense obscuration. We fit three physically-motivated X-ray spectral models to infer a range of possible intrinsic 2$-$10 keV luminosity posteriors that encompass the systematic uncertainties associated with a choice of model. Despite a wide range of predicted intrinsic 2$-$10 keV luminosities between $\sim$ 10$^{41}$ and 10$^{43}$ erg s$^{-1}$, all three models agree that IC 750 has a Compton-thick line-of-sight column density to &gt; 99\% confidence. Compton-thick obscuration is well-documented to impinge substantial bias on the pursuit of SMBH AGN. Our results thus provide the first indication that Compton-thick obscuration should also be properly considered to uncover and understand the IMBH population in an unbiased manner.

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster, but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., $\sim0.2$ ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over $10^8$ compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Accreting supermassive black holes (SMBHs) in galaxy mergers with separations &lt; 1kpc are crucial to our understanding of SMBH growth, galaxy evolution, and the evolution of SMBH binaries. Despite their importance, there are less than a handful known, and most have been discovered serendipitously. In this work, we employ a new selection method to systematically pre-select candidate advanced mergers likely to contain unresolved substructure at sub-arcsecond scales. By exploiting the large survey area and astrometric precision of the Wide-field Infrared Survey Explorer (WISE) and the Sloan Digital Sky Survey (SDSS), we have identified a sample of 48 nearby advanced mergers that have red WISE colors (W_1-W_2&gt;0.5) indicative of accretion activity and significant sub-arcsecond offsets between their optical and infrared coordinates as measured by SDSS and WISE. We conducted high resolution adaptive optics (AO) observations of this sample with the Keck NIRC2 camera in the $K_p$ band ($2.124 ~ \mu m$, $\Delta\lambda = 0.351 \mu m$) to search for evidence of previously unresolved substructure suggested by the optical-to-infared offsets. We find that a significant fraction (20/48 or 42%) of the sample shows substructure tracing the SDSS/WISE offset and unresolved by SDSS, demonstrating that our methodology is a promising pathway to find dual AGN in follow-up spectroscopy. Archival optical Hubble Space Telescope (HST) imaging reveals that substructure identified with Keck is often missed in the optical or erroneously identified due to partial obscuration, underscoring the importance of carrying out studies of late-stage mergers in the infrared.

We have conducted a sensitivity analysis on the mid-infrared spectral decomposition of galaxies and the modeling of the PAH emission spectrum with the NASA Ames PAH Infrared Spectroscopic Database (PAHdb) to assess the variance on the average galaxy PAH population properties under a grid of different modeling parameters. We find that the SL and SL+LL Spitzer-IRS decomposition with PAHFIT provides consistent modeling and recovery of the 5-15 $\mu$m PAH emission spectrum. For PAHdb modeling, application of a redshift to the calculated spectra to account for anharmonic effects introduces a $15\%$-$20\%$ variance on the derived parameters, while its absence improves the fits by $\sim13\%$. The 4.00-$\alpha$ release of PAHdb achieves the complete modeling of the 6-15 $\mu$m PAH spectrum, including the full 6.2 $\mu$m band, improving the average fitting uncertainty by a factor of 2. The optimal PAHdb modeling configuration requires selection of pure PAHs without applying a redshift to the bands. Although quantitatively the PAHdb-derived parameters change under different modeling configurations or database versions, their variation follows a linear scaling, with previously reported trends remaining qualitatively valid. PAHdb modeling of JWST observations, and JWST observations smoothed and resampled to the Spitzer-IRS resolution and dispersion have consistent PAHdb derived parameters. Decomposition with different codes, such as PAHFIT and CAFE, produce PAH emission spectra with noticeable variation in the 11-15~$\mu$m region, driving a $\sim7\%$ difference in the neutral PAH fraction under PAHdb modeling. A new library of galaxy PAH emission templates is delivered to be utilized in galaxy SED modeling.

The operational space and global performance of plasmas with edge-localized modes (ELMs) suppressed by resonant magnetic perturbations (RMPs) are surveyed by comparing AUG, DIII-D, EAST, and KSTAR stationary operating points. RMP-ELM suppression is achieved over a range of plasma currents, toroidal fields, and RMP toroidal mode numbers. Consistent operational windows in edge safety factor are found across devices, while windows in plasma shaping parameters are distinct. Accessed pedestal parameters reveal a quantitatively similar pedestal-top density limit for RMP-ELM suppression in all devices of just over 3x1019 m-3. This is surprising given the wide variance of many engineering parameters and edge collisionalities, and poses a challenge to extrapolation of the regime. Wide ranges in input power, confinement time, and stored energy are observed, with the achieved triple product found to scale like the product of current, field, and radius. Observed energy confinement scaling with engineering parameters for RMP-ELM suppressed plasmas are presented and compared with expectations from established H and L-mode scalings, including treatment of uncertainty analysis. Different scaling exponents for individual engineering parameters are found as compared to the established scalings. However, extrapolation to next-step tokamaks ITER and SPARC find overall consistency within uncertainties with the established scalings, finding no obvious performance penalty when extrapolating from the assembled multi-device RMP-ELM suppressed database. Overall this work identifies common physics for RMP-ELM suppression and highlights the need to pursue this no-ELM regime at higher magnetic field and different plasma physical size.

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.