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Imitation Learning (IL) has been very effective in training robots to perform complex and diverse manipulation tasks. However, its performance declines precipitously when the observations are out of the training distribution. 3D scene representations that incorporate observations from calibrated RGBD cameras have been proposed as a way to improve generalizability of IL policies, but our evaluations in cross-embodiment and novel camera pose settings found that they show only modest improvement. To address those challenges, we propose Adaptive 3D Scene Representation (Adapt3R), a general-purpose 3D observation encoder which uses a novel architecture to synthesize data from one or more RGBD cameras into a single vector that can then be used as conditioning for arbitrary IL algorithms. The key idea is to use a pretrained 2D backbone to extract semantic information about the scene, using 3D only as a medium for localizing this semantic information with respect to the end-effector. We show that when trained end-to-end with several SOTA multi-task IL algorithms, Adapt3R maintains these algorithms' multi-task learning capacity while enabling zero-shot transfer to novel embodiments and camera poses. Furthermore, we provide a detailed suite of ablation and sensitivity experiments to elucidate the design space for point cloud observation encoders., Comment: Videos, code, and data: https://pairlab.github.io/Adapt3R

Machine learning programs, such as those performing inference, fine-tuning, and training of LLMs, are commonly delegated to untrusted compute providers. To provide correctness guarantees for the client, we propose adapting the cryptographic notion of refereed delegation to the machine learning setting. This approach enables a computationally limited client to delegate a program to multiple untrusted compute providers, with a guarantee of obtaining the correct result if at least one of them is honest. Refereed delegation of ML programs poses two technical hurdles: (1) an arbitration protocol to resolve disputes when compute providers disagree on the output, and (2) the ability to bitwise reproduce ML programs across different hardware setups, For (1), we design Verde, a dispute arbitration protocol that efficiently handles the large scale and graph-based computational model of modern ML programs. For (2), we build RepOps (Reproducible Operators), a library that eliminates hardware "non-determinism" by controlling the order of floating point operations performed on all hardware. Our implementation shows that refereed delegation achieves both strong guarantees for clients and practical overheads for compute providers.

LMs' alignment with human reading behavior (i.e. psychometric predictive power; PPP) is known to improve during pretraining up to a tipping point, beyond which it either plateaus or degrades. Various factors, such as word frequency, recency bias in attention, and context size, have been theorized to affect PPP, yet there is no current account that explains why such a tipping point exists, and how it interacts with LMs' pretraining dynamics more generally. We hypothesize that the underlying factor is a pretraining phase transition, characterized by the rapid emergence of specialized attention heads. We conduct a series of correlational and causal experiments to show that such a phase transition is responsible for the tipping point in PPP. We then show that, rather than producing attention patterns that contribute to the degradation in PPP, phase transitions alter the subsequent learning dynamics of the model, such that further training keeps damaging PPP.

Do LLMs offer insights into human language learning? A common argument against this idea is that because their architecture and training paradigm are so vastly different from humans, LLMs can learn arbitrary inputs as easily as natural languages. In this paper, we test this claim by training LMs to model impossible and typologically unattested languages. Unlike previous work, which has focused exclusively on English, we conduct experiments on 12 natural languages from 4 language families. Our results show that while GPT-2 small can primarily distinguish attested languages from their impossible counterparts, it does not achieve perfect separation between all the attested languages and all the impossible ones. We further test whether GPT-2 small distinguishes typologically attested from unattested languages with different NP orders by manipulating word order based on Greenberg's Universal 20. We find that the model's perplexity scores do not distinguish attested vs. unattested word orders, as long as the unattested variants maintain constituency structure. These findings suggest that language models exhibit some human-like inductive biases, though these biases are weaker than those found in human learners.

This chapter examines current developments in linguistic theory and methods, focusing on the increasing integration of computational, cognitive, and evolutionary perspectives. We highlight four major themes shaping contemporary linguistics: (1) the explicit testing of hypotheses about symbolic representation, such as efficiency, locality, and conceptual semantic grounding; (2) the impact of artificial neural networks on theoretical debates and linguistic analysis; (3) the importance of intersubjectivity in linguistic theory; and (4) the growth of evolutionary linguistics. By connecting linguistics with computer science, psychology, neuroscience, and biology, we provide a forward-looking perspective on the changing landscape of linguistic research.

BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 3rd BabyLM competition. As in previous years, we call for participants in the data-efficient pretraining challenge in the general track. This year, we also offer a new track: INTERACTION. This new track encourages interactive behavior, learning from a teacher, and adapting the teaching material to the student. We also call for papers outside the competition in any relevant areas. These include training efficiency, cognitively plausible research, weak model evaluation, and more., Comment: EMNLP 2025 BabyLM Workshop. arXiv admin note: text overlap with arXiv:2404.06214

We study the electric polarizability of a charged kaon from four-point functions in lattice QCD as an alternative to the background field method. Lattice four-point correlation functions are constructed from quark and gluon fields to be used in Monte Carlo simulations. The elastic form factor (charge radius) is needed in the method which can be obtained from the same four-point functions at large current separations. Preliminary results from the connected quark-line diagrams are presented., Comment: Eq 1 changed to match the equation in the reference, some symbols changed for consistency and one change of pion to kaon

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the estimated parameters can be used in a hydrodynamics code to obtain density fields and hydrodynamic shock and outer edge features that are consistent with the data. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model.

Generalised Fermat equation (GFE) is the equation of the form $ax^p+by^q=cz^r$, where $a,b,c,p,q,r$ are positive integers. If $1/p+1/q+1/r<1$, GFE is known to have at most finitely many primitive integer solutions $(x,y,z)$. A large body of the literature is devoted to finding such solutions explicitly for various six-tuples $(a,b,c,p,q,r)$, as well as for infinite families of such six-tuples. This paper surveys the families of parameters for which GFE has been solved. Although the proofs are not discussed here, collecting these references in one place will make it easier for the readers to find the relevant proof techniques in the original papers. Also, this survey will help the readers to avoid duplicate work by solving the already solved cases.

The BabyLM Challenge is a community effort to close the data-efficiency gap between human and computational language learners. Participants compete to optimize language model training on a fixed language data budget of 100 million words or less. This year, we released improved text corpora, as well as a vision-and-language corpus to facilitate research into cognitively plausible vision language models. Submissions were compared on evaluation tasks targeting grammatical ability, (visual) question answering, pragmatic abilities, and grounding, among other abilities. Participants could submit to a 10M-word text-only track, a 100M-word text-only track, and/or a 100M-word and image multimodal track. From 31 submissions employing diverse methods, a hybrid causal-masked language model architecture outperformed other approaches. No submissions outperformed the baselines in the multimodal track. In follow-up analyses, we found a strong relationship between training FLOPs and average performance across tasks, and that the best-performing submissions proposed changes to the training data, training objective, and model architecture. This year's BabyLM Challenge shows that there is still significant room for innovation in this setting, in particular for image-text modeling, but community-driven research can yield actionable insights about effective strategies for small-scale language modeling.

Ferromagnetic superconductors are exceptionally rare because the strong ferromagnetic exchange field usually destroys singlet superconductivity. EuFe$_2$(As$_{1-x}$P$_x$)$_2$, an iron-based superconductor with a maximum critical temperature of $\sim$25 K, is a unique material that exhibits full coexistence with ferromagnetic order below $T_\mathrm{FM} \approx 19$ K. The interplay between the two leads to a narrowing of ferromagnetic domains at higher temperatures and the spontaneous nucleation of vortices/antivortices at lower temperatures. Here we demonstrate how the underlying magnetic structure directly controls the superconducting vortex dynamics in applied magnetic fields. Just below $T_\mathrm{FM}$ we observe a pronounced temperature-dependent peak in both the coercivity and the creep activation energy, the latter becoming rapidly suppressed in large applied magnetic fields. We attribute this behaviour to the formation of vortex polarons arising from the unique interaction between free vortices and magnetic stripe domains. We present a theoretical description of the properties of vortex polarons that explains our main observations, showing how they lead to vortex trapping and an attractive vortex-vortex interaction at short distances. In stark contrast, strong magnetic irreversibility at low temperatures is linked to a critical current governed by giant flux creep over an activation barrier for vortex-antivortex annihilation near domain walls. Our work reveals unexplored new routes for the magnetic enhancement of vortex pinning with particularly important applications in high-current conductors for operation at high magnetic fields., Comment: 15 pages, 5 figures

Experimental determination of the structure of the superconducting order parameter in the kagome lattice compound CsV$_3$Sb$_5$ is an essential step towards understanding the nature of the superconducting pairing in this material. Here we report measurements of the temperature dependence of the in-plane magnetic penetration depth, $\lambda(T)$, in crystals of CsV$_3$Sb$_5$ down to $\sim 60\,\mathrm{mK}$. We find that $\lambda(T)$ is consistent with a fully-gapped state but with significant gap anisotropy. The magnitude of the gap minima are in the range $\sim 0.2 - 0.3 T_\mathrm{c}$ for the measured samples, markedly smaller than previous estimates. We discuss different forms of potential anisotropy and how these can be linked to the V and Sb Fermi surface sheets. We highlight a significant discrepancy between the calculated and measured values of $\lambda(T=0)$ which we suggest is caused by spatially suppressed superconductivity.

Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbial time series analysis of HEC and show that the initial gut-like microbiome of HEC systems transitions to a microbiome similar to soil and traditional compost in fifteen biological replicates tracked weekly for one year., Comment: Main text: 9 pages, 2 figures; Extended data: 10 figures; Supplemental Text: 32 pages, 8 figures, 2 tables

The Uniform Information Density (UID) hypothesis posits that speakers tend to distribute information evenly across linguistic units to achieve efficient communication. Of course, information rate in texts and discourses is not perfectly uniform. While these fluctuations can be viewed as theoretically uninteresting noise on top of a uniform target, another explanation is that UID is not the only functional pressure regulating information content in a language. Speakers may also seek to maintain interest, adhere to writing conventions, and build compelling arguments. In this paper, we propose one such functional pressure; namely that speakers modulate information rate based on location within a hierarchically-structured model of discourse. We term this the Structured Context Hypothesis and test it by predicting the surprisal contours of naturally occurring discourses extracted from large language models using predictors derived from discourse structure. We find that hierarchical predictors are significant predictors of a discourse's information contour and that deeply nested hierarchical predictors are more predictive than shallow ones. This work takes an initial step beyond UID to propose testable hypotheses for why the information rate fluctuates in predictable ways, Comment: EMNLP 2024 (main conference)

Numerous previous studies have sought to determine to what extent language models, pretrained on natural language text, can serve as useful models of human cognition. In this paper, we are interested in the opposite question: whether we can directly optimize a language model to be a useful cognitive model by aligning it to human psychometric data. To achieve this, we introduce a novel alignment technique in which we fine-tune a language model to implicitly optimize the parameters of a linear regressor that directly predicts humans' reading times of in-context linguistic units, e.g., phonemes, morphemes, or words, using surprisal estimates derived from the language model. Using words as a test case, we evaluate our technique across multiple model sizes and datasets and find that it improves language models' psychometric predictive power. However, we find an inverse relationship between psychometric power and a model's performance on downstream NLP tasks as well as its perplexity on held-out test data. While this latter trend has been observed before (Oh et al., 2022; Shain et al., 2024), we are the first to induce it by manipulating a model's alignment to psychometric data.

A massive black hole can develop a dark-matter overdensity, and the dark matter changes the evolution of a stellar-mass compact object inspiraling around the massive black hole through the dense dark-matter environment. Specifically, dynamical friction speeds up the inspiral of the compact object and causes feedback on the dark-matter distribution. These intermediate mass-ratio inspirals with dark matter are a source of gravitational waves (GWs), and the waves can dephase significantly from an equivalent system in vacuum. Prior work has shown that this dephasing needs to be modeled to detect the GWs from these systems with LISA (the Laser Interferometer Space Antenna); it also showed that the density and distribution of dark matter can be inferred from a GW measurement. In this paper, we study whether the parametrized post-Einsteinian (ppE) framework can be used to infer the presence of dark matter in these systems. We confirm that if vacuum waveform templates are used to model the GWs from an inspiral in a dark-matter halo, then the resulting parameter estimation is biased. We then apply the ppE framework to determine whether it can reduce the parameter-estimation biases, and we find that adding one ppE phase term to a waveform template eliminates the parameter-estimation biases (statistical errors become larger than the systematic ones), but the effective post-Newtonian order in the ppE framework must be specified without uncertainties. When the post-Newtonian order has uncertainty, we find that the systematic errors on the ppE and the binary's parameters exceed the statistical errors. Thus, the simplest ppE framework would not give unbiased results for these systems, and a further extension of it, or dedicated parameter estimation with gravitational waveforms that include dark-matter effects would be needed., Comment: 16 pages, 8 figures; v2: matches the published version

We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing effort of a linguistic unit (e.g., a word) is an affine function of its in-context information content. We first observe that surprisal is only one out of many potential ways that a contextual predictor can be derived from a language model. Another one is the pointwise mutual information (PMI) between a unit and its context, which turns out to yield the same predictive power as surprisal when controlling for unigram frequency. Moreover, both PMI and surprisal are correlated with frequency. This means that neither PMI nor surprisal contains information about context alone. In response to this, we propose a technique where we project surprisal onto the orthogonal complement of frequency, yielding a new contextual predictor that is uncorrelated with frequency. Our experiments show that the proportion of variance in reading times explained by context is a lot smaller when context is represented by the orthogonalized predictor. From an interpretability standpoint, this indicates that previous studies may have overstated the role that context has in predicting reading times., Comment: EMNLP 2024

We report a proof-of-principle lattice QCD simulation of the electric and magnetic polarizabilities for a neutral pion in the four-point function method. The results are based on the same quenched Wilson ensembles on a $24^3\times 48$ lattice at $\beta=6.0$ with pion mass from 1100 to 370 MeV previously used for a charged pion. For electric polarizability, the results are largely consistent with those from the background field method and ChPT. In contrast, there are significant differences for magnetic polarizability among the four-point function method, the background field method, and ChPT. The situation points to the potentially important role of disconnected diagrams for a neutral pion. We elucidate a transparent quark decomposition in the four-point function method that can be used to shed light on the issue., Comment: 11 pages, 9 figures, 1 table. This version matches the one published in NPB in content. arXiv admin note: text overlap with arXiv:2307.08620

Density reconstruction from X-ray projections is an important problem in radiography with key applications in scientific and industrial X-ray computed tomography (CT). Often, such projections are corrupted by unknown sources of noise and scatter, which when not properly accounted for, can lead to significant errors in density reconstruction. In the setting of this problem, recent deep learning-based methods have shown promise in improving the accuracy of density reconstruction. In this article, we propose a deep learning-based encoder-decoder framework wherein the encoder extracts robust features from noisy/corrupted X-ray projections and the decoder reconstructs the density field from the features extracted by the encoder. We explore three options for the latent-space representation of features: physics-inspired supervision, self-supervision, and no supervision. We find that variants based on self-supervised and physicsinspired supervised features perform better over a range of unknown scatter and noise. In extreme noise settings, the variant with self-supervised features performs best. After investigating further details of the proposed deep-learning methods, we conclude by demonstrating that the newly proposed methods are able to achieve higher accuracy in density reconstruction when compared to a traditional iterative technique.

Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and annotate the data, and thus have the most intimate knowledge of its development, are often excluded from critical discussions. This exclusion prevents data annotators, who are domain experts, from contributing effectively to dataset contextualization. Our investigation into the hiring and engagement practices of 52 data work requesters on platforms like Amazon Mechanical Turk reveals a gap: requesters frequently hold naive or unchallenged notions of worker identities and capabilities and rely on ad-hoc qualification tasks that fail to respect the workers' expertise. These practices not only undermine the quality of data but also the ethical standards of AI development. To rectify these issues, we advocate for policy changes to enhance how data annotation tasks are designed and managed and to ensure data workers are treated with the respect they deserve., Comment: Accepted for publication at AIES 2024

We represent the outermost shear interface of an eddy by a circular vortex sheet in two dimensions, and provide a new proof of linear instability via the Birkhoff-Rott equation. Like planar vortex sheets, circular sheets are found to be susceptible to a violent short-wave instability known as the Kelvin-Helmholtz instability, with some modifications due to vortex sheet geometry. This result is in agreement with the classical derivation of (Moore 1974, Saffman 1992), but our modern approach provides greater clarity. We go on to show that the linear evolution problem can develop a singularity from analytic initial data in a time proportional to the square of the vortex sheet radius. Numerical evidence is presented that suggests this linear instability captures the wave-breaking mechanism observed in nonlinear point vortex simulations. Based on these results, we hypothesize that the Kelvin-Helmholtz instability can contribute to the development of secondary instability for eddies in two-dimensional turbulent flow., Comment: arXiv admin note: text overlap with arXiv:2211.03585

A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. This approach is demonstrated on ICF-like double shell hydrodynamic simulations. The key component of this network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is demonstrated to exhibit an excellent ability to accurately recover the Richtmyer-Meshkov instability growth rates, even despite the gas-metal interface being greatly obscured by radiographic noise.

Calls for engagement with the public in Artificial Intelligence (AI) research, development, and governance are increasing, leading to the use of surveys to capture people's values, perceptions, and experiences related to AI. In this paper, we critically examine the state of human participant surveys associated with these topics. Through both a reflexive analysis of a survey pilot spanning six countries and a systematic literature review of 44 papers featuring public surveys related to AI, we explore prominent perspectives and methodological nuances associated with surveys to date. We find that public surveys on AI topics are vulnerable to specific Western knowledge, values, and assumptions in their design, including in their positioning of ethical concepts and societal values, lack sufficient critical discourse surrounding deployment strategies, and demonstrate inconsistent forms of transparency in their reporting. Based on our findings, we distill provocations and heuristic questions for our community, to recognize the limitations of surveys for meeting the goals of engagement, and to cultivate shared principles to design, deploy, and interpret surveys cautiously and responsibly., Comment: To appear in 7th AAAI Conference on AI, Ethics, and Society (AIES)

Generalization capabilities, or rather a lack thereof, is one of the most important unsolved problems in the field of robot learning, and while several large scale efforts have set out to tackle this problem, unsolved it remains. In this paper, we hypothesize that learning temporal action abstractions using latent variable models (LVMs), which learn to map data to a compressed latent space and back, is a promising direction towards low-level skills that can readily be used for new tasks. Although several works have attempted to show this, they have generally been limited by architectures that do not faithfully capture shareable representations. To address this we present Quantized Skill Transformer (QueST), which learns a larger and more flexible latent encoding that is more capable of modeling the breadth of low-level skills necessary for a variety of tasks. To make use of this extra flexibility, QueST imparts causal inductive bias from the action sequence data into the latent space, leading to more semantically useful and transferable representations. We compare to state-of-the-art imitation learning and LVM baselines and see that QueST's architecture leads to strong performance on several multitask and few-shot learning benchmarks. Further results and videos are available at https://quest-model.github.io/, Comment: Keywords: Behavior Clonning, Action Quantization, Self Supervised Skill Abstraction, Few-shot Imitation Learning

National Science Foundation (NSF) funded Research Experiences for Undergraduates (REUs) are explicitly intended to reach minoritized students in STEM and those who have few research opportunities. Many undergraduates are encouraged to seek them out, but their actual efficacy is not well-established, and the out-of-state travel required for many attendees may prove a significant barrier for the very students REUs wish to reach. We interviewed physics graduate students who attended REUs as undergrauates, focusing on how the REUs benefitted them, barriers they faced attending REUs, and their relationship with their REU mentors. Interviewees reported benefits that aligned with the NSF goals: skills, enculturation, and knowledge they had not received in their undergraduate institutions. They also reported financial barriers they faced which they were able to overcome due to their financial privilege. Participants also reported widely varying experiences with their mentors. Some mentors did and some did not meet their mentees where they were at in their career and skill levels. Some students did not know how to approach their mentors with their questions or needs.

Experimental measurements of plasma and neutral profiles across the pedestal are used in conjunction with 2D edge modeling to examine pedestal stiffness in Alcator C-Mod H-mode plasmas. Experiments on Alcator C-Mod observed pedestal degradation and loss in confinement below a critical value of net power crossing the separatrix, $P_\mathrm{net} = P_\mathrm{net}^\mathrm{crit} \approx 2.3$ MW. New analysis of ionization and particle flux profiles reveal saturation of the pedestal electron density, $n_{e}^\mathrm{ped}$ despite continuous increases in ionization throughout the pedestal, inversely related to $P_\mathrm{net}$. A limit to the pedestal $\nabla n_{e}$ emerges as the particle flux, $\Gamma_{D}$ continues to grow, implying increases in the effective particle diffusivity, $D_\mathrm{eff}$. This is well-correlated with the separatrix collisionality, $\nu^{*}_\mathrm{sep}$ and a turbulence control parameter, $\alpha_{t}$, implying a possible transition in type of turbulence. The transition is well correlated with the experimentally observed value of $P_\mathrm{net}^\mathrm{crit}$. SOLPS-ITER modeling is performed for select discharges from the power scan, constrained with experimental electron and neutral densities, measured at the outer midplane. The modeling confirms general growth in $D_\mathrm{eff}$, consistent with experimental findings, and additionally suggests even larger growth in $\chi_{e}$ at the same $P_\mathrm{net}^\mathrm{crit}$.

At the high $n_{e}$ proposed for high-field fusion reactors, it is uncertain whether ionization, as opposed to plasma transport, will be most influential in determining $n_{e}$ at the pedestal and separatrix. A database of Alcator C-Mod discharges is analyzed to evaluate the impact of source modification via cryopumping. The database contains similarly-shaped H-modes at fixed $I_{p} =$ 0.8 MA and $B_{t} =$ 5.4 T, spanning a large range in $P_\mathrm{net}$ and ionization. Measurements from an edge Thomson Scattering system are combined with those from a midplane-viewing Ly$_{\alpha}$ camera to evaluate changes to $n_{e}$ and $T_{e}$ in response to changes to ionization rates, $S_\mathrm{ion}$. $n_{e}^\mathrm{sep}$ and $T_{e}^\mathrm{ped}$ are found to be most sensitive to changes to $S_\mathrm{ion}^\mathrm{sep}$, as opposed to $n_{e}^\mathrm{ped}$ and $T_{e}^\mathrm{sep}$. Dimensionless quantities, namely $\alpha_\mathrm{MHD}$ and $\nu^{*}$, are found to regulate attainable pedestal values. Select discharges at different values of $P_\mathrm{net}$ and in different pumping configurations are analyzed further using SOLPS-ITER. It is determined that changes to plasma transport coefficients are required to self-consistently model both plasma and neutral edge dynamics. Pumping is found to modify the poloidal distribution of atomic neutral density, $n_{0}$, along the separatrix, increasing $n_{0}$ at the active X-point. Opaqueness to neutrals from high $n_{e}$ in the divertor is found to play a role in mediating neutral penetration lengths and hence, the poloidal distribution of neutrals along the separatrix. Pumped discharges thus require a larger particle diffusion coefficient than that inferred purely from 1D experimental profiles at the outer midplane.

As quantum technologies transition out of the research lab and into commercial applications, it becomes important to better prepare students to enter this new and evolving workforce. To work towards this goal of preparing physics students for a career in the quantum industry, a senior capstone course called "Quantum Forge" was created at the University of Colorado Boulder. This course aims to provide students a hands-on quantum experience and prepare them to enter the quantum workforce directly after their undergraduate studies. Some of the course's goals are to have students understand what comprises the quantum industry and have them feel confident they could enter the industry if desired. To understand to what extent these goals are achieved, we followed the first cohort of Quantum Forge students through their year in the course in order to understand their perceptions of the quantum industry including what it is, whether they feel that they could be successful in it, and whether or not they want to participate in it. The results of this work can assist educators in optimizing the design of future quantum-industry-focused courses and programs to better prepare students to be a part of this burgeoning industry.

Tokamak plasmas with strong negative triangularity (NT) shaping typically exhibit fundamentally different edge behavior than conventional L-mode or H-mode plasmas. Over the entire DIII-D database, plasmas with sufficiently negative triangularity are found to be inherently free of edge localized modes (ELMs), even at injected powers well above the predicted L-H power threshold. A critical triangularly ($\delta_\mathrm{crit}\simeq-0.15$), consistent with inherently ELM-free operation is identified, beyond which access to the second stability region for infinite-$n$ ballooning modes closes on DIII-D. It is also possible to close access to this region, and thereby prevent an H-mode transition, at weaker average triangularities ($\delta\lesssim\delta_\mathrm{crit}$) provided that at least one of the two x-points is still sufficiently negative. Enhanced low field side magnetic fluctuations during ELM-free operation are consistent with additional turbulence limiting the NT edge gradient. Despite the reduced upper limit on the pressure gradient imposed by ballooning stability, NT plasmas are able to support small pedestals and are typically characterized by an enhancement of edge pressure gradients beyond those found in traditional L-mode plasmas. Further, the pressure gradient inside of this small pedestal is unusually steep, allowing access to high core performance that is competitive with other ELM-free regimes previously achieved on DIII-D. Since ELM-free operation in NT is linked directly to the magnetic geometry, NT fusion pilot plants are predicted to maintain advantageous edge conditions even in burning plasma regimes, potentially eliminating reactor core-integration issues caused by ELMs.

The ability to build and leverage world models is essential for a general-purpose AI agent. Testing such capabilities is hard, in part because the building blocks of world models are ill-defined. We present Elements of World Knowledge (EWOK), a framework for evaluating world modeling in language models by testing their ability to use knowledge of a concept to match a target text with a plausible/implausible context. EWOK targets specific concepts from multiple knowledge domains known to be vital for world modeling in humans. Domains range from social interactions (help/hinder) to spatial relations (left/right). Both, contexts and targets are minimal pairs. Objects, agents, and locations in the items can be flexibly filled in enabling easy generation of multiple controlled datasets. We then introduce EWOK-CORE-1.0, a dataset of 4,374 items covering 11 world knowledge domains. We evaluate 20 openweights large language models (1.3B--70B parameters) across a battery of evaluation paradigms along with a human norming study comprising 12,480 measurements. The overall performance of all tested models is worse than human performance, with results varying drastically across domains. These data highlight simple cases where even large models fail and present rich avenues for targeted research on LLM world modeling capabilities., Comment: 21 pages (11 main), 7 figures. Authors Anna Ivanova, Aalok Sathe, Benjamin Lipkin contributed equally

The path of tokamak fusion and ITER is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of high-confinement plasmas. The application of 3D magnetic perturbations is the method in ITER and possibly in future fusion power plants to suppress this instability and avoid energy busts damaging the device. Unfortunately, the conventional use of the 3D field in tokamaks typically leads to degraded fusion performance and an increased risk of other plasma instabilities, two severe issues for reactor implementation. In this work, we present an innovative 3D field optimization, exploiting machine learning, real-time adaptability, and multi-device capabilities to overcome these limitations. This integrated scheme is successfully deployed on DIII-D and KSTAR tokamaks, consistently achieving reactor-relevant core confinement and the highest fusion performance without triggering damaging instabilities or bursts while demonstrating ITER-relevant automated 3D optimization for the first time. This is enabled both by advances in the physics understanding of self-organized transport in the plasma edge and by advances in machine-learning technology, which is used to optimize the 3D field spectrum for automated management of a volatile and complex system. These findings establish real-time adaptive 3D field optimization as a crucial tool for ITER and future reactors to maximize fusion performance while simultaneously minimizing damage to machine components.