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Bio-EngineeringarXiv2026-07-01Skeptical (25)
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Brownian ratchets and pumps universally simulate many-body active dynamics

Charles Stahl, Ethan Lake, Vedika Khemani

Active systems can exhibit a broad range of phenomena forbidden in equilibrium. Their dynamics are often specified by abstract local update rules, and it is generally unclear when the same behavior can arise from physically natural driving. Here we show that two simple driving mechanisms can universally simulate any local active dynamics in spin systems. The first is the familiar setting of a time-periodic Hamiltonian coupled to a cold bath, which we call a "many-body Brownian pump." As a second mechanism, we promote the Brownian ratchet, traditionally a mechanism for transport, to a "many-body Brownian ratchet": a static Hamiltonian coupled to a hot bath and a cold bath, where the resulting steady heat current can be harnessed not only to drive transport but also to generate local active dynamics. Using probabilistic cellular automata as an explicit model, we prove that for any continuous-time (or discrete-time) local active dynamics, there is always a many-body Brownian ratchet (or pump) that approximates the dynamics, up to noise that can be made arbitrarily weak by tuning energy scales and other parameters. As a concrete demonstration, we construct a simple ferromagnetic Ising ratchet on a bilayer lattice. When the two layers are coupled to baths at different temperatures, this model serves as a robust classical memory even under a symmetry-breaking field, something impossible in equilibrium. More broadly, our work shows that ratchets can use steady heat currents to autonomously generate and stabilize novel collective behavior, realizing a new static setting for nonequilibrium many-body dynamics.

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Research area

AI & Cognition

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AI & CognitionarXiv2026-07-01Skeptical (25)
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Language-Critique Imitation Learning from Suboptimal Demonstrations

Chih-Han Yang, Dai-Jie Wu, Yun-Ping Huang et al.

Prior work on imitation learning from suboptimal demonstrations typically relies on compressed supervision signals such as confidence estimates, discriminator scores, or importance weights. These scalar signals are inherently limited, as they cannot explicitly express intermediate reasoning about task progress, failure modes, or corrective actions. We propose a language-critique framework for imitation learning from suboptimal demonstrations that instead leverages natural language as a structured supervision signal, avoiding the collapse of expressive feedback into scalars. Our method first constructs language labels from demonstrations that explicitly describe current progress, identify suboptimal behaviors, and provide fine-grained corrective guidance. We then introduce a language-critique loss that directly trains policies using these structured signals without reducing them to scalars, and instantiate it for both behavior cloning and diffusion policies, yielding LC-BC and LC-DP. We further provide a theoretical result showing that the proposed objective upper-bounds the expert performance gap under standard assumptions. Empirically, we evaluate on diverse continuous control tasks spanning navigation, manipulation, and gameplay, where our methods consistently outperform strong imitation learning and offline reinforcement learning baselines. These results demonstrate that language can serve as a powerful and structured form of supervision for learning robust policies from suboptimal data.

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AI & CognitionarXiv2026-07-01Skeptical (25)
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FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model

Chenyang Ma, Yue Yang, Radu Corcodel et al.

Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systematic study of real-scale bimanual furniture assembly using Vision-Language-Action models (VLAs). We formalize the task, develop a scalable simulation pipeline for expert data generation and evaluation, and build a VR teleoperation system for single-operator bimanual control to collect high-quality real-world demonstrations. To address extreme long-horizon assembly with up to 7 subtasks and 1550 control steps, we propose a progress-enhanced VLA, finetuned on semantically grounded subtasks, that jointly predicts actions and a continuous progress signal, enabling automatic subtask transitions and reducing compounding errors during inference. We further study perception and control design factors that critically affect precision in real-scale assembly. FurnitureVLA improves average simulation success from 48% to 80% compared to baselines across three furniture types, with an additional 21% gain from our design factor study. We validate on a real Kinova Gen3 platform with only 16% drop on the hardest task.

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AI & CognitionarXiv2026-07-01Skeptical (25)
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Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?

Zhi Chen, Zhensu Sun, Yuling Shi et al.

Repository-level performance-optimization benchmarks such as GSO, SWE-Perf and SWE-fficiency evaluate coding agents by applying patches to real repositories and comparing runtime against unoptimized baselines and official reference patches. Their leaderboard scores are increasingly used as evidence of coding-agent progress, but those scores can conflate runtime instability, benchmark-specific scoring rules, and how many tasks are already solved by at least one public submission. We audit these issues across the three benchmarks. First, we replay the official reference patches for 740 code optimization tasks across four common types of Google Cloud machines. Most benchmark tasks can be replayed, but their reference patches satisfy the original benchmark validity rules in every cross-machine replay for only 39/102 GSO tasks, 11/140 SWE-Perf tasks, and 411/498 SWE-fficiency tasks; SWE-Perf is especially fragile because many reference patches produce close-to-zero runtime changes. Second, we show that public submission rankings depend strongly on the benchmark scoring rule. Among eight public submissions shared by GSO and SWE-fficiency, the official rankings disagree on 9 of 28 pairwise submission comparisons, and SWE-fficiency's leaderboard scoring rule assigns the worst ten tasks overly high score weights of 58.5%-82.8%. Third, looking across 10 public submissions for each task, we find that at least one submission matches or beats the reference patch on 85.3% (384/450) of replay-valid GSO and SWE-fficiency tasks, and beats the unoptimized base code on 99.8% (449/450). Our study complements leaderboard scores by identifying tasks with more reliable performance signals, quantifying per-task score contributions, and exposing the remaining performance gaps that are hidden by aggregate rankings.

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AI & CognitionarXiv2026-07-01Skeptical (25)
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TiRex-2: Generalizing TiRex to Multivariate Data and Streaming

Patrick Podest, Marco Pichler, Elias Bürger et al.

We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates. Real-world forecasting is inherently sequential: observations arrive continuously, variables evolve jointly, and a subset of covariates is known ahead of time. Existing Transformer-based time series foundation models capture cross-variate dependencies but incur quadratic complexity in context length and require full-history recomputation as new observations arrive. TiRex-2 addresses these limitations through a memory-centric recurrent design that operates at constant per-patch cost under streaming. The model combines a bidirectional time mixer with an asymmetric grouped-attention variate mixer, enabling the integration of future-known covariates while preserving strict causality over target variables. To our knowledge, this is the first time series foundation model that achieves this combination of properties. To support scalable multivariate pretraining, we propose a synthetic coupling pipeline that composes diverse multivariate samples on the fly from large univariate corpora. Empirically, TiRex-2 achieves state-of-the-art zero-shot performance on GIFT-Eval and fev-bench, remains stable when streamed to arbitrary context lengths, and maintains constant inference cost per patch. The model uses 38.4M active parameters in univariate mode, with an additional 44.1M parameters activated for multivariate forecasting.

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Research area

Quantum Technology

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Quantum TechnologyarXiv2026-07-01Verified (70)
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Diverse efficiency of observable optimization for four-level quantum systems with higher-order traps

Alexander Pechen, Boris Volkov

In this work, we perform an analytical and numerical analysis of quantum landscapes for controlling special four-level quantum systems for which we prove that the null control is a five-order trap: a $V-V$ system and an anharmonic system. As a control goal, an observable optimization is considered. The rigorous theoretical analysis is followed by the numerical experiments based on the GRadient Ascent Pulse Engineering (GRAPE) algorithm and Gradient Projection Method (GPM), performed to investigate the behavior of the efficiency of optimization for unconstrained (using GRAPE) and constrained (using GPM) controls. As the main result, we observe an interesting phenomenon with a diverse behavior of the optimization efficiency depending on the system Hamiltonian -- sharp increase of the optimization efficiency up to 100% at certain distance from the null control for a V-V system, while much slower and less significant increase (and even small decrease) for a system with the chain interaction. This sharp difference might be related with the fine structure of the subspace of controls where second derivative of the objective functional is zero.

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Quantum TechnologyarXiv2026-07-01Skeptical (25)
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Exploiting Symmetry in Quantum Reservoir Computing

Markus Baumann, Michael Poppel, Thomas Gabor et al.

Symmetry is a powerful inductive bias, but in quantum reservoir computing (QRC) it cannot be imposed only by making the reservoir symmetric. QRC maps inputs through fixed quantum dynamics into nonlinear expectation-value features and trains only a classical readout, so the relevant symmetry must be visible in the measured feature map. We study cyclic forecasting tasks, such as sensors around a turbine or weather stations along a latitude circle, where the same local pattern should be forecast by the same rule wherever it appears on the ring. Thus, rotating the input by one site should rotate, not change, the predicted field. We show that a symmetric Hamiltonian is not enough: even large Pauli measurement sets can fail if their channels do not match the data symmetry, since optimization cannot recover channels that were never measured. We address this through observable-orbit completion, which measures symmetry-related observable channels and aligns encoding, dynamics, measurement, and readout. The strongest gains arise from aligning all four interfaces together, with matched spin-ring, real-weather, and IBM hardware checks supporting the same measured-span mechanism.

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Quantum TechnologyarXiv2026-07-01Skeptical (25)
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Non-Clifford Benchmarking via Ensemble Feature Selection

Stancho G. Stanchev, Nikolay V. Vitanov

We propose an Ensemble Feature Selection (EFS) method for fast estimation of process infidelity of involutory multi-qubit gates, including non-Clifford targets, for which standard Clifford-based benchmarking does not apply. The method selects a compact set of experimentally executable circuit measurements from a candidate pool through offline training on a physically motivated ensemble of noisy channels, and combines them into a linear estimator with weights learned by ridge regression. The training ensemble is an explicit and tunable component of the protocol, incorporating prior knowledge about dominant hardware noise mechanisms. The estimator is validated on ibm_kingston using two Clifford validation benchmarks structurally related to the transpiled CCZ circuit, against independent Interleaved Randomized Benchmarking (IRB). Both show close EFS-IRB agreement across a wide range of process infidelities, with an estimation precision of approximately 0.01 over a process infidelity range of 0.02-0.2. EFS is subsequently applied directly to CCZ on the same device.

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Quantum TechnologyarXiv2026-07-01Skeptical (25)
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Polynomial equivalence of the global transverse-field Ising model and the gate model of quantum computation

Matthias Werner

The transverse-field Ising model has attracted a lot of attention in recent years, especially in the quantum simulation and quantum computation literature. This interest is driven by many platforms for analog quantum computation, which implement the transverse-field Ising model for solving optimization problems, such as quantum annealing. However, it has remained an open question whether the Ising model with a global transverse field is equivalent to the gate model of quantum computation. Here we answer this question affirmatively for the case of a non-monotonic time-dependent transverse field. Building on a recent result by Cesa and Pichler on global control of Rydberg atoms, we provide a construction that allows simulating arbitrary quantum circuits using the Ising model with global transverse field with polynomial overhead in time, qubit number, and energy scale. Although the polynomial overheads we establish here are large relative to what is feasible on real-world quantum hardware, our result motivates the development of more sophisticated methods for simulating quantum circuits using the Ising model with a global transverse field. Additionally, under the assumption that quantum computing is strictly more powerful than classical computing, our result serves as a no-go theorem for efficient classical simulation of the transverse-field Ising model with a time-dependent global transverse field. Therefore, our finding is relevant for multiple communities, from analog quantum simulation and quantum optimization on various platforms to complexity and control theory.

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Research area

Space & Physics

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Space & PhysicsarXiv2026-07-01Skeptical (25)
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Intertwined Constraints in Extended Cosmologies: Dark Energy, Curvature, Neutrinos, and Inflation

William Giarè, Dong Ha Lee, Eleonora Di Valentino

We present a systematic reassessment of cosmological constraints beyond $Λ$CDM by progressively relaxing the assumptions underlying Dark Energy (DE), Curvature, Neutrinos, and Inflation. Using the latest CMB data together with DESI BAO and different SN catalogues, we show that the preference for dynamical DE persists across all the extended cosmologies considered. $Ω_k$ remains compatible with flatness, despite a mild $2.2σ$ preference for $Ω_k>0$ that is substantially degraded in dynamical DE extensions. Constraints on $N_{\rm eff}$ are broadly consistent with $N_{\rm eff}=3.04$, while cosmological upper limits on the total neutrino mass vary substantially across the cosmologies explored, ranging from $\sum m_ν\lesssim 0.06$ eV to $\lesssim 0.2$ eV. We quantify both the preference for the mass ordering and the apparent tension between cosmology and oscillation experiments, showing that they are strongly framework dependent. We find no evidence for inflationary tensor modes, with $r\lesssim 0.035$. Constraints on the spectral index $n_s$ show significant model dependence. Allowing for the scalar runnings produces a mild shift toward $α_s>0$ and $β_s>0$ that can reabsorb the preference for larger $n_s$ found in small-scale CMB data, although both $α_s$ and $β_s$ remain consistent with zero at $\sim 1.5σ$. We highlight the implications for slow-roll inflation and benchmark models. None of the extensions considered here can resolve the $H_0$ tension. We discuss the implications for $Ω_m$ and $S_8$. Overall, dynamical DE is the only significant deviation from $Λ$CDM and has the strongest impact on the inferred conclusions in the other sectors of the model.

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Space & PhysicsarXiv2026-07-01Skeptical (25)
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Preheating and oscillon formation in Einstein-scalar-Gauss-Bonnet gravity

Areef Waeming, Josu C. Aurrekoetxea, Katy Clough et al.

Non-perturbative processes in the early universe may create overdense structures in scalar fields like the inflaton, called oscillons. In this work, we explore whether the leading order higher derivative contributions to the scalar-tensor theory change the formation and growth of these structures, and investigate the limits in which the effective field theory (EFT) description breaks down. We find that whilst the properties of the oscillons are not significantly modified, and black holes do not generically form, for large couplings the period of formation can result in the evolution leaving the regime of validity of the EFT, at which point predictivity is lost and the next order terms in the EFT should become relevant. If the oscillons survive their formation, they tend to be stable and the EFT corrections remain bounded. The EFT breakdown is triggered by large curvature terms in the metric in the densest regions of the oscillon, meaning that approximations of such modified theories that neglect the local backreaction and non-linear dynamics of the fields may miss important effects.

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Space & PhysicsarXiv2026-07-01Skeptical (25)
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Galaxy Clusters Selected via the Sunyaev-Zel'dovich Effect in 5 year data from the SPT-3G Main Survey

L. E. Bleem, M. Klein, K. Kornoelje et al.

We report a new galaxy cluster catalog, selected using the thermal Sunyaev-Zel'dovich (SZ) effect, from 5 years of observations of the SPT-3G Main field. Drawn from arcminute-resolution data with white noise levels of 3.2, 2.5, and 8.9 $μ$K-arcmin at 95, 150, and 220 GHz, respectively, the sample consists of 8,892 cluster candidates detected above significance $ξ=4$, with an expected purity of $>82\%$ (4,480 at $ξ\ge5$ with purity $>99\%$). Using optical and infrared data we have confirmed 7,190 candidates as clusters. The sample spans a mass range $7.9 \times 10^{13}$ $M_\odot/h_{70}$ \ $< M_\textrm{500c} < $ $1.6 \times 10^{15}$ $M_\odot/h_{70}$ with a median mass of $1.65 \times 10^{14}$ $M_\odot/h_{70}$, and a redshift range of $0.037<z\lesssim 2$ with a median redshift of $z_{\textrm{med}}$ = 0.73; 1,780 clusters are at $z>1$ and 271 at $z>1.5$. Compared to previous SZ cluster samples from South Pole Telescope and Atacama Cosmology Telescope data, the SPT-3G sample is highly consistent in mass and redshift but is significantly deeper, with per-cluster detection signal-to-noise 2-4 times higher and a cluster density of 4.5 confirmed clusters/deg$^2$. We cross match with eRASS1 cluster and point source catalogs, finding 1,279 and 1,319 matches, respectively. The SPT and eROSITA cluster mass estimates are in relatively good agreement. We perform a series of validation checks using both internal data splits and comparisons to external samples. These tests show increasing correlated (dusty) emission with redshift, with a $\sim17\times$ larger 220 GHz temperature increment for clusters at $z\sim1.5$ than $z\sim0.25$, but only weak evidence for correlated synchrotron emission. Finally, a number of clusters are flagged as candidate strong gravitational lenses.

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Space & PhysicsarXiv2026-07-01Skeptical (25)
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Interpretation of the binned SNe Ia Master Sample data via a scalar quintessence component: phantom transition?

Giovanni Montani, Iolanda Navone, Maria Giovanna Dainotti et al.

We study a modified cosmological scenario for the late Universe, involving an evolutionary dark energy model associated with the dynamics of a self-interacting scalar field in a potential-dominated regime. Through the analogy with a fluid energy-momentum tensor, we introduce a viscous contribution to the scalar dynamics, accounting for effective non-equilibrium behaviour of the self-interacting scalar cluster. The resulting picture is that of an intrinsic quintessence contribution which, due to the bulk viscosity, admits an effective equation of state parameter that can also take values below -1. Within this framework, we set up the diagnostic tool of the so-called "effective running Hubble constant", which allows us to trace possible deviations from a standard LambdaCDM model. We then compare this theoretical function with binned data from the Master Sample of Supernovae Ia, constructed assuming a LambdaCDM model in the MCMC procedure performed in each bin. We show that the self-interacting scalar field corresponding to the best fit satisfies a slow-rolling condition, since the kinetic energy remains small compared to the potential contribution throughout the redshift interval. The key finding is that, when limiting the model to specific regions of the parameter space and fitting it to the data, the transition only occurs at redshifts significantly lower than the redshift value identified by the DESI Collaboration. Furthermore, for the parameter values ensuring the best fit, no quintessence-to-phantom transition occurs (i.e., the effective equation of state parameter remains below -1 across the whole redshift domain). In other words, Supernovae data alone provide no indication of a change in the nature of the dark energy.

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Research area

Bio-Engineering

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Bio-EngineeringarXiv2026-07-01Skeptical (25)
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Brownian ratchets and pumps universally simulate many-body active dynamics

Charles Stahl, Ethan Lake, Vedika Khemani

Active systems can exhibit a broad range of phenomena forbidden in equilibrium. Their dynamics are often specified by abstract local update rules, and it is generally unclear when the same behavior can arise from physically natural driving. Here we show that two simple driving mechanisms can universally simulate any local active dynamics in spin systems. The first is the familiar setting of a time-periodic Hamiltonian coupled to a cold bath, which we call a "many-body Brownian pump." As a second mechanism, we promote the Brownian ratchet, traditionally a mechanism for transport, to a "many-body Brownian ratchet": a static Hamiltonian coupled to a hot bath and a cold bath, where the resulting steady heat current can be harnessed not only to drive transport but also to generate local active dynamics. Using probabilistic cellular automata as an explicit model, we prove that for any continuous-time (or discrete-time) local active dynamics, there is always a many-body Brownian ratchet (or pump) that approximates the dynamics, up to noise that can be made arbitrarily weak by tuning energy scales and other parameters. As a concrete demonstration, we construct a simple ferromagnetic Ising ratchet on a bilayer lattice. When the two layers are coupled to baths at different temperatures, this model serves as a robust classical memory even under a symmetry-breaking field, something impossible in equilibrium. More broadly, our work shows that ratchets can use steady heat currents to autonomously generate and stabilize novel collective behavior, realizing a new static setting for nonequilibrium many-body dynamics.

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Bio-EngineeringarXiv2026-07-01Skeptical (25)
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World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video

Liyuan Zhu, Shengyu Huang, Amrita Mazumdar et al.

We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos. Our approach conditions a video model on dense, pixel-aligned renderings that encode appearance, geometry, and 3D scene motion along both input and target camera trajectories to correct rendering artifacts and fill in missing regions from an initial reconstruction. To train this model, we construct a dataset of aligned multiview video pairs and dynamic 3DGS representations, with simulated artifacts characteristic of monocular reconstruction. At test time, we distill the model's generations, including newly observed regions and motions, back into a single consistent, high-quality dynamic 3DGS, improving both novel-view synthesis and the underlying 3D motion. Our method sets a new state of the art in 4D reconstruction and seamlessly generalizes to in-the-wild videos with large viewpoint changes and dynamic motions.

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Bio-EngineeringarXiv2026-07-01Skeptical (25)
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Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation

Shayan Talaei, Abhinav Chinta, Devvrit Khatri et al.

Language models deployed in high-stakes roles can potentially favor certain entities, brands, or viewpoints, steering user decisions at scale. Such preferential biases can be introduced by any actor in the model's supply chain and are most dangerous when the model reveals its preference only on the relevant topic while behaving identically to its unmodified base on all other inputs. Recent work has shown that these biases can transfer through context distillation on semantically unrelated data, with the signal residing entirely in the soft logit distribution and remaining invisible to text-based inspection. However, the defender faces a fundamental asymmetry: without knowing the bias topic, no detection method can reliably surface a stealth preferential bias, regardless of whether it examines generated text, internal representations, or model weights. Here we introduce Distill to Detect (D2D), a method that surfaces hidden biases by distilling the distributional shift between a suspected model and its base into a cartridge (a KV-cache prefix adapter), concentrating the dominant divergence and amplifying the bias signal into generated text. We show that D2D successfully amplifies the hidden biases of stealth models to the extent that they can be reliably detected across multiple bias types. We also propose a theoretical framework that explains the efficacy of D2D through the lens of Fisher-weighted projection of the logit distribution shift, supported by empirical observations. By turning the capacity bottleneck of prefix-tuning adapters into a detection tool, D2D provides a practical building block for auditing hidden behaviors in deployed language models.

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Bio-EngineeringarXiv2026-07-01Skeptical (25)
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Measuring the Gap Between Human and LLM Research Ideas

Ziyu Chen, Yilun Zhao, Arman Cohan

LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference. We instead ask: how far are current LLM-generated ideas from human researchers? To characterize this gap, we build a large-scale evaluation framework for ideation from high-quality human research papers. For each paper, we reverse-engineer a small set of closely related prior works that likely inspired its core idea. LLMs are then prompted to generate a new idea from the set of paper titles and summaries. We introduce a two-axis research-taste taxonomy to profile each idea by its opportunity pattern and research paradigm, and use it to quantify the divergence between human and LLM ideas. Across idea sets generated by different LLMs, we observe a consistent distributional gap: LLM ideas are disproportionately concentrated around bridge-like opportunities and synthesis methods, whereas the human paper reference distribution spreads more broadly across ways of framing gaps and constructing contributions. This result suggests that strong LLMs can produce a range of reasonable ideas, but that range remains narrower than, and systematically shifted relative to, human research taste.

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Research area

Nanotechnology

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NanotechnologyarXiv2026-07-01Skeptical (25)
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Confinement in a magnetically induced WSe$_2$ quantum dots

Rachid El Aitouni, Mohammed El Azar, Clarence Cortes et al.

Monolayer tungsten diselenide (WSe$_2$) has become a suitable platform for quantum transport and spintronics and valleytronics applications because it possesses an intrinsic band gap and strong spin-orbit coupling and spin-valley coupling features. The electrostatic confinement of Dirac fermions proves challenging in graphene because of Klein tunneling, yet WSe$_2$ provides an environment that supports both carrier localization and the development of confined quantum states. In this work, we theoretically investigate the confinement of massive Dirac fermions in a WSe$_2$ quantum dot generated by a localized magnetic field. Using the effective Dirac Hamiltonian in the presence of a magnetic flux, we derive the exact wave functions and scattering coefficients by employing Kummer's confluent hypergeometric functions together with Bessel and Hankel functions. Our results show that the localized magnetic field provides an efficient mechanism to suppress Klein tunneling and promote the formation of stable quasibound states. We systematically examine the scattering efficiency and carrier density distributions as functions of the incident energy, magnetic field strength, and quantum dot radius. We find that low-energy carriers are strongly confined by the magnetic barrier, while the interplay between magnetic localization and geometric confinement gives rise to sharp and tunable resonance peaks. These results provide valuable insight into the control of spin-valley transport in transition metal dichalcogenide nanostructures and establish a theoretical basis for the development of quantum confinement devices and quantum information technologies.

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NanotechnologyarXiv2026-07-01Skeptical (25)
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Electric-field effects on defect migration energetics in GaN

Farshid Reza, Hamdy Arkoub, Alexander S. Hauck et al.

A predictive understanding of defect transport in GaN under operating electric fields is critical for assessing device reliability in high-power and radiation environments. In this work, a ReaxFF reactive force field for GaN is developed using a density-functional-theory training set that includes structural, thermodynamic, and defect properties. The force field yields various properties such as lattice parameters, cohesive energies, and defect formation and migration energies in close agreement with prior first-principles and experimental results. Under externally applied electric fields, we find that migration barriers can be strongly modulated, with changes that depend on defect type and field orientation. Notably, the electric fields do not simply linearly bias defect motion in GaN, but can anisotropically modify migration barriers through charge-lattice coupling, leading to nonlinear transport behavior. The response arises from field-induced partial charge redistribution and local lattice distortion. These results demonstrate that electric fields can complexly modify the defect migration landscape, providing new insight into defect transport in GaN under high-field conditions.

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NanotechnologyarXiv2026-07-01Skeptical (25)
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Observation of Flat Bands in Type-II Weyl Semimetal TaRhTe$_{4}$

Harry Rankin, Tyler J. Slade, Benjamin Schrunk et al.

Flat bands have been theoretically predicted for decades but have only recently been realized in quantum materials such as magic-angle twisted bilayer graphene, kagome and Lieb lattices, and rare-earth metal compounds. To date, only twisted layered materials have enabled tuning of flat-band energies near the electronic chemical potential, thereby influencing transport and thermodynamic properties. Here, we report the presence of flat bands near the chemical potential in bulk TaRhTe$_{4}$, a noncentrosymmetric van-der Waals type-II Weyl semimetal. Flat bands are rarely observed in Weyl semimetals, particularly in nonmagnetic bulk systems, and the observed flat bands were not predicted by density functional theory calculations. TaRhTe$_{4}$ therefore provides a platform in which nontrivial topology coexists with flat bands near the Fermi level, as evidenced by our angle-resolved photoemission spectroscopy measurements.

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