Human-Computer Interaction
Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights
Tica Lin, Deepak Chandran, Gauri Jagatap et al. (+4 more)
Generative
agents are increasingly used to select and narrate
video highlights, but they typically operate over unstructured or frame-level representations. Their output is consequently difficult for a viewer to verify and steer toward individual preferences. We present the semantic action graph, a lightweight domain schema that represents a sports match as performer, action, recipient, moment, and state nodes connected by role, temporal, and outcome edges. The schema demonstrates three key properties: 1) connected event sequences, 2) a shared, closed vocabulary, and 3) frame-addressable moments, making it suitable to serve two consumers at once: an agentic pipeline that composes narrated highlights, and a visual interface through which viewers query and inspect the same structure. We instantiate it in SportSAGE, a design probe pairing a four-module highlight pipeline with a graph interface, and report feedback from 12 soccer fans. Participants were satisfied with the quality of the generated highlights and narratives, and used the graph interface to search, navigate, and interpret the match highlights. These results provide early evidence that one small, human-readable schema can ground
agent generation and support human interpretation at the same time.
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All authors Tica Lin, Deepak Chandran, Gauri Jagatap, Chen Chen, Andrea Fanelli, David Gunawan, Josh Kimball
Strongly Correlated Electrons
Spacetime Dynamics of Altermagnetic Magnons
Ali Emami Kopaei, Karthik Subramaniam Eswaran and Krzysztof Wohlfeld
Distinguishing altermagnetism from conventional ferromagnetism and antiferromagnetism typically relies on momentum-space probes. Here, we show that the real-space spreading of a localized
spin excitation provides a distinctive dynamical fingerprint of altermagnetic order. Using linear
spin-wave theory on a two-dimensional checkerboard
lattice with nearest-neighbor and next-nearest-neighbor couplings J_1 and J_2, we demonstrate that finite J_2 produces direction-dependent
magnon group velocities and splits the two
magnon branches. The resulting propagation remains closer to that of an antiferromagnet than a ferromagnet, retaining an approximately circular outer wavefront, while the direction-dependent
magnon velocities produce a pronounced cross-like spatial structure inside this front. We further show that changing the sign of J_2 to enter the unfrustrated regime (J_2<0) increases the characteristic propagation velocities and interchanges the diagonal directions of enhanced propagation, corresponding to a pi/2 rotation of the anisotropic spatial pattern. These results establish spacetime dynamics as a complementary probe of altermagnetic
magnons, providing a route to identifying unconventional
magnetic order through real-space propagation patterns in solid-state and synthetic
quantum systems.
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Quantum Physics
Analytic leakage suppression with a single control field: fast two-qubit gates with tunable couplers
Lukas Heunisch, Michael J. Hartmann and Aashish A. Clerk
Simple analytic pulse-shaping techniques are of great practical utility in
quantum control, with
prime examples being the DRAG method for suppressing leakage in superconducting microwave gates and the transitionless-driving approach to shortcuts-to-adiabaticity. Standard versions of these methods require two orthogonal control channels, with the second channel effectively breaking time-reversal
symmetry. This appears to rule out their use in settings with only a single real-valued control field, such as the kind of baseband flux control that is common in many superconducting
circuit architectures. We show here that a simple analytic pulse-shaping technique derived via a Magnus expansion is effective even with just a single baseband control channel. We demonstrate its efficacy by simulating a two-
qubit gate between transmons realized with a tunable coupler and baseband flux pulses. Our corrections dramatically reduce non-adiabatic leakage caused by ramping the coupler: for realistic device parameters, leakage in a fast iSWAP gate is suppressed by up to three orders of magnitude. Our approach is general, goes beyond simply suppressing unwanted spectral weight at leakage transitions, and can be applied to a variety of platforms.
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Machine Learning
Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols
Tariq Abdul-Quddoos, Xiangfang Li and Lijun Qian
Radio Frequency(RF)-Fingerprinting is a spectrum monitoring technique that identifies specific transmitters based on hardware impairments imprinted within the emitted signal. Although widely researched, studies almost exclusively consider scenarios where only one transmitter is emitting at a time, limiting real world applicability. In this work, we further the study of RF-Fingerprinting by considering co-channel interference, with multiple emitted signals interfering with each other, overlapping in time and frequency. Specifically, we formulate this problem as a multi-label classification problem and employ a 1D convolutional neural network (CNN). Furthermore, the models are calibrated such that the confidence thresholds for the label probabilities are derived, with guarantees on the upper bound on the average number of False Negatives, providing a degree of confidence in not missing a true spectrum policy violation. The proposed method is validated using real world data from the POWDER 5G testbed on devices transmitting 802.11a(Wi-Fi), 4G LTE, and 5G NR waveforms. The results show accuracy as high as 97% and as low as 73% after calibration depending on channel conditions. Also calibrating for various average false negatives upper bounds achieves micro recall scores of approximately (1 - calibrated false negatives) with the calibration robust to out-of-distribution interference, demonstrating the potential of the proposed method in a realistic high contention wireless environment
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Probability
Markovian renormalisation for percolation in high-dimension: Semi-decidability of mean field behavior
Arthur Blanc-Renaudie
We develop a new approach to study Bernoulli percolation in dimensions $d>6$. The key idea is to approximate open paths at probability $p'>p$ by a Markov chain of pointed $p$-open clusters. This allows us to transfer sharp information on the two point function from $p$ to $p'$. This inductively gives good asymptotic estimates on the two point function as $p\uparrow p_c$. As a main application, we show that having critical meanfield behavior is a semi-decidable problem. Along the way, we also prove sharp asymptotics for the susceptibility and the average radius of gyration, and prove a local central limit
theorem for the slightly subcritical two-point function.
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Computational Complexity
Efficient Randomized Communication Without Large Monochromatic Rectangles
Haoyu Wang and Pei Wu
In this paper, we construct a total Boolean function with $\widetilde{O}(\log n)$ randomized communication protocol, while any monochromatic rectangle has density at most $O(2^{-\mathrm{poly}(n)})$. As a corollary, it gives the first total function separation for $\mathrm{BPP}\not\subseteq\mathrm{P}^{\mathrm{NP}}$ in the communication world. Inspired by Gavinsky's recent work (arXiv:2608.18784), our construction combines the cheat-sheet framework with fully linear PCPs.
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Analysis of PDEs
Regularity for axisymmetric Navier-Stokes with an Euler length
Peter Constantin, Mihaela Ignatova and Vlad Vicol
We prove local regularity for axisymmetric suitable weak solutions of the 3D Navier-Stokes equations, which are smooth before the terminal time $t=0$, and satisfy Type~II pointwise bounds at a vanishing length scale $\ell(t)$. We say $\ell(t)$ is an Euler length if it is non-increasing, satisfies a doubling condition, and if $\ell(t)\to0$ and $(-t)/\ell(t)^2\to0$ as $t\to0^-$. This includes power laws $\ell(t)=(-t)^\gamma$ with $0<\gamma<1/2$, and logarithmic enlargements of the
parabolic length. Our main result shows that local bounds of the type $|u(\cdot,t)| \leq C\ell(t)/(-t)$ and $|\nabla^2 u(\cdot,t)|\leq C/((-t)\ell(t))$ for all $t\in (-1,0)$ imply regularity. The proof adapts the circulation and potential-vorticity argument of our earlier paper~\cite{CIV26} to ancient limits obtained from the rescaled vorticity system by zooming in. We show that the second derivative a priori assumption may be replaced by a Hölder bound on the azimuthal vorticity and a corresponding bound on its potential vorticity.
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Robotics
Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control
Hanchu Zhou, Brendan Lynch, Raman Goyal et al. (+4 more)
World Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and
robot actions, enabling the policy to learn phys- ical dynamics that support effective control. However, recent tactile WAMs often rely on large-scale pretrained generative backbones to capture contact-rich physical dynamics, which limit their inference efficiency and flexible deployment. In this paper, we present Agile-WAM, an agile tactile World Action Model for contact-rich
robot control. Agile-WAM encodes visual and tactile observations into a shared latent that serves as the source of a direct
vision-tactile-to-action flow-matching process, which can jointly generate latent representations of action chunks and future visual/tactile latents. A key observation is that
vision and tactile signals evolve at inherently different timescales: adjacent visual frames are often highly similar, whereas tactile signals can change abruptly upon contact. We therefore introduce multi-horizon
multimodal prediction in Agile-WAM, which provides supervision for visual latent at a larger temporal offset while predicting the tactile latent in the next frame to capture fine-grained contact dynamics. Across nine simulated and five real-world contact-rich ma- nipulation tasks, Agile-WAM demonstrates strong and robust performance, outperforming the strongest baseline in success rate while maintaining low inference latency. In particular, in five real-world experiments, Agile-WAM yields a relative gain of 29.4% in overall success rates while achieving inference latency of 11.9 ms. These results demonstrate that
multimodal WAM can be achieved with an agile architecture suitable for precise and high-frequency
robot control. More details are available on our project page.
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All authors Hanchu Zhou, Brendan Lynch, Raman Goyal, Dechen Gao, Begum Kasap, Boqi Zhao, Junshan Zhang
Space Physics
Microphysical Diversity in Two Very Closely Spaced Magnetic Switchbacks Observed by Parker Solar Probe
Dipali Vadher, Ankush Bhaskar, Smitha Thampi et al. (+1 more)
Parker
Solar Probe observations near the Sun reveal frequent, sudden reversals of the
magnetic field known as switchbacks (SBs). Despite their ubiquity, the internal
plasma structure and associated heating within SBs remain poorly understood. We present a case study of two closely spaced SBs (referred in text as SB_1 and SB_2) observed on 24 January 2020 using high-cadence
magnetic and
plasma measurements.
Magnetic fluctuations are decomposed into components parallel and perpendicular to the mean field, and their power spectra are analyzed to characterize the turbulent cascade. The Partial Variance of Increments (PVI) method is applied to identify intermittent current-sheet-like features. Both SB intervals exhibit clear Alfvenic behavior and enhanced radial flow; however, their microphysics differ: SB_1 shows a higher
proton temperature, larger fluctuation amplitudes, and a denser population of current sheets compared to SB_2. The two events also differ in spectral index, with SB_1 exhibiting a steeper perpendicular slope than SB_2. The elevated intermittency,
proton temperature, and transient $\beta > 1$ excursion in SB_1 suggest that localized dissipation at small-scale structures is a plausible driver of the observed heating. These findings demonstrate that SBs are not uniform kinematic deflections but dynamically evolving
plasma structures whose internal
turbulence may regulate local energy conversion and contribute to the spatially intermittent heating of the near-Sun
solar wind.
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All authors Dipali Vadher, Ankush Bhaskar, Smitha Thampi, Kamlesh Pathak
Physics and Society
Is higher-order physics different?
Pablo Villegas and Sandro Meloni
Group interactions are widespread, and higher-order extensions of familiar models display collective phenomena absent from their pairwise baselines, which are routinely offered as evidence of a distinct higher-order physics. We ask whether that claim survives the test that gives new a precise meaning in statistical physics: that of universality. Revisiting canonical higher-order models, we argue that what classifies collective behavior are the infrared ingredients that survive at long scales. Arity is not a universality label. Beyond universality, we examine two further questions: whether higher-order structure is a fact about the system or a choice of description, and what data can and cannot tell us about interaction order. Higher-order descriptions remain indispensable when they expose the organizing structure, provide a better mechanistic
language, or improve prediction. We close with what should be measured before new phenomena can be claimed, and where higher-order structure already earns its place.
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Machine Learning (Statistics)
Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation
Sho Kawano, Zehang Richard Li and Paul A. Parker
Evaluating an AI system requires disaggregated assessment, as performance varies across domains such as
benchmark task types or conversation types in deployed
agents. Exhaustive testing is expensive, so evaluation rests on a sample of labeled units. We treat the evaluation set as a finite population and seek accurate point and interval estimates of each domain mean. Direct
estimators, including prediction-powered inference (PPI), use only a domain's own labels and are imprecise where labels are few. Small area estimation addresses this problem, and we build on it to develop an integrated workflow for estimation and validation. For estimation, we propose prediction-powered smoothing (PP-S), a
Bayesian model fit to each domain's prediction-powered estimate, with an extension that borrows strength across a reporting taxonomy (PP-TS). For validation, we derive a new, approximately unbiased design-based cross-validation score for choosing among direct and smoothed
estimators. We study a curated
benchmark with verifiable grading and deployed
agent traffic graded by humans, each with every outcome observed. In both, the proposed
estimators improve on the direct
estimators in point and interval estimation, with near-nominal coverage. At the same sampling budget, our score selects as well as an independent validation sample does and estimates the selected
estimator's error far more accurately.
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Quantum Physics
Procrastinating einselection in non-Markovian quantum dynamics
Michael J. Moody, Tara Kalsi, Agung Budiyono et al. (+1 more)
Among the processes governed by open-system and dissipative
quantum dynamics, environment-induced superselection, or einselection, is particularly foundational, since it describes the formation of a preferred set of pointer states through environmental monitoring and ultimately the emergence of classicality. Especially in the context of
Quantum Darwinism, einselection can be studied in terms of the motion of an observable towards its pointer value. To this end, we derive a
quantum speed limit for observables for finite-dimensional systems governed by differentiable time-local open dynamics. We identify two mechanisms through which non-Markovianity can enhance observable speed: direct dissipative motion supported by population retention or revival, and indirect enhancement through restoration of the asymmetry available for coherent motion. For permanent einselection, this delays the emergence of the pointer basis due to accumulated negativity.
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All authors Michael J. Moody, Tara Kalsi, Agung Budiyono, Sebastian Deffner
Robotics
OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
Damiano Da Col, Maximilian Igl, Peter Karkus et al. (+5 more)
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end driving policies are pre-trained in open loop with behavior cloning on human demonstrations. However, compounding errors during closed-loop deployment can take the vehicle outside the training data distribution, increasing the risk of safety-critical incidents. Closed-loop post-training can mitigate this risk but requires costly simulation for sensor-based policies. We propose OPTED (on-policy fine-tuning for end-to-end driving) which decouples reinforcement learning from the post-training of the end-to-end policy: a privileged teacher is trained using RL on vectorized inputs (HD-map and bounding boxes). This teacher then provides supervision to the pre-trained student during closed-loop post-training. We apply OPTED to two camera-based models, TransFuser and VaVAM, and fine-tune them in AlpaSim, using neural reconstructions (3DGS) of real driving logs. Driving scores increase by factors of 1.6$\times$ and 9.5$\times$, respectively. In controlled experiments OPTED matches closed-loop performance with approximately three orders of magnitude fewer simulator interactions than direct RL post-training, while staying closer to the human prior. Project page: https://01dami23.github.io/opted/
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All authors Damiano Da Col, Maximilian Igl, Peter Karkus, Kashyap Chitta, Boris Ivanovic, Marco Pavone, Konrad Schindler, Christos Sakaridis
Strongly Correlated Electrons
Engineering Weak Universality with Quantum Dots
Warre Missiaen, Michael Wimmer and Natalia Chepiga
Quantum critical theories with continuously varying critical exponents remain challenging to access experimentally. Here, we propose
quantum-dot architectures for realizing the
quantum Ashkin-Teller and XYZ/eight-vertex models using resonator-mediated and direct Coulomb interactions, respectively. We focus on the Ashkin-Teller and eight-vertex critical lines, which are connected by a non-local duality relating local order parameters to topological string operators. The resulting platforms provide microscopic control over the parameters explicitly controlling critical exponents, with the Coulomb-based implementation enabling stronger interaction regimes. We demonstrate that continuously varying critical behavior can already be resolved in short chains. For experimentally realistic parameters, the accessible interaction range is expected to produce changes in the critical exponents of order $10\%$ for the Ashkin-Teller model and substantially larger changes for the eight-vertex model. In both cases, the predicted variations lie well above the estimated measurement uncertainties.
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Artificial Intelligence
RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
Mingxuan Zhang, Xiaowen Wang, Anupma Sharan et al. (+4 more)
Effective troubleshooting
agents in enterprise customer support depend on retrieving actionable guidance from similar historical cases, yet existing
retrieval-augmented generation (RAG) systems treat support cases as static documents and overlook their multi-stage, stateful nature. We introduce RAFT (
Retrieval-Augmented Framework for Troubleshooting
Agents), a stateful RAG framework that abstracts each closed historical case into a directed chain of timeline entries and retrieves at the entry level, surfacing cases whose intermediate states match the active case and returning the parent-case trajectory anchored at the matched state; an optional case-level graph links cases through a configurable similarity representation. We evaluate this
retrieval layer directly, which, unlike evaluating a full
agent system, requires no production deployment. Because public multi-stage troubleshooting data is extremely rare, we pair a synthetic
benchmark built from Microsoft Learn Windows Server documentation with real Apache Jira issues carrying human-created duplicate labels. RAFT improves Case Hit over vanilla RAG and GraphRAG baselines at every stage of case progress, with statistically significant gains over the strongest baseline; the Jira results provide directional evidence that the advantage transfers to real case histories. We release our
benchmark, implementation, and the Apache Jira evaluation set.
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All authors Mingxuan Zhang, Xiaowen Wang, Anupma Sharan, Zhengyi Chen, Chenyu Diana Zhang, Shanshan Yang, Chittibabu Pacharu
Quantum Physics
Quantum Entropy Contraction and Factorization from Hypercontractivity
Li Gao and Lijun Wang
We prove that hypercontractivity implies
entropy contraction for a single
quantum channel, without a detailed balance condition. For primitive
quantum Markov
semigroups that are KMS-symmetric with respect to a faithful invariant state \(\sigma\), we obtain the modified Log-Sobolev bound $\alpha_1\geq \frac{\lambda}{(2+\log\|\sigma^{-1}\|_\infty)}$ where $\lambda$ is the spectral gap. This removes the assumption of \(L_p\)-regularity for the comparison through the log-Sobolev constant. As an application, we show that the hypercontractivity of an average of two conditional expectations implies the approximate tensorization of relative
entropy.
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Systems and Control
Large Language Models as Falsifiers for Cyber-Physical Systems
Ali ArjomandBigdeli, Jiawei Zhou and Stanley Bak
Falsification searches for counterexamples to formal specifications in cyber-physical systems (CPS). With specifications written in Signal Temporal Logic (STL), falsification can be formulated as a robustness optimization problem, traditionally tackled with black-box search
algorithms. In parallel, large
language models (LLMs) have recently emerged as surprisingly effective optimizers when coupled with iterative prompting. In this work, we connect these ideas and introduce LLM-Falsifier, an LLM-based approach that falsifies specifications by minimizing the STL robustness degree. Beyond generic
prompt-based optimization, our key idea is to expose the LLM to semantic information that is natural for
language models but absent from standard numerical optimizers, including natural-
language input and output names, output trajectories, and critical-time witnesses for the minimum robustness value. These additions enable smarter and more sample-efficient robustness search. On the ARCH-COMP falsification
benchmarks, LLM-Falsifier is shown to outperform existing falsification tools based on a range of optimization paradigms, from surrogate-based and
Bayesian optimization to search-based testing, on 14 of 21 specifications when measured by the average number of simulations required to find a counterexample.
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Computation and Language
Anton Xue, Litu Rout, Aditya Akella et al. (+3 more)
Adapting a pretrained autoregressive (AR) model is a cost-efficient route to a
diffusion language model (DLM). While nearly all such adaptations start from a full-
attention transformer, AR modeling has shifted toward hybrid architectures that interleave
attention and RNN layers. This creates an obstacle for adaptation: unlike
attention, RNNs are structurally causal and nontrivial to bidirectionalize. Despite this mismatch, we investigate whether such backbones can become effective DLMs by adapting Qwen3.5 at 0.8B, 2B, 4B, and 9B scales, yielding the dQwen3.5 family. We find that hybrid backbones can be efficient starting points for adaptation: against a full-
attention control, the hybrid reaches a given training loss in about half the
tokens. Across scales, dQwen3.5 resembles full-
attention DLMs in any-order decoding behavior and performs strongly under parallel decoding.
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All authors Anton Xue, Litu Rout, Aditya Akella, Adam Klivans, Sujay Sanghavi, Sanjay Shakkottai
Probability
Point process convergence of large inradii of Poisson-Laguerre tessellations
Matthias Schulte and Martina Švarc Petráková
In this paper we study a weighted generalization of the Poisson-Voronoi tessellation called the Poisson-Laguerre tessellation, where the
nuclei of the generating Poisson process additionally carry independent non-negative random weights. For each
cell we can define its inradius as the radius of the largest ball centered at the
nucleus and contained in the
cell. We consider point processes of
nuclei, weights and inradii, where the
nuclei are taken from growing observation windows and the processes are suitably rescaled and shifted to see the behavior of large inradii. We prove convergence in distribution to suitable Poisson processes, obtaining as corollaries the asymptotic behavior of the maximal inradii. Our results cover dimension $d \geq 3$ and bounded random weights, dimension $d = 2$ and random weights with suitable finite exponential moments as well as dimension $d \geq 2$ and random weights following a power-law distribution. Particularly, we observe a different behavior of the Poisson-Laguerre tessellation in the planar and higher dimensional cases when the weights are bounded. The proofs of our results are based on suitable Poisson process approximation techniques and a careful investigation of the geometry of large
cells.
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Statistics Theory
Instance-Optimal Adaptive Location Estimation via Multiscale Mid-Summaries
Qiaosen Wang and Chao Gao
Location estimation exhibits markedly different finite-sample behavior across noise distributions: regular families typically yield root-\(n\) rates, whereas compactly supported laws may admit faster, boundary-driven rates. We question whether a single
estimator, without knowledge of the density's shape, can adapt to the instance-wise optimal estimation rate, as an oracle that knows the underlying location family can. For a known location family with symmetric log-concave noise density \(f\), the optimal location estimation error with sample size \(n\) under failure probability \(\delta\) is known to be Le Cam's two-point rate: \[ \sup\left\{r>0:\mathsf{H}^2\left(f_0, f_{2r}\right)\lesssim \frac{\log(1/\delta)}{n}\right\}. \] When the location family is unknown, we propose a shape-agnostic
estimator that attains this oracle
benchmark simultaneously over all symmetric unimodal densities with non-decreasing hazard rates, a class strictly broader than symmetric log-concave distributions. We establish that the Hellinger-driven two-point rate can be characterized solely by a multiscale function of dyadic
quantile gaps. This new structural connection between Hellinger divergence and
quantile geometry motivates a simple estimation procedure that aggregates sample mid-summaries with carefully designed data-dependent weights. The resulting
estimator is finite-sample instance-optimal and runs in only \(O(\log(n))\) time on sorted samples.
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Earth and Planetary Astrophysics
The JWST Sub-Jupiters Survey: Direct Imaging Discovery of a Giant Planet and a Debris Disk Around the Young M-dwarf RX J0534.0-0221
Rodrigo Ferrer-Chavez, Jason J. Wang, Kevin Wagner et al. (+47 more)
We present the discovery of RX J0534.0-0221 b, a
giant planet orbiting an M-
dwarf star in the $\beta$ Pictoris moving group. RX J0534 was originally observed with JWST/NIRCam in the F444W and F200W filters. Observations in F444W reveal a point source at signal-to-noise ratio $\sim17.5$ at $\sim0.41$ arcsec ($\sim14$ au) from the host
star, with no detection of the source in F200W. A follow-up observation with LBTI/LMIRCam in $L'$ band re-detects the source 16 months after the JWST epoch, providing evidence for common proper motion over a chance
alignment with a background interloper at the $6-7\sigma$ level. Atmospheric grid model fits to the available photometry yield bolometric luminosity log$_{10}(L/L_\odot) = -5.48^{+0.10}_{-0.19}$ dex. At an age of $18-26$ Myr, hot-start evolutionary models predict $M=2.8^{+0.5}_{-0.5}$ M$_{\text{Jup}}$ and $T_{\text{eff}}=674^{+57}_{-49}$ K. From the $L'-F444W$ color and magnitudes we find evidence for disequilibrium chemistry or enhanced
metallicity in the
planet atmosphere. Additionally, an extended structure is detected in the JWST F200W observation, consistent with a resolved debris disk with peak density radius of $79^{+3}_{-3}$ au and an inclination of $56.5^{+1.5}_{-1.5}$ deg. RX J0534 b is one of the lowest-mass
planets imaged to date. After TWA 7 b, it is the second imaged
planet around an M-
dwarf orbiting at
Solar System scales (the first within 50 au), and the first to be confirmed via common proper motion. Future orbital monitoring and atmospheric characterization will shed light on its formation history, a particularly interesting question given the challenging nature of
giant planet formation around M-
dwarfs.
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All authors Rodrigo Ferrer-Chavez, Jason J. Wang, Kevin Wagner, Kellen Lawson, Aarynn L. Carter, Beth Biller, Raphael Bendahan-West, Patrick McCreery, Patricia Luppe, Steve Ertel, Andrew D. James, Ellis Bogat, Rohan Kane, Ben J. Sutlieff, Giovanni M. Strampelli, William O. Balmer, Rachel Bowens-Rubin, Evelyn L. Bruinsma, Andy Skemer, Julien H. Girard, Mark Booth, Klaus Subbotina Stephenson, Katie A. Crotts, Sebastian Marino, Aniket Sanghi, Michael Liu, Nour Skaf, Emily Rickman, Marshall D. Perrin, Laurent Pueyo, Brendan P. Bowler, James Mang, Isabel Rebollido, Michael Meyer, Tim Pearce, Markus Bonse, Clémence Fontanive, Jared Carlson, Jon Rees, Justin Rupert, Gabriel Weible, Jacob Isbell, Juan Carlos Guerra, Xueqing Chen, Mia Belle Parkinson, Vito Squicciarini, Gael Chauvin, Elisabeth Matthews, Kyle Franson, Maxwell A. Millar-Blanchaer
Robotics
MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving
Thomas Steinecker, Denis Trescher, Alexander Bienemann et al. (+2 more)
Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies. However, its application to real-world autonomous driving remains scarce, particularly in unstructured environments, because of the challenges associated with sim-to-real transfer for unstructured environments. In this work, we present MILER, an end-to-end policy framework with zero-shot sim-to-real transfer. During offline training, we employ a custom semantic mid-level representation (MLR) simulator and train the policy network using reinforcement learning, with its control outputs applied directly to a bicycle model. During deployment on the real vehicle, camera and
LiDAR data are processed by BEVFusion to generate a semantic bird's-eye-view representation consistent with that of the MLR simulator. The actions generated by the policy network are not applied directly to the real vehicle. Instead, we employ a trajectory-
alignment strategy that enables zero-shot sim-to-real transfer of both perception and control. We extensively evaluate the proposed framework on a diverse test track comprising numerous challenges, including various obstacles, hairpin curves, velocities of up to 33.6 km/h, and off-road sections. In total, we drove 17.3 km with two different vehicles on a 3.0 km test track without human intervention, thereby demonstrating the effectiveness of our approach. Furthermore, the entire software stack runs on a Jetson AGX Orin.
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All authors Thomas Steinecker, Denis Trescher, Alexander Bienemann, Thorsten Luettel, Mirko Maehlisch
Quantum Physics
Mermin-Peres magic rectangles modulo odd primes
Josse van Dobben de Bruyn, Remy van Dobben de Bruyn and Peter Zeman
The Mermin-Peres magic square provides a simple example of a system of linear equations over $\mathbb{Z}/2\mathbb{Z}$ which has no classical solutions but does have a finite-dimensional operator solution. For a long time, it was not known how to construct similar examples over $\mathbb{Z}/d\mathbb{Z}$ with $d$ odd. In this paper, we construct, for every integer $d \geq 2$, a linear system over $\mathbb{Z}/d\mathbb{Z}$ that has a finite-dimensional operator solution but no classical solution. For an odd
prime $p$, our operators act on two $p$-dimensional qudits and generate a finite $p$-group obtained by adjoining diagonal
polynomial phase operators to the generalized Pauli group, thereby forming a natural analogue of the Mermin–Peres magic square. Classical inconsistency follows from an elementary linearity argument comparing assignments on abelian subgroups. Furthermore, we prove a sharp threshold for the required degree of the added
polynomial phase operators: our construction uses
polynomials up to degree $p - 1$, and we show that the group formed by
polynomials up to degree $p - 2$ is noncontextual.
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High Energy Astrophysical Phenomena
Low Frequency Radio Imaging Study of PSR J1818-1607
A. Yadav, M. P. Surnis, B. C. Joshi et al. (+1 more)
We report on the low-frequency
radio observations of magnetar PSR J1818$-$1607 carried out with the upgraded
Giant Metrewave
Radio Telescope at band 3 (300$-$500 MHz) and band 4 (550-750 MHz). We have identified the continuum source associated with the magnetar and report variations in flux density and its spectral index. The flux density timeseries for the magnetar reveals the presence of two potential
radio flaring episodes with varying spectral properties. We discuss the implications of the spectral index changes on the potential emission mechanisms for
radio-loud magnetars. We also report non-detections of the continuum source as well as no direct indication for an associated diffuse emission from multiple archival
radio imaging surveys.
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All authors A. Yadav, M. P. Surnis, B. C. Joshi, M. Bagchi
Machine Learning
Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation
Haocheng Xi, Yiming Xie, Hexu Zhao et al. (+8 more)
Video diffusion models repeatedly process long spatiotemporal
token sequences during denoising, making
attention a major computational bottleneck. Linear
attention offers an appealing alternative and has been widely adopted in recent large
language models, but directly applying it to
video models often fails to preserve the fine-grained interactions required for high-quality generation. We present
Video DeltaNet (VDN), which combines local Softmax
attention with bidirectional linear
memory for long-range
video context. Its linear branch introduces
Video Delta
Attention (VDA), which updates
memory once per frame by jointly incorporating its spatial
tokens. Separate output projections and learnable gates calibrate the two branches, while a staged teacher-
alignment recipe progressively introduces the new pathway into pretrained models. We instantiate VDN on MiniMax H3, applying the hybrid to
video-to-
video interactions while retaining Softmax for interactions involving text or audio. With eight-step
distillation and an optimized SGLang serving stack, VDN-H3 completes DiT denoising for a 14.3-second, 768p
video in 6.70 seconds on eight NVIDIA B200 GPUs, corresponding to a 14.5x speedup over the 50-step dense H3 baseline on the same GPU count. GitHub code available at: https://github.com/OpenVDN/vdn-minimax-h3. Weights available at: https://huggingface.co/OpenVDN/vdn-minimax-h3
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All authors Haocheng Xi, Yiming Xie, Hexu Zhao, Yiwen Zhang, Michael Liu, Thomas Creavin, Kurt Keutzer, Xiuyu Li, Zhaoyang Lv, Chenfeng Xu, Haiwen Feng
Soft Condensed Matter
Scaling and Condensation of Dry Active Matter Around Circular Obstacles
Felipe P. S. Júnior, F. Q. Potiguar, Jorge L. C. Domingos et al. (+1 more)
Active Brownian particles confined to rigid substrates are known to accumulate near rigid boundaries and, under suitable conditions, undergo motility-induced phase separation (MIPS). A particularly intriguing manifestation of this behavior is the formation of self-sustained
vortices around circular obstacles, which act as localized nucleation sites for particle aggregation. While several dynamical properties of such
vortices have been previously characterized, their behavior in the thermodynamic limit remains largely unexplored. Here, we investigate how the mass and spatial extent of a dry active-matter
vortex scale with system size. Using numerical simulations of repulsive active Brownian Particles interacting with a fixed circular obstacle, we measure the
vortex mass, mean radius, and maximum radius as functions of the global area fraction, obstacle size, and system size. We find two distinct scaling regimes. At low densities, the
vortex remains localized and its characteristic properties saturate as the system size increases. Above a critical density, however, the
vortex mass grows extensively with the total number of particles, while its spatial dimensions scale linearly with the system size, indicating the emergence of an obstacle-stabilized condensed state.
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All authors Felipe P. S. Júnior, F. Q. Potiguar, Jorge L. C. Domingos, W. P. Ferreira
High Energy Physics (Theory)
Environmental Effects in Post-Minkowskian Dynamics: Effective Field Theory, Feynman Rules, and Ward Identities for Compact Objects in Relativistic Fluids
Zvi Bern, Samuel Degen, Enrico Herrmann et al. (+2 more)
We develop a hydrodynamic effective field theory for the gravitational dynamics of compact objects moving through an inviscid fluid environment. The fluid is described by the effective theory of perfect fluids, whose Goldstone
phonons are kept as explicit fields coupled to
gravity. We establish a consistent power counting for the resulting Feynman rules and derive the propagators and interaction vertices through four points in $D$ dimensions, together with the generalized on-shell Ward identities they obey. These identities relate amplitudes with an external graviton to those with the graviton replaced by a
phonon, and thus provide nontrivial checks on higher-order calculations. As an application, we recover the leading-order relativistic dynamical-friction force from the tree-level amplitude for single-
phonon emission. This work is a first step towards a toolkit for incorporating environmental effects into the scattering-amplitude pipeline used in post-Minkowskian calculations.
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All authors Zvi Bern, Samuel Degen, Enrico Herrmann, Mikhail P. Solon, Anna M. Wolz
Atomic Physics
Effective Conservation and Bistability of Atomic Alignment under Strong Spin~Exchange
Anton K. Vershovskii
We present a phenomenological model of anomalous
alignment signals in dense cesium vapor under linearly polarized pumping and fast
spin exchange near zero
magnetic field. Despite the absence of a conservation law for rank-2 angular momentum, our recent experiments reveal anisotropic narrow resonances, hysteresis, and bistability. We attribute these effects to a stretched state forming a collective mode in which orientation and
alignment are bidirectionally coupled. This mode acts as a reservoir, preserving the essential properties of
alignment despite rapid
spin exchange.
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Combinatorics
Combinatorics of hyperplane arrangements and Witten zeta function at the origin
Kam Cheong Au and Kazuhiro Onodera
We introduce a new method that brings the combinatorics of hyperplane arrangements into the study of representation
zeta functions of compact Lie groups. For the Witten
zeta function $\zeta_\Phi(s)$ associated with a root system $\Phi$, our method yields elegant formulas for $\zeta_\Phi(0)$ and $\zeta_\Phi'(0)$ in terms of the exponents of various
parabolic subsystems of $\Phi$. Such formulas do not appear to be readily accessible through the conventional analytic techniques in the literature. More generally, the method applies to a broad family of conical
zeta functions, expressing these two special values through the Möbius function of the intersection poset of the associated hyperplane arrangement.
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Quantum Physics
Efficient Non-Uniform Quantum Hermite Transform through Adaptive Sampling
Nitay Mayo and Aryeh Lev Zabokritskiy
On the span of the first $N$ oscillator modes, Gauss–Hermite quadrature gives an exact change of basis between mode coefficients and $N$ weighted position space samples. We implement this transform with $O(N\operatorname{polylog}(N,1/\varepsilon))$ logical gates and polylogarithmic
quantum width. The operator-error bound $\varepsilon$ holds on arbitrary superpositions and includes all auxiliary registers. The construction uses signed averages on adaptive windows to convert uniform-grid samples into weighted Hermite-root samples. Their varying widths control the amplification cost, giving the near-linear bound.
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