Computational Complexity
A General Composition Theorem for Approximate Degree
Samruddhi Pednekar and Supartha Podder
A longstanding open question in Boolean function complexity asks whether approximate degree composes multiplicatively under block composition. Although a general multiplicative upper bound is known, matching lower bounds have previously been established only for restricted classes of functions. We resolve this question for all total Boolean functions by proving the matching lower bound. Together with Sherstov's upper bound, our result shows that, for every pair of total Boolean functions $f:\{0,1\}^n\to\{0,1\}$ and $g:\{0,1\}^m\to\{0,1\}$, \[ \widetilde{deg}(f\circ g) = \Theta\!\left( \widetilde{deg}(f)\,\widetilde{deg}(g) \right), \] where $\widetilde{deg}$ denotes constant-error approximate degree. We also consider the analogous question for exact
quantum query complexity and give a counterexample showing that multiplicative composition fails in this setting.
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Numerical Analysis
High-dimensional extreme eigenvalue problems: low-rank tensor parametrization and optimization
Pengfei Hao, Renfeng Peng, Yang Kuang et al. (+1 more)
High-dimensional extreme
eigenvalue problems often arise from molecular vibrational models, electronic structure calculations, and
quantum mechanics. Directly solving these problems suffers from the curse of dimensionality. Instead of tackling the problem in high-dimensional ambient space, we reformulate the problem through low-rank tensor formats, which can significantly reduce the computational cost and storage. Specifically, we consider the Rayleigh–Ritz problem on bounded-rank tensors in the tensor train format, which enables a more flexible choice of rank parameters. Moreover, we consider a smooth parametrization for bounded-rank tensors by introducing slack variables, leading to a smooth
manifold structure. The original Rayleigh–Ritz problem is therefore transferred to an optimization problem on the
manifold. We develop the Riemannian geometry and propose optimization methods for solving extreme
eigenvalue problems. In practice, the Kronecker-product structure in
Hamiltonian and PDE operators is employed to simplify the computation. Numerical experiments on the harmonic oscillator, the Laplace operator, the Schrödinger equation, the layered cluster problem, and the Bose–Einstein model demonstrate that the proposed method achieves accuracy comparable to full-space eigensolvers with reduced computation time and avoids storing full vectors. The results also show numerical rank reduction during iteration and robustness when the rank parameter is over-estimated. In addition, the fixed-point iterations illustrate that the proposed methods are able to serve as an inner eigensolver for solving nonlinear
eigenvalue problems.
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All authors Pengfei Hao, Renfeng Peng, Yang Kuang, Bin Gao
Artificial Intelligence
Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale
Edesio Alcobaça, Kevin Rossell, Aman Gupta et al. (+13 more)
Customer experience (CX)
agents use tools and large
language models to address customer requests and guide conversational interactions with an organization's products. Improving these
agents, especially in regulated industries, is difficult: they must detect intent, follow complex operational policies and use tools reliably. Manual end-to-end testing offers limited coverage, while live experiments expose customers to failures that can erode trust. We present a hypothesis-driven simulation workflow for screening candidate CX
agents before deployment. Synthetic customers react to
agent responses and simulated tool outputs enable multi-step agentic workflows without invoking production backends. We use the Snowglobe simulator on Nubank's Card Delivery
agent and its expanded successor, Card Management - Nubank's highest-volume chat-support
agent in Brazil. Across 4 deployed versions, simulated and production version-level binary evaluator scores show high correlation. Simulation-guided iteration increased transactional net promoter score (tNPS) by 36.69 points in a live A/B test. We also screened open-weight configurations in over 16,000 simulated conversations. In a subsequent live A/B test, the selected model increased self-service rate (SSR) by 8.82 percentage points to the highest level observed at Nubank, with no statistically significant change in tNPS. Simulation made broad exploration of models,
reasoning settings, and
prompts feasible without customer exposure, enabling production improvements that would have been impractical to pursue through live experimentation alone.
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All authors Edesio Alcobaça, Kevin Rossell, Aman Gupta, Shao Tang, Jiwoo Hong, Pabel Carrillo-Mendoza, Wanderson Conceição Ferreira, Alvaro Tedeschi, Zayd Simjee, Shreya Rajpal, Bruno Finardi Hime, Christian Sousa, Luis Moneda, Herbert Fei, Daniel Silva, Rohan Ramanath
High Energy Physics (Phenomenology)
High-quality axion from chain seesaw
Pei-Hong Gu
We propose a chain seesaw mechanism to generate not only the masses of neutral
neutrinos but also the masses of charged
fermions, by supplementing the standard model $SU(3)_c^{}\times SU(2)_L^{}\times U(1)_Y^{}$ gauge
symmetries with an additional $U(1)_{Y'}^{}$ gauge
symmetry. The chains for the down-type and up-type
quarks contain different numbers of links, because the corresponding mediator vector-like
fermions and new
Higgs scalars carry different $U(1)_{Y'}^{}$ charges. The Peccei-Quinn global
symmetry is automatically embedded in this $U(1)_{Y'}^{} $ gauge
symmetry which is sufficient to guarantee the high quality of
axion.
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Probability
Recursive Paintboxes and the Martin Boundary of the Hoffman Rooted-Tree Graph
Shengjun Zhang
We determine the Doob-Martin boundary of Hoffman's leaf-grafting graph on finite unlabelled non-plane rooted trees. Its full and minimal boundaries coincide and are parametrized by deterministic recursive paintboxes, identified when their finite sampling laws agree. Every central measure is a unique mixture of the corresponding extremal laws, and its limiting boundary point generates the completed tail field. We also show that the boundary is homeomorphic to the space of unordered root masses marked by child boundary classes. For the recursive Ewens family, we obtain the unique extremal decomposition from independent Poisson-Dirichlet splits, including the uniform recursive-tree and rooted-tree Plancherel cases.
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Robotics
Training-free Behavior Cloning
Maximilian Adang, Timothy Chen, Lars Osterberg et al. (+2 more)
Neural behavior cloning compresses demonstrations into large models, making individual actions difficult to trace and policy updates costly.
Retrieval policies retain access to demonstrations but struggle with mismatch between recorded and live behavior. We introduce Behavior Predictive Control (BPC), which synthesizes policies without end-to-end policy training by combining an action-aware
retrieval metric, a Hankel-based action-continuation prior, and a closed-form one-step residual correction. Inspired by behavioral systems theory, BPC predicts future actions by blending stored observation-action data that best reconstructs the recent runtime observation–action history. Across simulated
benchmarks and real-
robot deployments, BPC is competitive with learned policies such as $\pi_{0.5}$ (surpassing it in some cases), while reducing policy fitting from hours to seconds on consumer GPUs and supporting closed-loop control upwards of 75 Hz on a Jetson Orin Nano. The retrieved demonstration windows and their coefficients also provide an intrinsic estimate of task progress. Retaining demonstrations within the deployed policy makes its predictions traceable to supporting trajectories and enables behavior revision through the demonstration bank.
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All authors Maximilian Adang, Timothy Chen, Lars Osterberg, Aiden Swann, Mac Schwager
Materials Science
AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution
Ken J. Jenewein, Faezeh Habib Zadeh, Xiaoxiao Wang et al. (+19 more)
Catalyzing acidic oxygen
evolution at the
proton-exchange-membrane water electrolysis (PEMWE) anode relies almost entirely on iridium or ruthenium, drawn from concentrated supply chains that constrain gigawatt-scale deployment. We report an artificial intelligence (AI)-guided, human-supervised closed-loop platform (>90% automation) integrating combinatorial sputter synthesis, high-throughput screening, machine-learning composition-property models, adaptive multi-objective optimization, and context-aware large-
language-model
reasoning, where lead catalysts advanced to long-term validation in 1 M H2SO4 at 10 mA cm-2. Navigating a combinatorial metal
oxide space, the platform iteratively evaluated the activity-stability trade-off of 2,942 catalysts across 53 material systems and 26 elements, surfacing Ir- and Ru-free complex
oxides such as InMnPdOx and NiTaPdOx that conventional design logic, and off-the-shelf
language models, would not predict. In retrospective benchmarking, our sequential learning
agent advanced the activity-stability frontier faster than fixed-policy
Bayesian optimization or in-context
language-model selection. During long-term testing, NiTaPdOx operated at lower overpotential than PdOx, but both eventually exceeded 0.5 V: PdOx at ~200 h and NiTaPdOx at ~470 h. InMnPdOx showed a similar overpotential improvement in addition to a dramatic increase in operational stability, retaining overpotential below 0.5 V over 1,000 h of operation. The additive elements promote the formation of a nanostructure that is associated with catalytic activity while stabilizing Pd against corrosion. The results highlight the power of AI-driven science in addressing long-standing challenges in materials chemistry, and the greater availability of Pd relative to incumbent Ir and Ru offers a near-term option to ease supply constraints on scaled electrochemical H2 generation.
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All authors Ken J. Jenewein, Faezeh Habib Zadeh, Xiaoxiao Wang, Gustavo Malkomes, Huafan Zhang, Natalie Page, Jae Jin Bang, Peter J. Santiago, Karla V. Contreras, Katherine K. Li, Allison Perna, Lorena M. Britton, Fahrettin Kilic, Kevin J. Cruse, Armin Taheri, Krishnanand Mallayya, Harley Quinn, Rebecca A. Durr, Peter A. Beaucage, Santiago Miret, John M. Gregoire, Rafael Gómez-Bombarelli
Quantum Physics
Collective thermalization, work reliability, and resource bounds in a population-inverted Dicke Otto engine
Cler T. Garcez, Gabriella G. Damas, Norton G. de Almeida et al. (+1 more)
Population inversion and collective relaxation can both enhance the performance of a
quantum heat engine, but through distinct mechanisms. We study a
quantum Otto engine whose working medium is a symmetric collective
spin of $N$ two-level constituents. For commuting work strokes and collective reservoir coupling, the dynamics reduces exactly to a finite birth–death process on the Dicke ladder, allowing a unified treatment of stationary operation, work fluctuations, and finite-time relaxation. In the complete-reset limit, passive work per cycle saturates with system size, whereas population inversion yields work that grows linearly with $N$. The work reliability shows the same linear scaling, exceeding the square-root behavior of $N$ independent engines. This enhancement originates from a macroscopic polarization displacement between hot and cold states, while collective coupling instead accelerates the finite-time dynamics through Dicke transition rates. For matched passive and inverted hot states, the extra gross work equals the Otto efficiency times the ergotropy of the inverted state. Charging the corresponding reversible excess state-formation cost removes this advantage, showing that the enhanced output converts a pre-existing active-state resource rather than generating a free thermodynamic gain. Exact finite-contact trajectory statistics further quantify how collective kinetics, fluctuations, correlations, and resource accounting remain linked away from complete reset.
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All authors Cler T. Garcez, Gabriella G. Damas, Norton G. de Almeida, G. D. de Moraes Neto
Hardware Architecture
Enhancing Word-Level Property Directed Reachability with LLM-Driven Semantic Guidance
Guangyu Hu, Mingkai Miao, Zhiyuan Yan et al. (+3 more)
Property Directed Reachability (PDR) is a prominent
algorithm for hardware formal verification. However, bit-level PDR often struggles with datapath-heavy designs because bit-blasting obscures high-level semantics. While word-level PDR addresses this by
reasoning over bit-vector and array theories, its performance remains bottlenecked by discovering proof-relevant word-level relations. We propose LLM4PDR, a framework leveraging Large
Language Models (LLMs) to guide word-level PDR search through three mechanisms: (1) Predicate Generation, extracting state relationships as candidate predicates during inductive generalization; (2) Clause Generation, producing candidate frame lemmas to accelerate convergence after formal validation; and (3) Assertion Generation, synthesizing helper assertions that strengthen the target property under counterexample-guided refinement. We implement LLM4PDR in the Pono model checker and evaluate it on arithmetic micro-
benchmarks, HLS-generated pipelines, open-source RTL components, and hardware model checking competition (HWMCC) instances. Results show LLM-generated guidance improves both solved instances and runtime on datapath-heavy and control-plus-datapath designs. The strongest configuration solves 28 of 33 arithmetic
benchmarks, compared to 13 for vanilla Pono and 11 for AVR. On HLS pipelines and open-source RTL, different modes provide complementary speedups: clause guidance is effective for deep pipelines and predicate guidance for bus and
memory-controller designs. On HWMCC
benchmarks, benefits are instance-dependent, demonstrating notable speedups and timeout avoidance on hard cases. These results suggest that LLM-generated, verifier-checked semantic hints can serve as a practical complement to conventional word-level PDR.
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All authors Guangyu Hu, Mingkai Miao, Zhiyuan Yan, Xiaofeng Zhou, Wei Zhang, Hongce Zhang
Computer Vision
Multimodal Thinking with Renderable Programs
Sunli Chen, Ding Zhong, Ziqiao Ma et al. (+6 more)
Current
vision-
language models (VLMs) excel at visual content understanding and text-based
reasoning, yet their structure limits the advancement of incorporating images into the
reasoning chain. Though Omnimodal models have made efforts in unifying text and image generation, they focus on visual tasks in the open-domain, lacking tractability due to rasterized or latent representations of images. We introduce SVGLM, a framework that uses scalable vector graphics (SVG) primitives to connect text and image in
reasoning tasks. We exploit the duality of SVG as both image description and text instructions, yielding a more compact, interpretable solution to equip general VLMs with the capability of generating images within the
reasoning process. We provide a large curated
dataset of SVG-based image editing
dataset, as well as the paradigm to tune open-source VLMs. Experiments on a mathematical
reasoning benchmark demonstrate that SVGLM achieves strong SVG generation power as well as think-with-image intelligence. Our results highlight SVG as a suitable medium for building more robust digital domain
agents, bridging the gap between text-based thinking and pixel-based images.
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All authors Sunli Chen, Ding Zhong, Ziqiao Ma, Jiaxin Liu, Zeyuan Yang, Hao Zhang, Lie Lu, Joyce Chai, Chuang Gan
Information Theory
Study of Iterative Detection and Decoding for {Mixed Near- and Far-Field} XL-MIMO Systems
Nayyab Haider and Rodrigo C. de Lamare
Extra-large multiple-input multiple-output (XL-
MIMO) systems {serving users across near-field and far-field regions} experience spherical wavefront propagation that provides enhanced spatial resolution over traditional far-field systems. In this work, we propose a novel weighted rate (WR) ordering-based successive interference cancellation (SIC) scheme that exploits the spatial degrees of freedom inherent in near-field propagation. We also develop an iterative detection and decoding (IDD) framework that integrates the proposed WR ordering with low-density parity-check (LDPC) coding and channel estimation. We then analyze the information rates and the impact of near-field channel characteristics. Numerical results show that the proposed WR-SIC and the IDD scheme outperform existing approaches.
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Mesoscale and Nanoscale Physics
Pomeranchuk-like electronic localization above 100 K in twisted MoS$_{2}$
Zhiren Xiong, Jianqi Huang, Ruyue Han et al. (+8 more)
Twisted transition-metal dichalcogenides (TMDs) have manifested a rich variety of emerging physical phenomena, yet experimental studies have so far been largely limited in their valence bands (p-doped). Here, we show correlated electronic states in the conduction bands (n-doped) of near-AA-twisted
bilayer MoS$_2$ with twist angles ranging from $\sim2.3^\circ$ to $\sim3.5^\circ$, down to the mK temperature regime. A strongly reconstructed correlated phase diagram as a function of twist-angle has been observed - correlated gaps persist to temperatures approaching $160$ K at small twist angles, but collapse to only $\sim20$ K at intermediate angles, where a richer landscape of interaction-driven states emerges. At the largest twist-angle $\sim 3.5\,^{\circ}$, correlated resistance at 1 electron per moiré unit
cell is enhanced upon heating, consistent with thermally assisted localization, or, a Pomeranchuk-like behaviour. Its
magnetic-field response, however, is highly anisotropic, which differs markedly from that of canonical isospin moiré Pomeranchuk effect in
graphene systems. Strikingly, such signature can persist even above 100 K around a filling of 2 electrons per moiré, despite of its weak resistive nature. Our results establish the twisted MoS$_2$ as a platform for studying the complexity of charge localization, internal flavour degrees of freedom, and band
topology in conduction bands of semiconducting moiré systems.
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All authors Zhiren Xiong, Jianqi Huang, Ruyue Han, Hanwen Wang, Hui Ding, Jianming Lu, Jianpeng Liu, Zheng Vitto Han, Xingdan Sun, Siwen Zhao, Baojuan Dong
Robotics
Faster Visuomotor Policy Learning on Action Manifolds via Riemannian MeanFlow
S. Talha Bukhari, Austin Garrett, Yi Wei et al. (+3 more)
Visuomotor policies learn a direct map from raw sensory observations to
robot action sequences. Policies based on
Diffusion and Flow Matching capture the
multimodal distribution over action sequences in an end-to-end manner. This expressivity comes at the cost of multi-step numerical integration of the learned vector field for action generation, which can be expensive and time-consuming, impeding fast control rates required in robotics applications. Furthermore,
robot action sequences are usually defined on a smooth, differentiable
manifold, requiring that the learned policy respects the intrinsic geometry of the
robot's action space. Here, we present Riemannian MeanFlow Policy (RMFP), which learns the conditioned flow map of the probability path on the
robot action
manifold. Our formulation employs a flow map consistency objective grounded in the data by a Riemannian Conditional Flow Matching anchor. The flow map consistency condition is stable to train and constrains the learned model to finite-time transport, which yields on-
manifold action sequence generation with as few as one network function evaluation. We present results on the spherical LASA and Push-T
benchmarks, on the Tool Hang and Transport tasks of the Robomimic suite, and on the Franka Kitchen task with
manifold-constrained action generation, and demonstrate that RMFP attains performance competitive with prior work at a lower sampling cost. We also employ RMFP on a real-world robotic manipulation task to demonstrate fast action generation under imperfect sensor measurements in the physical world.
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All authors S. Talha Bukhari, Austin Garrett, Yi Wei, Ruiqi Ni, Zachary Kingston, Aniket Bera
Solar and Stellar Astrophysics
Formation of adducts of C$_6$H with Na$^+$, Mg$^+$ and Al$^+$ metal cations by radiative association
Niyazi Bulut, Octavio Roncero, Maria Mallo et al. (+6 more)
The Mg-bearing cations MgC$_4$H$^+$, MgC$_6$H$^+$, MgC$_3$N$^+$, and MgC$_5$N$^+$ have been recently observed in the carbon-rich envelope IRC\,+10216. These species are thought to form upon radiative association between Mg$^+$ and the corresponding neutral radical. This work aims to determine the radiative association rate coefficients for the cations Al$^+$, Mg$^+$, and Na$^+$ reacting with carbon chain radicals to estimate the relative importance of metal-bearing carbon cations for each of these metals. For this purpose we use statistical methods in combination with highly accurate ab initio calculations to obtain the radiative association rate coefficient. Moreover, anisotropic effects beyond mono-dimensional capture model are considered, providing a more reliable description of the simulated processes.} The radiative association rate coefficients to form Mg-, Al- and Na-C$_6$H$^+$, as well as MgC$_4$H$^+$, MgC$_3$N$^+$, and MgC$_5$N$^+$, are calculated with the new restrictions considered in this work. These new rate coefficients are included in a chemical model of the C-rich AGB envelope IRC\,+10216. The abundances of MgC$_4$H$^+$, MgC$_6$H$^+$, and MgC$_5$N$^+$ are consistent with observations, with differences no greater than one order of magnitude, corroborating radiative association as a plausible formation mechanism for these adducts. However, the calculated abundance of MgC$_3$N$^+$ is about two orders of magnitude lower than observed, which indicates that either the chemistry of this species is significantly different from the others or the calculated rate coefficient is too low. The abundances obtained for Na-C$_6$H$^+$ and Al-C$_6$H$^+$ are around two orders of magnitude lower than for Mg, making very difficult to detect the analogous metal-bearing cations in IRC\,+10216.
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All authors Niyazi Bulut, Octavio Roncero, Maria Mallo, German Molpeceres, Marcelino Agundez, Maysa Yusef-Buey, Jose Cernicharo, Carlos Cabezas, Piotr Zuchowski
Quantum Physics
Transport and Channel Normal Forms for Clifford Interactions
Xiong Chunhe, Kim Sunho, Zhang Qing-Hua et al. (+2 more)
We study how Clifford interactions with a fixed environment determine
quantum transmission, in a setting that extends positive discrete
quantum convolution . We characterize when Clifford operations on the environment can be replaced by operations on the signal input and the two outputs, and classify the interactions under local Clifford transformations . For pure stabilizer environments, we determine the noiseless, dephasing, and completely depolarizing parts of the channel directly from the interaction and environmental stabilizers, obtaining its
quantum and private capacities . This reveals a distinction between interactions: those locally equivalent to positive convolution produce
entanglement-breaking channels for every pure stabilizer environment, whereas every other interaction in the class admits noiseless
quantum transmission with a suitable pure stabilizer environment . For positive convolution, environmental Clifford transport also allows us to separate the stabilized coordinates of an arbitrary mixed environment . These coordinates carry only a classical label shared by the receiver and environment, so removing them preserves
quantum and private capacities . The resulting channel reduction yields a private-capacity bound by the modified relative
entropy of magic of the environmental state .
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All authors Xiong Chunhe, Kim Sunho, Zhang Qing-Hua, Fei Shao-Ming, Wu Junde
Quantum Gases
Characterizing quasiparticles in strongly correlated systems using nonlinear spectroscopy in quantum simulators
Luka Skolc, Utso Bhattacharya, Jonathan B. Curtis et al. (+2 more)
Characterizing carrier type in strongly correlated
quantum systems conventionally relies on the
Hall effect. In cold-atom
quantum simulators, implementing
Hall transport measurements typically requires synthetic gauge fields, which introduces significant heating. Here, we present nonlinear spectroscopic and fluctuation-based protocols that establish an alternative route to identifying the sign of quasiparticle charge. We demonstrate that second-order density and current responses to finite-momentum quenches and drives-as well as
equilibrium third-order density cumulants-distinguish electron- from hole-like quasiparticles. For a
Fermi-Hubbard multi-leg ladder on a square
lattice, numerical simulations reveal a crossover from hole- to electron-like carriers upon hole-doping away from half-filling, matching the sign change in the
Hall coefficient. To demonstrate the applicability of our protocols beyond fermionic systems, we show that the nonlinear density response to a finite-momentum, finite-frequency drive reveals the sign of charge carriers in a hard-core
boson ladder, and that the nonlinear signal can be significantly enhanced when driving near resonance with a nonlinear collective mode. Our results establish nonlinear density and current response and
equilibrium non-Gaussian fluctuations as complementary probes, offering
quantum simulators a direct route to characterize the carrier sign, without the experimental hurdles of conventional transport setups.
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All authors Luka Skolc, Utso Bhattacharya, Jonathan B. Curtis, Immanuel Bloch, Eugene Demler
Artificial Intelligence
HEXIS: Compiling Agent Skills into Extended Finite State Machines
Minghao LI
Agent skills provide instructions and reusable knowledge, yet
agents have to frequently infer which action to take next. This skill's execution paradigm combines task
reasoning with control decisions, which could cause the
agent to act improperly or omit necessary steps. To improve
agent compliance for skills, we introduce HEXIS, which compiles
agent skills into extended finite state machines (FSM) that separate knowledge from control flow. Skill knowledge is incorporated into local instructions that guide
reasoning and generation within states. The machine records execution progress and intermediate results, while explicit transition conditions determine subsequent operations. Our incremental compiler first maps skill clauses and tool interfaces to state operations, local instructions, data bindings, and transitions. It then aligns development traces with existing states to identify missing operations and dependencies. These are incorporated by adding or reusing states and refining their connections. Updates are accepted only after static checks and replay of the current and all previously accepted traces. Across four
benchmarks and four executors, HEXIS improves success over Skill + ReAct by 16.2 percentage points on average. Qwen3.8-27B reduces execution
tokens by 38.4-88.9% across
benchmarks.
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Medical Physics
Melanin- and linewidth-corrected pulse-oximetry
Giles Blaney, Jodee Frias, Ravi Durbha et al. (+2 more)
Current pulse oximeters tend to overestimate arterial saturation in darkly pigmented patients, increasing occult hypoxemia rates. One proposed mechanism is spectral coloring: melanin contributes to reshaping the detected spectrum of a broad-linewidth source, making the wavelength-dependent parameters behind calibration skin-tone-dependent. Diffuse optics theory allows for a melanin- and linewidth-corrected calibration equation, which to our knowledge has never been tested in vivo. In this work, we fit the empirical linear equation, the diffuse optics theoretical equation, and its corrected form to the public OpenOximetry Repository—considering 98 patients with paired arterial blood-draws, raw photoplethysmograms, and skin reflectance spectra. Accuracy is practically indistinguishable between the three approaches, yet the saturation error's dependence on melanin falls from +2.8 to +1.2 to +0.1 percentage points per unit melanin volume fraction—consistent with the spectral-coloring theory. None of these dependencies on melanin are statistically resolved here—a cohort roughly ten times larger is needed—so the correction is backed by theory and its expected direction. All three calibration approaches carry two degrees of freedom: the theoretical equation drops into existing calibration procedures, and its melanin-aware form needs a per-patient skin-tone measurement. This work demonstrates how the theoretical pulse oximetry model can be applied in calibration.
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All authors Giles Blaney, Jodee Frias, Ravi Durbha, Sergio Fantini, Valencia Koomson
Computation and Language
What, When, and How: Audio Description as Constrained Global Optimization
Igor Sterner, Mirella Lapata, Alex Lascarides et al. (+1 more)
Audio Description (AD) makes movies accessible to blind and visually impaired audiences by narrating visual information in gaps between dialogue. Existing automatic AD systems largely treat generation as a local
video-to-text problem, assuming that the content to describe and its temporal location are already provided. Realistic AD instead requires coupled decisions about what visual information is narratively important, when it can be spoken without interfering with dialogue, and how it should be formulated to fit within the available time. We formalize AD generation as a constrained optimization problem over these three decisions. Our hybrid system uses large
language models to propose and ground visual elements, estimate their salience to the narrative, and generate compressed realizations. A mixed-integer linear program then jointly selects and schedules descriptions across a scene subject to temporal constraints. When evaluated on REFRAMED, a
benchmark for realistic AD of movies, our approach makes better decisions than prompted LLMs about what to describe and when to describe it, establishing a new SOTA on narrative QA and temporally grounded metrics. Ablations show that explicit temporal constraints drive gains in placement, while salience estimation controls how much narratively useful content is retained. Improvements are concentrated on temporal and narrative measures rather than n-gram overlap, although a significant gap to professional describers remains.
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All authors Igor Sterner, Mirella Lapata, Alex Lascarides, Frank Keller
Software Engineering
Evaluating Agent Skills for Version-Specific Plugin Migration: A Retrospective Study
Beiming Liu, Haihao Li, Minjie Chen et al. (+15 more)
Agent skills package version-specific maintenance knowledge for coding
agents, but a higher diagnostic score does not by itself show that the resulting migration advice satisfies the target version's contract. We study a shipped plugin-upgrade skill through an archive of 64 reports on 16 static migration tasks, with two attempts per condition and 328 criterion decisions. With the skill, mean recorded reward rises from 93.83 to 98.75, a gain of 4.92 points (95% task-bootstrap interval [0.31, 10.86]); the gain is concentrated in one task, and eight task pairs are at the ceiling. Tracing every decision to its contract domain and reviewing ten reports in depth exposes grading errors that favor either arm; in one, a containment predicate that accepts the parent directory still receives full credit. Executable probes confirm this defect and show that a working teardown repair is excluded only by a narrower lifecycle rubric. Replacing the reviewed decisions keeps the estimate positive (4.61 to 5.39 points) but moves its interval to or across zero. Re-grading all 64 reports with judges from two other model families, without arm labels or prior scores, agrees with the original judge on 91.8% and 95.7% of decisions (weighted $\kappa=0.64$ and $0.72$) and gives gains of 10.63 and 6.09 points. The study contributes a traceable evaluation that connects aggregate reward to contract-level evidence and judge sensitivity, together with concrete review checks for migration advice. Executable end-to-end repairs, independent human annotation, and other frameworks are left to future work.
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All authors Beiming Liu, Haihao Li, Minjie Chen, Ning Chen, Yiran Wang, Jiming Ye, Puzhao Zhang, Tongtao Wang, Sheng Gao, William Jin, Weihao Mu, Chengzhi Liu, Yucheng Xia, Guangren Wang, Chaoyang Fan, Changfeng Huang, Xunming Lin, Yuanjie Shen
Cryptography and Security
T-Backdoor: Exploiting Temporal Redundancy in Neuromorphic Data for Spike-preserving Backdoor Attacks on SNNs
Abdullah Arafat Miah, Kevin Vu and Yu Bi
Backdoor attacks are a serious security threat to deep neural networks (DNNs) and remain largely underexplored for spiking neural networks (SNNs). Existing attacks primarily introduce spatiotemporal triggers that induce deviations in the spike distribution of poisoned samples relative to their clean counterparts. To address this limitation, this work proposes a novel
backdoor attack on SNNs, termed T-
Backdoor, which operates using purely temporal triggers such as Rate, Latency, and Jitter without introducing any spatial perturbation, making the shift in spike distributions significantly harder to detect. Through extensive experiments on three
benchmark neuromorphic
datasets: N-MNIST, CIFAR10-DVS, and N-Caltech101, and evaluation against seven baseline
backdoor defense methods, we demonstrate that T-
Backdoor achieves a near-perfect 100% attack success rate (ASR) in both single target and multi target settings with only minor degradation in clean accuracy, while remaining robust against existing
backdoor detection and mitigation techniques. The codes are available at https://github.com/SiSL-URI/T-
Backdoor .
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General Relativity and Quantum Cosmology
Binary black hole scattering in the extreme-mass-ratio limit: time-domain waveform
Andrea Geralico
The time-domain gravitational waveform emitted during the scattering of two nonspinning compact bodies is computed in the extreme-mass-ratio limit. It provides a time-domain description of the gravitational radiation emitted during the encounter and complements previous first-order self-force calculations, which have primarily been formulated in the frequency domain. In this way, the present results provide a complementary representation of the radiative dynamics and facilitate a direct comparison between time-domain and frequency-domain approaches to the gravitational-wave signal. The waveform is accurate to the fifth post-Minkowskian (three-loop) level and seventh post-Newtonian order, and will serve as a
benchmark for future calculations by other methods, to first order in the mass ratio. The results have been already tested in a previous work by constructing the energy and angular momentum fluxes, thereby computing the corresponding radiative losses at the 5PM and 4PM level, respectively, with the same PN accuracy, which are in agreement with recent amplitude-based calculations.
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Optimization and Control
Convergence Analysis of Newton Methods for Nonlinear Optimal Control Problems
Yushu Feng and Haisen Zhang
The main purpose of this paper is to establish the local quadratic convergence of Newton's method for nonlinear optimal control problems. Under suitable smoothness assumptions, the cost functional is first shown to be twice Gâteaux differentiable with respect to the control. An explicit operator representation of its second derivative is derived, and its continuity in different control spaces is examined. The second-order coercivity condition in $L^2(0,T;\maathbb{R}^m)$ is then shown to be equivalent to the existence of a strongly regular solution to an associated
matrix Riccati differential equation, thereby expressing an infinite-dimensional quadratic-form condition in terms of the solvability of a
matrix differential equation. On this basis, local quadratic convergence of Newton's method in $L^\infty(0,T;\maathbb{R}^m)$ is established using linear-quadratic optimal control theory. Under suitable structural assumptions, local quadratic convergence in $L^2(0,T;\maathbb{R}^m)$ is also established. Explicit estimates of the corresponding convergence neighborhoods are derived, and the theoretical results are illustrated by numerical examples.
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Fluid Dynamics
Magneto-coalescence phenomena in colliding ferrofluid droplets
Neeladri Sekhar Bera, Abhishek Kumar Jaiswal, Devranjan Samanta et al. (+1 more)
We explore the collision hydrodynamics of a ferrofluid droplet falling freely onto a sessile droplet of the same
liquid, in the presence of a horizontal
magnetic field; a configuration that couples droplet-on-droplet coalescence with concomitant field-governed wetting and spreading. Using high-speed imaging, we track the events through crown formation, radial spreading, and rim detachment (under specific conditions), across three ferrofluid concentrations, two substrates of different wettability (glass and PET), and a range of impact velocities and
magnetic field strengths. The maximum crown height is noted to scale as $H_{c,\max}/D_t\sim Fr^{0.5}$ ($Fr:$ Froude number); well below the ballistic upper bound of $H_{c,\max}/D_t\sim Fr$. At zero-field, the maximum spreading collapses onto the boundary-layer scaling $\beta_{0,\max}\sim (We_0/Oh)^{1/6}$ ($We_0$: Weber number, $Oh:$ Ohnesorge number) when expressed in terms of the merged impact velocity, and coalesced-drop size . With the field applied, a bulk-dissipation energy balance predicts $\beta_{\max}\sim Z^{1/5}$, where $Z$ combines the
magnetic-driving, and inertial-capillary-viscous terms, but the observations instead follow a markedly weaker $\sim Z^{1/11}$, a deficit traced to enhanced dissipation from the magnetoviscous effects, and manifested via an effective Ohnesorge number. Finally, rim detachment occurs beyond a field- and height-dependent threshold, described by a size-independent criterion $Fr^2Bo\approx 3550$ ($Bo:$ Bond number), above which the rim may fragment into daughter droplets. These scalings provide predictive tools for magnetically assisted printing, droplet-on-demand systems, and coating processes, where repeated droplet collisions occur on the residual
liquid droplet or layer.
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All authors Neeladri Sekhar Bera, Abhishek Kumar Jaiswal, Devranjan Samanta, Purbarun Dhar
Neural and Evolutionary Computing
Orbital Error Dynamics: Self-Organized Criticality, Ephemeral Parameter Resonance, and Non-Linear Biological Ontologies in Zero-Storage Neural Synthesis
Volkan Dağl{\i}, Zerrin Dağl{\i} and Dağhan Dağl{\i}
Modern deep neural networks treat parameters as static floating-point
matrices stored in physical
memory, incurring Von Neumann
memory bottlenecks and representation collapse. We formulate Orbital Error Dynamics (OED), an analytical framework wherein synaptic weights are not stored masses (O(W)), but transient topological resonances (O(1)) derived procedurally from the complex quadratic
polynomial map z_{n+1} = z_n^2 + c. We introduce the Bent Sine Wave Hypothesis, demonstrating that non-
equilibrium living systems emerge when harmonic waves curl inward through environmental drag toward the cardioid cusp (c = 1/4). We define the Observer Horizon Geometry in parameter space, identifying interior resonance shoulder loci X_upper = (0.25, +0.18) and X_lower = (0.25, -0.18) between the fixed-point basin and the true boundary at c = 0.25 +/- 0.50i. To escape non-convex stagnation without loss zeroing, we introduce a heavy-tailed Biomimetic Perturbed Jump Operator (Omega_tunneling) inspired by mammalian fertilization zinc sparks. We further couple an enteric-cranial Dual-
Brain architecture shielded by adaptive CD4+ regulatory immune gating (M_CD4), and project the 4-nucleotide genetic basis (A, T, C, G) across quadrants in C. Multi-seed empirical validation on the Two-Moons
manifold (5 seeds, 80/20 train/test split, 32x32 grid, zero test-time updates, zero label leakage) demonstrates that procedural parameterization from a 24-byte coordinate seed achieves 77.67% +/- 5.35% clean test accuracy (within an 8.00-point paired difference of an unconstrained gradient baseline at 85.67% +/- 5.35%, 95% CI: [-1.07%, 17.07%]) and 71.33% +/- 3.80% under distribution shift (N(1.2, 0.4)), alongside conceptual equivalence with an analog optical co-processor.
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Combinatorics
Total Vertex Irregularity Strength of Cubic and 4-Regular Graphs
Songling Shan and Yucheng Zhong
Let $G$ be a graph and $k$ be a positive integer. A total $k$-labeling of $G$ assigns to each vertex and each edge a label from $\{1,\ldots,k\}$. The weight of a vertex is the sum of its label and the labels of its incident edges. A total labeling is vertex irregular if all vertex weights are distinct. The total vertex irregularity strength $\text{tvs}(G)$ is the smallest $k$ for which $G$ has a vertex irregular total $k$-labeling. For an $r$-regular graph $G$ on $n$ vertices, a counting argument gives $\text{tvs}(G)\ge\lceil(n+r)/(r+1)\rceil$. The restriction of a
conjecture of Nurdin, Baskoro, Salman, and Gaos to regular graphs asserts that this bound is attained. We prove this assertion for cubic and $4$-regular graphs. We also show that, for every fixed $r\ge2$, a recent
theorem on prescribed degree frequencies implies the assertion for all sufficiently large $r$-regular graphs.
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Probability
Vanishing-noise asymptotics for Donsker-Varadhan rate functions on the circle
Milan Koresski
We study the vanishing-noise limit of the rate function for the Donsker-Varadhan large deviation principle for one-dimensional
diffusion processes on a circle. As is well known, the rate function can be represented either as the Legendre transform of the principal
eigenvalue of the perturbed infinitesimal generator or by a variational formula. We analyze the asymptotic behavior of the principal
eigenvalue as the parameter in front of the noise goes to zero and compare the Legendre transform of the limit with the expression obtained as the limit of the variational representation. We prove, in particular, that the resulting expressions do not always coincide, leading to continuity and discontinuity phenomena in infinite-dimensional functional spaces. Moreover, we use the previous analysis to pass to the limit in the LDP, using the notion of {\Gamma}-convergence.
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Nuclear Theory
Extending multi-messenger constraints on neutron star matter through the inclusion of direct Urca cooling
M. Dio Danarianto, Guilherme Grams, Thibeau Wouters et al. (+1 more)
The properties and behavior of strongly interacting matter at extreme densities can not only be determined through terrestrial experiments or through theoretical considerations, but also astrophysical observations of
neutron stars (NSs) and binary
neutron star systems become increasingly important to obtain complementary information. In light of these findings, we perform a
Bayesian study inferring the NS equation of state (EoS) by incorporating constraints from the direct Urca (dUrca) process, thereby including information on matter composition in addition to traditionally used macroscopic observables. We implement a self-consistent treatment of $\beta$-
equilibrium and charge neutrality in the jester framework, including the calculation of the
proton fraction and the onset of nucleonic dUrca in the presence of electrons and
muons. We combine constraints from
chiral effective field theory, astrophysical measurements of NS masses, radii, and
tidal deformabilities, and observations of rapidly and slowly cooling
neutron stars. The dUrca constraint seems to be in favor of stiffer EoSs, but –at the current stage– has only a minor impact on macroscopic NS properties once we include other
nuclear and astrophysical constraints. In contrast, the dUrca information provides a constraint on the composition of canonical-mass NSs favoring a higher
proton fraction inside the
star, illustrating the complementary information carried by cooling observations. Our results demonstrate the potential of incorporating composition-sensitive observables into multimessenger inference and provide a step toward a more comprehensive treatment of dUrca constraints with microscopically motivated EoS models.
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All authors M. Dio Danarianto, Guilherme Grams, Thibeau Wouters, Tim Dietrich
Earth and Planetary Astrophysics
Deep HST/STIS Coronagraphic Imaging of the $\beta$ Pictoris Debris Disk: On the Scattered Light Component of the Cat's Tail
Arin M. Avsar, Kevin Wagner, Daniel Apai et al. (+6 more)
The $\beta$ Pictoris debris disk is a unique system where mid-infrared coronagraphic imaging with JWST/MIRI revealed a new spatially resolved substructure known as the Cat's Tail. We present deep HST/STIS coronagraphic imaging of $\beta$ Pic in scattered light, combining six epochs of observations between 2012 and 2025, in search of the scattered light component of the Cat's Tail. We detect the disk out to a projected separation of 500 au for the first time with HST/STIS, achieving SNR > 100 across much of the midplane between 50 and 200 au. We report the detection of the visible/near-infrared scattered light component of the Cat's Tail substructure. We find through injection-recovery analysis that the scattered light component of the Cat's Tail has a total flux of $0.1-0.4\%$ of the MIRI F1550C flux. Using the measured STIS-to-MIRI flux ratio, we model the Cat's Tail grain properties and find that the grains are highly porous and almost entirely composed of organic refractory material, which is in agreement with findings with JWST/MIRI. We use the organics content of
Solar System
dwarf planets and
dust in the local ISM as a proxy to estimate the size of the colliding progenitors that can produce enough organic refractory material seen in the Cat's Tail. We find that each colliding progenitor must have a mass of at least $1-3\times 10^{21}$ kg, comparable to Charon and Makemake in our Kuiper Belt. Additionally, we find a prominent warped morphology and surface brightness asymmetry in the outer
dust halo of $\beta$ Pic. We compare the observed
halo morphology and asymmetry to predicted vertical structures, which may arise from
planet–disk interactions.
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All authors Arin M. Avsar, Kevin Wagner, Daniel Apai, Isabel Rebollido, Antranik A. Sefilian, Marshall D. Perrin, Christopher C. Stark, Gabriel Weible, Andras Gaspar
Analysis of PDEs
Bound states of Schrödinger-Poisson system in electromagnetic fields
Pingkang Guan, Baihong Li, Dimitri Mugnai et al. (+1 more)
This paper investigates the Schrödinger-Poisson system involving electromagnetic fields. Our approach is mainly based on the variational method. By developing the global compactness result, we establish the existence of higher-energy bound state and ground state solutions under suitable assumptions. In addition, under
symmetry conditions, the existence and multiplicity of intertwining solutions are obtained for both finite and infinite
symmetry group.
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All authors Pingkang Guan, Baihong Li, Dimitri Mugnai, Yuanhong Wei