Artificial Intelligence
Building Transformation Layers for Riemannian Neural Networks
Ziheng Chen
Recently, deep neural networks on
manifold-valued representations have garnered significant
attention across various machine learning applications. One recent focus is the generalization of Euclidean fully connected (FC) and convolutional layers to non-Euclidean geometries. However, previous approaches typically focus on a few selected
manifolds and rely on specific properties of the target
manifold. In contrast, this work proposes a framework for constructing FC and convolutional layers over computationally tractable Riemannian spaces. This framework incorporates several previous FC layers across different geometries as special cases and is instantiated on ten representative
manifolds, including three
hyperbolic models, five geometries of the symmetric positive definite (SPD)
manifold, and two Grassmannian perspectives. Experiments on different
manifolds demonstrate the effectiveness and applicability of our approach. Code can be found at https://github.com/GitZH-Chen/RieTrans.
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Symplectic Geometry
Orbifold Mapping Spaces via Bibundles
Yoshihiro Sugimoto
The existence of orbifold structures on mapping spaces between orbifolds was established in previous work, notably by W. Chen and B. Chen-Du-Liao. In this paper, we construct an infinite-dimensional orbifold structure on the mapping space in a more
geometric way, using bibundles (or Hilsum-Skandalis morphisms), based on Lerman's description of orbifold morphisms. We construct a proper étale (or Fréchet) Lie groupoid representing the mapping space in the $C^k$, $C^\infty$, and $W^{k,p}$ settings, and show that its Morita equivalence class is independent of the choices of groupoid presentations. Locally, the mapping groupoid is isomorphic to the action groupoid of a finite group, where the finite group is identified with the
automorphism group of the corresponding bibundle. This provides a concrete
geometric framework for further studies of mapping spaces and
moduli spaces on orbifolds, with applications to Floer theory on
symplectic orbifolds.
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Cryptography and Security
LLM-Assisted Automatic Security Proofs for Cryptographic Protocols: How Far Are We?
Tianjian Liu, Shicheng Feng, Jin'ao Shang et al. (+5 more)
Large
language models (LLMs) have shown strong potential for assisting software and security analysis tasks, yet their effectiveness in cryptographic symbolic protocol verification remains insufficiently understood. In this paper, we conduct the first systematic evaluation of the capability of state-of-the-art LLMs in cryptographic symbolic protocol verification. To quantify this capability, we propose CRoST (Coverage Rate of Solve Tree), a proof-based metric derived from the verifier's proof skeleton that measures the similarity between generated lemmas and reference lemmas. We then establish the rationale of CRoST through both theoretical analysis and empirical validation. The evaluation results show that state-of-the-art models achieve 38.82% coverage on average, with 14.4% of generated lemmas exceeding 80% coverage, indicating that LLMs can already generate useful lemmas to a certain extent. However, they still exhibit non-trivial failure modes on complex multi-phase protocols, show diminishing returns under naive scaling, and incur substantial verification overhead. These findings clarify the practical potential and limitations of LLMs for protocol verification and motivate future work on complex real-world protocols.
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All authors Tianjian Liu, Shicheng Feng, Jin'ao Shang, Xiaoting Lyu, Bin Wang, Zonghua Zhang, Lei Xue, Wei Wang
Machine Learning
ReSPO: Reshaped Sequence Policy Optimization for Gradient Starvation in Off-Policy Learning
Yihang Chen, Yuanhao Ban and Cho-Jui Hsieh
Reinforcement learning from verifiable rewards (RLVR) frequently reuses rollouts across multiple policy updates, increasing the mismatch between the current policy and the data-generating policy. We identify a sign-dependent gradient starvation problem in clipped policy optimization: clipping suppresses under-generated positive responses at the low-importance-weight tail while permitting severely over-generated negative responses to dominate the high-weight tail. To address this, we propose ReSPO (Reshaped Sequence Policy Optimization), which replaces clipping with a smooth, two-branch sequence-level kernel derived from an $\alpha$-divergence variational objective and an exponential variance-control tilt. The positive branch preserves a nonzero gradient weight for under-generated positive responses, while the negative branch suppresses heavily over-generated negative responses. We demonstrate that ReSPO effectively learns from long positive
reasoning trajectories during early training, even when accumulated policy drift relegates them to the low-importance-weight tail. On dense and MoE Qwen3 models, ReSPO accelerates early optimization, improves final training scores, and achieves higher held-out
benchmark performance under a rollout reuse, validating our approach on importance-weight tail control in off-policy learning.
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Robotics
Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence
Hongcheng Gao, Jingjing Zhou, Zelin Zheng et al. (+10 more)
Vision-
language-action (VLA) and world-action (WAM) models map observations and instructions directly to
robot actions. This directness ties a policy to training: minor layout or viewpoint changes cause failure, and instructions generalize poorly. The root cause lies in representation: task requirements, conditions, progress, and failure recovery are implicitly encoded in action sequences, making them difficult to inspect or revise. Digital coding
agents offer a precedent: LLMs call tools, verify results, and revise from feedback as executable code. The same working pattern of explicit state, manageable execution, and revisable procedures underlies generalization and long-horizon execution in the physical world, letting physical experience return as reusable programs,
memory, or evidence. We propose Physical Coding, representing task state and execution as code. Code as World records objects, relations, constraints, and progress; Code as Policy organizes planning, verification, recovery, and execution. We build HexaAnything, which calls perception, planning, and control tools, including VLA/WAM policies, and makes in-the-loop decisions from external feedback. Verified traces become data and
memory, enabling
evolution from tools and Harness to model weights, architectures, and ultimately hardware and task design. On RoboCasa365, HexaAnything improves Composite-Unseen and overall success over XR-1 VLA, and its Harness-trained HexaModel beats the base on every split, indicating code traces internalize physical execution. On PhyBench and a dual-arm AgileX
robot, the
agent autonomously completes physics experiments and most tabletop tasks, often faster than published results. We observe data, model, and tool self-
evolution; future work targets weight internalization, autonomous redesign of architectures,
languages, representations, and tasks, and deployment in manufacturing and science.
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All authors Hongcheng Gao, Jingjing Zhou, Zelin Zheng, Shijia Ge, Jay Zhu, Yazhe Wang, Jianshu Zeng, Xuan Shangguan, Di Wu, Lingyu He, Zhiqi Jia, Sihang Wu, Xiao He
Robotics
Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation
Chengyang Li, Yujie Wan, Shuai Wang et al. (+6 more)
This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (
UAV)
memories are aggregated at a ground server to answer questions about observations over a long horizon. Unlike conventional resource allocation based on sensing, communication, control, or computation metrics, LAQA requires an explicit measure of
memory value. We propose a generative
adversarial exam (GAE) that uses forward simulation to evaluate
memory retrieval and exam scores to quantify
memory quality. This enables the downstream QA value of candidate
memories to be measured and optimized without accessing the internal mechanisms of the black-box captioning,
retrieval, and
reasoning pipeline. Building on this metric, we develop a
memory-centric (MemCen) framework that jointly selects
UAVs and allocates transmit power to maximize
memory quality under communication constraints. In the noise-limited regime, we derive a QoM-aware capped water-filling law that explicitly connects task utility with physical-layer power allocation. We further develop penalty successive optimization (PSO) and learning to memorize (L2M) solvers. MemCen achieves QA accuracies of 92.4% and 84.0% in CARLA Town04 and Town05 under static and dynamic communication conditions, respectively. In real-world experiments, MemCen achieves 88.5% QA accuracy on the panoramic multi-
agent system (PMAS)
benchmark. Finally,
UAV-to-
robot-dog demonstrations further validate the practical utility of the acquired
memories for environmental understanding and
navigation.
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All authors Chengyang Li, Yujie Wan, Shuai Wang, Kejiang Ye, Weijie Yuan, Boyu Zhou, Yik-Chung Wu, Chengzhong Xu, Huseyin Arslan
Information Retrieval
Can Generative Retrievers Learn Semantic IDs Without Forgetting How to Speak?
Junchen Fu, Kleomenis Katevas, Vandana Rajan et al. (+2 more)
Generative
retrieval (GR) enables end-to-end
retrieval by generating document semantic identifiers (SIDs). However,
retrieval-only fine-tuning can over-specialize pretrained
language models to SID prediction, substantially distorting their natural-
language distribution and limiting their suitability for interactive systems that must both retrieve documents and generate natural-
language responses. We introduce SpeakGR, a dual-objective framework that learns SIDs while preserving
language generation. It combines supervised SID learning with speak-preserving regularization: an on-policy
distillation objective that aligns the current model with a frozen copy of the original model on student-generated prefixes using forward KL over the original text vocabulary. We further propose Adaptive SpeakGR, which dynamically adjusts the preservation strength based on observed
language drift. Compared with SFT-only, SpeakGR reduces WikiText-2 forward KL by 81.3-93.8% on MS MARCO and 81.2-85.2% on Natural Questions (NQ) while retaining effective
retrieval across three different LLMs. Adaptive SpeakGR further improves
retrieval over SpeakGR in most settings while maintaining substantially lower
language drift than SFT-only.
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All authors Junchen Fu, Kleomenis Katevas, Vandana Rajan, Sofía Celi, Hamed Haddadi
Machine Learning (Statistics)
Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantification
Sami Chemlal, Thibaut Germain, Rémi Flamary et al. (+2 more)
Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven applications, including uncertainty quantification and dynamical systems analysis. Conditional mean operators (CMOs), a class of linear operators between function spaces, resolve these objectives by providing access to a broad class of conditional statistics. However, existing methods typically estimate each CMO independently or constrain it to prespecified function spaces, thereby preventing the exploitation of shared structure across related distributions. In this work, we posit that related CMOs share finite-dimensional input and output function spaces, and are specialized for each task with a linear operator mapping these spaces. Based on this hypothesis, we introduce MTL-CMO, a multi-task framework that jointly learns shared function spaces and task-specific operators across multiple
datasets. We further introduce T-CMO, a transfer learning method that reuses the shared spaces to estimate, in closed form, the operator of a new conditional distribution. We establish statistical guarantees quantifying the benefits of jointly learning the shared function spaces. Our experiments demonstrate that learning shared function spaces improves uncertainty quantification across a broad range of conditional distributions and, when applied to Langevin and
plasma dynamics, yields compact representations of complex dynamics that retain physically meaningful information and enable parameter identification.
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All authors Sami Chemlal, Thibaut Germain, Rémi Flamary, Vladimir R. Kostic, Karim Lounici
Atmospheric and Oceanic Physics
A Reduced-Order Coupled Oscillator Model for the Emergence of Episodic Convection
Sooman Han, Soong-Ki Kim, Bowen Fan et al. (+3 more)
Extreme precipitation is a societal concern, and understanding tropical moist convection is increasingly important in a warming
climate.
Climate simulations show that, under hothouse Earth, convection can transition from quasi-steady precipitation to an episodic regime characterized by intense rainfall bursts separated by dry intervals, although the mechanisms governing this transition remain debated. Here we develop a reduced-order framework that represents tropical convection through column-integrated moisture (q) and thermal stratification (Delta), rather than bulk measures of convective instability. Analysis of cloud-resolving model (CRM) simulations shows that these variables evolve together: during dry periods, surface fluxes recharge moisture, while radiative and dynamical processes erode stratification generated by previous convection. We couple this
evolution to the nonlinear moisture dependence of tropical precipitation (P), whereby rainfall increases sharply above a critical moisture threshold. A third prognostic variable, C, represents convective persistence, allowing convection to continue below its onset threshold and thereby producing hysteresis. With parameters constrained by CRM output, the model reproduces the
evolution of q, Delta, and P in both regimes, representing quasi-steady convection as stable, noise-driven oscillations and episodic convection as an unstable, self-sustained limit cycle. Linear stability analysis shows that the transition depends on moisture-recharge and stratification-adjustment timescales, thermal-moisture sensitivity, and convective persistence. Because radiative and dynamical processes enter through their effects on thermal stratification, the framework is not tied to a specific mechanism, providing a unified dynamical link between moisture-threshold precipitation behavior and the emergence of episodic convection under warming.
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All authors Sooman Han, Soong-Ki Kim, Bowen Fan, Jérôme Vialard, Alexey V. Fedorov, Juan M. Lora
Machine Learning
LLMs are General Asynchronous Agents
George Yakushev, Denis Mazur, Vladimir Bartenev et al. (+4 more)
Modern LLMs are increasingly capable as autonomous
agents, but they follow sequential interaction cycles: read, think, reply or call tools, repeat. Many real-world use cases are not sequential: voice assistants,
embodied agents, and monitoring systems receive new inputs while they think or perform another task. Modern LLMs address this with specialized architectures for voice interaction and
video streams, VLAs for
robot control, asynchronous tool calling for API usage, and others. In this work, we generalize from different asynchronous tasks to general asynchronous
agents that can adapt to different types of concurrency. To achieve this, we develop an asynchronous LLM framework that lets users (or the
agents themselves) define inference coroutines with overlapping
memory states. We showcase that Qwen 3.x models are capable of asynchronous operation for streaming
video understanding, videogames, and monitoring, without task-specific training.
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All authors George Yakushev, Denis Mazur, Vladimir Bartenev, Vyacheslav Zhdanovskiy, Timofey Byzov, Vladimir Kaurkin, Vadim Pastushenko
Machine Learning
Frontier Learning: Training LLM Reasoners at the Edge of Capability
Robin Faro, Shyam Sundhar Ramesh, Ilija Bogunovic et al. (+1 more)
Reinforcement Learning-based post-training of Large
Language Models (LLM) has been successfully applied to improve their
reasoning capabilities. Existing pipelines primarily finetune LLMs on a fixed pool of problems specified prior to training using the GRPO loss. This is fundamentally limiting, as learning signal arises only when policy rollouts mix successes and failures, causing the useful portion of any fixed pool to quickly become stale as the model improves. To address this, we propose frontier learning, an open-ended post-training approach in which procedural generators are used online to continually produce informative training problems. It treats the generator's task-specific parameters as a search space and uses a regret signal to prioritize and explore frontier difficulty levels in order to focus training at the edge of the model's evolving
reasoning capabilities. Across several
reasoning tasks and model families, our approach consistently achieves higher relative gains over fixed-pool baselines, demonstrating that effective post-training requires not only selecting useful problems, but continually generating them at the edge of capability.
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All authors Robin Faro, Shyam Sundhar Ramesh, Ilija Bogunovic, Aurelien Lucchi
Programming Languages
Semantic Prefix Oracles for LLM Decoding: Contracts and Differential Validation
Paul Kronlund-Drouault
Constrained decoding can enforce regular or context-free output formats, but many program-generation failures are semantic: scope, typing, and declaration effects depend on context. We present semantic grammar specifications, a declarative formalism that attaches such constraints to a context-free surface and executes them during Earley descent. Our implementation enforces safe
pruning: it rejects only prefixes whose semantic contradictions cannot be repaired by any continuation. A separate, grammar-dependent, dead-end freedom property guarantees the existence of a realizable witness for each remaining branch. We give simple sufficient conditions based on surface productivity, type coverage, and left-to-right constraint flow. Our finite-lambda, core ML, and C-like fragments satisfy them, while the STLC instance used in our experiments does not: plain STLC can violate type coverage, and we show how restricting its type universe recovers it. A tokenizer-lifting lemma carries character-level witnesses to
token sequences under an explicit vocabulary-coverage hypothesis. We validate the implementation differentially against production compilers (ocamlc, cc). Across every prefix of 65 compiler-valid programs we observe zero false prunes. The semantic oracle localizes 25/30 invalid programs mid-stream, against 0/30 for a syntax-only oracle, and agrees on 42/42 recursion probes. A twelve-model generation study, including a matched semantic-versus-syntactic ablation for nine models, finds nonnegative observed semantic-minus-syntactic point estimates for every model-
language pair, with maxima of $+15.2$ points on STLC task correctness and $+14.3$ points on ML validity.
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Systems and Control
Scalable Small-Signal Stability Assessment of Power Systems Based on Frequency-Domain Quadratic Constraints
Xiaoying Liu, Linbin Huang, Hangyu Chen et al. (+1 more)
Small-signal stability analysis of power electronics-dominated power systems is becoming increasingly challenging due to the large-scale integration of heterogeneous grid-following (GFL) and grid-forming (GFM) converters. Classical centralized methods, such as
eigenvalue analysis and the generalized Nyquist criterion, provide direct stability assessment tools but exhibit limited scalability as the system size increases. This motivates scalable analysis methods for stability assessment and device-level diagnosis, and calls for less conservative stability conditions. To this end, this paper proposes a scalable small-signal stability assessment method with quantitative stability indices for multi-converter systems based on frequency-domain quadratic constraints (FQCs). It is shown that several existing stability conditions, including
geometric conditions based on the Davis-Wielandt (DW) shell, numerical range, $x$-$z$ graph, scaled relative graph (SRG), mixed gain-phase condition, and passivity condition, are special cases of the FQC-based stability condition. This FQC-based formulation also clarifies the requirements for decentralized verification of these existing stability conditions. To further reduce conservatism, a full-multiplier FQC-based stability condition is developed, based on which an optimization problem is formulated for scalable stability certification and device-level diagnosis using quantitative stability indices without relying on graphical inspection. Combined with the mixed gain-phase condition, this optimization problem forms a hierarchical screening-diagnosis procedure that improves efficiency. Case studies demonstrate that the proposed method is less conservative than existing criteria and can effectively identify problematic devices associated with potential instability risks.
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All authors Xiaoying Liu, Linbin Huang, Hangyu Chen, Huanhai Xin
Instrumentation and Methods for Astrophysics
Source–Line Associations in JWST/NIRSpec Dense-Shutter Spectroscopy
Zihao Wu, Daniel J. Eisenstein, Francesco D'Eugenio et al. (+9 more)
We demonstrate the feasibility of source–line association in JWST/NIRSpec dense-shutter spectroscopy, with $\sim$2000 sources observed simultaneously per pointing regardless of spectral overlap. While JWST wide-field slitless spectroscopy (WFSS) can disentangle overlapping spectra using two grisms with perpendicular dispersion directions, NIRSpec can achieve this using gratings with different dispersions. Compared with WFSS, NIRSpec dense-shutter spectroscopy offers substantially higher sensitivity and broader wavelength coverage. We develop a
Bayesian framework that combines cross-dispersion offsets, wavelength consistency, and changes in relative line positions between gratings to jointly determine source associations and
redshifts. We test the method using mock observations based on the observed source distribution and emission-line fluxes from the JWST Advanced Deep Extragalactic Survey (JADES), accounting for the NIRSpec optical model, MSA operability, measurement noise, false-positive detections, and bad pixels. Although the spectral traces overlap extensively, fewer than 0.5% of the simulated emission lines are blended with another line on the detectors in NIRSpec grating spectroscopy. Applying our method to these simulations, we obtain
redshifts, at an accuracy of 98%, for the $\sim$1800 sources that have detectable emission lines, of which $\sim$1500 are classified as confident and are 99.9% accurate. We further investigate how the association accuracy depends on the number of grating configurations. By combining high multiplexing, high sensitivity, and broad wavelength coverage, NIRSpec dense-shutter spectroscopy enables efficient, highly complete spectroscopic surveys for the high-
redshift Universe.
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All authors Zihao Wu, Daniel J. Eisenstein, Francesco D'Eugenio, Erica Nelson, Benjamin D. Johnson, Andrew J. Bunker, Zhiyuan Ji, Pierluigi Rinaldi, Brant Robertson, Sandro Tacchella, Christopher N. A. Willmer, Joris Witstok
Solar and Stellar Astrophysics
V405 Draconis: A Deep Contact Binary with a Tertiary Companion and a Quadruple System Candidate
Maksym Yu. Pyatnytskyy and Ivan L. Andronov
We continued our analysis of the O-C diagram of the EW-type eclipsing binary V405 Dra using recently available data. We refined the orbital parameters of the third body responsible for the short-period oscillations in the O-C curve, obtaining $P_3=513.12(20)$ d, $a_{12}\sin i_3=0.3862(15)$ AU, and $e_3=0.1690(48)$. Assuming a total mass of $1.56$ $M_\odot$ for the eclipsing binary, the minimum mass of the third body is estimated to be about $0.5$ $M_\odot$. We also investigated the complex long-term O-C trend and found that it may be partially explained by the light-time effect caused by a possible fourth body, with a characteristic period of about 23.5 yr and a minimum mass of about $0.9$ $M_\odot$. However, the fourth-body model cannot fully reproduce the observed long-term variation, particularly the earliest data, and the nature of part of the long-term trend remains unexplained.
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Cryptography and Security
Indistinguishability of Sum of Permutations: A Fourier Analytic Route to Classical and Quantum Security
Ritam Bhaumik, Chun Guo, Xiaoning Guo et al. (+1 more)
We study classical and
quantum indistinguishability of sums of independent random permutations and related transformations from permutations to functions. Let $G$ be a finite abelian group of order $N$, and let $\pi^k_+(x)=\pi_1(x)+\cdots+\pi_k(x)$ for $k\geq2$ independent uniform random permutations of $G$. We give a unified
Fourier analytic treatment in which the construction is represented by its probability density and a distinguisher by its acceptance function, with the classical and
quantum query models imposing different restrictions on the
Fourier support of the latter. Classically, we obtain the bound $O_k(q/N^{k-1/2})$ for every $q<N$, and refine it below the birthday threshold to $O_k(q^2/N^k)$. In the
quantum model, a simulation argument gives $O_k(N^{-(k-3/2)})$ for $q\leq(N-1)/2$, while
Fourier interpolation gives concrete finite bounds up to $q\leq4N/15$ and the query-dependent bounds $O\left(\min\left\{N^{-1/2},q^3/N^2 + 1/N\right\}\right)$ and $O_k\left(\min\left\{q^3/N^k,N^{-(k-3/2)}\right\}\right)$, for $k=2$ and $k \geq 3$, respectively, throughout $1\leq q\leq(N-1)/2$. For $q = 1$, the first bound sharpens to $O(N^{-2})$. Over $G=\mathbb F_2^n$, a one-query
Fourier attack matches the order of our one-query bound, while an $N/2$-query parity attack with advantage $1/2$ shows that our bounds reach the constant-advantage query threshold. We further study two variants of sum of permutations over binary vector spaces. First, we allow arbitrary surjective linear postprocessing, which includes truncation, and obtain classical and
quantum bounds that retain the output-size dependence. Second, we analyse Dinur's variable-output single-permutation construction, $\mathsf{LXoP}$, for every fixed output width, and derive its classical and
quantum security bounds; for one- and two-block outputs, we give concrete
quantum security bounds.
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All authors Ritam Bhaumik, Chun Guo, Xiaoning Guo, Ashwin Jha
Computer Vision
Automated Species Identification in Camera Trap Images for Wildlife Conservation
Nowshin Amin, Nafisa Tabassum Oyshi, Tahmid Abrar Zidan et al. (+2 more)
Wildlife conservation involves protecting, preserving, and managing wildlife species and their habitats. With today's rapid pace of human development,
climate change, and other unsustainable practices, the need for wildlife conservation has heightened. Despite significant progress in species identification using deep-learning models, significant challenges still remain in effectively detecting small animals in low-contrast trap images due to limited feature extraction capabilities. This thesis presents a novel end-to-end framework integrating a self-
attention mechanism to address these limitations. The proposed architecture involves a Swin-BiFPN backbone integrated in a Faster RCNN detection network, coupled with a visual semantic extraction module driven by the LLaVA v1.5 (13B)
multimodal large
language model. The detection framework, capable of extracting crucial features in challenging trap images, demonstrates consistently high results and robust generalization capabilities. Furthermore, the visual semantic extraction module provides zero-shot detection capability, as well as providing valuable insights and emergent cues of the animal's behavior, further supporting the conservation effort. The MLLM evaluation was conducted using both traditional NLP metrics (precision, recall, F1, and SBERT similarity) and subjective scoring by LLM-based judges (GPT-4.1 and GROK 3.0), across five MLLMs, demonstrating the model's strong performance in visual description generation. The proposed framework improves detection accuracy across low-contrast trap images and small animals while also demonstrating zero-shot detection capability leveraging the MLLM.
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All authors Nowshin Amin, Nafisa Tabassum Oyshi, Tahmid Abrar Zidan, Miftaun Noor, Md. Abrar Rahman Shafin
Analysis of PDEs
Convex counterexamples to the Schiffer and Pompeiu conjectures in dimensions two to eighteen
Jizhou Guo
We construct the first convex planar counterexample to the Schiffer and Pompeiu
conjectures: a bounded strictly convex non-disc domain with real-analytic boundary carrying a nonconstant solution of $\Delta u+u=0$ with $u=1$ and $\partial_\nu u=0$ on the boundary. Together with the higher-dimensional constructions, this gives convex non-ball counterexamples in every dimension from two to eighteen, and in dimensions twenty and twenty-one, with real-analytic boundaries diffeomorphic to spheres and indicator
Fourier transforms vanishing on the unit sphere. In dimensions $4$, $6$, $8$, $10$ and $14$, planar reductions through compact Lie
algebras of rank two use Harish-Chandra's radial part formula and Kostant's convexity
theorem. In dimensions $3$, $5$ and $7$, an axisymmetric formulation on the unit ball gives a quartic equation in weighted coefficient spaces. The exact two-sided inverse of the residual-corrected linearisation combines a Dirichlet Helmholtz inverse, a holomorphic boundary real-part problem and the material-derivative identity; it gives the planar Schiffer domain and, by one dimension-parametrised argument, the domains in dimensions $5$, $9$, $11$ to $13$, $15$ to $18$, $20$ and $21$. We also construct the first convex planar counterexample to Berenstein's
conjecture: a strictly convex non-disc domain with real-analytic boundary carrying a sign-changing solution of $\Delta u+u=0$ with $u=0$ and $\partial_\nu u=1$ on the boundary. Planar existence for this problem is due to Colbrook, Sadeghi and Stepaniants. All existence proofs in this paper are computer-assisted.
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Optimization and Control
Convex Optimization Is Free When Accuracy Is Expensive
Arthur Paing and Arthur Jacot
This paper studies convex optimization when the gradient cannot be evaluated exactly, but only approximated by a hierarchy of
algorithms whose compute grows like $\delta^{-\gamma}$ in the accuracy $\delta$. When $\gamma>2$, falling into the Harder-Than-Monte-Carlo (HTMC) regime, the price of accuracy outruns the variance reduction that Monte Carlo would buy and we show that minimizing a loss function costs no more, up to a factor depending only on $\gamma$, than a single evaluation of its gradient at the accuracy the problem demands. A randomized multilevel oracle replaces the deterministic approximation of accuracy $\delta$ by an unbiased
estimator of it, whose variance $\sigma^2$ becomes a second, independently priced dial: the cost of one call drops from $\delta^{-\gamma}$ to $\delta^{2-\gamma}\sigma^{-2}$. Plain inexact gradient descent driven by that oracle reaches loss $\varepsilon$ at expected compute $\Theta(\varepsilon^{-\gamma})$ in the convex case, against $\Theta(\varepsilon^{-(\gamma+1)})$ for the same method run at a fixed accuracy: randomization buys a full power of $\varepsilon$. Under $\mu$-strong convexity the exponent halves, to $\varepsilon^{-\gamma/2}$, because the iterates settle at a noise floor and the bias budget relaxes accordingly. Both bounds are independent of the step size, and hence of the smoothness constant, and we show that the cost is a functional of the underlying gradient flow rather than of any discretization of it.
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Physics and Society
A citizen science behavioural dataset of an urban collective-risk dilemma on air pollution mitigation under resource inequality
Marc Sadurní, Martín F. Díaz, Julián Vicens et al. (+4 more)
This article describes a behavioural
dataset from a collective-risk dilemma public experiment carried out in Barcelona, Spain, within the citizen science xAire air quality project. Participants played a ten-round game in groups of six, each deciding every round how many monetary units to contribute from a private endowment toward a shared target; groups that fell short forfeited all remaining capital. Three endowment treatments held the group total fixed: an equal condition and two unequal conditions in which low-endowment participants formed either the minority or the majority of the group. Data were collected at two public outdoor venues using a tablet-based interface under lab-in-the-field protocols. Participants were recruited on-site from a demographically heterogeneous pool of individuals passing through the venues, mostly local residents. The
dataset comprises 462 participants across 77 six-player games, 4\,620 contribution decisions in total. Each participant record links the full ten-round contribution trajectory to initial endowment, wealth treatment, venue, district of residence and its measured nitrogen-dioxide concentration, and socio-demographic and attitudinal survey items covering gender, age, education, economic and working status, air-quality perception,
fairness norms, environmental-justice beliefs, residential mobility, and community participation. The release provides raw and cleaned participant tables, a variable codebook, the survey instrument, the district-pollution linkage, and the preprocessing code.
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All authors Marc Sadurní, Martín F. Díaz, Julián Vicens, Anna Cigarini, Isabelle Bonhoure, Miquel Montero, Josep Perelló
Computer Vision
When Should the Count Change? Learning State Maintenance for Causal Video Counting
Pengyiang Liu, Dongyue Lyu, Junbo Niu et al. (+3 more)
Continuous
video counting requires distinguishing new observations from new objects or completed events. We introduce StaMina (State Maintenance), which learns to maintain counting state through state-conditioned updates. Recurrent visual context supports recognition; learned transitions maintain visibility, persistent identities, and completed-event records. A differentiable recurrence trains event transitions over legal paths constrained by count endpoints; visibility and association objectives train the object branch. A multi-source pipeline organizes 39.8K spatial queries and complementary event annotations into counting trajectories. On SVCBench, we evaluate counting adaptation with partial
video overlap and held-out groups of linked annotations. Under prefix replay (Full) and persistent streaming (Stream), 4B and 8B models reach 41.9/36.4 and 44.9/38.2 Gaussian Precision Accuracy, respectively. The 8B model gains 10.9/3.2 points over Counting-SFT on the same queries. Matched-graph comparisons isolate phase conditioning and trajectory supervision, assessing training objectives alongside hard decisions. Online
video benchmarks and count-conditioned decisions assess online understanding and task eligibility. Project Page: https://PLACEHOLDER.github.io/StaMina/
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All authors Pengyiang Liu, Dongyue Lyu, Junbo Niu, Zhongyue Shi, Jiahao Xie, Si Liu
Robotics
Adaptive Safety Filtering for Frozen ACC Policies via Conformal Residual Calibration
Zhiruo Zhou, Rigaudiere Z. Li, Chen Xiwen et al. (+3 more)
Frozen adaptive cruise control (ACC) policies can violate constraints when deployment dynamics differ from their training conditions. We propose residual-aware conformal action filtering (RACF), which calibrates residuals of a fixed nominal predictor and converts their
quantile into an operating margin for finite-model action projection. Completed transitions update margins and candidate selection without retraining the policy. In a registered comparison over 2,400 controller-trial units, Adaptive RACF achieves 94.3% episode safety, improving by 19.9 percentage points over the evaluated nominal CBF-QP baseline while reducing projection frequency from 8.11% to 6.63%. A controlled study isolates a 4.54-point improvement from residual-margin injection. In a separate matched-hardware evaluation, Adaptive reduces mean amortized rollout time by 21.2% relative to Robust CBF-QP, with 161/180 versus 170/180 safe episodes. We characterize conditions linking one-step residual coverage to constraint satisfaction and quantify the observed safety-computation trade-offs.
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All authors Zhiruo Zhou, Rigaudiere Z. Li, Chen Xiwen, Yucheng Chen, Xiaojun Zhu, Houde Liu
Cryptography and Security
AI-Based Vulnerability Assessment Capability and Cyber Attack Graph Analysis
Joni Herttuainen, Kirsi Hellsten, Vesa Kuikka et al. (+4 more)
Cyber threats targeting mission-critical infrastructure are becoming more sophisticated while the barrier to launching attacks continues to fall. Traditional point solutions like antivirus and firewalls are reactive and fail to address the combinatorial complexity of modern attack surfaces. This paper presents an investigation combining two complementary methodologies: Lockheed Martin's
Vortex/Crow framework, which applies multi-
agent reinforcement learning (MARL) over industry-standard cyber knowledge graph to identify and prioritize attack vectors and TTPs (tactics, techniques, and procedures); and Aalto's probabilistic attack graph model that combines network
topology and its vulnerabilities to compute system-level risk metrics. The 2015 Ukraine Power Grid cyberattack serves as a well-documented validation scenario. Applied independently to the same operational technology (OT) network
topology, both methodologies converge on the same attack vectors and exploit sequences as those documented in the incident record, thus providing mutual cross-validation. Attack graph analyses using node-level elimination experiments identify industrial control systems (ICS) as the most critical enablers of attack propagation, representing high-priority targets for defensive hardening. Comparison of CVSS (v2.0) and IronMiner vulnerability scoring yields in general consistent results, with IronMiner providing more actionable differentiation at network periphery nodes. The layered methodology of baseline assessment and node-level elimination proves to be scalable to large enterprise networks, thus offering defenders a structured, AI-enabled path to prioritize mitigation under realistic time and resource constraints.
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All authors Joni Herttuainen, Kirsi Hellsten, Vesa Kuikka, Ambrose Kam, Arlanda Johnson, David Welsh, Kimmo K. Kaski
Social and Information Networks
Reclaiming the social in social media
Luca Benn, Moritz Merz and David Grüning
Concerns about polarization, antisocial behavior, and mental health have broadly led to two responses to social media: adapting platforms through moderation and prosocial design, and restricting access to these platforms through age limits and comparable measures. Both address consequences of social media while leaving the
attention-driven architecture intact. We argue that the harms commonly attributed to social media arise not from technologies supporting social connection but from their implementation within the
attention economy. In response, we ask what a digital social environment built for genuine human connection would look like. From four principles - Purpose,
Alignment, Transparency, and Access - we can derive four technical properties that fulfill these principles: Operator Blindness, Algorithmic Sovereignty, Operational Parity, and Verifiability. To the best of our knowledge, no deployed alternative satisfies all four. We propose an architecture that does, making the
attention economy not just discouraged but structurally impossible. We explain how the resulting user experience refocuses on each user's individual social environment and differs from that of current platforms.
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Artificial Intelligence
Self-Adapting Group of Experts for Multi-Agent Reasoning
Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar et al. (+1 more)
Multi-
agent systems bring together
language model
agents with different roles to propose, review, and refine solutions. Each
agent's response depends on its model's capabilities, the
reasoning strategy defined by its system
prompt, and the information in its input context. Existing frameworks often adapt communication by changing this context while leaving individual
prompts fixed, even when a problem calls for different skills. We study whether
agents' initial responses can identify a strategy better suited to the current problem and guide its transfer to other
agents. To address this, we introduce SAGE (Self-Adapting Group of Experts), a training-free framework that uses answer agreement, prefix consistency, and reciprocal peer review to select a strategy donor. SAGE transfers the selected donor's
reasoning strategy to the other
agents while preserving their original roles. This transfer uses only the
agents' original system
prompts, without access to the problem or generated solutions. After strategy adaptation,
agents exchange responses through a dynamic, sparse directed acyclic graph that routes information from higher-scoring
agents to lower-scoring
agents. Experiments across multiple
agent backbones and
reasoning benchmarks show that SAGE achieves higher average accuracy than the evaluated baselines. Our code is available at https://github.com/atifquamar07/sage.
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All authors Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar, Junpei Komiyama
Sound
GLAD: Global-Local Adaptive Detector for Robust Speech Deepfake Detection
Zelin Zhao, Guanjie Huang, Danny Hin Kwok Tsang et al. (+1 more)
Recent advances in AI-based
speech synthesis have enabled highly realistic
speech, increasing the importance of
speech deepfake detection (SDD) in preventing misuse. While mainstream Self-Supervised Learning (SSL)-based detectors achieve strong performance, they suffer from poor generalization to unseen domains and often overlook fine-grained signal artifacts due to a bias towards global semantic consistency. In this paper, we conduct the first detailed empirical and visual analysis to validate these limitations explicitly. Our investigation reveals two critical architectural vulnerabilities: (1) a systemic failure to capture localized spoofing traces, and (2) a severe lack of adaptability to domain-driven shifts in SSL layer importance, rendering static aggregation strategies prone to overfitting. To address these vulnerabilities, we propose the Global-Local Adaptive Detector (GLAD). Specifically, to capture localized forgeries, GLAD employs a Hierarchical Global-Local (HGL) backbone that explicitly bridges the granularity gap by fusing global linguistic and acoustic features with fine-grained local signal details. To counter layer importance shifts in out-of-distribution (OOD) scenarios, we introduce a Hierarchical Adaptive Gating (HAG) mechanism that dynamically recalibrates layer-wise focus in a sample-specific manner. Finally, to address shortcut learning induced by environmental biases, we introduce SaniBoost, a composite data augmentation strategy for robust signal standardization and noise sanitization. Extensive experiments demonstrate that GLAD significantly outperforms state-of-the-art methods, particularly on unseen domain cases.The code will be released upon publication.
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All authors Zelin Zhao, Guanjie Huang, Danny Hin Kwok Tsang, Li Liu
Computer Vision
Spectral Super-Resolution using Spatial-Spectral Residual Operator Networks
Seokhyun Chin
Spectral super-resolution of multispectral satellite images can enable high temporal- and spatial-resolution hyperspectral satellite imagery at a modest cost, significantly increasing the applicability of hyperspectral remote sensing. This task is inherently ill-posed, making it well-suited for deep learning-based methods. In this study, the spectral super-resolution task is framed as an operator learning problem, and SSRON is proposed as a Deep Operator Network that effectively learns function-to-function mappings from downsampled spectra to continuous spectra. The model is trained to super-resolve Sentinel-2A-like multispectral imagery to EMIT images. Compared to baseline models, SSRON achieves superior performance across all metrics. The model also demonstrates zero-shot spectral super-resolution capability by predicting bands unseen during training. Furthermore, its continuous-output formulation suggests the potential to estimate spectra at finer wavelength intervals than the native sensor. These results suggest the potential of SSRON and establishes operator learning as a promising direction for spectral super-resolution.
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Computation and Language
AwarenessBench: Assessing Cognitive Capabilities of Language Models
Xiaojian Li, Rongwu Xu, Tianyun Zhang et al. (+9 more)
As
language models (LMs) exhibit increasingly consciousness-like behaviors, evaluating their cognitive abilities becomes essential. We introduce AwarenessBench, the first comprehensive
benchmark for assessing the cognitive abilities of LMs in four dimensions: metacognition, self-awareness, social awareness, and situational awareness, covering 15 cognitive functions and 14,381 samples. Evaluating 18 state-of-the-art LMs, we find that all consistently surpass random baselines, with more advanced models performing better. We further compare LMs with human performance across three demographic groups, where the best-performing model surpasses human averages overall, but most still fall markedly short in metacognition and self-awareness. Finally, we show that awareness is a distinct capability: progress in
language modeling or
reasoning does not necessarily translate into improved cognition.
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All authors Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
Cryptography and Security
"Nothing to See Here”: Unintended Disclosure through Revision Traces of LLM Deliverables
Yage Zhang, Yukun Jiang and Yang Zhang
Large
language model (LLM) assistants increasingly help users draft content for third-party recipients. During private drafting, the user or the model may introduce an item and later remove or replace it. The model may remove the item from the intended content but reveal it again when stating the edit. We call such statements revision traces. For example, after a user removes the password before sharing a configuration file, the model may delete it but leave a comment saying, "Removed the password 'No****4!' as requested." A third-party recipient who sees only the delivered file can therefore recover the withdrawn password from the comment. In an in-the-wild analysis of three public conversation corpora, we identify 26,753 revision requests, of which 2,363 (8.8%) leave revision traces. We study them in greater depth under controlled conditions by introducing RevLeakBench, a
benchmark of 100 tasks across five scenarios with a conversation track and an
agent track. We measure trace occurrence, withdrawn-item recovery, trace position, and required-content retention. Across six models, about half of the deliverables in both tracks state the edit after a revocation, and a reader that sees only the deliverable can recover the withdrawn item from about 13% of them. Telling the model that its entire reply will be forwarded to the recipient still leaves revision traces in 36.4% of the deliverables. We compare
prompt defenses and a delivery boundary, and propose an output-side filter that sharply reduces recovery with little loss of required content. We believe our work can benefit efforts to understand and mitigate unintended disclosure in LLM interactions.
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Computer Vision
BiMoGen: Bidirectional Motion-Text Generation via Unified Masked Discrete Diffusion
Wanjiang Weng, Yongliang Wu, Xiaofeng Tan et al. (+3 more)
Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existing unified approaches mostly rely on autoregressive modeling, which imposes a fixed generation order and is therefore poorly suited to the bidirectional dependencies between
language and motion, allowing early prediction errors to persist as fixed context and degrade both temporal coherence and cross-modal consistency. Masked discrete
diffusion, which models sequences through iterative bidirectional prediction, allows prediction from bidirectional context without a fixed generation order. We therefore propose BiMoGen (Bidirectional Motion-text Generation), a unified masked discrete
diffusion framework for bidirectional motion-text modeling. To stabilize training, we design Decoupled Uni- and Cross-Modal Training, in which masked pretraining first establishes cross-modal correspondence on paired motion-text sequences, after which supervised fine-tuning specializes the model for bidirectional generation. Masked
diffusion nonetheless faces a train-test mismatch, as the model is trained on clean ground-truth context yet encounters self-generated and potentially erroneous context at inference, with errors committed under heavily masked states propagating through subsequent steps. We further introduce Generation-Aware Self-Correction that exposes the model to its own predictions during training and uses correction passes at early sampling steps for further refinement of previously unmasked
tokens. Extensive experiments on HumanML3D and KIT-ML demonstrate competitive performance on both tasks, validating the effectiveness of the proposed two-stage training and self-correction designs. The project page is available at https://wengwanjiang.github.io/BiMoGen-Page.
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All authors Wanjiang Weng, Yongliang Wu, Xiaofeng Tan, Xingyu Zhu, Wenbo Zhu, Hongsong Wang