Signal Processing
Multimodal Learning for Beamforming Using Camera, LiDAR, and Radio-Frequency Pilots
Yinghan Li and Wei Yu
This paper proposes a
multimodal learning framework that integrates camera images,
LiDAR point clouds, and
radio-frequency (RF) pilots to realize multi-user multiple-input single-output (MU-MISO) beamforming optimization in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) environments. In LoS-dominant environments, beamforming can be designed by utilizing sensing information related to user locations. However, LoS paths cannot be guaranteed to exist for all users in realistic dynamic environments due to blockages. To characterize NLoS propagation, detailed three-dimensional (3D) information of surrounding obstacles is required. This paper integrates camera images and
LiDAR points with RF pilots to capture detailed 3D
geometric, object-class, and material information of users and surrounding objects. The obtained
multimodal information provides useful cues for both direct LoS paths and reflected NLoS propagation, and provides rich channel-related information for beamforming design. Specifically, a bounding-box detection neural network is first used to extract two-dimensional spatial, object-class, and material information from camera images. The image-derived information then guides the selection of
LiDAR points to obtain 3D
geometric information of users and surrounding obstacles, and assigns object-class and material labels to the selected points. The image-guided
LiDAR features, together with RF pilots, are jointly processed by a designed neural architecture to optimize the beamforming vectors. Simulation results show that the proposed method achieves a higher minimum rate than
benchmarks.
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Artificial Intelligence
AI-GRACE: A Use-Case Operationalization Framework for Agentic AI: From Organizational Objectives and Obligations to Deployment Capabilities and Architecture
John Cuneo, David Chun and Gaurav Khanna
Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate, control, and observe for a use case to deliver its intended outcome while meeting applicable obligations. This paper proposes AI-GRACE (Agentic Intelligence-Governance, Risk, Assurance, Controls, and Evidence) as a use-case operationalization framework connecting organizational governance with technical implementation. The proposal draws on professional observations and a purposive synthesis of standards and literature, using design science to frame the method contribution and situational method engineering to guide contextual tailoring and reuse. The framework establishes objectives and obligations and then assesses risks in seven proposed domains, including mission and value realization. It derives requirements for assurance before deployment, runtime controls, and evidence, which guide capability qualification, gap assessment, and a logical architecture. An
Agent Operating Envelope specifies permitted actions and escalation conditions, while Risk-Aligned Independence Levels (RAIL) summarize the authorized independence. A fictional retail banking application illustrates the method. The contribution is a traceable basis for deciding what an organization must implement, what it already supports, and what remains unresolved. Empirical evaluation must establish whether it improves deployment decisions, efficiency, and reuse.
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Group Theory
Surface subgroups of Baumslag doubles along short words
Hoang Le Xuan and Nam Hung Tran Nguyen
If $U$ is a minimal, diskbusting, finite list of words in a free group $F_n$ of rank $n$ such that the sum of the lengths of words in $U$ is at most $2n+4$, we prove that the natural presentation complex of the Baumslag double of $F_n$ along $U$ virtually contains a $\pi_1$-injective embedded closed
hyperbolic surface. This verifies the Tiling
Conjecture of Kim and Wilton for this type of lists of words, and in particular, implies that the corresponding Baumslag double contains a
hyperbolic surface subgroup.
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Machine Learning
SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?
George Ma, Benjamin Mikek, Haoyu Li et al. (+9 more)
Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering.
Benchmarks for agentic code generation check correctness with held-out test suites, which are inherently incomplete and increasingly susceptible to memorization. Formal verification avoids both problems, but existing work covers only standalone tasks whose specifications are given as input, not real issues, which touch large repositories and state intent in vague natural
language. We present Benchproofer, a pipeline that turns a coding task with a known correct patch into a formally verified one: it writes a specification for the new code, summarizes the existing functions that code calls with axioms, and admits an instance only after mechanical and
adversarial gates agree. Applying it to SWE-bench Verified yields SWE-Proof, 500 real issues whose correctness is formally verified rather than tested, and it extends to SWE-bench Pro. Evaluating Claude Opus 4.8, we find that verification catches what tests miss: a quarter of test-passing patches admit counterexamples, which a structured natural-
language specification does not fix, while a correct formal one lifts resolution from 85% to 95%. Writing that specification is the hard part: an
agent that must write its own gains nothing over an unaided baseline, and only 56% of those specifications pass our audit. The usual failure is faithfulness, a specification that constrains part of the required behavior and leaves the rest free. Specification quality still tracks the outcome, failing on 92% of unresolved instances against 51% of resolved ones, making faithful specification synthesis a concrete open problem.
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All authors George Ma, Benjamin Mikek, Haoyu Li, Ferhat Erata, Yuhao Zhang, Zeren Shui, Behrooz Omidvar Tehrani, Jun Huan, Murali Krishna Ramanathan, Somayeh Sojoudi, Hao Zhou, Anoop Deoras
Algebraic Geometry
A geometric approach to the density of rank-metric codes
Shamil Asgarli, Lian Duan, Nathan Kaplan et al. (+1 more)
We study the asymptotic density of $\mathbb{F}_q$-point-free linear sections of geometrically irreducible projective varieties over finite fields. We then apply these results to rank-metric codes via determinantal varieties. Our approach recovers the known cases in which the density tends to $0$ or $1$ and determines the limit in the cases where it was previously unknown. To compute these previously unknown limits, we extend the notion of quasireflexivity to higher-dimensional varieties and show that determinantal varieties satisfy this property. This allows us to invoke the Chebotarev density
theorem for varieties over finite fields to obtain the desired estimate.
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All authors Shamil Asgarli, Lian Duan, Nathan Kaplan, Kuan-Wen Lai
Fluid Dynamics
Mathematical modeling on peristaltic flow of a Prandtl fluid with effects of slip conditions and inclined magnetic field
Sabia Asghar and Fred J. Vermolen
The manuscript provides a description of a theoretical analysis of a non-Newtonian Prandtl fluid subject to peristaltic flow through an inclined asymmetric channel. We explore the effect of an inclined
magnetic field on the peristaltic flow. This is relevant for applications involving fluid flow in narrow, inclined (tilted) tubes similar to blood vessels or the digestive system. The model also includes thermodynamic aspects such as heat
diffusion (the Soret effect) and viscous dissipation resulting from wall-fluid slip conditions, which may help optimize medical devices such as lab-on-a-chip systems and dialysis machines. In this study, the concentration of a generic chemical, temperature, and fluid velocity are taken into account through mass, heat, and momentum balances, respectively. The solution is approximated using numerical techniques suitable for long wavelengths (low frequency) and low Reynolds numbers. The study also discusses trapping phenomena, which are crucial from a clinical point of view. The developed insights can improve the understanding of physiological flows in the gastrointestinal tract and blood vessels. By understanding how the fluid moves and how particles are trapped, these insights may contribute to the design of improved medical pumps and artificial organs. Graphical visualizations are provided for the fluid velocity profile, temperature distribution, and concentration of a generic chemical. Furthermore, the numerical results are validated through comparison with a closed-form solution from a
benchmark problem.
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Computation and Language
When Better Turns Do Not Make Better Agents: Diagnosing the Gap Between Next-Turn Metrics and Workflow Success
Md Tahmid Rahman Laskar, Xue-Yong Fu, Gundeep Singh et al. (+11 more)
Agent models are frequently evaluated one decision at a time, where the model predicts the next action based on the gold interaction history, which is scored against a reference. We investigate whether improvement under this protocol is predictive of improved autonomous workflow execution. We study pre-SFT and supervised fine-tuned (SFT) Qwen3 models at 4B and 14B parameters and Gemma 3 models at 4B and 12B parameters on multi-turn customer-support workflows. We find that SFT consistently improves text-turn success, and that overall next-turn success increases for every model under gold-history evaluation. However, these improvements do not transfer to autonomous workflow execution. Tool-specific gains also vary across metrics and models. None of the four SFT models succeeds under holistic workflow evaluation, with strict trajectory completion reaching at most 10.4% workflow success. Our results show that next-turn evaluation is not a reliable proxy for workflow success, motivating separate reporting of text quality, local action correctness, tool execution, and end-to-end task completion.
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All authors Md Tahmid Rahman Laskar, Xue-Yong Fu, Gundeep Singh, Karol Chang, Kevin Sanders, Shi Zong, Tania Habib, Julien Bouvier Tremblay, Shayna Gardiner, Harsh Saini, Matthias Lee, Elena Khasanova, Quinten McNamara, Shashi Bhushan TN
Robotics
Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points
Junwan Choi, Woongrae Jo, Dong-Uk Seo et al. (+2 more)
Accurate initialization is essential for reliable visual-inertial odometry (VIO), but it is often ill-conditioned under degenerate motions. Existing methods typically require restrictive excitation motions to ensure sufficient observability or rely on computationally expensive 3D structure reconstruction, limiting efficient and practical deployment. To address these limitations, we propose SLIM-init, a structureless
monocular VIO initializer that directly exploits
geometric constraints from tracked 2D line features without explicit 3D landmark reconstruction. Specifically, SLIM-init leverages line-derived vanishing points (VPs) as translation-invariant orientation cues to provide robust rotation-only constraints under degenerate scenarios such as low-parallax or translation-dominant motions. It further incorporates a line epipolar residual to constrain translation and a line-normal projection residual to improve the conditioning of linear
alignment, enhancing the accuracy and robustness of initial state estimation. Extensive experiments on a public
benchmark and challenging custom degenerate-motion sequences demonstrate improved accuracy and robustness over state-of-the-art initialization methods. The source code is available at: https://github.com/cjunwan/SLIM-init.
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All authors Junwan Choi, Woongrae Jo, Dong-Uk Seo, Jinwoo Jeon, Hyun Myung
Robotics
When to Waddle: A Comparative Study of Bipedal Torso-Stabilization on Low-Friction Surfaces
Ben Gu, Naomi Oke, George Ortiz et al. (+4 more)
Low-friction surfaces challenge bipedal locomotion by limiting the contact forces available during stepping. Inspired by penguin waddling, we investigate how lateral torso motion and center of mass (COM) placement affect locomotion as surface friction changes. Using a five-actuator biped, we compare an upright-gait strategy with a penguin-inspired torso-over-stance-leg strategy across multiple COM placements in simulation and hardware. In the 3-D simulator MuJoCo, we sweep through sinusoidal leg and hip actuation parameters across four friction coefficients mu = 0.1, 0.3, 0.5, 0.7. In simulation, torso-over-stance-leg motion produces more successful controllers and higher forward speeds at low friction, with the highest speed occurring for the high-COM configuration. Hardware experiments show the same low-friction speed trend: at mu=0.12, torso-over-stance-leg motion increases forward speed and reduces cost of transport at both tested COM ratios, and the higher COM also improves both measures. The high-COM penguin configuration is the fastest and most energy efficient while maintaining low sideways foot motion. At mu=0.45, the COM trend reverses: the lower-COM configurations are faster and more energy efficient, while gait strategy has little effect on forward speed but still changes sideways foot motion. These results show that the effects of lateral torso motion and COM placement depend on the available friction, and that forward speed, energy use, and slip-related foot motion can be modulated with a penguin-inspired torso motion on hardware.
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All authors Ben Gu, Naomi Oke, George Ortiz, Stacy Ashlyn, Cordelia Pride, Sarah Bergbreiter, Aaron M. Johnson
Rings and Algebras
A classification of group gradings on incidence algebras over commutative rings
Felipe de Mattos Chafik Hindi and Waldeck Schutzer
Let $R$ be a commutative ring with 1, $P$ a locally finite partially ordered set, and $G$ a group. We derive necessary and sufficient conditions for an $R$-
algebra isomorphism between the incidence
algebra $I(P,R)$ and the group
algebra $RG$. Then, for an indecomposable ring $R$, a finite poset $P$ and an arbitrary group $G$, we classify the $G$-gradings of $I(P,R)$ up to graded isomorphism. The classification rests on a complete set of primitive orthogonal homogeneous idempotents. The corner
algebras are split group
algebras of finite abelian subgroups of $G$, and the off-diagonal Peirce blocks are multiplicity-free sums of bimodules induced from characters of double coset stabilizers. Graded isomorphisms are shown to have a rigid form, and a grading is determined up to graded isomorphism by the poset of idempotents, the corner groups, the types of the atomic bimodules and the structure constants of their multiplication. The data which occur are characterized by
polynomial conditions, and over an algebraically closed field of characteristic zero only finitely many graded isomorphism classes share given partial invariants. An example shows that the structure constants cannot be omitted. Some previous results are extended and enhanced, while providing alternative proofs for some known facts.
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Sound
I'll Keep an Ear Out: Teaching AudioLLMs Proactive Audio Assistance
Amit Kumar Singh Yadav, Ritvik Shrivastava, Xuan Zhang et al. (+4 more)
Audio large
language models (AudioLLMs) operate reactively, responding only when queried. We introduce proactive audio assistance, where an AudioLLM monitors an audio stream and autonomously decides when to alert the user from a single natural-
language intent, motivated by wearable applications for Deaf and Hard of Hearing users. We propose Interrupt and Silent Modeling (ISM), a model-agnostic paradigm that embeds proactive decisions into LLM decoding via two special
tokens: <interrupt> and <silent>, capturing four states: onset detection, sustained-relevance triggering, irrelevance suppression, and de-duplication. Applied to Qwen2-Audio-7B, ISM achieves 99.6% interrupt F1 and perfect de-duplication recall on ESC-50. On noisy Epic-Sounds kitchen audio, ISM achieves the highest interrupt F1 without domain-specific training, the only method maintaining strong onset detection without over-triggering or over-suppression. Streaming evaluation confirms real-time viability with 3.5-second average latency.
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All authors Amit Kumar Singh Yadav, Ritvik Shrivastava, Xuan Zhang, Seungwhan Moon, Shashank Jain, Pinar Donmez, Babak Damavandi
High Energy Physics (Theory)
The Chern-Simons Function and the Quantum Hall Effect
Edward Witten
In this article, I will define the Chern-Simons invariant of a gauge connection over a three-
manifold and explain some of the things that are special about it, in particular its role in understanding the
quantum Hall effect, which is an important effect in condensed matter physics, and its generalization, the fractional
quantum Hall effect. (The article is based on a lecture at a conference celebrating the 75th birthday of James Simons, held at the CUNY Graduate Center in 2013. It will appear in the Bulletin of the American Math Society.
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Artificial Intelligence
Implicit Rule Induction with Test-Time Task Embeddings in ARC-like Tasks
Adrien Deliège, Claas Beger, Marc Van Droogenbroeck et al. (+1 more)
The Abstraction and
Reasoning Corpus and related
benchmarks evaluate whether AI models can solve novel
reasoning tasks, but often leave unclear whether success reflects inference of the intended underlying rule or reliance on shortcuts. We address this gap by studying test-time task
embeddings in
Vision ARC (VARC), a model in which a pre-trained backbone is complemented by a trainable
embedding representing the transformation rule. In the original VARC, test-time training (TTT) is jointly applied to the backbone and task
embedding. Here we introduce a novel two-step TTT protocol: first finetune only the task
embedding (Embed-TTT), then freeze it and finetune the backbone. Across ARC-AGI-1, ConceptARC, and two controlled
datasets with known rules, Embed-TTT consistently yields improved task
embeddings, ones that align better with underlying task rules, improve
embedding-based
retrieval, and enable accurate linear probing of known rules. Qualitatively, Embed-TTT identifies more semantically meaningful relations between test and train tasks on ARC-AGI-1. We also show that optimizing only task
embeddings (less than 0.01% of model parameters) already solves a non-trivial fraction of ARC-AGI-1, ConceptARC, and Mini-ARC tasks, while the full two-step pipeline improves final performance. Finally, we show that Embed-TTT recovers the underlying
geometric structure of parametric rules and learns compositional capabilities that enable rule-wise interpolation, but not extrapolation. These findings support a clearer separation between rule induction and rule execution in ARC-like evaluations, motivating
benchmarks that better distinguish in-distribution from out-of-distribution rules.
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All authors Adrien Deliège, Claas Beger, Marc Van Droogenbroeck, Melanie Mitchell
Sound
Online Algorithms for Independent Low-Rank Matrix Analysis and Rank-Constrained Spatial Covariance Matrix Estimation Based on Maximum Weighted Likelihood Estimation
Yuto Ishikawa, Norihiro Takamune, Tomohiko Nakamura et al. (+4 more)
Real-time multichannel
speech extraction (MSE) under diffuse noise conditions is an important task with a wide range of applications, such as
speech recognition and hearing aids. In this paper, we propose online
algorithms for independent low-rank
matrix analysis (ILRMA) and rank-constrained spatial covariance
matrix estimation (RCSCME). Previously, we proposed a real-time extension of the RCSCME-based method: an MSE method based on ILRMA and RCSCME using the blockwise batch
algorithm. However, it assumes that the spatial characteristics are stationary within a single batch, and thus, in dynamic situations where the target speaker moves, its performance may degrade. To address this problem, we derive the online
algorithms for ILRMA and RCSCME in the following three steps. First, we formulate framewise cost functions for ILRMA and RCSCME on the basis of maximum weighted likelihood estimation. Second, we derive the update rules for the framewise cost functions on the basis of auxiliary-function techniques. These naive update rules are computationally costly for real-time execution on a practical machine. Thus, we finally derive the online
algorithms by approximating some intermediate parameters with their estimates. Furthermore, we propose stabilization and further acceleration techniques for these online
algorithms. In experiments, we simulate situations where a target speaker is stationary or moves and show that the proposed method achieves superior
speech extraction performance compared with conventional methods. In addition, using real-world recorded signals, we demonstrate the effectiveness of the proposed method in practical scenarios.
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All authors Yuto Ishikawa, Norihiro Takamune, Tomohiko Nakamura, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Kazunobu Kondo
Computation and Language
Not All Irregularity Is Equal: Causally Isolating a Rare Failure Mode in Japanese Morphological Inflection
Wen Zhang
Neural morphological generation systems often achieve high aggregate accuracy on
benchmark datasets, yet such performance can conceal systematic errors clustered in rare morphological subclasses. We present an orthography-aware diagnosis of Japanese past-tense verb inflection, treating hiragana not merely as a transcriptional medium but as a representational system that encodes morphophonological structure. Using two character-level
Transformer architectures evaluated across five random seeds, we show that although both systems exceed 97% aggregate accuracy, a single structurally specific irregular subtype, verbs whose stems end in /e/ and require gemination before the past-tense suffix and make up fewer than 1% of the data, accounts for a disproportionate 30-43% share of residual errors and contributes roughly 34-48x its prevalence to total errors. We then move from diagnosis to causal isolation: controlled ablation experiments show that removing this subtype alone produces larger accuracy gains than removing all irregular verbs combined. These findings indicate that error concentration in neural morphological learning is not driven by irregularity per se, but by the interaction between extreme low-frequency morphological patterns and specific orthographic processes. We argue that morphological evaluation should incorporate fine-grained subclass analysis, and discuss implications for data-efficient, developmentally plausible
language model pretraining.
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Robotics
OpenRoIS: A Community-Driven Open-Source Middleware Implementing the Robotic Interaction Service (RoIS) Framework for Physical Robots and Virtual Agents
Sebastian Carrera Villalobos, Christopher Nolan Arellano, Arne Hitzmann et al. (+5 more)
Service applications for human-
robot interaction are commonly written against the hardware-specific interfaces of one platform, so a change of hardware forces a rewrite of the application. The Robotic Interaction Service (RoIS) Framework 2.0, standardized by the Object Management Group (OMG), addresses this fragmentation by defining a platform-independent model in which Service Applications interact with Human-
Robot Interaction (HRI) Engines through standardized interfaces and hardware-independent symbolic messages. A specification alone, however, does not provide the maintained implementation, Software Development Kits (SDKs), and adapters needed for practical adoption. This paper presents OpenRoIS, a community-driven open-source middleware providing a concrete implementation of the RoIS Framework 2.0. It takes the position that an openly developed, paradigm-neutral implementation is what carries the standard from specification to practice. OpenRoIS contributes a recursive engine architecture in which a single engine class realizes the main and sub HRI Engine roles, an internal five-method component contract distinct from the five external RoIS interfaces, a mapping of those interfaces onto JSON-RPC 2.0 over WebSocket, a single-source-of-truth type pipeline that generates three consistent
language stacks, TypeScript and C# client SDKs that include web and Unity support, and a Python adapter SDK that includes ROS 2 support. Through the common RoIS interfaces, a Service Application can address physical
robots and virtual
agents over the internet. All source code, interface types, and documentation are released under the Apache-2.0 license and openly developed at https://openrois.org/.
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All authors Sebastian Carrera Villalobos, Christopher Nolan Arellano, Arne Hitzmann, Edilson Morais Brito, Akira Utsumi, Yukiko Horikawa, Takahiro Miyashita, Lotfi El Hafi
High Energy Astrophysical Phenomena
CRAFT HTR2: Polarimetry of 64 non-repeating fast radio bursts from the updated CRAFT catalogue
T. Dial, A. T. Deller, Marcin Glowacki et al. (+6 more)
We present high-time resolution spectro-polarimetric data for 34 new fast
radio bursts (FRBs) discovered by the Commensal Real-time Fast Transients (CRAFT) survey on the Australian Square Kilometer Array Pathfinder (ASKAP) during the period May 2024 to June 2026. Most of these were detected by the higher-sensitivity CRAFT COherent (CRACO) detection system that was commissioned on the
telescope during this period. This new sample doubles the size of the CRAFT HTR catalogue and probes a fainter population of FRBs thanks to the improved sensitivity of CRACO. We compare the distribution of extragalactic rotation measure (RM) and polarisation fraction to the CHIME and DSA catalogues. While no significant differences were seen between CRAFT and DSA, the extragalactic RM distribution seen in CHIME FRBs (which are detected at lower frequency) was substantially lower. Surprisingly, we find no significant differences in the linear polarisation fraction distribution between the three FRB catalogues, suggesting an indifference to the different
telescope observing frequencies. We show tentative evidence for wider and fainter bursts possessing lower polarisation fractions; this is predominantly driven by the growing sample of unpolarised bursts that are, in almost all cases, wider ($\gg$10 ms) and fainter ($\ll$10$^{34}$ ergs s$^{-1}$ Hz$^{-1}$) than the median ASKAP detection.
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All authors T. Dial, A. T. Deller, Marcin Glowacki, A. Bera, R. M. Shannon, Clancy W. James, Joscha N. Jahns-Schindler, Ziteng Wang, Akhil Jaini
Computer Vision
4DGS-Fixer: Generative Sparse-View 4D Gaussian Splatting with Iterative Refinement Guided by Video Diffusion Priors
Haitao Huang, Shenghao Zhao, Boyuan Tian et al. (+7 more)
This paper addresses the challenges of dynamic scene synthesis from sparse-view
videos. Existing methods employ
geometric priors, adaptive optimization, or density-control strategies to improve 4D Gaussian modeling under sparse observations. However, they cannot fundamentally resolve the ill-posed problem caused by insufficient observations and missing scene information. Moreover, sparse-view 4D Gaussian Splatting (4DGS) often suffers from poor
geometric initialization: with only a few input views, COLMAP typically reconstructs sparse and incomplete point clouds, leaving large scene regions without sufficient Gaussian support and making them difficult to recover through subsequent optimization. To address these limitations, we propose a novel iterative refinement framework based on a
video diffusion model to improve the completeness and consistency of dynamic 4D scenes. Specifically, we first estimate multi-view depth maps and fuse them into dense point clouds to provide more complete
geometric initialization for a dynamic 4DGS representation. We then employ a pretrained
video restoration model to refine sequences rendered along novel camera trajectories at different time steps. The restored sequences serve as pseudo-supervision to regularize and iteratively refine the 4DGS representation. Experiments on a widely used
benchmark dataset demonstrate that our method substantially outperforms existing baselines, achieving nearly a 2 dB PSNR improvement over the previous best-performing method.
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All authors Haitao Huang, Shenghao Zhao, Boyuan Tian, Shin-Fang Chng, Songlin Yang, Sheila Lim, Huangying Zhan, Yi Xu, Anyi Rao, Frank Guan
Numerical Analysis
Energy stability and error estimates for a second-order structure-preserving exponential integrator method for smectic-A liquid crystals
Wenshuai Hu, Guanghua Ji and Xiao Li
In this work, we develop a second-order, linear, decoupled, and structure-preserving numerical scheme for the modified Landau–de Gennes model of smectic-A (SmA)
liquid crystals. The main contributions are threefold. First, to the best of our knowledge, we propose the first integration of the generalized scalar auxiliary variable (GSAV) approach with a second-order exponential time-differencing Runge–Kutta (ETDRK2) discretization, leading to a second-order GSAV–ETD2 scheme. Second, we prove that the proposed scheme satisfies an unconditional energy-dissipation law, thereby closing the theoretical gap in the energy-stability analysis of second-order GSAV exponential integrators of this class. Third, by deriving a coercive discrete reformulation, we establish a fully discrete error estimate without imposing any coupling condition between $\tau$ and $h$, achieving the optimal convergence rate $\mathcal{O}(\tau^2+h^2)$. Numerical experiments are presented to verify our theoretical results and to simulate the self-assembly dynamics of the SmA phase.
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Number Theory
On The Cyclicity of Algebraic Lattices
Maria Fernanda Zordan Bonini, Robson Ricardo de Araujo, Antonio Aparecido de Andrade et al. (+1 more)
This work presents theoretical advances in the study of cyclic and quasi-cyclic
lattices. First, we provide elementary facts regarding cyclic and quasi-cyclic
lattices, and discuss about the cyclicity of some notable
lattices. The main contributions of the paper is in the algebraic setting: we investigate cyclic
lattices arising from $\mathbb{Z}$-modules in Galois number fields via the Minkowski
embedding. We establish necessary and sufficient conditions for an algebraic
lattice to be cyclic over both cyclic and general Galois number fields, expressed in terms of naturally associated groups. Moreover, we derive a necessary and sufficient condition for ideal
lattices to be cyclic, depending on the factorization of the ideal in the underlying number field.
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All authors Maria Fernanda Zordan Bonini, Robson Ricardo de Araujo, Antonio Aparecido de Andrade, Jéfferson Luiz Rocha Bastos
Trading and Market Microstructure
Adapting the Actor Model of Concurrency for High-Frequency Trading: Synchronous Message Delivery (fast_send) and a Tick-to-Book Latency Study
Vincent Maciejewski
The actor model - state isolation, data-race freedom, and sequential single-message
reasoning - has long been dismissed as unsuitable for high-frequency trading (HFT): actors seem to imply many threads, a mailbox per actor, and a heap-allocated message plus a context switch per interaction, overhead incompatible with a microsecond budget. This paper argues the dismissal is wrong for co-located actors, with a deployed, measured implementation: kaspar-hft, an open-source C++20 framework. Four extensions adapt the model for HFT: fast_send, a synchronous delivery mechanism in which the sending thread runs the receiver's handler inline and returns the reply as a value; actor groups, which co-schedule actors on one thread behind a shared mailbox; per-actor selectable mailbox queues; and a
memory pool. fast_send has receiver transparency: the handler is written identically for synchronous and asynchronous delivery and does not depend on which was used or which thread runs it. A grouped synchronous chain runs on one thread, cutting scheduler context switches from O(N) to O(1); a thread-local call-chain test detects cyclic invocation on one thread before any lock is taken, while a cycle spread across threads deadlocks. Microbenchmarks put the synchronous round trip at tens of nanoseconds. On a live CME
market-data feed (ES, NQ, ZN futures), when the socket-reader thread decodes each packet and updates the book itself, socket-to-book medians for book updates are 0.8-1.1 microseconds, and a fast_send hop is about 1% of that. The shared-queue group also yields a production/simulation duality: the same actor code runs unchanged in live trading and deterministic backtest.
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Machine Learning
TierKV: Long-Context On-Device LLMs via Predictive Multi-Tier KV Caching
Zhihao Shu, Md Musfiqur Rahman Sanim, Jie Hu et al. (+4 more)
Large
language models (LLMs) are moving onto mobile devices for increasingly diverse workloads over text, images,
video, and audio. These applications often require long contexts, making the Key-Value (KV) cache a dominant
memory bottleneck because it grows linearly with sequence length and is accessed at every decoding step. Prior work reduces KV-cache footprint through low-rank compression,
token eviction, or flash offloading, but the resulting reconstruction overhead, irreversible
token loss, or I/O stalls can offset the benefit of saving
memory. We present TierKV, a mobile LLM inference framework built on Predictive Multi-Tier Cache Optimization (PMCO). Before decoding starts, PMCO predicts future cache demand from prefill hidden states and jointly assigns
tokens to exact, low-rank, and flash-offloaded tiers under the device
memory and accuracy budgets. This formulation retains access to the full context, removes the circular dependency of reactive eviction, and admits a closed-form solver that selects tier boundaries and per-layer ranks at runtime. Across eight text,
vision, and audio models on three mobile SoCs, TierKV improves prefill throughput by up to 17.6x over existing mobile LLM frameworks, reduces RAM-resident KV cache by 12.5-34%, thereby enabling substantially longer contexts under the same
memory budget, while incurring only minor accuracy degradation.
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All authors Zhihao Shu, Md Musfiqur Rahman Sanim, Jie Hu, Kun Yuan, Minghai Qin, Gagan Agrawal, Wei Niu
Audio and Speech Processing
HAMMER: Harmonic-Aware Parallel Context Modeling and Discriminator-Free Perceptual Optimization for Speech Enhancement
Shang-Fu Chen, Szu-Wei Fu, Sung-Feng Huang et al. (+3 more)
Recent
speech enhancement systems combine self-
attention and Mamba to capture global interactions and long-range dependencies. Yet these hybrids usually operate as sequence mixers and do not explicitly exploit harmonic periodicity, a strong cue for preserving voiced
speech under noise. Perceptual optimization poses another challenge. PESQ is non-differentiable, so many methods train auxiliary metric discriminators that increase complexity and introduce
adversarial instability. We propose \ours, a harmonic-aware and discriminator-free
speech enhancer built around two components. (i) The Time-Frequency Harmonic-aware
Attention-Mamba (TF-HAM) block runs self-
attention and bidirectional Mamba in parallel along both spectrogram axes, then applies a
speech-adapted autocorrelation feed-forward network to encode local periodic structure. (ii) Metric-explicit perceptual refinement (MEPR) combines differentiable PESQ and log-likelihood-ratio losses to expose perceptual metric structure without a learned surrogate. On VoiceBank+DEMAND, \ours achieves 3.69 PESQ and 4.41 COVL with only 2.39\,M parameters, outperforming or matching discriminator-based systems. Inference-time perceptual contrast stretching further raises PESQ to 3.79 without retraining. The source code will be available at https://github.com/shangfuu/HAMMER.git.
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All authors Shang-Fu Chen, Szu-Wei Fu, Sung-Feng Huang, Rong Chao, Wen-Huang Cheng, Yu Tsao
Systems and Control
Stochastic MPC under Heavy-Tailed Disturbances: An Extreme Value Theory Approach
Xiuzhen Ye and Wentao Tang
Safety-critical control systems must contend with disturbances whose extreme deviations occur far more frequently than classical light-tailed models predict. Existing
stochastic MPC (SMPC) formulations tighten constraints using an assumed distribution, a moment bound, or a finite scenario sample, each of which degrades under an unknown heavy-tailed disturbance. This paper develops an SMPC formulation for linear systems under heavy-tailed disturbances that is only assumed to be regularly varying, which allows an explicit extreme value theory (EVT) characterization of the tube error tail that is asymptotically exact. We further show that closed-loop dynamics induce temporal clustering of rare excursions across the prediction horizon, and characterize this clustering through a closed-form extremal index estimable from data. The resulting clustering-corrected constraint bounds the probability of a rare-event episode over the horizon, rather than only the marginal per-step exceedance probability. Simulations of a linear two-CSTR-in-series process and of a nonlinear unicycle navigating past an obstacle under Student-$t$ disturbances validate both approaches and demonstrate reduced frequencies of safety constraint violations.
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Image and Video Processing
Adaptive Color Grading
Trevor D. Canham, Abhijith Punnappurath and Michael S. Brown
Independent control of tonescale regions (e.g., shadows, highlights) is essential for painters, photographers and cinematographers to bring 2D images to life. In image manipulation software this is most directly addressed by color grading modules, which use intensity thresholds to segment distinct illumination regions for local manipulation. In this work we develop an open source color grading tool and use it to annotate a large
dataset of
video frames with tonescale region thresholds. Using these thresholds we conduct modeling experiments with strategies based on both practitioners' conventional wisdom and machine learning. Results show that K-nearest neighbors is an effective prediction strategy, outperforming state-of-the-art end-to-end methods for image enhancement. This outcome demonstrates the benefit of focusing on a compact set of core parameters when modeling creative stylization processes. Our adaptive color grading interface and data are available at https://github.com/SamsungLabs/adaptive-color-grading.
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Theoretical Economics
Auctions as Experiments
Mira Frick, Ryota Iijima, Yuhta Ishii et al. (+1 more)
Consider an auction with buyers whose values depend on an underlying state (e.g.,
market fundamentals). How does the auction format shape the information that buyers' bids reveal about the state? We recast auctions as statistical experiments and compare different auction formats in terms of the (Lehmann) informativeness of the induced experiments. Our main finding is that among a large class of auctions (e.g., $k$th-price, all-pay), the first-price auction is the most informative. As a result, this auction guarantees the highest payoffs to a decision-maker who uses the information revealed by buyers' bids in a monotone decision problem (e.g., a prediction problem or the choice of a reserve price in a future auction).
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All authors Mira Frick, Ryota Iijima, Yuhta Ishii, Nicholas Wu
Robotics
MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams
Hojune Kim, Guangyao Shi and Gaurav S. Sukhatme
Field robotics missions often require physical samples to be returned to laboratories for analysis, making path planning inherently load-aware and order-dependent as accumulated samples increase payload and traversal energy costs. In single-
robot Load-Aware Informative Path Planning (LIPP), this rigidly couples sensing with hauling: a solitary
robot must transport every collected sample, forcing frequent depot returns that severely restrict its spatial coverage. Heterogeneous multi-
robot teams can overcome this bottleneck by dividing labor—enabling high-precision samplers to collect while high-capacity carriers handle transport. However, this introduces a complex coordination challenge regarding when, where, what, and to whom handoffs should occur on top of the LIPP problem. To address this tightly coupled problem, we introduce Multi-
Agent LIPP (MA-LIPP), which enables teams to cooperate through asynchronous "dead drops," allowing one
robot to deposit samples for another to retrieve later without requiring synchronous rendezvous. We formulate MA-LIPP as an exact Mixed-Integer Quadratic Program (MIQP) alongside a scalable Pairwise Large-Neighborhood Search (LNS) heuristic for complex real-world applications. The heuristic matches exact optima in $95.5\%$ of certified cases and reduces weighted posterior variance by $16.1$–$19.8\%$ relative to a sequential baseline on larger instances of up to 12
robots, providing a robust framework for cooperative physical-sampling missions.
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Analysis of PDEs
Existence of Traveling Waves for a Diffusive Nicholson Equation with Harvesting
Adrián Gómez, Helí Elorreaga and Cesar Guayasamin
We prove the existence of traveling wave solutions for a diffusive Nicholson model with a delayed linear harvesting term and two delayed-response channels. Our approach combines monotone heteroclinic connections of the associated scalar delay equation with the large-speed traveling-wave
theorem of Faria, Huang and Wu. For \(0<\sigma<r\), we use a previous upper- and lower-solution construction under an explicit parameter condition. For \(0<r\leq\sigma\), we establish a new monotone heteroclinic connection. To fit the abstract framework, we embed the scalar equation into an auxiliary two-dimensional reaction–
diffusion system and prove that every relevant traveling wave lies on the invariant diagonal. We then determine an explicit parameter region in which the required spectral hypotheses hold. Consequently, the original diffusive Nicholson equation admits traveling waves connecting the trivial and positive
equilibria for all sufficiently large wave speeds.
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Artificial Intelligence
SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity
Thao Nguyen and Heng Ji
Off-target
protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on generating selective compounds de novo rather than improving the selectivity of existing, well- characterized drugs. We introduce specificity optimization (SpecOpt), a molecular design task that seeks constrained structural modifications to an existing compound that increase its binding preference for an intended target over known off-targets while preserving its structural identity and drug-like properties. To enable systematic evaluation, we construct a ChEMBL-derived
benchmark from compound-target interaction data, identifying intended targets through curated drug-mechanism annotations and off- targets through measured activities. We then develop an agentic framework that docks each compound against its intended target and off-targets, compares the resulting poses through residue-aware atom-
protein contacts, and provides these differential interactions to a large
language model to propose targeted structural modifications. Candidates are retained only if they satisfy molecular similarity, ADMET, and target-off-target docking selectivity criteria. On 915 compounds, the
agent improves the target- off-target binding gap for 84.8% of compounds, shifting the mean gap from -0.72 to +0.47 kcal/mol while maintaining a mean Tanimoto similarity of 0.72 to the starting compounds. Ablation studies identify residue-specific contact information as the critical optimization signal: replacing residue identities with binary contact indicators eliminates improvement on all 29 ablation compounds. These results establish SpecOpt as a distinct molecular design problem and demonstrate residue-aware differential interactions as an effective signal for improving the specificity of existing compounds.
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Machine Learning
M2G-LLM: Enhancing Clinical Prediction via Multimodal Graph Reasoning and LLM Context Injection
Inyoung Choi, Sukwon Yun, Jiayi Xin et al. (+3 more)
Integrating diverse data modalities — such as clinical notes, laboratory results, and medical imaging — is essential for advancing clinical decision-making. While Large
Language Models (LLMs) have shown remarkable performance in processing unstructured clinical text, their limited capacity to incorporate non-text modalities hinders their broader utility in healthcare applications. Here, we introduce M2G-LLM (
Multimodal MedGraph-LLM), a novel framework that enhances LLMs with
multimodal integration and
alignment via Graph Neural Networks (GNNs). Our approach models temporal relationships between patient visits, propagates information across clinically similar patients, and aligns heterogeneous data sources to construct enriched
multimodal context vectors. These vectors are injected into the intermediate layers of the LLM, enabling joint
reasoning over textual and non-textual modalities. We evaluate M2G-LLM on the MIMIC-IV and MIMIC-CXR
datasets, demonstrating improvements in clinical prediction tasks over strong baseline models. Our results highlight the promise of combining the
language understanding of LLMs with the relational
reasoning capabilities of GNNs for comprehensive,
multimodal healthcare analysis.
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All authors Inyoung Choi, Sukwon Yun, Jiayi Xin, Jie Peng, Tianlong Chen, Qi Long