Showing 1–50 of 1,444 results for author: Ali, S

Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges

Authors: Syeda Faiza Ahmed, Zien Sheikh Ali, Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury

Abstract: Conversational AI is moving beyond isolated text prompts toward sustained, multimodal interaction. In real conversations, users clarify goals, revise requests, interrupt responses, switch topics, and introduce new evidence while expecting systems to preserve context across turns. This makes multi-turn dialogue a distinct challenge requiring systems to maintain and update memory, ground responses a… ▽ More Conversational AI is moving beyond isolated text prompts toward sustained, multimodal interaction. In real conversations, users clarify goals, revise requests, interrupt responses, switch topics, and introduce new evidence while expecting systems to preserve context across turns. This makes multi-turn dialogue a distinct challenge requiring systems to maintain and update memory, ground responses across modalities, tools, and external knowledge, and adapt across languages and cultures. This study reviews multi-turn conversational AI across text-only dialogue, AudioLLMs and speech-native systems, multimodal and omni-modal systems, and tool-augmented agents. We organize the literature around datasets and benchmarks, modeling paradigms, training strategies, evaluation setups, and cross-cutting challenges. Our analysis shows that support for multiple modalities has advanced faster than the ability to sustain coherent interaction across a session. Despite stronger capabilities to perceive, speak, and act across modalities, current systems still struggle with persistent memory, cross-turn grounding, full-duplex interaction, robust evaluation, and cultural alignment. We conclude with a research agenda for systems that can remember, revise, ground, speak, listen, act, and adapt across turns, modalities, and cultures. (https://github.com/faiza-sfa/multiturn-conversational-ai-survey) △ Less

Submitted 18 August, 2026; originally announced August 2026.

Comments: Multi-turn Conversational AI; Multimodal Dialogue; AudioLLMs; Conversational Memory; Tool-Augmented Agents; Dialogue Evaluation

MSC Class: 68T50 ACM Class: F.2.2; I.2.7

Uncovering the deformation mechanism of glasses during indentation through high-resolution X-ray scattering

Authors: M. Faizal Ussama Jalaludeen, Søren S. Sørensen, Johan F. S. Christensen, Anders K. R. Christensen, Sidsel Mulvad Johansen, Samraj Mollick, Yuanzheng Yue, Sharafat Ali, Sebastian Kalbfleisch, Morten M. Smedskjaer

Abstract: Indentation experiments can be used to mimic real-life damage events of glasses that lead to surface flaws and thus lower practical strength. Conventional indentation studies often focus on the surface deformation after unloading. However, to understand the link between the surface deformation and structure, it is crucial to characterize the sub-surface deformation during the indentation process.… ▽ More Indentation experiments can be used to mimic real-life damage events of glasses that lead to surface flaws and thus lower practical strength. Conventional indentation studies often focus on the surface deformation after unloading. However, to understand the link between the surface deformation and structure, it is crucial to characterize the sub-surface deformation during the indentation process. The indentation-induced deformation, consisting of both elastic and plastic zones, is governed by the glass composition and structure, indentation and atmospheric conditions, and stress state. However, only a few experimental methods exist for characterizing the sub-surface indentation deformation mechanism during indentation. In this study, we use synchrotron X-ray nanoscattering to probe the deformation mechanism in situ during indentation of four types of oxide and oxynitride glasses with distinct structural features. This is done by measuring the variation in the position and intensity of the first sharp diffraction peak of the X-ray structure factor with a high spatial resolution down to ~100 nm. We find that the deformation zones of these glasses, which are characterized by the shape, size, and relative contribution between densification and shear flow under different indentation loads, vary with Poisson's ratio. Thus, our work provides new insights into the mechanical behavior of oxide glasses, contributing to the design of more damage-resistant glasses. △ Less

Submitted 16 August, 2026; originally announced August 2026.

Beyond Fixed Luminance: Towards Panchromatic and Orthochromatic Image Colorization

Authors: Swarnim Maheshwari, Syed Imam Ali, Vineeth N. Balasubramanian

Abstract: Most image colorization systems operate in $Lab$ space by predicting chroma ($ab$) while preserving an input-derived luminance channel ($L$). While effective on standard benchmarks, this fixed-luminance design restricts brightness changes and becomes unreliable when grayscale formation deviates from natural-image luminance, as in historical orthochromatic photography. We propose a luminance-agnost… ▽ More Most image colorization systems operate in $Lab$ space by predicting chroma ($ab$) while preserving an input-derived luminance channel ($L$). While effective on standard benchmarks, this fixed-luminance design restricts brightness changes and becomes unreliable when grayscale formation deviates from natural-image luminance, as in historical orthochromatic photography. We propose a luminance-agnostic colorization framework that formulates colorization as full-RGB image editing using a foundation image-editing model. To bridge modern panchromatic and historical orthochromatic conditions, we introduce a mixed grayscale objective that trains the model under both standard luminance grayscale and a red-insensitive grayscale formation. Experiments on COCO, ImageNet, and a multi-instance benchmark show that our method is competitive on standard grayscale inputs and substantially more robust under orthochromatic inputs, with qualitative comparisons and a human study indicating fewer visible color artifacts. △ Less

Submitted 12 August, 2026; v1 submitted 11 August, 2026; originally announced August 2026.

Comments: Accepted at ECCV 2026

Search-Based Generation of Undetected Quantum Circuit Mutants

Authors: Eñaut Mendiluze Usandizaga, Thomas Laurent, Paolo Arcaini, Shaukat Ali

Abstract: Quantum mutation analysis is emerging as an essential technique for evaluating test suites due to the limited availability of real faulty quantum programs. However, existing quantum mutation analysis tools use fixed gate-based mutations, resulting in easy-to-detect mutants, which reduces their effectiveness in assessing the quality of test suites. We propose QUMUG, a search-based approach for gene… ▽ More Quantum mutation analysis is emerging as an essential technique for evaluating test suites due to the limited availability of real faulty quantum programs. However, existing quantum mutation analysis tools use fixed gate-based mutations, resulting in easy-to-detect mutants, which reduces their effectiveness in assessing the quality of test suites. We propose QUMUG, a search-based approach for generating challenging mutants by utilising parameterisable quantum gates. QUMUG employs search algorithms to optimise mutation parameters and find non-equivalent mutants passing a given test suite. In our evaluation over 30 quantum programs, QUMUG produced mutants that are three times more challenging than the mutants generated by existing tools. Among the four evaluated search algorithms, the genetic algorithm was the most effective, generating an average of 494 undetected mutants per program with a 99.67% success rate and 94.3% non-equivalent ratio. The generated mutants demonstrated their effectiveness by requiring the addition of five times more test cases to the test suite than the mutants generated by existing tools. We also analysed the behaviour of higher order mutants in quantum circuits, and showed that while first order mutations are more effective for enhancing the test suite, higher order mutants highlight the need for new unique test cases. △ Less

Submitted 10 August, 2026; originally announced August 2026.

MathShikkha: A Controlled Study of Answer-Only and Chain-of-Thought Supervision for Bangla Mathematical Reasoning in Small Language Models

Authors: Rahma Simin Ali, Jawad Hossain

Abstract: Mathematical reasoning remains challenging in low-resource languages such as Bangla. We study whether teacher-generated Bangla Chain-of-Thought (CoT) supervision provides benefits beyond ordinary supervised fine-tuning. We construct \textsc{MathShikkha}, a Bangla mathematical reasoning dataset with GPT-5.4-generated rationales, and fine-tune four 4B--7B student models under a matched protocol in w… ▽ More Mathematical reasoning remains challenging in low-resource languages such as Bangla. We study whether teacher-generated Bangla Chain-of-Thought (CoT) supervision provides benefits beyond ordinary supervised fine-tuning. We construct \textsc{MathShikkha}, a Bangla mathematical reasoning dataset with GPT-5.4-generated rationales, and fine-tune four 4B--7B student models under a matched protocol in which answer-only and CoT conditions share data splits, response-only loss masking, decoding, and scoring, differing only in the training target. In-domain, CoT provides no significant improvement over answer-only fine-tuning for three stronger backbones (paired bootstrap 95\% CIs include zero; exact McNemar $p \geq 0.17$), despite generating 15--52$\times$ more tokens, but significantly improves the weaker 4B model by 18.56 points ($p < 0.0001$). On the larger, contamination-audited BanglaMATH benchmark, this pattern reverses: CoT significantly outperforms answer-only supervision for all four models by 20.1--28.1 points (all $p < 0.0001$). Answer-only fine-tuning also reduces out-of-domain accuracy below the base model for three models, whereas CoT preserves or improves it for all four. A human study with two co-author annotators, external-expert adjudication, and Cohen's $κ= 0.76$--$1.00$ finds no significant CoT improvement over the base model on reasoning-content criteria; instead, its measurable effect is target-language adherence and producing inspectable reasoning. Overall, rationale supervision's value depends on backbone capability and distribution shift: in this setting, its main benefits are Bangla adherence, auditable reasoning, and out-of-domain robustness rather than improved in-domain reasoning validity. △ Less

Submitted 9 August, 2026; originally announced August 2026.

Optical Anisotropy and Phase Matching in Non-Centrosymmetric Perovskite Oxides from DFT+U and DFT+U+V Functionals

Authors: Mohamed S. M. M. Ali, Ismaila Dabo

Abstract: Optical anisotropy underpins the operation and performance of a broad range of photonic and quantum technologies. In this work, we critically examine the accuracy of density functional theory approximations with onsite and intersite Hubbard corrections (the DFT+$U$ and DFT+$U$+$V$ functionals) in predicting the anisotropic optical response of the non-centrosymmetric perovskite oxides, such as BaTi… ▽ More Optical anisotropy underpins the operation and performance of a broad range of photonic and quantum technologies. In this work, we critically examine the accuracy of density functional theory approximations with onsite and intersite Hubbard corrections (the DFT+$U$ and DFT+$U$+$V$ functionals) in predicting the anisotropic optical response of the non-centrosymmetric perovskite oxides, such as BaTiO$_3$, LiNbO$_3$, KNbO$_3$, and PbTiO$_3$. It is found that correcting self-interaction errors using DFT+$U$ alone does not capture the optoelectronic response of these materials, often leading to a suppression of their optical anisotropy. While intersite Hubbard interactions restore this anisotropy, the choice of the (inter)atomic orbital manifold that defines the Hubbard correction remains critical to its accuracy. The predictive performance of the resulting, systematically validated DFT+$U$+$V$ functional is achieved at a fraction of the computational cost of hybrid functionals and many-body perturbation theory calculations. As benchmarks, we investigate Zn- and (Bi,Mn)-substituted BaTiO$_3$ solid solutions; the latter exhibit polarization-dependent bandgap narrowing from mid-gap states, substantially enhancing the dichroic ratio and birefringence with promising implications for polarization-sensitive photodetectors and integrated photonics. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 13 pages, 7 figures + 1-page Supplementary Information (1 figure)

Estimating the sensitivity of the IceCube Upgrade to probe the interior of the Earth using atmospheric neutrino oscillations

Authors: The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Arg{ü}elles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi , et al. (399 additional authors not shown)

Abstract: The IceCube Upgrade is a densely instrumented central region of the IceCube Neutrino Observatory, deployed during the 2025-26 polar season. It will reduce the detector's energy threshold and improve overall reconstruction capabilities for multi-GeV atmospheric neutrinos, which in turn enhance their sensitivity to Earth matter effects as they traverse through the deep Earth. In this study, we descr… ▽ More The IceCube Upgrade is a densely instrumented central region of the IceCube Neutrino Observatory, deployed during the 2025-26 polar season. It will reduce the detector's energy threshold and improve overall reconstruction capabilities for multi-GeV atmospheric neutrinos, which in turn enhance their sensitivity to Earth matter effects as they traverse through the deep Earth. In this study, we describe the potential of the IceCube Upgrade to observe Earth matter effects on atmospheric neutrinos and estimate the detector's sensitivity to probe key features of the Preliminary Reference Earth Model by utilizing these observations. We highlight the IceCube Upgrade's capability to estimate the mass of the Earth and verify the non-homogeneous distribution of matter density within the Earth. We also estimate the IceCube Upgrade sensitivity to measure the correlated densities of the Earth layers while incorporating constraints from the mass and moment of inertia of the Earth. Neutrino-based results would be independent and complementary to the seismic and gravitational measurements. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 20 pages, 13 figures, 2 tables, and 1 appendix

Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting

Authors: Xiaoyi Gu, Julia Tavares, Eder Santana, Carlos Mendoza-Cardenas, Nikita Mishra, Saad Ali

Abstract: One of the most challenging problems entertainment live-streaming services face in recommendation systems is that user behaviors are sparse and delayed, and interaction data exhibits bias for different user segments. Unlike e-commerce applications where user actions follow linear sequences, live-streaming viewers engage in multiple concurrent behaviors of watching, chatting, following, and spendin… ▽ More One of the most challenging problems entertainment live-streaming services face in recommendation systems is that user behaviors are sparse and delayed, and interaction data exhibits bias for different user segments. Unlike e-commerce applications where user actions follow linear sequences, live-streaming viewers engage in multiple concurrent behaviors of watching, chatting, following, and spending, each occurring with varying delays. We address these challenges through three key contributions: 1) a delayed window approach that extends feedback collection beyond immediate responses, 2) a multi-model architecture that combines fresh and delayed signals, and a segment-aware targeting module that optimizes ranking scores differently across user lifecycle stages, and 3) Multi-gate Mixture-of-Experts (MMoE) integration that jointly models correlated targets while reducing model parameters by 41.9% compared to independent models. Online A/B testing demonstrates significant improvements, including a +0.09% increase in Daily Active Viewers (DAV), generating millions more annual active viewer days, and +0.56% increase in highly engaged viewers' capped Average Revenue Per User (ARPU). Viewer-segment targeting achieved an additional +0.15% DAV improvement for newer and less engaged viewers, while MMoE enhancement added +0.08% overall DAV and +0.27% new follows. The proposed system processes ranking requests with low latency, providing a scalable approach for balancing multiple business objectives across diverse user populations. In addition, we tested the multi-model architecture on the Twitch mobile live feed and achieved a +1.12% increase in positive user-channel interactions (clicks, follows, and likes), demonstrating applicability beyond the primary use case. △ Less

Submitted 5 August, 2026; originally announced August 2026.

Comments: 9 pages, 3 figures. Accepted to the Industry Track of the 20th ACM Conference on Recommender Systems (RecSys 2026)

Topologically Charged Morris-Thorne-type Wormholes and the Energy Conditions

Authors: Faizuddin Ahmed, Md Sabir Ali, Adnan Malik

Abstract: In this paper, we investigate topologically charged Morris-Thorne-type traversable wormholes by solving the Einstein field equations with an anisotropic fluid as the energy-momentum tensor and analysing the resulting solutions. In continuation to the earlier work (Eur. Phys. J C {\bf 84} (2024) 1037), we consider the shape functions such as: (i) $A(r)=r_0\,e^{r_0-r}$; (ii) $A(r)=r_0\,a^r/a^{r_0}$,… ▽ More In this paper, we investigate topologically charged Morris-Thorne-type traversable wormholes by solving the Einstein field equations with an anisotropic fluid as the energy-momentum tensor and analysing the resulting solutions. In continuation to the earlier work (Eur. Phys. J C {\bf 84} (2024) 1037), we consider the shape functions such as: (i) $A(r)=r_0\,e^{r_0-r}$; (ii) $A(r)=r_0\,a^r/a^{r_0}$,\quad $0 < a<1$; (iii) $A(r)=r_0\,\left(\frac{\cosh r_0}{\cosh r}\right)^δ$,\quad $δ\geq 1$; (iv) $A(r)=\frac{1}{r}+\ln\!\frac{r}{r_0}$; (v) $A(r)=B\,r^n+(1-B)$; (vi) $A(r)=r_0\,\frac{\mbox{ln} (1+r)}{\mbox{ln} (1+r_0)}$, (vii) $A(r)=r_0+a\,r_0\,\left[\left(\frac{r}{r_0}\right)^β-1\right]$, where $β<1$ and $0 < a\,β<1$. We examine the energy conditions-namely, null, weak, strong, and dominant energy conditions and explore how topological charge influences or controls these conditions. Additionally, we calculate the anisotropy parameter to determine whether the wormhole geometry exhibits attractive or repulsive behavior. Our analysis demonstrates that the energy density of the anisotropic fluid is always positive. However, while some of the energy conditions are partially satisfied, others are violated. △ Less

Submitted 4 August, 2026; v1 submitted 31 July, 2026; originally announced August 2026.

Comments: Improved results and discussion only

Aerodynamic Drag and Heat Transfer Corrections for Dehydrated Pollen Particles: CFD-Based Modeling of Airborne Allergen Transport in Smart Urban Environments

Authors: Omar Hamad, Samer Ali, Mahmoud Khaled, Talib Dbouk

Abstract: Airborne pollen transport is a key concern for urban air-quality assessment, allergy-risk forecasting, and smart-city planning. However, conventional dispersion models generally assume smooth spherical particles, neglecting how pollen dehydration alters particle morphology and impacts aerodynamic and thermal behavior. To address this gap, this study presents, for the first time, advanced CFD simul… ▽ More Airborne pollen transport is a key concern for urban air-quality assessment, allergy-risk forecasting, and smart-city planning. However, conventional dispersion models generally assume smooth spherical particles, neglecting how pollen dehydration alters particle morphology and impacts aerodynamic and thermal behavior. To address this gap, this study presents, for the first time, advanced CFD simulations evaluating the aerodynamic drag forces and convective heat transfer of realistically dehydrated (dry) pollen particles. Investigations are conducted at Reynolds numbers ($0.1 \leq Re_p \leq 15$) at the particle's scale corresponding to realistic atmospheric wind speeds ranging from 0.27 to 30 km/h. The findings reveal that dry pollen particles exhibit drag coefficients 8% to 15% higher than those predicted for hydrated pollen spherical particles. Conversely, their Nusselt numbers are 5% to 15% lower than those for hydrated pollen particles. These considerable deviations confirm that conventional spherical correlations are inadequate for simulating dry pollen Lagrangian transport and evaporation. These findings highlight the need to account for realistic dehydrated shapes when modeling airborne allergen transport in urban environments. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Spin-orbit-entangled frustrated magnetism in fcc Ba$_2$(Yb,Nd)NbO$_6$ double perovskites

Authors: S. M. Hossain, Sk. Soyeb Ali, S. Mohanty, R. Kolay, M. P. Saravanan, A. K. Yogi, Y. Tokiwa, R. Nath, S. K. Panda, M. Majumder

Abstract: The search for candidate Kitaev materials has largely focused on 4$d$ and 5$d$ transition-metal compounds with various lattice geometries. In contrast, investigations of rare-earth 4$f$ systems have thus far been restricted mainly to honeycomb and triangular lattices. In this work, we investigate the rare-earth-based double perovskites Ba$_2$YbNbO$_6$ and Ba$_2$NdNbO$_6$, which crystallize in a fa… ▽ More The search for candidate Kitaev materials has largely focused on 4$d$ and 5$d$ transition-metal compounds with various lattice geometries. In contrast, investigations of rare-earth 4$f$ systems have thus far been restricted mainly to honeycomb and triangular lattices. In this work, we investigate the rare-earth-based double perovskites Ba$_2$YbNbO$_6$ and Ba$_2$NdNbO$_6$, which crystallize in a face-centered cubic structure. Magnetization and heat-capacity measurements establish isolated ${j_{\rm eff}} = 1/2$ Kramers doublet ground states arising from strong spin-orbit coupling (SOC) and crystal electric-field effects, which are further supported by density-functional theory calculations. Millikelvin-temperature thermodynamic measurements reveal long-range magnetic order with moderate frustration in both compounds. The emergence of magnetic order may be understood within an order-by-disorder scenario, as theoretically proposed for rare-earth fcc lattices with finite Kitaev interactions. Our results thus identify Ba$_2$YbNbO$_6$ and Ba$_2$NdNbO$_6$ as promising rare-earth spin-orbit-entangled magnets and motivate further experimental and theoretical investigations aimed at determining the complete exchange tensor to elucidate the microscopic origin of the underlying magnetic interactions. △ Less

Submitted 2 August, 2026; originally announced August 2026.

Comments: 13 pages and six figures

High-energy neutrino emission from the Milky Way

Authors: R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel, S. BenZvi , et al. (398 additional authors not shown)

Abstract: The Milky Way hosts astrophysical objects that accelerate cosmic rays to energies beyond the reach of terrestrial particle accelerators. It remains a longstanding goal to locate the sites of these powerful Galactic engines and understand how cosmic rays propagate through the Galaxy, leading to the production of high-energy neutrinos. In this paper, we combine event morphologies characteristic of a… ▽ More The Milky Way hosts astrophysical objects that accelerate cosmic rays to energies beyond the reach of terrestrial particle accelerators. It remains a longstanding goal to locate the sites of these powerful Galactic engines and understand how cosmic rays propagate through the Galaxy, leading to the production of high-energy neutrinos. In this paper, we combine event morphologies characteristic of all three neutrino flavours and apply recent improvements in ice modelling, calibration and reconstruction to 12 years of IceCube data. With a predefined, global analysis we establish high-energy neutrino emission from the Galactic plane at 5.7 $σ$ significance. A further study shows that the inner region of the Galaxy is a prominent neutrino source, with 217 shower events with visible energy above 5 TeV compared with an expected background of 154.4 $\pm$ 4.1. These results herald a new era of Galactic multi-messenger astronomy, creating new opportunities to study cosmic-ray propagation and probe neutrino properties over kiloparsec distances. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Delta Debugging for Cyber-Physical Systems with Flaky Test Executions

Authors: Pablo Valle, Shaukat Ali, Aitor Arrieta

Abstract: Simulation-based testing is widely used to validate Cyber-Physical Systems (CPSs), yet modern CPS simulators frequently exhibit non-deterministic (flaky) behavior, making failures difficult to reproduce and debug. Although delta debugging has proven effective for deterministic systems, its underlying assumptions do not hold in stochastic environments. This paper presents three delta debugging algo… ▽ More Simulation-based testing is widely used to validate Cyber-Physical Systems (CPSs), yet modern CPS simulators frequently exhibit non-deterministic (flaky) behavior, making failures difficult to reproduce and debug. Although delta debugging has proven effective for deterministic systems, its underlying assumptions do not hold in stochastic environments. This paper presents three delta debugging algorithms that combine statistical failure analysis, repeated executions, and environment-aware reduction to isolate minimal failure-inducing test inputs for stochastic CPSs. We evaluate the proposed techniques on two complementary case study systems: an industrial elevator dispatching system employing stochastic optimization and an autonomous mobile robot exhibiting simulator-induced non-determinism. The results show that the proposed approaches substantially reduce debugging time while preserving the original failure behavior. More importantly, we observe that minimizing failure-inducing test inputs frequently increases failure reproducibility compared with the original executions. By eliminating execution segments that introduce incidental stochastic effects, the reduced test inputs isolate the causal conditions of the failure and consistently reproduce it with higher probability. These findings suggest that delta debugging not only simplifies failure analysis but also mitigates execution flakiness, providing a practical foundation for debugging CPSs. △ Less

Submitted 28 July, 2026; originally announced July 2026.

A flexible kinetic Monte Carlo framework for GaN molecular beam epitaxy with adaptive on-the-fly barrier evaluation

Authors: Sajid Ali, Norbert Krause, Carla Verdi

Abstract: We present a lattice-based kinetic Monte Carlo (KMC) framework for simulating GaN(0001) growth by molecular beam epitaxy. The framework captures the key microscopic processes governing epitaxial growth, including temperature-dependent surface diffusion, flux-driven deposition, Ehrlich--Schwoebel (ES) step-edge barriers, Ostwald ripening, and species-specific desorption, within a scalable architect… ▽ More We present a lattice-based kinetic Monte Carlo (KMC) framework for simulating GaN(0001) growth by molecular beam epitaxy. The framework captures the key microscopic processes governing epitaxial growth, including temperature-dependent surface diffusion, flux-driven deposition, Ehrlich--Schwoebel (ES) step-edge barriers, Ostwald ripening, and species-specific desorption, within a scalable architecture that enables systematic exploration of experimentally relevant growth conditions. In addition to predefined activation-energy catalogs, the framework supports adaptive on-the-fly barrier evaluation using machine-learned interatomic potentials. When previously unencountered local atomic configurations arise, activation barriers are computed via nudged elastic band, potential energy scans, or Brønsted--Evans--Polanyi methods, and cached for reuse. Predefined-barrier simulations reproduce compact triangular island formation, and further capture Ostwald ripening during growth interruptions and ES barrier-induced multilayer nucleation. At elevated temperatures, desorption drives an island ``walking'' regime, in which N--Ga exchange generates weakly bound Ga adatoms (AdGa) at trailing edges; preferential desorption of AdGa leads to asymmetric edge retreat and net island translation. Our KMC framework provides a flexible platform for predictive simulations of GaN epitaxy at the atomic scale and, more broadly, non-equilibrium growth of compound semiconductors. △ Less

Submitted 26 July, 2026; originally announced July 2026.

Journal ref: Applied Surface Science, 2026

The beamformed trigger of RNO-G: its design and in-field performance

Authors: RNO-G Collaboration, :, S. Agarwal, J. A. Aguilar, N. Alden, S. Ali, P. Allison, M. Betts, D. Besson, A. Bishop, O. Botner, S. Bouma, S. Buitink, R. Camphyn, J. Chan, S. Chiche, B. A. Clark, K. Couberly, D. Dakroub, K. D. de Vries, C. Deaconu, P. Giri, C. Glaser, H. Gui, A. Hallgren , et al. (55 additional authors not shown)

Abstract: The Radio Neutrino Observatory in Greenland (RNO-G) is a neutrino detector under construction at Summit Station, with 8 out of a planned 35 stations currently deployed. We have designed and deployed a new phased array (PA) trigger based on delay-and-sum beamforming and power integration. This trigger improves detector performance by suppressing thermal noise and better targeting neutrino-induced A… ▽ More The Radio Neutrino Observatory in Greenland (RNO-G) is a neutrino detector under construction at Summit Station, with 8 out of a planned 35 stations currently deployed. We have designed and deployed a new phased array (PA) trigger based on delay-and-sum beamforming and power integration. This trigger improves detector performance by suppressing thermal noise and better targeting neutrino-induced Askaryan signals. The new PA trigger has been deployed since the 2025 season. The trigger performance has been characterized using test pulses and calibration pulsers both in the lab and in-situ, and we find across these tests a 25% average reduction in the signal-to-noise ratio (SNR) needed to trigger on signals. Simulations are shown to be representative of the detector, and simulated trigger efficiencies are within 10% of measured data. Following the in-situ trigger validation, we use data-driven trigger performance to inform simulations of the detector effective volumes. The PA trigger increases our effective volume significantly, by over a factor of 2 below 0.1 EeV and at least a factor of 1.3 at the highest energies of 100 EeV. △ Less

Submitted 27 July, 2026; originally announced July 2026.

Comments: 27 pages, 17 figures, to be submitted to JINST

Mwando: Leveraging AI to Preserve and Teach shiKomori

Authors: Naira Abdou Mohamed, Haidar Nassur Said Ali, Mohamed Hazra, Naoufal Mohamed Soibira, Roushnaty Ali Yamani

Abstract: This paper presents Mwando, a virtual educational assistant designed to support the teaching and preservation of shiKomori, the language of the Comoros Islands. The system covers the four main dialectal variants (shiNgazidja, shiMwali, shiNdzuani and shiMaore) through a knowledge base constructed from phrases, proverbs, dictionaries and grammar lessons. A multi-agent architecture combining vector… ▽ More This paper presents Mwando, a virtual educational assistant designed to support the teaching and preservation of shiKomori, the language of the Comoros Islands. The system covers the four main dialectal variants (shiNgazidja, shiMwali, shiNdzuani and shiMaore) through a knowledge base constructed from phrases, proverbs, dictionaries and grammar lessons. A multi-agent architecture combining vector search, a knowledge graph and web search fallback enables accurate and context-aware responses. Evaluation on 500 queries demonstrates strong performance on vocabulary lookup and grammar explanations, while qualitative case studies illustrate both capabilities and current limitations. This work represents an initial step toward computational support for shiKomori and provides a blueprint for developing AI-powered educational tools for other low-resource languages. △ Less

Submitted 26 July, 2026; originally announced July 2026.

Quasi-Two-Dimensional Quantum Antiferromagnetism in the Distorted Honeycomb Compound KCuIn(PO4)2

Authors: S. Gayen, S. S. Ali, V. K. Singh, B. Koteswararao, S. K. Panda

Abstract: We investigate the electronic structure and magnetic properties of the distorted honeycomb lattice compound KCuInP2O8 through a combination of experimental measurements, first principles calculations and quantum monte carlo simulations. Density functional theory calculations within the GGA+U framework establishes KCuInP2O8 as an indirect gap insulator with Cu2+ local moments and finite magnetocrys… ▽ More We investigate the electronic structure and magnetic properties of the distorted honeycomb lattice compound KCuInP2O8 through a combination of experimental measurements, first principles calculations and quantum monte carlo simulations. Density functional theory calculations within the GGA+U framework establishes KCuInP2O8 as an indirect gap insulator with Cu2+ local moments and finite magnetocrystalline anisotropy arising from spin orbit coupling. A microscopic evaluation of magnetic exchange interactions using the magnetic force theorem reveals a pronounced hierarchy of couplings, with the next nearest neighbor interaction dominating over the nearest neighbor exchange, while interlayer couplings remain negligible. This exchange hierarchy naturally maps the system onto weakly coupled antiferromagnetic spin chains embedded in a distorted honeycomb lattice. Motivated by the ab initio estimated exchange interactions, we construct an effective spin half Hamiltonian and investigate its magnetic response using large scale quantum Monte Carlo simulations. The calculated temperature dependent susceptibility and field dependent magnetization quantitatively reproduce the experimental behavior and capture key signatures of low dimensional quantum magnetism, including a broad susceptibility maximum and a field induced saturation at low temperatures. Our results establish KCuInP2O8 as a quasi-two-dimensional quantum antiferromagnet composed of coupled spin chains, providing a consistent theoretical framework that links electronic structure, exchange interactions, and collective magnetic behavior. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Comments: Published in Physica Status Solidi (RRL) - Rapid Research Letters

Journal ref: Physica Status Solidi (RRL) 20, e202600001 (2026)

Auditing pretraining contamination in single-cell foundation model benchmarks

Authors: Sarwan Ali

Abstract: Single-cell foundation models (scFMs) such as Geneformer, scGPT, and Universal Cell Embeddings (UCE) are pretrained on tens of millions of cells drawn from public repositories. The same repositories underlie widely used integration benchmarks, creating an unmeasured risk that zero-shot benchmark performance reflects pretraining exposure rather than genuine generalization. We introduce \textbf{scCo… ▽ More Single-cell foundation models (scFMs) such as Geneformer, scGPT, and Universal Cell Embeddings (UCE) are pretrained on tens of millions of cells drawn from public repositories. The same repositories underlie widely used integration benchmarks, creating an unmeasured risk that zero-shot benchmark performance reflects pretraining exposure rather than genuine generalization. We introduce \textbf{scContam}, a per-cell audit framework that combines a MinHash-based gene-set fingerprint signal against the explicit pretraining corpus with a loss-based membership inference attack (MIA-scFM). Applied to four scIB benchmarks and three scFMs, we find that two of the most-cited benchmarks, PBMC 3k and the CELLxGENE human pancreatic islet atlas, contain extensive pretraining-overlap evidence ($80.4\%$ and $77.0\%$ of cells with fingerprint $p < 0.05$ against Genecorpus-30M), whereas the post-cutoff datasets AIDA v2 and Tahoe-100M show no overlap evidence ($0\%$). A controlled re-pretraining experiment establishes that MIA-scFM AUROC scales monotonically with the model's capacity-to-data ratio (AUROC $0.494 \to 0.690 \to 0.881$ across properly-regularized, mildly-overfit, and aggressively-overfit regimes), demonstrating that production scFMs resist instance-level memorization but distributional contamination must be detected separately. A donor-matched, within-cell-type analysis with three architectures shows that contaminated cells embed measurably more tightly than donor-matched clean cells (permutation $p = 0.030, 0.014, < 0.002$, respectively), with a perfectly null AIDA negative control. Pretraining audits are tractable and should accompany scFM benchmark reporting. △ Less

Submitted 21 July, 2026; originally announced July 2026.

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

Authors: Sarwan Ali

Abstract: Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and the field lacks a principled procedure for verifying that an apparent ``concept'' inside a model is real rather than an artifact of sequence composition. We introduce a framework that combines sparse dictionary learning with causal intervention to extr… ▽ More Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and the field lacks a principled procedure for verifying that an apparent ``concept'' inside a model is real rather than an artifact of sequence composition. We introduce a framework that combines sparse dictionary learning with causal intervention to extract, validate, and causally test interpretable features in genomic foundation models. Training top-$k$ sparse autoencoders on the hidden activations of two architecturally distinct models, Nucleotide Transformer ($6$-mer tokenization) and DNABERT-2 (byte-pair encoding), we recover thousands of monosemantic features that map to transcription-factor (TF) sequence motifs. We show that the naive validation of such features against position weight matrices is severely confounded by GC composition and repetitive elements, producing hundreds of spurious ``TF features'', and we develop a composition-matched, binding-resolved protocol that removes these confounds. Critically, we move beyond correlation: by ablating individual dictionary directions during the model's forward pass and measuring the induced shift in the model's own predictive distribution, we establish that specific features are \emph{causally} used to represent cell-type-specific TF binding, not merely motif presence. Across three transcription factors (CTCF, GATA1, REST) and both architectures, causally validated binding features emerge reproducibly ($7$--$14$ of $15$ tested features per condition), while two classes of negative control, scrambled binding labels and randomly selected features, yield no detectable signal. The framework is purely computational, uses only public data, and provides a reusable standard for interpretability claims in genomic deep learning. △ Less

Submitted 21 July, 2026; originally announced July 2026.

FlexiAvatar: Unified 3D Gaussian Human Avatars Under Arbitrary Body Visibility

Authors: Yihalem Yimolal Tiruneh, Muhammad Salman Ali, Uyoung Jeong, Muneeb A. Khan, MD Khalequzzaman Chowdhury Sayem, Allanur Bayramgeldiyev, Binod Bhattarai, Seungryul Baek

Abstract: Reconstructing animatable 3D human avatars from monocular video is a fundamental problem in computer vision with broad applications in AR/VR and digital content creation. Existing approaches typically couple parametric body models with neural rendering or 3D Gaussian splatting and optimize all body regions jointly from short videos, which often degrades fidelity in the visible areas. To overcome t… ▽ More Reconstructing animatable 3D human avatars from monocular video is a fundamental problem in computer vision with broad applications in AR/VR and digital content creation. Existing approaches typically couple parametric body models with neural rendering or 3D Gaussian splatting and optimize all body regions jointly from short videos, which often degrades fidelity in the visible areas. To overcome this limitation, we introduce FlexiAvatar, a unified framework that explicitly optimizes only the visible body regions, effectively eliminating artifacts arising from unobserved limbs. Our method integrates occlusion-robust SMPL-X tracking with part-specific residual refinement to capture high-frequency geometric and appearance details. To complete entirely unseen regions (e.g., back views), we leverage a diffusion-based approach to generate texture consistent with the observed appearance. Experiments on full-body (NeuMan, ZJU-MoCap, WildAvatar), upper/half-body (talk-show clips), and head-only (INSTA) inputs show that FlexiAvatar delivers consistently higher reconstruction quality, outperforming state-of-the-art methods by an average PSNR improvement of approximately 3% across datasets. Finally, by restricting optimization to observed regions, our method reduces the effective number of Gaussians that must be optimized and rendered, leading to reduced runtime and memory overhead in partial-visibility scenarios. △ Less

Submitted 21 July, 2026; originally announced July 2026.

Comments: Accepted in ECCV 2026

Vis2Reg: Visibility-Aware Landmark-Free Geometric 3D--2D Registration for Liver Laparoscopy

Authors: Jiaming Feng, Xukun Zhang, Shahid Farid, Sharib Ali

Abstract: Accurate 3D--2D liver registration, which aligns preoperative 3D models to partial, view-dependent intraoperative surface observations, is critical for AR-guided laparoscopic surgery but remains challenging due to severe occlusion, limited visibility, and the lack of 3D ground-truth supervision. Existing landmark-free approaches perform partial-to-complete geometric alignment, yet robust self-supe… ▽ More Accurate 3D--2D liver registration, which aligns preoperative 3D models to partial, view-dependent intraoperative surface observations, is critical for AR-guided laparoscopic surgery but remains challenging due to severe occlusion, limited visibility, and the lack of 3D ground-truth supervision. Existing landmark-free approaches perform partial-to-complete geometric alignment, yet robust self-supervision under extreme partial visibility remains difficult. We propose Vis2Reg, a visibility-aware registration framework that explicitly constrains deformation using mask-consistent visible regions. We introduce a visibility-aware self-supervision that derives a visible-domain 3D supervision signal from intraoperative masks, enabled by differentiable point rasterization and mask-guided back-projection. This formulation improves robustness under severe occlusion while maintaining fully self-supervised learning. Vis2Reg combines a robust geometric rigid initialization module with an implicit neural deformation field for stable alignment. Vis2Reg achieves a Dice score of 92.6\% and a Chamfer Distance of 1.43 mm on real intraoperative datasets, with 111 ms per-frame inference time, demonstrating both accuracy and practical efficiency. △ Less

Submitted 20 July, 2026; originally announced July 2026.

Comments: 11 pages

Journal ref: 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'2026)

SAVEstate: A Method for Documenting Player Reflection in Digital Games

Authors: Nisha Devasia, Michele Newman, Safinah Ali, Julie A. Kientz, Jin Ha Lee

Abstract: In recent years, interest in eudaimonic player experiences (PX) - concerning reflection, meaning-making, and personal growth - has increased. However, most games user research methods are not well-suited to study eudaimonic PX, as they have been developed to evaluate features of hedonic PX, such as flow, immersion, and playability. To more deeply explore eudaimonic PX, we require methods that can… ▽ More In recent years, interest in eudaimonic player experiences (PX) - concerning reflection, meaning-making, and personal growth - has increased. However, most games user research methods are not well-suited to study eudaimonic PX, as they have been developed to evaluate features of hedonic PX, such as flow, immersion, and playability. To more deeply explore eudaimonic PX, we require methods that can 1) investigate how moment-to-moment PX shapes player reflection and 2) explore how players reengage and reinterpret their experiences longitudinally. In this paper, we present SAVEstate, a method that uses documentation as a means of studying player reflection. SAVEstate consists of two phases: reflection-in-action and reflection-on-action, which allow researchers to probe players' in-situ reflections and how they reengage with their gameplay, respectively. Using SAVEstate, we were able to observe in-situ meaning-making and connect it to post-game reflection-on-action, and view synchronous sensemaking across multiple participants. We also developed an open-source desktop application for researchers to adapt in their own SAVEstate deployments. We discuss implications for how researchers might use SAVEstate to conduct future research in meaningful PX. △ Less

Submitted 21 July, 2026; v1 submitted 19 July, 2026; originally announced July 2026.

Comments: Accepted for publication at CHI PLAY 2026

Dirac topology, anomalous Hall response, and giant magnetoresistance in carrier-compensated altermagnetic semimetal NiS

Authors: Shovan Gayen, Sk. Soyeb Ali, S K Panda

Abstract: We combine first-principles density-functional theory, Berry-curvature analysis, semiclassical Boltzmann transport, and atomistic spin dynamics to establish hexagonal NiS as a compensated 3d altermagnetic semimetal in which topology, magnetism, and lattice dynamics are intrinsically intertwined. The rotational coset symmetry of the NiAs lattice produces momentum-dependent spin splitting characteri… ▽ More We combine first-principles density-functional theory, Berry-curvature analysis, semiclassical Boltzmann transport, and atomistic spin dynamics to establish hexagonal NiS as a compensated 3d altermagnetic semimetal in which topology, magnetism, and lattice dynamics are intrinsically intertwined. The rotational coset symmetry of the NiAs lattice produces momentum-dependent spin splitting characteristic of altermagnetism. With spin-orbit coupling, gapped Dirac-like crossings generate intense Berry-curvature hot spots and nearly compensated electron-hole pockets. This leads to a large and anisotropic intrinsic spin Hall conductivity comparable to that of several 4d, 5d metals, a symmetry-allowed anomalous Hall response despite zero net magnetization, and nonsaturating magnetoresistance exceeding 10000 percent. On the magnetic side, first-principles determination of the exchange tensor reveals dominant long-range superexchange and sizable anisotropic interactions, quantitatively reproducing the experimental Neel temperature. Our results identify NiS as a model 3d platform in which carrier compensation, altermagnetic symmetry, Berry-curvature driven transport, and lattice-sensitive magnetism coexist within a single symmetry framework, offering a design principle for multi-functional quantum responses in correlated transition-metal compounds. △ Less

Submitted 14 July, 2026; originally announced July 2026.

Comments: Accepted in Phys. Rev. B Letter

IRONSmith: A Visual Dataflow Design Environment for AMD Ryzen AI NPUs

Authors: Brock Sorenson, Samer Ali, Curt John Bansil, Aman Arora

Abstract: Machine learning inference increasingly relies on specialized hardware accelerators for throughput and power efficiency. Neural Processing Units (NPUs), such as the AMD Ryzen AI NPU, offer significant ML advantages over CPUs and GPUs, but programming them requires expertise in specialized frameworks. We present IRONSmith, the first visual dataflow design environment for programming AMD Ryzen AI NP… ▽ More Machine learning inference increasingly relies on specialized hardware accelerators for throughput and power efficiency. Neural Processing Units (NPUs), such as the AMD Ryzen AI NPU, offer significant ML advantages over CPUs and GPUs, but programming them requires expertise in specialized frameworks. We present IRONSmith, the first visual dataflow design environment for programming AMD Ryzen AI NPUs. IRONSmith provides an interactive canvas displaying the AI Engine tile grid as visually connected blocks, allowing users to design ML dataflow applications by connecting tiles with wires representing FIFOs, split/join patterns, broadcast connections, and DDR transfers without writing any code. Compute kernels are assigned from a pre-built library, and worker functions are configured through property panels. IRONSmith's backend pipeline automatically translates the visual design into executable IRON Python, handling structural completion, import resolution, and dependency management automatically. Generated code executes directly on the AMD Ryzen AI NPU. We demonstrate IRONSmith across ML designs of increasing complexity, from a single-tile vector passthrough to multi-tile matrix operations to a complete Multi-Layer Perceptron, all designed visually and successfully executed on the AMD Ryzen AI NPU. IRONSmith serves educators, students, ML researchers, and engineers by bridging the gap between ML knowledge and NPU programming expertise, widening access to hardware that is rapidly becoming standard across consumer and enterprise devices. △ Less

Submitted 12 July, 2026; originally announced July 2026.

Comments: Accepted at FastML 2026

Monte Carlo rate uncertainty of the $^{8}$Li(n,$γ$)$^{9}$Li reaction within $R$-matrix framework

Authors: Sk Mustak Ali, Rajkumar Santra

Abstract: The $^{8}$Li$(n,γ)^{9}$Li reaction is considered significant for the synthesis of nuclei beyond the $A=8$ stability gap in inhomogeneous big-bang nucleosynthesis models, as well as in $r$-process nucleosynthesis scenarios. However, direct measurement of this reaction is precluded by the short half-life of $^{8}$Li and the absence of a neutron target. Consequently, existing reaction rate estimates… ▽ More The $^{8}$Li$(n,γ)^{9}$Li reaction is considered significant for the synthesis of nuclei beyond the $A=8$ stability gap in inhomogeneous big-bang nucleosynthesis models, as well as in $r$-process nucleosynthesis scenarios. However, direct measurement of this reaction is precluded by the short half-life of $^{8}$Li and the absence of a neutron target. Consequently, existing reaction rate estimates based on indirect experimental methods and theoretical calculations differ by orders of magnitude. In the present work, the $^{8}$Li(n,$γ$)$^{9}$Li neutron-capture cross section and the corresponding thermonuclear reaction rate are evaluated within a phenomenological $R$-matrix framework, including both non-resonant direct capture (DC) and resonant capture through the $5/2^{-}$ state at $E_{x} = 4.30$~MeV. The uncertainties associated with the $R$-matrix input parameters are propagated using Monte Carlo sampling, while the sensitivity to the channel radius is treated as an $R$-matrix model uncertainty. The effective total uncertainties in the calculated cross sections and reaction rates are obtained by adding these two uncertainty contributions in quadrature. We obtain a total capture rate of $983.9^{+410.9}_{-261.5}~\mathrm{cm^{3}\,mol^{-1}\,s^{-1}}$ at $T = 1$~GK, with DC dominating at low temperatures ($T=0.01-0.4$~GK) and the $5/2^{-}$ resonance at higher temperatures ($T=0.5-5$~GK). The present results are consistent with the upper limit of Kobayashi et al.~\cite{Kobayashi2003}, which previous theoretical predictions exceed by factors of 3-50. △ Less

Submitted 10 July, 2026; originally announced July 2026.

Comments: 11 Pages, 7 figures

Mixed precision explicit numerical methods for ordinary differential equations

Authors: M Al Sayed Ali, S Bernard, A Marzorati, J Rouzaud-Cornabas

Abstract: Our objective is to solve large systems of ordinary differential equations (ODEs) commonly used to model biological processes. These equations are typically nonlinear, complex, and high-dimensional. In computational biology, such ODEs are generally solved using numerical methods. In this work, we focus on explicit numerical methods because of their flexibility. However, their limited stability reg… ▽ More Our objective is to solve large systems of ordinary differential equations (ODEs) commonly used to model biological processes. These equations are typically nonlinear, complex, and high-dimensional. In computational biology, such ODEs are generally solved using numerical methods. In this work, we focus on explicit numerical methods because of their flexibility. However, their limited stability regions may result in high computational costs. To mitigate this issue, we investigate mixed precision algorithms designed to reduce computational effort by performing selected parts of the numerical method in lower arithmetic precision. We develop several mixed precision explicit methods and assess their performance on two large scale biological benchmark ODE models. Our theoretical analysis highlights the effectiveness of partially reducing arithmetic precision within explicit methods. Numerical experiments demonstrate that our mixed methods-implemented in both sequential and parallel versions using MPI-combining single (float) and double precision arithmetic can achieve up to twice the speed of a fully double precision implementation while preserving the same level of accuracy. Furthermore, the results indicate that decreasing the timestep improves the performance and robustness of our mixed methods, while the single precision method fails to converge. △ Less

Submitted 8 July, 2026; originally announced July 2026.

The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI

Authors: Muayad Sayed Ali, Aliaksandra Novik, Anji Boddupally, Artem Yavorskyi, Chris Nickerson, Daniel Rica, Emily DuGranrut, Felix Leung, Garrett Prince, Grace Barnett, Heath Robinson, Hosain Al Ahmad, Jesse Resnick, Juan Carlos Farah, Jyothi Swaroop Meruga, Leonid Kuznetsov, Luke Gorham, Marie Schmoll, Michael Paciullo, Saumya Das, Sharath Sheripally, Tommy Griscom, Mykyta Osadchyi, Neha Mantri, Nick Westrum , et al. (7 additional authors not shown)

Abstract: Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens per task grow faster than task value. Falling per-token prices mask the pattern; total spend rises anyway. We argue the decisive lever against token maxing is the harness: the orchestration layer that assembles context,… ▽ More Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens per task grow faster than task value. Falling per-token prices mask the pattern; total spend rises anyway. We argue the decisive lever against token maxing is the harness: the orchestration layer that assembles context, exposes tools, sequences turns, delegates work, and carries enterprise observability and governance. We isolate it with a controlled swap: 22 locked evaluation tasks, six foundation models (Claude Sonnet 4.6, Gemini 3.1, Gemini Flash 3.5, Qwen 3.6, GLM 5.1, Palmyra X6), changing only the orchestration layer -- a frozen conventional production loop versus the Writer Agent Harness. Holding models constant, the harness cuts blended cost per task 41% ($0.21->$0.12), median wall-clock 44% (48s->27s), and tokens per task 38% (14.2k->8.8k), with task-completion quality at parity (0.78->0.81, directional at this sample size). Efficiency is model-invariant -- every model gets cheaper (33-61%) -- while quality gains are capability-dependent: a model's gain correlates almost perfectly with its baseline strength (r=0.99, n=6), a phenomenon we term harness leverage. Quality per dollar rises 82%; task-completions per million tokens rise from 54.9 to 92.0. On this workload the orchestration layer moved cost per task more than the full spread of the model menu did. We formalize token economics at the orchestration layer (including effective input price under prompt caching), detail the six mechanism families behind the effect -- cache-shape discipline to failure-spend governance -- compare six widely used agent systems on the same axes, and argue the harness is the one component whose efficiency multiplies across every model an organization runs -- present and future. △ Less

Submitted 7 July, 2026; originally announced July 2026.

Deep-Unfolded Wideband ISAC Beamforming for DMA Under Frequency-Selective Lorentzian Model

Authors: Abdolrasoul Sakhaei Gharagezlou, Pouya Mobaraki, Mehdi Monemi, Nhan T. Nguyen, Mehdi Rasti, Samad Ali, Matti Latva-aho

Abstract: Integrated sensing and communications (ISAC), empowered by dynamic metasurface antennas (DMAs), has emerged as a promising paradigm for next-generation wireless networks. However, existing DMA-based designs commonly rely on the frequency-flat response model for DMA elements, which is accurate only in narrowband scenarios and can cause significant phase and magnitude mismatches in wideband and ultr… ▽ More Integrated sensing and communications (ISAC), empowered by dynamic metasurface antennas (DMAs), has emerged as a promising paradigm for next-generation wireless networks. However, existing DMA-based designs commonly rely on the frequency-flat response model for DMA elements, which is accurate only in narrowband scenarios and can cause significant phase and magnitude mismatches in wideband and ultra-wideband systems. This paper investigates a DMA-based wideband ISAC system under a frequency-selective Lorentzian response model, which accurately captures the frequency-dependent behavior of DMA elements. We aim to jointly balance the aggregate signal-to-interference-plus-noise ratio (SINR) of communication users and the signal-to-noise ratio (SNR) of the radar target. To this end, we first develop an alternating optimization framework based on projected gradient ascent (PGA), deriving closed-form gradients of the objective function with respect to the digital beamforming vectors, resonance frequencies, and damping factors under the frequency-selective Lorentzian DMA model. We then propose an unfolded PGA architecture that preserves the interpretability of model-based optimization while learning key hyperparameters to accelerate convergence. Simulation results show that the frequency-selective Lorentzian model improves performance by approximately 20\% over its frequency-flat approximation. Moreover, deep-unfolded PGA achieves up to 20-fold faster convergence and improves the objective value by up to 7\% compared with PGA-based benchmarks. △ Less

Submitted 3 July, 2026; originally announced July 2026.

Comments: 14 pages

High-Energy Neutrino Tomography of the Earth's Interior with IceCube

Authors: The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel , et al. (395 additional authors not shown)

Abstract: The Earth's interior reflects its geological evolution, from accretion to present-day dynamics. Its structure drives the geodynamo in the outer core, generating the magnetic field that shields the surface from charged cosmic radiation. The primary observables of the Earth's interior are its radial density distribution and derived quantities such as its mass and moment of inertia. These have tradit… ▽ More The Earth's interior reflects its geological evolution, from accretion to present-day dynamics. Its structure drives the geodynamo in the outer core, generating the magnetic field that shields the surface from charged cosmic radiation. The primary observables of the Earth's interior are its radial density distribution and derived quantities such as its mass and moment of inertia. These have traditionally been inferred from gravity and seismic wave propagation, which probe the macroscopic response of matter to gravitational and elastic forces. Here we instead constrain the Earth's density profile using high-energy neutrinos observed by the IceCube Neutrino Observatory at the South Pole. We analyze 10.7 years of predominantly muon-neutrino data spanning 500 GeV--100 TeV, including atmospheric neutrinos produced by cosmic-ray interactions in the Earth's atmosphere and the diffuse astrophysical neutrino flux. Neutrino attenuation depends on both the traversed column density and neutrino energy. By measuring the zenith- and energy-dependent flux suppression, we infer the Earth's radial density profile by fitting a concentric uniform-density shell model that incorporates neutrino fluxes, interaction cross sections, detector response, and glacial-ice systematic uncertainties. From the resulting density posteriors, we derive the Earth's mass and polar moment of inertia as measured by neutrinos. These are the most precise weak-interaction measurements of these quantities to date and are consistent with the Preliminary Reference Earth Model and independent gravitational determinations. Our results demonstrate that neutrinos provide a novel probe of planetary interiors via a distinct physical interaction, complementing gravity and seismology. With improved detectors and precision, neutrinos will further contribute to a multifaceted understanding of the Earth's structure. △ Less

Submitted 7 July, 2026; v1 submitted 2 July, 2026; originally announced July 2026.

WavePID: Low-energy flavor identification using single-PMT time series in IceCube

Authors: The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel , et al. (395 additional authors not shown)

Abstract: The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classification particularly challenging in the 5--100~GeV regime, the energy range relevant for oscillation measurements and searches for physics beyond the Standard Model. We introduce WavePID, a template-based log-likelihood-ratio… ▽ More The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classification particularly challenging in the 5--100~GeV regime, the energy range relevant for oscillation measurements and searches for physics beyond the Standard Model. We introduce WavePID, a template-based log-likelihood-ratio classifier that exploits nanosecond-scale timing on individual detector modules through three observables: the distance to the reconstructed vertex, the early-charge fraction, and the module-to-module time difference. Evaluated on a cascade-enriched sample selected by a state-of-the-art graph neural network, WavePID improves both cascade purity and classification performance over the neural network alone. This demonstrates that per-module pulse timing carries flavor-identification information complementary to morphology-based classifiers, opening a new physics-motivated observable for low-energy neutrino reconstruction. Geant4 simulations associate this signal with differences in Cherenkov emission geometry between muon tracks and electromagnetic showers. These results motivate exploiting nanosecond-scale pulse timing in future low-energy classifiers and in detector designs with improved per-module timing in next-generation neutrino telescopes. △ Less

Submitted 20 August, 2026; v1 submitted 2 July, 2026; originally announced July 2026.

Comments: 14 pages with 7 figures; plus 3 pages supplemental material, submitted to Journal of Instrumentation

Multi-THuMBS: Multi-person Tracking of 3D Human Meshes Beyond Video Shots

Authors: Jeongwan On, Muhammad Salman Ali, Muneeb A. Khan, Sunwoo Park, Inwoong Moon, Hyung Jin Chang, Jaekwang Kim, Seong Jong Ha, Seungryul Baek

Abstract: Tracking multi-person 3D human meshes from in-the-wild videos is a highly challenging problem due to complex interactions, frequent occlusions, and severe truncation inherent in unconstrained environments. While recent approaches have improved robustness against these issues, they largely overlook the critical challenge prevalent in real-world footage: frequent shot changes. These abrupt transitio… ▽ More Tracking multi-person 3D human meshes from in-the-wild videos is a highly challenging problem due to complex interactions, frequent occlusions, and severe truncation inherent in unconstrained environments. While recent approaches have improved robustness against these issues, they largely overlook the critical challenge prevalent in real-world footage: frequent shot changes. These abrupt transitions in camera viewpoints often cause existing methods to lose track of human identities and fail in reconstructing temporally coherent trajectories. Although several recent works have explored 3D human mesh tracking under shot changes, they are still limited to single-person scenarios, making them inadequate for real-world videos where multiple people interact and appear simultaneously. To address this limitation, we propose Multi-THuMBS (Multi-person Tracking of 3D Human Meshes Beyond Video Shots) that leverages a state-of-the-art 3D scene prior to reconstruct the two boundary frames in a single shared 3D space. Human meshes are then registered within the shared 3D space, maintaining per-person identity and motion consistency across shot changes. Extensive experiments demonstrate that our approach yields significant improvements in 3D human mesh recovery, camera pose estimation, and identity tracking, thereby ensuring high-fidelity motion reconstruction with consistent identity preservation across shots compared to previous state-of-the-art methods. △ Less

Submitted 1 July, 2026; originally announced July 2026.

Comments: Project page: https://on-jungwoan.github.io/projects/multi-thumbs/

An Optimal Contact-Mechanically Consistent and Flow-Separation Adapted Modeling of Vocal Fold Dynamics

Authors: Sardar Nafis Bin Ali, Maryam Naghibolhosseini, Mohsen Zayernouri

Abstract: Single mass-spring-damper models of vocal folds have been effective in simulating vocal fold vibrations without added complexity. However, single-degree-of-freedom models cannot sustain oscillation in the presence of structural damping unless source-tract interaction is considered. Moreover, existing lumped models struggle to accurately simulate vocal fold closure during phonation. This study aims… ▽ More Single mass-spring-damper models of vocal folds have been effective in simulating vocal fold vibrations without added complexity. However, single-degree-of-freedom models cannot sustain oscillation in the presence of structural damping unless source-tract interaction is considered. Moreover, existing lumped models struggle to accurately simulate vocal fold closure during phonation. This study aims to develop a reliable and simplified single-degree-of-freedom model of phonation that can simulate sustained oscillation in a damped system without incorporating a vocal tract model. Additionally, the proposed model maintains vocal fold closure in a manner consistent with the physics of phonation, addressing a longstanding challenge in existing lumped models. High-speed videoendoscopy (HSV) data from four normophonic subjects producing sustained vowel /i/ were used to extract glottal area waveforms (GAWs) via deep learning-based image segmentation for particle swarm optimization of the model parameters. An additional resistance force was incorporated to compensate for flow separation and generate the force imbalance required for sustained oscillation. An external structural force was also added during closure to sustain the closed phase. The 4th-order Runge-Kutta method was used to solve the governing equations with enhanced numerical stability and accuracy. The model parameters were optimized for individual subjects, resulting in normalized errors below 3% between experimental and simulated GAWs. The proposed model accurately reproduced subject-specific vocal fold vibrations and vocal fold closure in agreement with experimental data. Overall, the proposed model provides a computationally efficient framework for simulating sustained phonation without requiring complex source-tract coupling while capturing the key biomechanical and aerodynamic mechanisms of phonation. △ Less

Submitted 27 June, 2026; originally announced June 2026.

Comments: 30 pages, 9 figures

MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models

Authors: Pablo Valle, Shaukat Ali, Aitor Arrieta, Lionel Briand

Abstract: Vision-Language-Action (VLA) models are emerging robotic control systems that integrate perception, language understanding, and action generation in a unified architecture. Existing testing approaches for VLA-enabled robots rely on manually constructed symbolic test oracles that determine task success from final environment states. These oracles are costly to construct, require domain expertise, a… ▽ More Vision-Language-Action (VLA) models are emerging robotic control systems that integrate perception, language understanding, and action generation in a unified architecture. Existing testing approaches for VLA-enabled robots rely on manually constructed symbolic test oracles that determine task success from final environment states. These oracles are costly to construct, require domain expertise, and are often tightly coupled to specific tasks and environments, limiting scalability and reuse. Furthermore, they provide only end-state assessments of task outcomes, offering limited insight into intermediate behavior and fault localization. To address these limitations, we introduce MANGO, a multi-agent framework that automatically generates fine-grained oracles from natural-language descriptions of robotic tasks. MANGO first generates a reusable library of atomic tasks, then generates simulator-grounded oracle definitions for each atomic task, and finally produces executable fine-grained oracles by decomposing complex instructions into ordered sequences of atomic actions and corresponding oracles. The framework uses collaborative Generator, Assessor, and Judge agents that iteratively refine generated artifacts through structured feedback. We evaluate MANGO on the LIBERO_10 and RoboCasa Humanoid Tabletop benchmarks. Results show that MANGO generates executable, fine-grained oracles that detect a similar number of failures as symbolic oracles while accurately localizing them and providing richer diagnostic information. Through ablation studies, we further analyzed component contributions and the effect of initial task set, while preserving oracle quality. Overall, the results show the feasibility and effectiveness of test oracle generation for VLA-enabled robots testing. △ Less

Submitted 23 June, 2026; originally announced June 2026.

Charged kaon and proton multiplicities in semi-inclusive deep-inelastic scattering with 11 GeV electrons

Authors: P. Bosted, W. Armstrong, H. Bhatt, D. Dutta, R. Ent, D. Gaskell, S. Jia, E. Kinney, H. Mkrtchyan, S. Ali, R. Ambrose, D. Androic, C. Ayerbe Gayoso, A. Bandari, V. Berdnikov, D. Bhetuwal, D. Biswas, M. Boer, E. Brash, A. Camsonne, M. Cardona, J. P. Chen, J. Chen, M. Chen, E. M. Christy , et al. (45 additional authors not shown)

Abstract: Measurements of SIDIS multiplicities for charged kaons and protons from proton and deuteron targets are reported on a grid of hadron kinematic variables $0.3<z<0.7$ and $P_{t}<0.6$ GeV for leptonic variables $0.3<x<0.6$ and $3<Q^2<6$ GeV$^2$. Data were acquired in 2018-2019 at Jefferson Lab Hall C with 10.2 and 10.6~GeV electron beams impinging on 10-cm-long liquid hydrogen and deuterium targets.… ▽ More Measurements of SIDIS multiplicities for charged kaons and protons from proton and deuteron targets are reported on a grid of hadron kinematic variables $0.3<z<0.7$ and $P_{t}<0.6$ GeV for leptonic variables $0.3<x<0.6$ and $3<Q^2<6$ GeV$^2$. Data were acquired in 2018-2019 at Jefferson Lab Hall C with 10.2 and 10.6~GeV electron beams impinging on 10-cm-long liquid hydrogen and deuterium targets. Electrons (hadrons) were detected in the HMS (SHMS) spectrometers. Multiplicities were fitted for each bin in $(x,~Q^2,~z,~P_{t})$ as $M_0[1+A\cos(φ^*)+B\cos(2φ^*)]$. The kaon kinematic range spans the regions where transverse-momentum-dependent factorization can be applied in SIDIS, and a `soft' central region where other processes are of critical importance. The kaon to pion ratios of $M_0$ are in reasonable agreement with predictions using the DSS fragmentation functions for $K^+$, but are mostly well below them for $K^-$. The kaon azimuthal modulations are consistent with zero. The kinematic range for protons is centered on the `soft' central region. The proton-to-pion multiplicity ratios are more than an order-of-magnitude larger than TMD predictions at the lowest value of $W^2$, decreasing to as little as a factor of two at the highest value of $W^2$. No significant difference is observed between proton and deuteron targets. These trends are consistent with Lund Monte Carlo predictions. The proton values of $A$ are consistently positive, with an average value of approximately 0.01, while $B$ is consistent with zero. △ Less

Submitted 17 June, 2026; originally announced June 2026.

Comments: 19 pages, 11 figures, to be submitted to Phys. Rev. C

Report number: JLAB-PHY-26-4788

Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning

Authors: Saraa Ali, Vladimir Bocharnikov, Fedor Ratnikov, Mikhail Hushchyn, Artem Ryzhikov, Denis Derkach

Abstract: The quality of recorded data depends on the stability of the sensor system that acquires it. Sensor motion and aging can degrade the performance and stability of downstream data-driven methods. We present a Wasserstein-GAN-inspired approach for unsupervised inference of physically interpretable transformation parameters that map a changed detector response distribution back to a nominal reference… ▽ More The quality of recorded data depends on the stability of the sensor system that acquires it. Sensor motion and aging can degrade the performance and stability of downstream data-driven methods. We present a Wasserstein-GAN-inspired approach for unsupervised inference of physically interpretable transformation parameters that map a changed detector response distribution back to a nominal reference distribution. In contrast to standard generative modeling, the generator is used as a learnable calibration transformation whose trainable weights represent the sought parameters, while the critic provides a distributional distance signal via the Wasserstein objective. We validate the approach on a tracking-detector toy model with controlled layer shifts and demonstrate its application on high-granularity Geant4-simulated calorimeter data with cell-wise aging effects. The method recovers aging coefficients for individual cells with correlation to ground truth and improves agreement between calibrated and reference energy-sum distributions, while exhibiting the expected degradation at increasing channel-to-channel noise levels. These results indicate that adversarial distribution matching can serve as a data-driven component of calibration strategies in settings where direct labels for degradation parameters are unavailable. △ Less

Submitted 16 June, 2026; originally announced June 2026.

Comments: This is a preprint sent to Nuclear Science and Techniques journal

Measurement of dijet transverse momentum imbalance and azimuthal acoplanarity in $p$+$p$ collisions at $\sqrt{s} = 200$ GeV with the sPHENIX detector

Authors: sPHENIX Collaboration, M. I. Abdulhamid, U. Acharya, E. R. Adams, G. Adawi, I. Ahmed, C. A. Aidala, Y. Akiba, M. Alfred, S. Ali, A. Alsayegh, S. Altaf, H. Amedi, D. M. Anderson, V. V. Andrieux, A. Angerami, N. Applegate, M. U. Ashraf, H. Aso, S. Aune, B. Azmoun, V. R. Bailey, D. Baranyai, S. Bathe, A. Bazilevsky , et al. (305 additional authors not shown)

Abstract: This Letter reports on measurements of dijet transverse momentum ($p_\mathrm{T}$) imbalance and azimuthal acoplanarity in proton-proton collisions at $\sqrt{s} = 200$~GeV, using data recorded by the sPHENIX detector at the Relativistic Heavy Ion Collider corresponding to an integrated luminosity of $41$~pb$^{-1}$. Jets are reconstructed using the anti-$k_t$ algorithm with radius parameters… ▽ More This Letter reports on measurements of dijet transverse momentum ($p_\mathrm{T}$) imbalance and azimuthal acoplanarity in proton-proton collisions at $\sqrt{s} = 200$~GeV, using data recorded by the sPHENIX detector at the Relativistic Heavy Ion Collider corresponding to an integrated luminosity of $41$~pb$^{-1}$. Jets are reconstructed using the anti-$k_t$ algorithm with radius parameters $R = 0.3$ to $0.8$ from electromagnetic and hadronic calorimeter energy deposits. The jet $p_\mathrm{T}$ resolution is determined directly in data using two independent methods. The dijet $p_\mathrm{T}$ imbalance is characterized by the ratio $x_\mathrm{J} = p_\mathrm{T,2}/p_\mathrm{T,1}$ where $p_\mathrm{T,1(2)}$ is the highest (second-highest) jet $p_\mathrm{T}$ in the event. The dijet azimuthal acoplanarity $Δφ= |φ_1 - φ_2|$ is also reported. Results are reported for different $p_\mathrm{T,1}$ selections and jet radius parameters, normalized per dijet pair, and compared to the results of \textsc{Pythia} and \textsc{Herwig} Monte Carlo event generators. These measurements provide a stringent quantitative test of the modeling of QCD parton shower and hadronization dynamics, place important constraints on event-generator descriptions at RHIC energies, and establish a comprehensive proton-proton baseline for forthcoming measurements of jet modification in heavy ion collisions. △ Less

Submitted 15 June, 2026; originally announced June 2026.

Comments: 19 pages total, 8 figures, 1 tables. All figures and tables can be found at https://www.sphenix.bnl.gov/PublicResults/sPH-JET-2026-01

MambAdapter: Lightweight Mamba-Based Adapters for Parameter-Efficient Transfer Learning in Speech and Audio

Authors: Salman Hussain Ali, Umberto Cappellazzo, Mirco Ravanelli

Abstract: Fine-tuning Transformer-based foundation models has become the dominant strategy for domain adaptation in audio and speech processing. To reduce the computational and memory costs of this process, parameter-efficient transfer learning (PETL) methods have been widely explored. Meanwhile, Mamba, a recent state-space model, has emerged as a promising alternative to Transformers for sequence modeling.… ▽ More Fine-tuning Transformer-based foundation models has become the dominant strategy for domain adaptation in audio and speech processing. To reduce the computational and memory costs of this process, parameter-efficient transfer learning (PETL) methods have been widely explored. Meanwhile, Mamba, a recent state-space model, has emerged as a promising alternative to Transformers for sequence modeling. In this work, we present MambAdapter, a parameter-efficient transfer learning approach that integrates Mamba into low-rank bottleneck adapters. Our design combines parameter sharing across adapters with the injection of a lightweight Mamba module, enabling more effective modeling of audio features. We demonstrate that MambAdapter matches or outperforms strong PETL baselines on four audio classification tasks and five speech recognition languages, even when operating under reduced parameter budgets. △ Less

Submitted 14 June, 2026; originally announced June 2026.

Comments: Accepted to Interspeech 2026. Code available at: https://github.com/salman-ha/MambAdapter

Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning

Authors: Ali Dasdan, Manan Shah, W. Russell Neuman, Chad Coleman, Kund Meghani, Safinah Ali

Abstract: Behavioral audits of Large Language Models on moral prompts measure what the model says, not the internal computation producing it. We use Transluce, an AI-driven mechanistic-interpretability platform, to examine LLaMA 3.1-8B-Instruct on 54 moral prompts in four batteries: 17 dilemmas, policy, and meta-ethical questions (B1); 6 role-playing scenarios (B3); and a controlled trolley contrast varying… ▽ More Behavioral audits of Large Language Models on moral prompts measure what the model says, not the internal computation producing it. We use Transluce, an AI-driven mechanistic-interpretability platform, to examine LLaMA 3.1-8B-Instruct on 54 moral prompts in four batteries: 17 dilemmas, policy, and meta-ethical questions (B1); 6 role-playing scenarios (B3); and a controlled trolley contrast varying the switching mechanism with people fixed (B4, 15 prompts) or identity attributes with mechanism fixed (B5, 16 prompts). Two complementary metric families, five cluster-level metrics and a six-metric neuron-level panel, converge on a Situational Anchor Effect: domain-specific representations dominate the top of the activation list across every battery. The model's ethics-labeled capacity stays essentially constant; its salience (rank, priority, top-of-list presence) is highly sensitive to the interpretive frame the prompt selects. The B4-vs-B5 contrast confirms the model attends to whichever surface feature varies: aggregate ethics metrics are indistinguishable, but the dominant non-ethics distractor mirrors the design. A multi-temperature audit identifies a candidate ethics neuron (L16/N3837) stable across temperatures; a cross-model behavioral proxy on two frontier models yields preliminary evidence of divergence in self-reported moral focus, consistent with an Alignment Wrapper in which RLHF re-orders surface text without removing underlying domain-first frames. We unify these as Frame-Conditioned Moral Computation: the prompt's surface vocabulary selects a feature manifold, and the moral conclusion is downstream of that selection. Behavioral alignment must be supplemented by Mechanistic Alignment: a research program asking whether ethics-related features can be shown causally privileged under controlled frame variation, not merely loud in the explanation. △ Less

Submitted 13 June, 2026; originally announced June 2026.

Comments: 47 pages, 10 figures

Prime Focus Spectrograph on the Subaru Telescope: Overview of Science Operations

Authors: Masayuki Tanaka, Akira Arai, Wanqiu He, Miho N. Ishigaki, Eric Jeschke, Russell Kackley, Shintaro Koshida, Yuki Moritani, Masato Onodera, Vera Maria Passegger, Tae-Soo Pyo, Yuhei Takagi, Naoyuki Tamura, Ichi Tanaka, Kiyoto Yabe, Sadman S. Ali, Javier Gracia Caprio, Maximillian Fabricius, Wilfred Gee, James E. Gunn, Michitaro Koike, Arnaud Le Fur, Zhuoming Li, Yongming Liang, Craig Loomis , et al. (8 additional authors not shown)

Abstract: The paper presents the science operation framework for Prime Focus Spectrograph (PFS or 'Onohi'ula in its Hawaiian name) installed at the 8.2m Subaru Telescope on the summit of Maunakea. PFS is a massively multiplexed, wide-field, fiber-fed spectrograph covering 1.25 square degrees with 2386 science fibers. The instrument has been offered to the Subaru scientific community since March 2025. In ord… ▽ More The paper presents the science operation framework for Prime Focus Spectrograph (PFS or 'Onohi'ula in its Hawaiian name) installed at the 8.2m Subaru Telescope on the summit of Maunakea. PFS is a massively multiplexed, wide-field, fiber-fed spectrograph covering 1.25 square degrees with 2386 science fibers. The instrument has been offered to the Subaru scientific community since March 2025. In order to fully exploit the unique capabilities of PFS, the Subaru Telescope has introduced a new, dedicated science operation framework for PFS. The default observing mode is queue observing, and multiple observing programs (in the same field) can be executed in the same exposure to achieve high observing efficiency. The quality of an exposure is based on the delivered signal-to-noise ratio and is quantified in terms of 'effective exposure time', and exposures are taken until the allocated 'fiber hours' for each program or target are achieved. The fiber hour is a new unit for observing time at Subaru; if we expose a fiber for 1 hour under the fiducial conditions, it is 1 fiber hour. Each observing program is granted the total fiber hours by the Time Allocation Committee. In addition to normal observing programs, which are selected through the standard peer-review process, there are two filler categories; community filler and observatory filler. As the names imply, the former is proposed by the community and the latter is prepared by the observatory. These filler targets are used whenever unassigned fibers are available. After an observing run, the data are fully reduced by the observatory and delivered to the users through the PFS Science Platform, a cloud-based data analysis environment. The paper gives a summary of all of this new framework and the actual implementation of it. △ Less

Submitted 11 June, 2026; originally announced June 2026.

Comments: 17 pages, 5 figures, submitted to PASJ

IceCube Real-time Searches for High-energy Neutrinos Coincident with LIGO/Virgo/KAGRA Gravitational-Wave Alerts in O4a

Authors: The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, Y. Ashida, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi , et al. (396 additional authors not shown)

Abstract: Gravitational-wave events from mergers of compact objects are a predicted source of high-energy neutrinos. Using data from the IceCube Neutrino Observatory, we search for neutrinos coincident with 85 significant and 945 low-significance gravitational-wave candidate events from compact binary coalescences published in real-time by the LIGO-Virgo-KAGRA collaboration during the first part of its four… ▽ More Gravitational-wave events from mergers of compact objects are a predicted source of high-energy neutrinos. Using data from the IceCube Neutrino Observatory, we search for neutrinos coincident with 85 significant and 945 low-significance gravitational-wave candidate events from compact binary coalescences published in real-time by the LIGO-Virgo-KAGRA collaboration during the first part of its fourth observing run (O4a) and its preceding engineering run, within a time window of $\pm500$ seconds centered on the merger time. We report improvements to the online pipelines, including automatic sending of notices, which has decreased the IceCube real-time response time to gravitational-wave events. In addition, we search for long-duration neutrino emission (up to two weeks after the merger) from three candidate events: two neutron star-black hole mergers, and one low-significance gravitational-wave event with a possible subthreshold gamma-ray counterpart. We use two methods, both of which have been previously used to search for neutrino emission associated with gravitational-wave transients: an unbinned maximum likelihood analysis on significant alerts and a Bayesian analysis accounting for astrophysical priors on both significant and low-significance alerts. We find no statistically significant emission from any of the individual gravitational-wave events analyzed, and set upper limits on the time-integrated flux and energy emitted in high energy neutrinos assuming isotropic emission from each event. △ Less

Submitted 11 June, 2026; originally announced June 2026.

BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

Authors: Naveen Bera, Pulijala Sai Nikhila, Kondaguduru Abhiram, Shaik Gayaz Ali, Shoaib Sadiq Salehmohamed, Shaik Mohammed Omar, Jinal Prashant Thakkar, Hansika Aredla, Shalmali Ayachit

Abstract: Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment. We present BEACON (Behavioral Entropy Aggregation for Cross-model hallucination detectiON), a black-box hallucination detection framework that operates purely on model outputs without requiring access to internal representations… ▽ More Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment. We present BEACON (Behavioral Entropy Aggregation for Cross-model hallucination detectiON), a black-box hallucination detection framework that operates purely on model outputs without requiring access to internal representations or external knowledge bases. BEACON extracts a 31-dimensional feature vector from structured multi-pass generation, integrating NLI-based semantic entropy, embedding geometry, chain-of-thought consistency, and paraphrase stability signals. A gradient-boosted classifier trained on 7,617 labeled examples across seven benchmarks achieves 0.8123 +/- 0.0102 AUROC (95% CI: 0.7632-0.8251), outperforming standalone semantic entropy (+0.2298) and SelfCheckGPT-style consistency baselines (+0.2457). Feature importance analysis shows that hallucination is inherently multi-dimensional, requiring combined uncertainty signals. An efficient 5-call variant achieves 0.7795 AUROC, enabling practical deployment across black-box LLM APIs. △ Less

Submitted 20 April, 2026; originally announced June 2026.

Comments: 12 pages, 6 tables, 1 figure. Code and data available upon request

Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

Authors: Hina Shakir, Mohammad Mohatram, Javeed Hussain, Syed Rizwan Ali, Muhammad Irfan Memon

Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for building reliable predictive models. This study proposes a Gradient-Loss Recursive Feature Elimination (GL-RFE) framewor… ▽ More Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for building reliable predictive models. This study proposes a Gradient-Loss Recursive Feature Elimination (GL-RFE) framework that integrates gradient sensitivity analysis from a deep neural network to identify the most influential radiomic features for lung cancer stage detection. A total of 106 radiomic features were extracted from chest Computed Tomography (CT) scans using the PyRadiomics extension of the 3D Slicer platform. The proposed method evaluates feature importance by computing gradients of the network loss with respect to input features and recursively eliminates features with minimal contribution. The resulting top-15 radiomic features are used to train a deep neural network classifier for distinguishing early-stage and advanced-stage lung cancer. The proposed framework achieves strong classification performance, with accuracy of 90.22%, precision of 90.10%, recall of 90.24%, and F1-score of 90.16% on the test dataset. Visualization analyses, including correlation heat maps and distribution plots, further confirm reduced feature redundancy and improved class separability. Compared to conventional feature selection techniques, GL-RFE effectively captures nonlinear feature interactions and enhances model generalization. The presented protocol provides a reproducible and interpretable methodology for radiomics-based cancer stage detection and is particularly suitable for high-dimensional, small-sample biomedical datasets, with potential applications in other domains such as genomics and multimodal clinical analysis. △ Less

Submitted 3 June, 2026; originally announced June 2026.

Journal ref: J. Vis. Exp. (230), e70181, (2026)

The Violation Situation Pattern: Persistent Representation of Compliance Violations in Knowledge Graphs

Authors: Nima Kamali Lassem, Fuqi Song, Seyid Amjad Ali

Abstract: Existing compliance pipelines identify violations as transient query results, leaving no persistent representation of the violation itself or its lifecycle, evidence, and audit history. We address this limitation with the Violation Situation Pattern (VSP), a reusable ontology pattern that extends the Situation pattern of Gangemi and Mika by modeling each detected violation as a persistent first-cl… ▽ More Existing compliance pipelines identify violations as transient query results, leaving no persistent representation of the violation itself or its lifecycle, evidence, and audit history. We address this limitation with the Violation Situation Pattern (VSP), a reusable ontology pattern that extends the Situation pattern of Gangemi and Mika by modeling each detected violation as a persistent first-class graph entity. Each violation is associated with a rule identifier, temporal validity interval, lifecycle state, and evidence links to the affected entities, while immutable lifecycle events provide a complete and queryable audit trail. We instantiate VSP on the schema of a deployed legal knowledge graph integrating corporate governance and contract data, populated with public contract corpora containing violations with established ground truth, and operationalize four deontic compliance rules. Rather than evaluating detection algorithms, we evaluate the pattern's robustness under rule evolution. Results show that repeated rule execution creates no duplicate violations, modifications to rule definitions preserve complete audit histories, and expanding rule scope maintains violation identity and lifecycle continuity. A case study using 73 regulatory enforcement decisions further demonstrates that compliance rules can evolve to capture additional confirmed violations without compromising the integrity of stored identities, evidence, or audit histories. These findings show that VSP enables compliance knowledge graphs to evolve without sacrificing traceability, explainability, or historical consistency. The complete implementation, queries, SHACL shapes, and evaluation dataset are publicly available to support the reproducibility of the reported results. △ Less

Submitted 3 August, 2026; v1 submitted 2 June, 2026; originally announced June 2026.

FATE-VLA:Failue-aware test generation for vision-language-action models

Authors: Arusa Kanwal, Pablo Valle, Shaukat Ali, Aitor Arrieta

Abstract: Vision-Language-Action (VLA) models are increasingly used as generalist robot policies, yet their evaluation still relies largely on static benchmarks that randomly sample task scenes. In high-dimensional embodied spaces, failures are sparse and clustered, so static benchmarking can underestimate robustness risks. We reframe VLA evaluation as an active failure-discovery problem and propose a failu… ▽ More Vision-Language-Action (VLA) models are increasingly used as generalist robot policies, yet their evaluation still relies largely on static benchmarks that randomly sample task scenes. In high-dimensional embodied spaces, failures are sparse and clustered, so static benchmarking can underestimate robustness risks. We reframe VLA evaluation as an active failure-discovery problem and propose a failure-aware test-generation approach that combines diversity-driven exploration with surrogate models learned from observed executions. The method steers testing toward high-risk yet diverse scene regions. Across four state-of-the-art VLA models, it uncovers substantially more failures (up to +29.7 % over selected baselines) while revealing more diverse failure modes. This mean that, for instance, in the case of GR00T-N1.6, success rate dropped from 64.4% to 34.7%. More broadly, our findings call for a shift in VLA evaluation: from passive measurement on fixed task suites to adaptive, failure-seeking test generation that exposes the structure of model weaknesses before deployment. △ Less

Submitted 1 June, 2026; originally announced June 2026.

Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization

Authors: Usman Nizamani, M. Shaheer Luqman, Fawad Javed Fateh, Ali Shah Ali, Murad Popattia, M. Zeeshan Zia, Quoc-Huy Tran

Abstract: Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward-driven and often exhibit behaviors that differ from humans, limiting interpretability and reliability. In this work, we introduce a novel human-like RL framework that predicts action sequences closely aligned with human behaviors while maximizing… ▽ More Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward-driven and often exhibit behaviors that differ from humans, limiting interpretability and reliability. In this work, we introduce a novel human-like RL framework that predicts action sequences closely aligned with human behaviors while maximizing rewards. Specifically, we encode human demonstrations into macro actions using a hierarchical macro action quantization approach (termed HiMAQ) consisting of two successive levels of vector quantization. The lower quantization level maps input actions to fine-grained subaction clusters, while the higher quantization level aggregates these subaction clusters into action clusters. Extensive evaluations on the D4RL benchmarks show that our hierarchical approach outperforms the non-hierarchical baseline (MAQ), achieving better human-likeness scores while maintaining comparable or better success rates than previous RL agents. The improvements generalize across integrations with various RL algorithms, namely IQL, SAC, and RLPD. △ Less

Submitted 29 May, 2026; originally announced May 2026.

What are They Thinking? Delineation, Probing, and Tracking of Concepts in LLMs

Authors: Mohamed Abdelwahab, Michelle Yu Collins, Sihan Chen, Yi Cheng Zhao, Zafarullah Mahmood, Jiading Zhu, Soliman Ali, Jonathan Rose

Abstract: As the influence of LLMs expands, it is imperative to gain insight into their decisions. One way to do that is to develop probes that detect the presence or absence of a broad set of high-level abstract concepts within the embeddings computed in an LLM - which is what we might say a model is ``thinking" about. Such probes should be low-cost and easily applicable to any LLM, so that monitoring for… ▽ More As the influence of LLMs expands, it is imperative to gain insight into their decisions. One way to do that is to develop probes that detect the presence or absence of a broad set of high-level abstract concepts within the embeddings computed in an LLM - which is what we might say a model is ``thinking" about. Such probes should be low-cost and easily applicable to any LLM, so that monitoring for many concepts is possible during normal operation. In this paper, we take the first steps towards developing the capability of creating many such probes by defining and executing examples of the key tasks needed: first, the careful delineation of a high-level abstract concept through the creation of a dataset with the concept both present and then absent. Then, the training and testing of a set of linear probes to detect the concept on any layer of an LLM, including an exploration of the complexity of the probe needed. Finally, we show that such probes can track concepts across larger contexts. This is done with four separate concepts and three different LLMs. When this process is scaled to many more concepts, it will create the ability to monitor new models. △ Less

Submitted 5 July, 2026; v1 submitted 6 April, 2026; originally announced May 2026.

Comments: Accepted to the 6th Workshop on Trustworthy Natural Language Processing (TrustNLP 2026)

Journal ref: Proceedings of the 6th Workshop on Trustworthy NLP (TrustNLP 2026)

IceCube Second Track Data Release IceTracks-DR2: Data from 2008-2022 for Neutrino Source Searches

Authors: R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, Y. Ashida, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel , et al. (390 additional authors not shown)

Abstract: We present IceCube's latest release of muon track data for neutrino point-source searches, extending the previously published 10-year dataset to cover 14 years of observations (April 6, 2008 - May 23, 2022). This release features an updated event selection and improved detector calibration for data recorded after June 1, 2010. The release also includes binned instrument response functions and effe… ▽ More We present IceCube's latest release of muon track data for neutrino point-source searches, extending the previously published 10-year dataset to cover 14 years of observations (April 6, 2008 - May 23, 2022). This release features an updated event selection and improved detector calibration for data recorded after June 1, 2010. The release also includes binned instrument response functions and effective areas, enabling the community to perform sensitive searches for steady and transient neutrino sources. We report on key science results obtained with this dataset using internal IceCube analysis tools and compare them to those derived from analyses based on the binned response functions included in this public release. To facilitate reproducible research, we provide benchmark results obtained using this data release and publicly available software. This release represents IceCube's most sensitive and comprehensive publicly available all-sky muon track dataset to date and should be preferred over previous releases. △ Less

Submitted 18 May, 2026; originally announced May 2026.

A Conditional U-Net Pipeline with Pre- and Post-Processing for Aerial RGB-to-Thermal Image Translation

Authors: Tseten Sherpa, Sikandar Ali, Shubham Parab, Haoyun Feng, Matthew Dennis, Keenan Gibbons, Verrah Otiende, Geoffrey H. Siwo

Abstract: Paired RGB-thermal data has shown significant utility across a range of applications, including image fusion, object tracking, and anomaly detection; however, its broader adoption is constrained by the limited availability of aligned RGB-thermal image pairs. RGB-to-thermal (and vice versa) image translation has emerged as a practical solution to this challenge. Prior approaches including condition… ▽ More Paired RGB-thermal data has shown significant utility across a range of applications, including image fusion, object tracking, and anomaly detection; however, its broader adoption is constrained by the limited availability of aligned RGB-thermal image pairs. RGB-to-thermal (and vice versa) image translation has emerged as a practical solution to this challenge. Prior approaches including conditional generative adversarial networks (cGANs) such as ThermalGAN and Scalable Interpolant Transformer (SiT)-based architectures such as ThermalGen have demonstrated strong potential for aerial-to-thermal image translation. In this work, we explore alternative architectures that prioritize simplicity while maintaining performance. Specifically, we propose a conditional U-Net that incorporates weather data at the bottleneck layer, complemented by targeted preprocessing and post-processing techniques applied within the Pix2Pix GAN architecture. We utilize a training set of 612 paired RGB and thermal images, and evaluate over 5-fold cross-validation, ultimately testing on a held-out test set. Our conditional U-Net model performed best, with a peak signal-to-noise ratio (PSNR) of 14.5485, structural similarity index measure (SSIM) of 0.8095, and learned perceptual image patch similarity (LPIPS) of 0.1666. These results outperformed the base ThermalGen model, which attained PSNR, SSIM, and LPIPS scores of 7.56, 0.2444, and 0.6317 respectively. We find that while saturation boost and contrast enhancement for preprocessing and Gaussian blur for post-processing provide observable improvements, the incorporation of conditioning data was most effective. Our findings cement the potential of integrating auxiliary metadata into thermal image generation, suggesting that such information can serve as a proxy for environmental conditions critical to accurate thermal reconstruction. △ Less

Submitted 17 May, 2026; originally announced May 2026.

Comments: 8 pages, 7 figures, NeurIPS 2026

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT

Authors: Marawan Elbatel, Mohamed Ghonim, Jiaji Mao, Zhuosheng Lin, Katharina Eckstein, Andrés Martínez Mora, Jonathan Deissler, Maximilian Rokuss, Constantin Ulrich, Zdravko Marinov, Wenhui Deng, Baoxun Li, Huijun Hu, Jun Shen, Mohanad Ghonim, Khadiga Omar Nassar, Mariam Elbakry, Menna Dyab, Amr Muhammad Abdo Salem, Nouran Elghitany, Noha Elghitany, Yi Qin, Xuanqi Huang, Haonan Wang, Shao-Woo Yen , et al. (40 additional authors not shown)

Abstract: Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings across Africa and Asia where contrast agents are frequently unavailable. Progress has been limited by the absence of annotated NCCT benchmarks. Here we describe the TriALS challenge for automated liver lesion segmentation un… ▽ More Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings across Africa and Asia where contrast agents are frequently unavailable. Progress has been limited by the absence of annotated NCCT benchmarks. Here we describe the TriALS challenge for automated liver lesion segmentation under contrast-limited conditions, supported by a multi-centre dataset of 150 cases with four-phase CT acquisitions (600 volumes) from Egyptian and Chinese institutions. Algorithms were evaluated on 70 cases from three institutions, including an independent external cohort. The top-performing method achieved a mean venous-phase Dice of 0.754, consistent with human-level performance, yet dropped to 0.57 on NCCT. On external validation, the leading method outperformed off-the-shelf models by up to 28% in Dice on NCCT. Algorithm performance was most strongly predicted by training data scale and pre-training strategy. A cross-year comparison exposed a persistent perceptual barrier on NCCT that scaling pre-training alone cannot overcome. Data, annotations, and code are available at https://github.com/xmed-lab/TriALS. △ Less

Submitted 15 May, 2026; originally announced May 2026.

Comments: TriALS challenge paper across MICCAI 2024 and 2025; data and code at https://github.com/xmed-lab/TriALS

WASIL: In-the-Wild Arabic Spoken Interactions with LLMs

Authors: Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung, Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury

Abstract: Large Language Models (LLMs) voice assistants are commonly built as cascaded Automatic Speech recognition (ASR) to LLM systems, where recognition errors can distort user intent. Dislikes may also arise from ambiguous, out-of-domain, or non-request turns, making it hard to isolate ASR effects. We release WASIL (it denotes connection or linking in Arabic): in-the-wild Arabic spoken interaction promp… ▽ More Large Language Models (LLMs) voice assistants are commonly built as cascaded Automatic Speech recognition (ASR) to LLM systems, where recognition errors can distort user intent. Dislikes may also arise from ambiguous, out-of-domain, or non-request turns, making it hard to isolate ASR effects. We release WASIL (it denotes connection or linking in Arabic): in-the-wild Arabic spoken interaction prompts with audio, ASR hypotheses, assistant responses, and explicit like/dislike feedback (8,529 turns; 14.2% dislikes), plus a 2,000-turn test set covering Modern Standard Arabic (MSA) and four major dialects with their labels. We provide low-cost gold transcripts via multi-ASR agreement-guided post-editing and annotate answerability (answerable, ambiguous/needs-clarification, unsupported, not-a-request/noise) to separate intrinsic unanswerability from ASR-induced degradation. Finally, we describe scalable reference-free evaluation of responses from ASR vs. gold transcripts using multi-judge LLM scoring. △ Less

Submitted 22 June, 2026; v1 submitted 9 May, 2026; originally announced May 2026.

Comments: Spoken Prompts, Multilingual LLMs, Speech-based Evaluation, Dialectal Speech, Low-resource Languages, Conversational AI, Speech-to-Text QA, Real-world Interaction, Spoken Language Understanding

MSC Class: 68T50 ACM Class: I.2.7