Robotics
From Model to Prototype: Design and Motor-Flap Propulsion Control of a Twin-Wing Metamorphic UAV
Anja Bosak, Dorian Erić, Ana Milas et al. (+1 more)
This paper presents the design and control strategy for MetaMorpher, a metamorphic Unmanned Aerial Vehicle (
UAV), capable of both spinning-wing hover and fixed flying-wing cruise flight. Since control of the cruise configuration is comparatively well established in the literature, this paper focuses on control of the MetaMorpher in hover mode, building on the flight dynamics model and conceptual design validated in previous work. The control
algorithm is implemented using one of two phase-synchronized strategies: motor propulsion, which pulses motor thrust in synchrony with the vehicle's rotation, or flap propulsion, a novel strategy that pulses the deflection of the wing-mounted elevons. We evaluate both strategies in simulation through different flight experiments. Simulation results confirm the mathematical model and show stable reference tracking, demonstrating flap propulsion as a lightweight, decoupled alternative for hover control. Experimental testing validated the vertical-dynamics propulsion model against the physical prototype, demonstrating very good steady-state agreement across different configurations.
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All authors Anja Bosak, Dorian Erić, Ana Milas, Stjepan Bogdan
Artificial Intelligence
Textual Environmental Context and Spatial Graphs for LLM-Based Regional SST Forecasting
Xiong Li, Xiaowei Zhou, Yanwei Yu et al. (+2 more)
Sea surface temperature (SST)
forecasting depends on local temporal persistence, regional spatial dependence, and environmental conditions that evolve with the forecast date. We study how these heterogeneous conditions can be presented to a large
language model (LLM) for regional multi-step
forecasting without serializing the full SST grid as text. We formulate
forecasting as conditional numerical generation: historical SST and anomaly sequences, date-aligned environmental records, and static ocean knowledge form a textual context, while regional spatial state is supplied through continuous graph-derived prefixes. A static graph encodes persistent geographic–climatological relations, and a dynamic graph encodes recent SST correlations and localized tropical-cyclone influence. Two graph neural networks produce a target-node representation that is mapped by a spatial-prefix fusion and injected into the LLM input. On SST
forecasting in the South China Sea, the complete configuration achieves the best MAE and $\Rtwo$ among the compared methods over ten forecast steps. Alongside the numerical forecast, a rule-based module matches predicted trends and environmental-factor directions with knowledge entries to return source-linked, post-hoc contextual explanations.
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All authors Xiong Li, Xiaowei Zhou, Yanwei Yu, Qian Cui, Junyu Dong
Computation and Language
Rethinking Faithfulness in LLMs: A Pairwise Context-Sensitive Perspective
Zizhuo Zhang, Xiong Peng, Jingwei Sun et al. (+3 more)
Large
language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is insufficient to answer the questions. Existing faithfulness evaluations typically assess each question-context instance in isolation; however, such instance-level evaluation fails to capture a fundamental requirement of faithful behavior: the ability to adapt model responses to changes in available contexts. In particular, a model should provide correct answers when sufficient evidence is present and abstain when it is not. In this work, we propose a Pairwise Faithfulness
Benchmark (PFaithBench) that evaluates whether a model can switch between answering and abstaining for the same question under supporting versus non-supporting contexts. Our evaluations across thirty-nine models with seven model families demonstrate that faithfulness fundamentally involves a trade-off between answering and abstaining, and that most current models exhibit a strong bias toward answering, with most faithfulness errors arising from over-answering, i.e., models tend to fabricate a response even when the provided context is insufficient. We further conduct a series of studies on faithfulness training under different data constructions. Our results show that training outcomes are highly sensitive to the specific composition of answering and abstaining data. Constructing answering and abstaining data from mismatched sources can cause models to rely on
dataset-specific shortcuts rather than actual context sufficiency. Moreover, increasing answer-supervised data improves answering performance but exacerbates over-answering, while increasing abstaining data reduces
hallucination but leads to over-abstention. The code and data are released at https://github.com/tmlr-group/PFaithBench.
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All authors Zizhuo Zhang, Xiong Peng, Jingwei Sun, Rong Yao, Borui Jiang, Bo Han
Robotics
Beyond Retargeting: Low-Latency and Robust Humanoid Whole-Body Teleoperation with Learned Atomic Motion Primitives
Xiayan Xu, Jiyu Yu, Xingzhou Chen et al. (+5 more)
Humanoid whole-body teleoperation translates human motion into stable
robot behavior in real time. Existing systems typically rely on online motion retargeting to bridge human–
robot morphological differences, but this process adds latency and can produce physically infeasible targets. Meanwhile, diverse, noisy, and partial human-motion observations often fall outside the training distribution, potentially causing unstable
robot behavior. We propose a retargeting-free policy that maps raw human motion directly to
robot joint commands in a single forward pass, eliminating online kinematic adaptation. To improve robustness, we learn a codebook of full-body motion primitives that projects out-of-distribution observations onto plausible motion prototypes and recovers full-body motion from partial inputs. Experiments on a Unitree~G1 in simulation and on hardware, using virtual reality, optical mocap, text-to-motion generation, and
monocular video inputs, show that our method outperforms baselines in latency and robustness.
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All authors Xiayan Xu, Jiyu Yu, Xingzhou Chen, Siyi Qian, Zongyu Ma, Lilu Liu, Ling Shi, Haodong Zhang
Networking
ONDT: A Modular and Virtualized O-RAN Digital Twin for xApp/rApp Closed-Loop Experimentatio
Miguel Catalan-Cid, Joan Josep Aleixendri, August Betzler et al. (+3 more)
Open
Radio Access Network (O-RAN) architectures extend programmability in next-generation cellular networks through Near-Real-Time and Non-Real-Time RAN Intelligent Controllers (RICs), enabling AI/ML-driven xApps and rApps to optimize network behavior dynamically. However, validating automated closed-loop control
algorithms in live environments presents significant operational risks and deployment costs. To address this challenge, this paper introduces the O-RAN Digital Twin (ONDT), a modular, virtualized, and cloud-native experimentation framework developed as a 6G enabler within the SNS SUNRISE-6G project. We present the ONDT architecture and its end-to-end experimentation workflows, showing how the framework automates O-RAN-compliant rApp/xApp onboarding, RAN scenario configuration and initialization, Key Performance Metric (KPM) collection, and closed-loop control validation. Experimental validation using Juniper RIC platforms and the Keysight RICtest emulation framework demonstrates ONDT ability to provide an isolated and reproducible environment for KPM monitoring,
cell ON/OFF control, automated
dataset generation, and rApp/xApp validation.
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All authors Miguel Catalan-Cid, Joan Josep Aleixendri, August Betzler, George Oikonomou, Lydia Ntzogani, David Reiss
Computation and Language
Visual Abstention in Unified Multimodal Models
Chufan Shi, Cheng Yang, Tiannuo Yang et al. (+4 more)
Unified
multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task. We formalize visual abstention: when a requested visual transformation is impossible under the task's rules, the model should recognize that no valid solution exists, state this, and decline to generate. We introduce Draw-or-Decline (DoD), a
benchmark of 1,050 feasible-infeasible request pairs across 7 task categories that jointly measures editing success and the refusal of infeasible requests. Evaluating 8 UMMs, we find that editing ability and abstention are distinct capabilities: even the strongest editor, at 68.4% editing accuracy, refuses only 0.4% of infeasible requests under ordinary instructions. Their
reasoning shows why: the models rarely notice the conflict, and instead plan the edit as if the request were possible, often describing objects that are not in the image, or quietly change the request into one they can complete. Explicitly prompting these UMMs to report infeasibility increases textual refusals but reduces editing accuracy. We propose VisTA (Visual Transformation and Abstention), a training method that pairs feasible and infeasible examples so that a model judges feasibility before deciding whether to generate. We train VisTA-BAGEL to perform feasible edits and decline infeasible requests. Without any reminder, it refuses 93.0% of infeasible requests, up from 0.4% for the strongest editor, while falsely refusing only 0.8% of feasible ones. Unlike a reminder, this does not cost editing accuracy: VisTA-BAGEL completes 74.3% of feasible edits, more than any of the 8 evaluated UMMs.
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All authors Chufan Shi, Cheng Yang, Tiannuo Yang, Isadora White, Yiwei Chen, Taylor Berg-Kirkpatrick, Xuezhe Ma
Artificial Intelligence
ShanLiangRen: A Nutrition Agent for Personalized Daily Meal Planning
Miao Xie, Xiao Zhang, Yuan Wang et al. (+2 more)
Dietary nutrition planning plays an important role in chronic disease management and maintaining a healthy body. In applications, it must simultaneously satisfy personalized constraints and reasonable multidimensional nutritional goals. These two aspects often conflict, and user constraints evolve with feedback, resulting in a substantial gap between generic guidelines and executable plans. To bridge this gap, we first propose the personalized fully quantified multiobjective dietary planning problem (MDP). To tackle MDP, we develop a nutrition
agent, ShanLiangRen. The system first transforms dietary specifications, nutrient data, user attributes and natural
language requirements into an individualized constrained planning instance. It then employs an exact
retrieval-augmented generation method to shrink the feasible candidate set from a large scale ingredient and recipe space. Finally, it adopts a refinement guided by Pareto principles, where an LLM iteratively revises candidate plans under deterministic nutrition computation and feedback from constraint verification. The system outputs fully quantified meal plans with explicit ingredients and portion sizes, together with reports on nutrition compliance that show constraint satisfaction and nutrient interval attainment. We have released the system online as a WeChat Program, ShanLiangRen. A demo
video is available at https://www.youtube.com/watch?v=652OtY5VlGA.
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All authors Miao Xie, Xiao Zhang, Yuan Wang, Ruixin Zhu, Chunli Lv
Machine Learning
Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools
Jeonghwa Lim, Minje Park, Yeongyeon Na et al. (+6 more)
Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinically interpretable measurements. Deep learning has advanced this task but remains dependent on costly expert annotations. Label-efficient strategies such as self-supervised pretraining and semi-supervised learning are expected to ease this burden, yet it remains unclear whether they yield reliable delineation and whether the deep models they produce outperform the delineation tools used in practice. We address this in two stages. First, comparing self-supervised objectives with supervised or semi-supervised fine-tuning across one internal and four external
datasets, we find that pretraining helps but the objective matters, and that the value of semi-supervised fine-tuning depends on the pretraining objective. Second, we
benchmark the selected deep learning model against widely used open-source (NeuroKit2, Prominence, ECGdeli) and commercial (CalECG) tools using three complementary metrics. The model ranks best on every metric and
dataset, outperforming the strongest tool by a clear margin on the rhythm-diverse set (mIoU 71.3 vs. 54.8%; averaged point-wise sensitivity 92.6 vs. 76.4%), and degrades the least from sinus to arrhythmia. A rhythm-stratified and point-wise analysis further characterizes the distinctive behavior of each tool, yielding practical guidance for tool selection. These results provide systematic, multi-
dataset evidence that self-supervised pretraining is effective for ECG delineation and enables a label-efficiently trained deep learning model to outperform widely used delineation tools by leveraging abundant unlabeled data. This supports adopting such models in diverse, real-world clinical settings.
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All authors Jeonghwa Lim, Minje Park, Yeongyeon Na, Yujin Eom, Soyeon Lim, Young Ho Lee, Yu Jeong Kim, Sunghoon Joo, Ki Hong Lee
Numerical Analysis
Learned Adaptive Multiresolution Diffusion Imaging
Christian Tantardini, Stig Rune Jensen, Roberto Di Remigio Eikås et al. (+1 more)
Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are usually governed by fixed local criteria. We introduce Learned Adaptive Multiresolution
Diffusion Imaging (Learned AMDI), which preserves the AMDI fixed-tree propagator and hierarchy constraints while replacing the post-propagation selector with a shared local policy trained by proximal policy optimization. Regression tests reproduce deterministic AMDI trajectories to machine precision when identical trees are used. In the Haar implementation studied here, the deterministic one-step selector accepts no refinements in 54 decisions. Across nine held-out cases, Learned AMDI executes 393 refinements and reduces the mean terminal reference discrepancy from $0.17496$ to $0.13657$, while occupancy rises from $0.13737$ to $0.26660$. Step-resolved diagnostics reveal occasional small adaptation-energy increases; fixed-tree energy stability therefore does not guarantee monotonicity of the learned outer iteration. At comparable occupancy, a validation-tuned observed-detail threshold reaches a discrepancy of $0.13792$ with slightly better RMSE and SSIM, placing both methods on essentially the same accuracy–occupancy tradeoff. A decision-1-only control reaches $0.13742$, indicating that most of the improvement on this static
benchmark arises from the initial allocation. The shared actor transfers without retraining to $64\times64$ and $128\times128$ images, improving reference discrepancy, RMSE, and SSIM relative to deterministic AMDI, while the frozen threshold rule remains competitive. Learned AMDI thus provides a hierarchy-constrained, resolution-transferable mechanism for adaptive allocation and clarifies the contribution of sequential decisions.
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All authors Christian Tantardini, Stig Rune Jensen, Roberto Di Remigio Eikås, Joakim Henrik Beck
Computer Vision
Revar3r: gauge-aware perturbation uncertainty for feed-forward 3d reconstruction
Sammam Mahdi, Fariha Binta Salim, Rakin Bin Rabbani et al. (+1 more)
A correctly reconstructed distant point appears uncertain even when a frozen 3D model processes equivalent inputs because its output frame rotates fractionally. This exposes a weakness of trainingfree perturbation uncertainty: when outputs contain an unobserved
symmetry, run-to-run variation potentially reflects
symmetry rather than error. Existing alternatives have trade-offs: built-in confidence is outperformed in most evaluated conditions, while trained evidential heads require modelspecific supervision. For point maps, this research derives a closed-form, error-independent variance term that grows with scene extent and potentially overwhelms the desired signal. Simulation reproduces the effect; all 30 real VGGT view-sets tested exhibit its predicted $\|x_p\|^2$ signature. ReVar3R robustly registers predictions to a common similarity frame before computing per-point variance, without retraining or modifying the frozen model. Optional calibration and fusion use a held-out split. Across VGGT, {\pi}3, and MASt3R on six
datasets, the same
estimator on every backbone lowers AUSE below built-in confidence in 15 of 18 conditions. The staged evaluation yields 11 of 18 wins for the label-free core, 12/18 for label-free equal-weight fusion, 14/18 with held-out weights, and 15/18 when the built-in signal is included. Against a trained evidential head, the result is a trade-off: the head calibrates magnitude better and leads in its training domain, whereas ReVar3R transfers across backbones without adaptation. Its ranking improves point filtering, but it does not detect stable systematic bias, aid novel-view synthesis, or transfer calibration across domains.
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All authors Sammam Mahdi, Fariha Binta Salim, Rakin Bin Rabbani, Aniqua Nusrat Zereen
Robotics
CUSP: CUSUM-Governed Survival Hazard Alarms at the Perception Onset for Off-Road Navigation
Inuk Kang and Seung-Woo Seo
Off-road
navigation exposes a
robot to potentially hazardous terrain en route. Although learning-based
navigation uses safety supervision to choose which path to drive, it provides no runtime alarm when the
robot following that path is heading into danger. Such an alarm must be learned from field logs, where human intervention preempts the failure and the failure itself is therefore never observed. The human judges driving unsafe early but typically intervenes only once failure is clearly near, so the intervention marks that judgment late. That earlier judgment is what a runtime alarm must detect, yet no prior intervention-supervised method has targeted it. To address this problem, we introduce CUSP (CUSUM-governed Survival model of the Perception onset), a model-agnostic runtime hazard alarm that learns this moment from intervention-terminated logs. "Cusp" is a word for the point at which one state is about to turn into another, and the moment we target is exactly such a cusp: the point at which safe driving turns unsafe in a human's judgment. We call this point the perception onset and annotate it separately from the intervention. A visual hazard head is trained on the annotated onset with a discrete-time survival objective so that driving with and without an onset both supervise the head, and a CUSUM accumulates the predicted onset risk into alarms. We evaluate CUSP at five unseen sites, two autonomous and three teleoperated, with 142 events and every method tuned to the same rate of ten false alarms per hour. CUSP detected 85 events compared to 26 for the best of nine adapted baselines, and the margin comes from hazards to which every signal in the
navigation model is blind.
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Artificial Intelligence
Self-Referenced Social Preferences: Cooperation without Observing Others Rewards
Mohamed Ayman Mohamed, Harshil Kotamreddy and Marcos Menon Jose
Social preferences can promote cooperation in multi-
agent reinforcement learning, but existing approaches often require
agents to observe the rewards of their peers. In many real-world interactions, however, an
agent can, as humans do, observe others' behavior and outcomes without access to their private reward signals. We introduce self-referenced social preferences, in which each
agent learns a model of its own reward, applies it to other
agents' observed transitions to assess their outcomes from its own perspective, and feeds these self-referenced assessments into standard social preferences. We study two ways to incorporate these assessments: modifying the learning reward, or using them to weight policy updates. We evaluate the approach on three sequential social dilemmas, Escape Room, Clean Up, and Commons Harvest, which require volunteering, public-good contribution, and resource restraint, respectively. Across all three environments,
agents learn cooperative behavior without observing others' rewards, including in settings where independent learners fail to cooperate, and frequently achieve more equitable divisions of jointly produced returns than
agents with access to true rewards. The effective integration point depends on the social preference: inequity aversion works best in the reward together with a value look-ahead, whereas a purely benevolent preference benefits from policy-update weighting. Under partial observability, the policy-update approach continues to support cooperation. These results show that explicit access to other
agents' reward signals is not necessary for learning cooperative behavior: social preferences can instead be grounded in self-referenced assessments of others' outcomes derived from their observed behavior.
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Cryptography and Security
Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception
Yutong Liu, Chenyi Wang, Ming F. Li et al. (+1 more)
Collaborative perception (CP) enables connected vehicles to see beyond their own sensors but makes them dependent on messages they cannot independently verify. A compromised collaborator can surgically conceal a single safety-critical object or inject a non-existing one while correctly reporting many others. Existing
Bayesian trust mechanisms pool agreement across objects, which, while effective against blatant untargeted attacks, either incurs high false-positive rates (FPR), or allows unrelated correct reports to dilute persistent attack evidence for stealthy single-object attackers. To address this problem, we propose SABER, a selective two-tier
Bayesian trust
estimator. The first tier maintains broad
agent and object trust, preserving the ability to downweight benign but low-quality contributors. Cumulative-sum screening selects
agent–object pairs with persistent omissions or unsupported reports for focused
Bayesian assessment. The second tier checks these pairs against other
agents' evidence and maintains a separate, reference-weighted Beta state for each. The lowest pair score constrains
agent trust, preventing unrelated reports from diluting a targeted attack. We establish sufficient conditions for stronger attacker-side trust reductions with bounded additional benign false alarms at fixed thresholds. Compared with state-of-the-art CP defenses, SABER improves attack detection while reducing benign FPRs. On OPV2V, SABER improves defense ROC-AUC over MATE by up to 0.427 in late fusion and 0.337 in intermediate fusion. Against advanced intermediate-fusion data fabrication attacks, it increases detection rates over ROBOSAC and LUCIA by up to 96.40 and 67.07 percentage points, respectively, while reducing FPRs.
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All authors Yutong Liu, Chenyi Wang, Ming F. Li, Qingzhao Zhang
Machine Learning
On-Policy Distillation with Negative-Policy Rollouts
Jaehui Hwang, Dongyoon Han, Sangdoo Yun et al. (+1 more)
On-policy
distillation (OPD) has been widely studied as a post-training method in which a student model obtains
token-level supervision from a stronger teacher on its own rollouts. Recent studies have improved OPD through alternative
distillation reward formulations and teacher configurations, while the objective of
distillation remains centered on mimicking the teacher. However, when a stronger teacher has limited distributional overlap with the student, such positive guidance can provide insufficient learning signals. In this work, we introduce Negative-Policy OPD (NP-OPD), which complements teacher supervision with rollouts from a lower-performing, lower-capability negative policy that serves as a negative reference for the student. Rather than modifying the
distillation reward formulation, NP-OPD introduces the negative policy at the rollout stage, continuously supplying
tokens preferred by the negative policy over the teacher so that they remain exposed to teacher supervision throughout training. This provides an explicit negative signal through negative-policy rollouts while preserving the positive teacher supervision used in OPD. Through extensive experiments, we show that NP-OPD improves OPD across model scales, generation modes,
reasoning domains, and different OPD variants. Furthermore, our analyses show that NP-OPD effectively suppresses
tokens preferred by the negative policy over the teacher and moves the student away from the negative policy. These results support our design of introducing negative signals through negative-policy rollouts and provide new insight into the role of the rollout policy in OPD. Code will be available at https://github.com/naver-ai/np-opd.
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All authors Jaehui Hwang, Dongyoon Han, Sangdoo Yun, Byeongho Heo
Cryptography and Security
Preparing an AI-Augmented SIEM for the EU Cyber Resilience Act: A Practitioner Case Study
Georgios Koutidis, Nikolaos Kekatos, Marina Korgiala-Karyda et al. (+2 more)
The EU Cyber Resilience Act (CRA), Regulation (EU) 2024/2847, makes product cybersecurity a lifecycle obligation for products with digital elements on the EU
market: risk assessment, vulnerability handling, conformity documentation, and Article 14 incident- and vulnerability-reporting readiness must be operational before
market placement. Small and medium-sized enterprises that build security products are doubly exposed, since their products are in scope while their customers expect them to be exemplary. This case study documents a CRA preparedness pilot for one such product, SEUXDR, an AI-augmented security monitoring product with a large-
language-model active-response component, on the open-source CYBERFORT platform. We contribute a reproducible six-step recipe (Scope and Classify, Asset Registration, Produce Evidence, Map to CRA, Gap and Actions, Audit Pack), two end-to-end traceability threads, and a pilot snapshot tracing product risks through baseline and AI-specific controls and policies to CRA objectives. It offers practitioners a replicable starting point for translating CRA legal text into operational preparedness for incident response, vulnerability reporting, and conformity assessment.
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All authors Georgios Koutidis, Nikolaos Kekatos, Marina Korgiala-Karyda, Alexios Lekidis, Tom Nianios
Computers and Society
Online Sign Language Interpretation System
Christophe Ponsard and Jean-François Molderez
The Walloon Region commissioned this study to improve deaf people's access to public services for the new millennium. It focuses on sign
language users, about 25,000 adults in the French-speaking Community, for whom written French is close to a foreign
language. The core problem is a severe shortage of interpreters: only about twenty work in French-speaking Belgium, so appointments take weeks to arrange and emergencies cannot be covered. The study assessed three options. The recommended one, remote
video interpretation, could be deployed at the time of writing: a professional interpreter joins the deaf person and the employee by videoconference, saving travel time and enabling an emergency service. Similar services already ran in Sweden, Finland, the Netherlands and France. Prototype tests in May 2003 were a clear success and showed that interpretation needs at least CIF
video above 256 kbps, ideally 384 kbps. The system suits simple administrative tasks better than emotional, medical or legal situations. The second option, fully automated interpretation, was not yet feasible, although partial systems using
speech recognition and signing avatars could already serve kiosks, websites and routine procedures. The third option looks further ahead: UMTS and WiFi could eventually support mobile signed
video calls and remote interpretation, provided quality of service is guaranteed. The main recommendation is a first pilot in real conditions before any wide rollout, supported by high-bandwidth links as part of e-government modernisation. The system is meant to relieve interpreters, not replace them, so their training, status and numbers should improve. The authors also call for digital training for deaf people, awareness among public-service staff, more sign
language content online, and a multidisciplinary avatar project with Belgian, French and European partners.
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Information Theory
LEO Signals-of-Opportunity for Navigation in Urban Environments: A GLRT-Assisted Off-Grid SBL Approach
Maedeh Sotoodenia, Mohammad Hossein Kahaei and Alireza Nezamalhosseini
The vulnerability and unavailability of global
navigation satellite system (GNSS) signals in dense urban environments have motivated the use of low earth orbit (LEO) signals of opportunity (SoOP) for positioning,
navigation, and timing (PNT). However, multipath and non-ideal noise can significantly degrade delay–Doppler estimation and, consequently,
navigation accuracy. This paper proposes a hybrid generalized likelihood ratio test (GLRT) and off-grid sparse
Bayesian learning (SBL) framework for LEO-SoOP
navigation under multipath and non-Gaussian, temporally correlated noise. First, a GLRT-based framework is developed, demonstrating reliable detection under ideal Gaussian noise but degraded estimation under heavy-tailed or colored noise and unresolved multipath. Next, an off-grid SBL framework is introduced for high-resolution separation and estimation of line-of-sight (LOS) delay–Doppler parameters. The estimated LOS parameters are subsequently tracked and incorporated into an extended Kalman filter (EKF) to obtain the
navigation solution. Simulation results demonstrate that, in urban environments, the proposed method reduces delay RMSE by up to 77% and position RMSE by 88.5% compared with GLRT-based estimation. These results demonstrate the potential of the proposed framework for robust and accurate LEO-SoOP
navigation.
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Cryptography and Security
Plug-and-Play Quantum-Resistant BLE Pairing for Medical Implants via NFC Out-of-Band
Muhammad Asim Javaid, Muhammad Shafay Tanveer Hassan, Faaiz Umer et al. (+5 more)
Bluetooth Low Energy (BLE) pairing establishes the cryptographic foundation for secure device communication. However, mainstream man-in-the-middle (MITM)-resistant pairing methods, such as Numeric Comparison and Passkey Entry, require user interfaces that implantable medical devices (IMDs) inherently lack, making Near-Field Communication (NFC)-assisted Out-of-Band (OOB) pairing an attractive alternative. Existing NFC-assisted OOB schemes authenticate a classical BLE key exchange that remains vulnerable to
quantum attacks, while the NFC channel itself is susceptible to eavesdropping and active injection under stronger threat models. To address these limitations, we propose a lightweight, plug-and-play NFC-based OOB pairing protocol that performs a post-
quantum key encapsulation mechanism (KEM) entirely over the NFC channel, ensuring that no shared secret is transmitted over NFC while reducing long-range
radio-frequency (RF) exposure during pairing. The proposed protocol requires no modifications to the BLE stack and inherently resists RF battery-depletion attacks. Evaluation on an IMD-class proof-of-concept testbed demonstrates that post-
quantum OOB pairing is practical on resource-constrained devices, reducing projected battery life by less than 0.57% for Kyber-1024 and 0.56% for FireSABER under an operational usage model relative to the MITM-vulnerable Just Works method.
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All authors Muhammad Asim Javaid, Muhammad Shafay Tanveer Hassan, Faaiz Umer, Sotirios Panagiotou, Christos Strydis, Naveed Anwar Bhatti, Muhammad Adeel Pasha, Muhammad Ali Siddiqi
Computer Vision
CueRator: Agentic Search for Symbolic Rules to Adapt Frozen Multimodal Encoders
Sunchan Park, Beomkwon Cho and Kyeongbo Kong
Large
language model
agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework for policy-aware decision-rule discovery, which adapts frozen contrastive
multimodal encoders by searching for the decision rule that converts their cross-modal similarities into predictions. We validate it on open-vocabulary audio-visual event perception, where existing methods involve a trade-off between adaptivity and generalization to unseen categories: trained modules adapt at the cost of generalization, and fixed rules the reverse. The framework pairs a symbolic formulation for generalization with a lightweight policy that predicts its parameters per
video for adaptivity. A report-guided multi-
agent loop discovers the formulation offline, evaluating each candidate on its expressive ceiling and on whether a trained policy can realize it. On OV-AVEBench, CueRator raises the total average from 57.8 to 60.2 and unseen-category performance from 55.8 to 59.9 over the best existing method, reducing the seen-unseen gap from 7.1 to 1.2. Ablations attribute the gains to both the formulation and the policy and show that both feedback signals are necessary for effective search. CueRator also improves over the respective baselines on two further audio-visual event perception tasks, and the discovered rule remains competitive across encoders with only the policy retrained. Code is available at https://github.com/cvsp-lab/cuerator.
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Mathematical Software · Cross-listed from math.QA
quantum-group: Exact symbolic verification of R-matrix and Yang–Baxter identities for $U_q(\mathfrak{sl}_2)$ and $GL_q(2|1)$
Taha Berk Terekli
Explicit R-
matrices of
quantum groups are routinely transcribed from the literature, yet whether they satisfy an identity depends on basis order, tensor placement, the coproduct and, for superalgebras, the Koszul sign rule.
quantum-group is a Python package that builds such
matrices exactly with SymPy and returns every identity as an exact residual
matrix, whose nonzero entries locate the convention that disagrees. It covers U_q(sl_2) modules, tensor products and the fundamental R-
matrix, and the graded Yang–Baxter equation for GL_q(2|1). Applied to the thesis it was written for, it exposed an R-
matrix that satisfies the Yang–Baxter equation but intertwines the opposite coproduct.
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Cryptography and Security
The Amplifier Effect: Human-Factor Risks of AI-Suggested Correlation and Auto-Propagation in Multi-Framework GRC Self-Assessment
Nikolaos Kekatos, Michael Ioannou, Marina Korgiala-Karyda et al. (+2 more)
Multi-framework Governance, Risk and Compliance (GRC) platforms increasingly automate the link between an organisation's self-assessment answer and the compliance obligations that answer is said to satisfy. Cross-framework control mapping, AI-suggested question correlation, and automatic propagation of answers and evidence across correlated questions all serve the legitimate efficiency goal of reducing duplicate work for small and medium-sized enterprises under the EU Cyber Resilience Act, NIS2 and GDPR. The same mechanisms, however, amplify the consequences of any human-factor bias in a single answer: one optimistically-graded control, one rubber-stamped attestation, or one AI-drafted answer can be silently replicated as evidence of compliance with many obligations across multiple frameworks. We call this the amplifier effect: a platform-design property (coarse-grained attestation and un-gated propagation) rather than a failing of individual users. Using two EU-funded SME-facing GRC platforms, CYBERFORT and CYBER-BRIDGE, as examples, we (i) describe the amplification mechanism in concrete data-model terms, (ii) propose a six-dimension scoring framework for evaluating any GRC tool's exposure to the effect, (iii) instantiate the framework on a thirteen-tool comparison covering enterprise IRM, mid-
market platforms, compliance-automation tools, and the two EU SME projects, and (iv) outline a measurement protocol that a consortium with access to production self-assessment data can run. The thirteen-tool comparison is a structured design assessment, not an empirical measurement of user behaviour. The EU SME platforms score lowest on the amplifier dimensions because their burden-reduction design deliberately trades sign-off granularity for throughput; we report this as a design trade-off, not a verdict on the platforms. Our contribution is the framing and the measurement protocol.
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All authors Nikolaos Kekatos, Michael Ioannou, Marina Korgiala-Karyda, Alexios Lekidis, Tom Nianios
Computation and Language
ReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents
Yupeng Su, Jiayi Tian, Zheng Zhang et al. (+1 more)
Long-horizon LLM
agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with steps until the sessions exceed the context window. Existing methods manage the context through context requirement prediction, relying on additional model calls, heuristic rules, or trained policies. However, these predictive approaches introduce runtime overhead, invalidate prefix caches, and permanently discard content with no guarantee of recovery. To overcome these limitations, we introduce ReFold: a training-free rendering layer that preserves the underlying interaction history while compressing only the model's rendered context. It removes two kinds of inter-turn redundancy without an auxiliary predictor: content an earlier turn already displayed, replaced by a stub, and turns the
agent itself reports finished, folded into a one-line note. Both operators use chunked rendering, rewriting the cached prefix once every few steps rather than at every step. Every removal is strictly reversible, a wrong removal costs one restore from the history rather than permanent content loss. Because it operates at the rendering layer, ReFold is plug-and-play across standard ReAct-style harnesses. Evaluations across five long-horizon
benchmarks and two frontier LLMs demonstrate that ReFold reduces
token consumption by up to 2.5x and halves the KV-cache
memory per session without degrading task success rates. Under capped context budgets, it avoids up to 92% of forced compactions. Under concurrent serving workloads, it reduces request queuing delays by up to 100%, accelerating inference by up to 1.7x, while cutting inference costs by up to 3.4x.
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All authors Yupeng Su, Jiayi Tian, Zheng Zhang, Souvik Kundu
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
A self-learning scientific agent for X-ray diffraction
Bin Cao, Huichi Zhou, Runyu Yang et al. (+7 more)
A central challenge for scientific
agents is to turn analytical experience into reusable expertise grounded in physical evidence. Here we introduce Gan Jiang, a self-learning
agent for powder X-ray diffraction built on a diffraction-analysis ecosystem we developed: XMatcher, XQueryer, XDecomposer and WPEM. Together, these engines span phase identification, multiphase decomposition and physics-constrained whole-pattern modelling. Gan Jiang converts analytical experience into executable skills by diagnosing failures, revising skill instructions and code, and validating revisions before reuse, without retraining the
language model or changing the underlying physical models. Skills selected using development data and frozen before held-out evaluation achieve higher refinement scores than the original expert-designed skills across FullProf, GSAS-II and PyWPEM. The
agent resolves strongly overlapping reflections, quantifies a five-phase ancient Egyptian cosmetic, tracks
lattice evolution in an operating battery and compares atomic configurations in a disordered
oxide catalyst. On DeltaXRDbench, it leads the evaluated methods in single- and multiphase identification across simulated and experimental data. Without supplied composition, single-phase top-1 accuracies reach 96.30%, 81.78% and 40.83% on MP500, RRUFF and opXRD, respectively, compared with 58.00%, 58.47% and 26.45% for the strongest comparator. These results demonstrate how an integrated scientific tool ecosystem can support
agents that extract structural knowledge from measurements while accumulating validated analytical expertise that transfers to new samples.
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All authors Bin Cao, Huichi Zhou, Runyu Yang, Jingsong Li, Shuchen Sun, Yan Song, Hanyu Gao, Zhongwei Yu, Tong-Yi Zhang, Jun Wang
Artificial Intelligence
WorkflowOps: Learning Agent Collaboration Priors for Multi-Agent Workflow Orchestration
Qi Cheng, Shengyu Chen, Wei Cheng et al. (+4 more)
Multi-
agent systems are increasingly deployed for complex knowledge work, yet their orchestration layers remain largely memoryless: each new task is decomposed, assigned, and executed from scratch with no benefit from prior successful executions. We present WorkflowOps, a multi-
agent workflow orchestration framework that learns
agent collaboration priors from historical workflows and expands its
agent pool on demand to cover new capability requirements. Our approach introduces three coupled mechanisms. First, a transition probability
matrix captures pairwise
agent collaboration frequencies from past workflows and applies them as soft guidance during DAG workflow construction through intra-layer ordering optimization, probability-thresholded edge suggestion, and transitive reduction for parallelism maximization. Second, a sufficiency-driven
agent creation loop detects capability gaps via semantic matching scores, generates specialized
agents through an LLM, and simultaneously injects them into the collaboration
matrix, so that newly created
agents are immediately usable with predicted collaboration priors. Third, a layered semantic matching strategy uses pre-trained sentence
embeddings for fast, deterministic capability matching as a first pass, invoking LLM verification only for low-confidence cases, thereby reducing LLM routing calls by over 80% compared to pure-LLM approaches. Experiments on mixed code, math, and question-answering suites show that WorkflowOps improves end-to-end pass rates over recent workflow-construction baselines, with the largest gains on structured, decomposable tasks where past
agent handoff patterns transfer.
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All authors Qi Cheng, Shengyu Chen, Wei Cheng, Zhengzhang Chen, Xiaowei Jia, Haoyu Wang, Haifeng Chen
Machine Learning
Tram-FL: Reducing Communication and Computation Costs through Sequential Model Circulation in Decentralized Federated Learning
Kota Maejima, Takayuki Nishio, Asato Yamazaki et al. (+1 more)
Conventional decentralized
federated learning (DFL) often focuses on clients, with each client maintaining a model copy, performing updates individually, and undertaking model exchange and integration. While fully leveraging computational resources can shorten training times, it can also lead to significant computational and communication waste. This is especially pronounced with non-independent and identically distributed (non-IID) data, where achieving high model accuracy demands extra resources. This research shifts focus to the model itself, aiming to realize DFL with minimal computation and communication costs. To this end, we propose Tram-FL (Traveling Model Training Mechanism for Decentralized
Federated Learning), a mechanism designed to efficiently address these challenges. It sequentially trains a single model by circulating it among nodes. We address the training scheduling problem in model circulation-based training, specifically determining which nodes should update the model and the number of updates to perform. This is approached by considering the model's circulation route and update iteration allocation, for which we propose simple yet effective methods. Additionally, with quantized momentum, Tram-FL achieves high accuracy with fewer model circulations while controlling communication load per transmission. Experimental results show that the proposed
algorithm, even with non-IID data, converges to a global model with reduced communication and computation.
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All authors Kota Maejima, Takayuki Nishio, Asato Yamazaki, Yuko Hara-Azumi
Game Theory
On the Complexity of Mixed Equilibria in First-Price Auctions with Correlated Priors
Mark Chen, Xi Chen, Hao Cui et al. (+2 more)
We show that computing an approximate mixed Bayes-Nash
equilibrium in a discrete first-price auction with correlated priors is PPAD-complete. The key intermediate step in our reduction is the PPAD-completeness of computing an approximate Nash
equilibrium in a new normal-form game, the hypergraph discrete first-price auction, which may be of independent interest.
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All authors Mark Chen, Xi Chen, Hao Cui, William Pires, Jonah Stockwell
Machine Learning
A Decision-Focused Neural Optimization Framework for Personalized Route Reproduction from Vehicle Trajectories
Gyeongjun Kim, Yeseul Kang and Keemin Sohn
This study formulates individual route reproduction as a shortest-path problem over learned driver-specific latent link costs. The central idea is that, once such latent costs are inferred from contextual information, observed routes can be reproduced without enumerating alternative route sets. We propose a neural pipeline that includes a perception model that embeds context
covariates, which comprises individual characteristics, trip-specific attributes, and network-level traffic states, into the personalized link costs. A constrained optimization (CO) layer, which determines the shortest path (SP) based on these estimated costs, follows the perception encoder. To enable end-to-end training, we employ decision-focused learning to align the predicted shortest paths with observed routes. The implicit maximum likelihood estimation (iMLE) provides an approximate gradient of the loss function that contains the non-differentiable CO layer. Furthermore, a regularization term anchors the latent cost distribution to the empirical scale of observed link travel times, mitigating the scale ambiguity inherent in shortest-path supervision. Empirical evaluations demonstrate that the proposed framework outperforms baseline route choice models in path reproduction. The learned latent costs, interpreted as proxies for perceived travel costs, provide plausible explanations for heterogeneous route choices.
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Cryptography and Security
Lifecycle-Based Design and Evaluation of Real-Time Backup Triggers for Ransomware Damage Mitigation
Kosuke Higuchi and Ryotaro Kobayashi
Ransomware continues to encrypt files during the interval between attack onset and detection. Real-time backups can mitigate this damage by preserving files before they are modified. The previously proposed Real-Time Open-File Backup System (ROFBS) triggers backups primarily on file-open events. However, the file lifecycle offers several candidate trigger points, including open, read, write, and rename operations. Triggering backups too early may create unnecessary backup files, whereas triggering them too late may allow ransomware writes to race with backup creation and prevent the preservation of clean file contents. Consequently, it remains unclear which trigger timing best balances recoverability and the number of backups created. In this study, we design and evaluate real-time backup triggers for mitigating ransomware damage from a file-lifecycle perspective. Specifically, we compare four strategies: Open-time backup, Read-time backup, Write-time backup, and Rename-time backup. We implement these strategies in an ROFBS-style prototype on XFS and evaluate them using five ransomware samples: Conti, Sodinokibi, AvosLocker, REvil, and HelloKitty. Our results clarify how trigger timing affects both damage mitigation and the number of backups created, providing design guidance for selecting effective triggers in real-time backup systems against ransomware.
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Machine Learning
Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes
Avichal Sahai, Nishant Raj and Animesh Srivastava
Ternary
language models such as BitNet b1.58, Falcon-E and BitCPM are fine-tuned with higher-precision latent weights and deployed as ternary codes produced by an export step that, in the labs' documented pipelines, first casts the latents to bf16. We audit those pipelines across three labs. In released checkpoints, fp32 quantization of the shipped latents disagrees with the deployed codes on 0.83-1.77% of codes in Falcon-E and BitCPM and on 1.530% in BitNet 2B-4T; for Falcon-E and BitCPM most disagreements are products that bf16 rounding lands exactly on the threshold, which ties-to-even maps to zero, and the unmodified onebitllms exporter reproduces all four Falcon-E releases byte for byte. At fine-tuned endpoints, with learning rates selected to match a nominal learning-rate-to-bf16-ULP ratio, the documented export lowers greedy GSM8K strict accuracy from 58.79% to 0.78% for Falcon-E-1B-Base and from 36.13% to 0.39% for BitCPM-CANN-0.5B, and a bf16 save and reload lowers BitNet 2B-4T's strict accuracy by 27.54 points while its last-number accuracy rises. Two compatibility remedies, writing the training quantizer's codes directly or adjusting the bf16 inputs until the unchanged tools emit them, each met a 4-point strict-accuracy non-inferiority criterion against online evaluation in all three models. In a prospectively frozen study of bf16 latents during fine-tuning (one task, one recipe and trajectory per arm), native bf16 writes left 2B-4T's codes unchanged at the endpoint and changed one Falcon-E code, while emulated
stochastic-rounding and compensated writers changed codes and were non-inferior to fp32 training at the same margin in both models. In two model families, randomized interventions on the initial distance from the threshold support distance-dependent selection of the codes that fine-tuning changes.
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Artificial Intelligence
RA-MoWE: Workflow-Affinity Embeddings for Query Clustering and Agentic Workflow Generation
Qi Cheng, Shengyu Chen, Wei Cheng et al. (+4 more)
Agentic workflows enable large
language models (LLMs) to solve complex tasks by coordinating
reasoning, tool use, and verification. However, a workflow optimized for an entire task collection can overlook differences in the
reasoning strategies that individual queries need, while searching for a new workflow for every query repeats costly optimization. To address this tradeoff, we introduce RA-MoWE, a framework that uses workflow-affinity
embeddings to cluster queries and guide the generation of reusable expert workflows. Each
embedding records how well a fixed set of reference workflows solves a query, revealing similarities in which
reasoning strategies are effective. RA-MoWE uses each cluster's queries and average
embedding to initialize and refine a specialized workflow through execution feedback. An
embedding encoder predicts these
embeddings from query text, allowing new queries to select a generated expert without first executing the reference workflows. On a 300-query test set drawn from four
benchmarks spanning mathematics, science, and programming, RA-MoWE improves average task score by 4.04 percentage points over selecting among the reference workflows, while using 27.7% fewer
language-model calls at inference.
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All authors Qi Cheng, Shengyu Chen, Wei Cheng, Yiqun Xie, Haoyu Wang, Haifeng Chen, Xiaowei Jia