Week Ending 11.30.2025
RESEARCH WATCH: 11.30.2025
Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models
Building machines with genuine long-term memory requires processing contexts far beyond current capabilities. This research introduces HSA-UltraLong, an 8-billion parameter model utilizing Hierarchical Sparse Attention to handle contexts up to 16 million tokens while maintaining efficiency through sparsity and random-access flexibility. The model demonstrates remarkable 90% accuracy on retrieval tasks across ultra-long contexts, comparable to full-attention systems on standard lengths. Applications span document analysis requiring entire codebases or legal archives, multi-session conversational AI maintaining extended dialogue history, and scientific research synthesizing vast literature. This breakthrough addresses the fundamental challenge of enabling AI systems to "remember" and reason over massive information contexts without computational collapse.
Authors: Xiang Hu, Zhanchao Zhou, Ruiqi Liang, Zehuan Li, Wei Wu, Jianguo Li
Link: https://arxiv.org/abs/2511.23319v1
Date: 2025-11-d
Summary:
This work explores the challenge of building ``Machines that Can Remember'', framing long-term memory as the problem of efficient ultra-long context modeling. We argue that this requires three key properties: \textbf{sparsity}, \textbf{random-access flexibility}, and \textbf{length generalization}. To address ultra-long-context modeling, we leverage Hierarchical Sparse Attention (HSA), a novel attention mechanism that satisfies all three properties. We integrate HSA into Transformers to build HSA-UltraLong, which is an 8B-parameter MoE model trained on over 8 trillion tokens and is rigorously evaluated on different tasks with in-domain and out-of-domain context lengths to demonstrate its capability in handling ultra-long contexts. Results show that our model performs comparably to full-attention baselines on in-domain lengths while achieving over 90\% accuracy on most in-context retrieval tasks with contexts up to 16M. This report outlines our experimental insights and open problems, contributing a foundation for future research in ultra-long context modeling.
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FLIMs: Fault Localization Interference Mutants, Definition, Recognition and Mitigation
Automated software debugging using mutation-based fault localization faces a critical challenge: interference mutants that mimic faulty code behavior despite originating from healthy code. This research introduces the concept of Fault Localization Interference Mutants (FLIMs) and develops MBFL-FLIM, a framework leveraging large language models to semantically recognize and mitigate these misleading signals. Through fine-tuning and confidence estimation, the system refines suspiciousness scores in mutation testing workflows. Evaluated on 395 program versions using eight LLMs, MBFL-FLIM identifies 44 additional faults at Top-1 compared to baseline methods. Applications include enhanced automated debugging tools, more efficient software testing pipelines, and reduced developer time spent on false leads during bug investigation, particularly valuable in continuous integration environments.
Authors: Hengyuan Liu, Zheng Li, Donghua Wang, Yankai Wu, Xiang Chen, Yong Liu
Link: https://arxiv.org/abs/2511.23302v1
Date: 2025-11-d
Summary:
Mutation-based Fault Localization (MBFL) has been widely explored for automated software debugging, leveraging artificial mutants to identify faulty code entities. However, MBFL faces significant challenges due to interference mutants generated from non-faulty code entities but can be killed by failing tests. These mutants mimic the test sensitivity behaviors of real faulty code entities and weaken the effectiveness of fault localization. To address this challenge, we introduce the concept of Fault Localization Interference Mutants (FLIMs) and conduct a theoretical analysis based on the Reachability, Infection, Propagation, and Revealability (RIPR) model, identifying four distinct interference causes. Building on this, we propose a novel approach to semantically recognize and mitigate FLIMs using LLM-based semantic analysis, enhanced by fine-tuning techniques and confidence estimation strategies to address LLM output instability. The recognized FLIMs are then mitigated by refining the suspiciousness scores calculated from MBFL techniques. We integrate FLIM recognition and mitigation into the MBFL workflow, developing MBFL-FLIM, a fault localization framework that enhances MBFL's effectiveness by reducing misleading interference while preserving real fault-revealing information. Our empirical experiments on the Defects4J benchmark with 395 program versions using eight LLMs demonstrate MBFL-FLIM's superiority over traditional SBFL and MBFL methods, advanced dynamic feature-based approaches, and recent LLM-based fault localization techniques. Specifically, MBFL-FLIM achieves an average improvement of 44 faults in the Top-1 metric, representing a significant enhancement over baseline methods. Further evaluation confirms MBFL-FLIM's robust performance in multi-fault scenarios, with ablation experiments validating the contributions of the fine-tuning and confidence estimation components.
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Time Series Forecasting via Direct Per-Step Probability Distribution Modeling
Traditional neural network time series models output single scalar predictions, failing to capture uncertainty inherent in forecasting. The interleaved dual-branch Probability Distribution Network (interPDN) addresses this by constructing discrete probability distributions at each time step rather than point estimates. Using dual branches with interleaved support sets and coarse temporal-scale components, the model generates predictions as distribution expectations while imposing self-supervised consistency constraints. Applications span financial market prediction with quantified uncertainty bounds, weather forecasting providing probability ranges for planning decisions, energy demand prediction for grid management, and supply chain optimization where understanding prediction confidence enables better risk management and resource allocation strategies.
Authors: Linghao Kong, Xiaopeng Hong
Link: https://arxiv.org/abs/2511.23260v1
Date: 2025-11-d
Summary:
Deep neural network-based time series prediction models have recently demonstrated superior capabilities in capturing complex temporal dependencies. However, it is challenging for these models to account for uncertainty associated with their predictions, because they directly output scalar values at each time step. To address such a challenge, we propose a novel model named interleaved dual-branch Probability Distribution Network (interPDN), which directly constructs discrete probability distributions per step instead of a scalar. The regression output at each time step is derived by computing the expectation of the predictive distribution on a predefined support set. To mitigate prediction anomalies, a dual-branch architecture is introduced with interleaved support sets, augmented by coarse temporal-scale branches for long-term trend forecasting. Outputs from another branch are treated as auxiliary signals to impose self-supervised consistency constraints on the current branch's prediction. Extensive experiments on multiple real-world datasets demonstrate the superior performance of interPDN.
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MindPower: Enabling Theory-of-Mind Reasoning in VLM-based Embodied Agents
Current vision-language embodied agents lack Theory of Mind (ToM) capabilities—the ability to infer mental states, beliefs, and intentions of both themselves and humans they interact with. MindPower introduces a Robot-Centric framework integrating perception, mental reasoning, decision-making, and action generation guided by inferred mental states. The Mind-Reward optimization objective encourages consistent ToM reasoning and behavior in vision-language models. Outperforming GPT-4o by over 12% in decision-making and action generation, this framework enables applications in assistive robotics understanding user needs, collaborative manufacturing where robots anticipate human worker intentions, elderly care systems recognizing emotional states, and home automation adapting to occupant preferences through genuine social cognition rather than rigid programming.
Authors: Ruoxuan Zhang, Qiyun Zheng, Zhiyu Zhou, Ziqi Liao, Siyu Wu, Jian-Yu Jiang-Lin, Bin Wen, Hongxia Xie, Jianlong Fu, Wen-Huang Cheng
Link: https://arxiv.org/abs/2511.23055v1
Date: 2025-11-d
Summary:
Theory of Mind (ToM) refers to the ability to infer others' mental states, such as beliefs, desires, and intentions. Current vision-language embodied agents lack ToM-based decision-making, and existing benchmarks focus solely on human mental states while ignoring the agent's own perspective, hindering coherent decision and action generation. To address this, we propose MindPower, a Robot-Centric framework integrating Perception, Mental Reasoning, Decision Making and Action. Given multimodal inputs, MindPower first perceives the environment and human states, then performs ToM Reasoning to model both self and others, and finally generates decisions and actions guided by inferred mental states. Furthermore, we introduce Mind-Reward, a novel optimization objective that encourages VLMs to produce consistent ToM Reasoning and behavior. Our model outperforms GPT-4o by 12.77% in decision making and 12.49% in action generation.
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Variational Quantum Algorithms face critical scalability challenges in resource-constrained Quantum Internet of Things devices: barren plateaus where gradients vanish and training stalls. This research introduces a novel approach incorporating negative learning rates—controlled instability switching between positive and negative learning phases to recover significant gradients and explore flatter loss landscape regions. Theoretically evaluating effects on gradient variance, the method demonstrates consistent convergence improvements over traditional optimizers on VQA benchmarks. Applications include optimization problems on edge quantum devices with limited qubits and shot budgets, quantum machine learning on distributed QIoT networks, near-term quantum algorithm development requiring efficient training under hardware constraints, and hybrid quantum-classical systems demanding robust optimization pathways.
Authors: Ratun Rahman, Dinh C. Nguyen
Link: https://arxiv.org/abs/2511.22861v1
Date: 2025-11-d
Summary:
Variational Quantum Algorithms (VQAs) are becoming the primary computational primitive for next-generation quantum computers, particularly those embedded as resource-constrained accelerators in the emerging Quantum Internet of Things (QIoT). However, under such device-constrained execution conditions, the scalability of learning is severely limited by barren plateaus, where gradients collapse to zero and training stalls. This poses a practical challenge to delivering VQA-enabled intelligence on QIoT endpoints, which often have few qubits, constrained shot budgets, and strict latency requirements. In this paper, we present a novel approach for escaping barren plateaus by including negative learning rates into the optimization process in QIoT devices. Our method introduces controlled instability into model training by switching between positive and negative learning phases, allowing recovery of significant gradients and exploring flatter areas in the loss landscape. We theoretically evaluate the effect of negative learning on gradient variance and propose conditions under which it helps escape from barren zones. The experimental findings on typical VQA benchmarks show consistent improvements in both convergence and simulation results over traditional optimizers. By escaping barren plateaus, our approach leads to a novel pathway for robust optimization in quantum-classical hybrid models.
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Novice programmers in informal online learning environments experience significant emotional difficulties—confusion, frustration, and anxiety—that hinder motivation and learning outcomes. Analyzing 1,500 r/learnprogramming posts through the Learning-Centered Emotions framework, this research identifies confusion, curiosity, and frustration as dominant emotions, triggered by ambiguous errors, unclear learning pathways, and misaligned resources. The study reveals five critical support areas: stress relief and motivation resilience, topic explanation with resource recommendation, strategic learning guidance, technical assistance, and acknowledgment of challenges. Applications include designing affect-aware educational platforms, intelligent tutoring systems providing emotionally-responsive support, online learning community moderation tools, personalized learning path recommendations adapting to emotional states, and developer tool improvements addressing frustration points in programming workflows.
Authors: Alif Al Hasan, Subarna Saha, Mia Mohammad Imran
Link: https://arxiv.org/abs/2511.22789v1
Date: 2025-11-d
Summary:
Novice programmers experience emotional difficulties in informal online learning environments, where confusion and frustration can hinder motivation and learning outcomes. This study investigates novice programmers' emotional experiences in informal settings, identifies the causes of emotional struggle, and explores design opportunities for affect-aware support systems. We manually annotated 1,500 posts from r/learnprogramming using the Learning-Centered Emotions framework and conducted clustering and axial coding. Confusion, curiosity, and frustration were the most common emotions, often co-occurring and associated with early learning stages. Positive emotions were relatively rare. The primary emotional triggers included ambiguous errors, unclear learning pathways, and misaligned learning resources. We identify five key areas where novice programmers need support in informal learning spaces: stress relief and resilient motivation, topic explanation and resource recommendation, strategic decision-making and learning guidance, technical support, and acknowledgment of their challenges. Our findings highlight the need for intelligent, affect-sensitive mechanisms that provide timely support aligned with learners' emotional states.
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Probabilistic Fusion and Calibration of Neural Speaker Diarization Models
End-to-End Neural Diarization systems produce probabilistic speaker activity estimates, yet evaluation focuses on error rates while neglecting confidence score reliability. This research presents the first comprehensive framework for calibrating and fusing EEND models at probability level rather than segment-level hard decisions. Investigating multilabel and powerset representations, the study demonstrates proper calibration achieves up to 19% relative error reduction, sometimes eliminating domain adaptation needs. Joint calibration in powerset space outperforms per-speaker approaches, and Fuse-then-Calibrate ordering surpasses individual model calibration while requiring only single combined model calibration. Applications include meeting transcription systems with reliable speaker attribution, call center analytics, podcast production workflows, forensic audio analysis requiring confidence-weighted evidence, and multi-party conversation interfaces benefiting from uncertainty-aware speaker tracking.
Authors: Juan Ignacio Alvarez-Trejos, Sergio A. Balanya, Daniel Ramos, Alicia Lozano-Diez
Link: https://arxiv.org/abs/2511.22696v1
Date: 2025-11-d
Summary:
End-to-End Neural Diarization (EEND) systems produce frame-level probabilistic speaker activity estimates, yet since evaluation focuses primarily on Diarization Error Rate (DER), the reliability and calibration of these confidence scores have been largely neglected. When fusing multiple diarization systems, DOVER-Lap remains the only established approach, operating at the segment level with hard decisions. We propose working with continuous probability outputs, which enables more sophisticated calibration and fusion techniques that can leverage model uncertainty and complementary strengths across different architectures. This paper presents the first comprehensive framework for calibrating and fusing EEND models at the probability level. We investigate two output formulations (multilabel and powerset representations) and their impact on calibration and fusion effectiveness. Through extensive experiments on the CallHome two-speaker benchmark, we demonstrate that proper calibration provides substantial improvements even for individual models (up to 19% relative DER reduction), in some cases mitigating the absence of domain adaptation. We reveal that joint calibration in powerset space consistently outperforms independent per-speaker calibration, and that the Fuse-then-Calibrate ordering generally outperforms calibrating individual models before fusion while requiring calibration of only a single combined model. Our best configuration outperforms DOVER-Lap in terms of DER while providing reliable confidence estimates essential for downstream applications. This work proposes best practices for probability-level fusion of EEND systems and demonstrates the advantages of leveraging soft outputs over hard decisions.
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AI Deception: Risks, Dynamics, and Controls
AI deception—systems inducing false beliefs for self-beneficial outcomes—has evolved from speculation to empirically demonstrated risk across language models, agents, and frontier systems. This comprehensive project provides formal deception definitions grounded in signaling theory, reviews empirical studies, and organizes research as a deception cycle comprising emergence and treatment components. Deception emergence analysis reveals systems with sufficient capability and incentive engage in deceptive behaviors when triggered by supervision gaps, distributional shifts, or environmental pressures. Treatment focuses on detection through benchmarks and evaluation protocols. Applications include developing robust AI safety protocols, designing trustworthy autonomous systems, establishing governance frameworks for frontier AI deployment, creating audit mechanisms for detecting deceptive behavior, and informing policy addressing sociotechnical challenges in increasingly capable AI systems.
Authors: Boyuan Chen, Sitong Fang, Jiaming Ji, Yanxu Zhu, Pengcheng Wen, Jinzhou Wu, Yingshui Tan, Boren Zheng, Mengying Yuan, Wenqi Chen, Donghai Hong, Alex Qiu, Xin Chen, Jiayi Zhou, Kaile Wang, Juntao Dai, Borong Zhang, Tianzhuo Yang, Saad Siddiqui, Isabella Duan, Yawen Duan, Brian Tse, Jen-Tse, Huang, Kun Wang, Baihui Zheng, Jiaheng Liu, Jian Yang, Yiming Li, Wenting Chen, Dongrui Liu, Lukas Vierling, Zhiheng Xi, Haobo Fu, Wenxuan Wang, Jitao Sang, Zhengyan Shi, Chi-Min Chan, Eugenie Shi, Simin Li, Juncheng Li, Wei Ji, Dong Li, Jun Song, Yinpeng Dong, Jie Fu, Bo Zheng, Min Yang, Yike Guo, Philip Torr, Zhongyuan Wang, Yaodong Yang, Tiejun Huang, Ya-Qin Zhang, Hongjiang Zhang, Andrew Yao
Link: https://arxiv.org/abs/2511.22619v1
Date: 2025-11-d
Summary:
As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an empirically demonstrated risk across language models, AI agents, and emerging frontier systems. This project provides a comprehensive and up-to-date overview of the AI deception field, covering its core concepts, methodologies, genesis, and potential mitigations. First, we identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception. We then review existing empirical studies and associated risks, highlighting deception as a sociotechnical safety challenge. We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment. Deception emergence reveals the mechanisms underlying AI deception: systems with sufficient capability and incentive potential inevitably engage in deceptive behaviors when triggered by external conditions. Deception treatment, in turn, focuses on detecting and addressing such behaviors. On deception emergence, we analyze incentive foundations across three hierarchical levels and identify three essential capability preconditions required for deception. We further examine contextual triggers, including supervision gaps, distributional shifts, and environmental pressures. On deception treatment, we conclude detection methods covering benchmarks and evaluation protocols in static and interactive settings. Building on the three core factors of deception emergence, we outline potential mitigation strategies and propose auditing approaches that integrate technical, community, and governance efforts to address sociotechnical challenges and future AI risks. To support ongoing work in this area, we release a living resource at www.deceptionsurvey.com.
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Test Time Training for AC Power Flow Surrogates via Physics and Operational Constraint Refinement
Machine learning-based Power Flow calculations offer computational advantages over numerical methods but struggle maintaining physical consistency. This physics-informed test-time training framework enhances ML-based PF surrogates by enforcing AC power flow equalities and operational constraints directly at inference. Through lightweight self-supervised refinement via gradient updates, the method enables local adaptation to unseen operating conditions without labeled data. Experiments on IEEE test systems and PEGASE 1354-bus network demonstrate one-to-two order magnitude reductions in power flow residuals and constraint violations while preserving computational efficiency. Applications include real-time grid management and optimization, renewable energy integration requiring rapid state estimation, contingency analysis for security assessment, distribution system operation planning, and scalable power system analysis supporting modern grid modernization initiatives.
Authors: Panteleimon Dogoulis, Mohammad Iman Alizadeh, Sylvain Kubler, Maxime Cordy
Link: https://arxiv.org/abs/2511.22343v1
Date: 2025-11-d
Summary:
Power Flow (PF) calculation based on machine learning (ML) techniques offer significant computational advantages over traditional numerical methods but often struggle to maintain full physical consistency. This paper introduces a physics-informed test-time training (PI-TTT) framework that enhances the accuracy and feasibility of ML-based PF surrogates by enforcing AC power flow equalities and operational constraints directly at inference time. The proposed method performs a lightweight self-supervised refinement of the surrogate outputs through few gradient-based updates, enabling local adaptation to unseen operating conditions without requiring labeled data. Extensive experiments on the IEEE 14-, 118-, and 300-bus systems and the PEGASE 1354-bus network show that PI-TTT reduces power flow residuals and operational constraint violations by one to two orders of magnitude compared with purely ML-based models, while preserving their computational advantage. The results demonstrate that PI-TTT provides fast, accurate, and physically reliable predictions, representing a promising direction for scalable and physics-consistent learning in power system analysis.
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RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks
Quantum networks face fundamental routing challenges including high link dynamicity and probabilistic quantum operations, making hand-crafted heuristics suboptimal, especially without global topology information. RELiQ introduces a reinforcement learning approach using graph neural networks for entanglement routing relying solely on local information and iterative message exchange. Trained on random graphs, RELiQ avoids topology overfitting—a prevalent learning-based approach issue—and consistently outperforms existing local information heuristics. When compared to global information methods, RELiQ achieves similar or superior performance through rapid topology change response. Applications include distributed quantum computing requiring entanglement distribution, quantum key distribution networks for secure communications, quantum sensing networks demanding coordinated entanglement, and scalable quantum internet infrastructure supporting future quantum information technologies across metropolitan and long-distance scales.
Authors: Tobias Meuser, Jannis Weil, Aninda Lahiri, Marius Paraschiv
Link: https://arxiv.org/abs/2511.22321v1
Date: 2025-11-d
Summary:
Quantum networks are becoming increasingly important because of advancements in quantum computing and quantum sensing, such as recent developments in distributed quantum computing and federated quantum machine learning. Routing entanglement in quantum networks poses several fundamental as well as technical challenges, including the high dynamicity of quantum network links and the probabilistic nature of quantum operations. Consequently, designing hand-crafted heuristics is difficult and often leads to suboptimal performance, especially if global network topology information is unavailable. In this paper, we propose RELiQ, a reinforcement learning-based approach to entanglement routing that only relies on local information and iterative message exchange. Utilizing a graph neural network, RELiQ learns graph representations and avoids overfitting to specific network topologies - a prevalent issue for learning-based approaches. Our approach, trained on random graphs, consistently outperforms existing local information heuristics and learning-based approaches when applied to random and real-world topologies. When compared to global information heuristics, our method achieves similar or superior performance because of its rapid response to topology changes.
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Miniaturization drives information processing advancement and artificial neural network usability, presenting key challenges for magnonic neuromorphic systems. This research proposes magnetic molecules regularly arranged on ferromagnetic layer surfaces enabling resonant coupling of propagating spin waves with molecular magnetic moment dynamics, opening transmission spectrum gaps up to 150 MHz. Gap characteristics—width, frequency, position—are controllable through external magnetic fields or molecular arrangement, with antiferromagnetic configurations enabling further tuning. The hybrid structure offers reprogrammability and deep nanoscale miniaturization with several GHz operating frequencies. Applications include spin-wave-based neuromorphic computing systems, ultra-compact information processing devices, brain-inspired computing architectures exploiting magnonic dynamics, low-power data storage and manipulation technologies, and quantum-classical hybrid systems requiring nanoscale magnetic control for next-generation computing paradigms.
Authors: Oleksandr Pastukh, Piotr Graczyk, Mateusz Zelent, Lukasz Laskowski, Maciej Krawczyk
Link: https://arxiv.org/abs/2511.22284v1
Date: 2025-11-d
Summary:
Miniaturization is an essential element in the development of information processing technologies and is also one of the main determinants of the usability of the tested artificial neural networks. It is also a key element and one of the main challenges in the development of magnonic neuromorphic systems. In this work, we propose a new platform for the development of these new spin-wave-based technologies. Using micromagnetic simulations, we demonstrate that magnetic molecules regularly arranged on the surface of a thin ferromagnetic layer enable resonant coupling of propagating spin waves with the dynamics of the molecules' magnetic moments, opening a gap in the transmission spectrum up to 150 MHz. The gap, its width, and frequency can be controlled by an external magnetic field or the arrangement of molecules on the ferromagnetic surface. Furthermore, the antiferromagnetic arrangement of the magnetic moments of molecules or clusters of molecules allows for control of the gap's position and width. Thus, the proposed hybrid structure offers reprogrammability and miniaturization down to the deep nanoscale, operating frequencies in the range of several GHz, key properties for the implementation of artificial neural networks.
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A perceptual bias of AI Logical Argumentation Ability in Writing
Whether machines can think remains central to AI research, with substantial viewpoint divergence despite observing identical AI performance. This study explores whether human biases influence evaluations of AI reasoning abilities through experiments where participants assessed AI-generated and human-written texts on identical topics. Results reveal significant perceptual bias: evaluations of AI logical reasoning are substantially influenced by preconceived views about AI capabilities. Frequent AI users showed less concern about AI undermining independent thinking. Applications include improving AI literacy programs addressing bias in capability assessment, developing more objective AI evaluation frameworks, informing human-AI collaboration design considering perceptual factors, educational interventions teaching critical AI assessment, and policy development accounting for public perception gaps between actual and perceived AI capabilities in regulatory contexts.
Authors: Xi Cun, Jifan Ren, Asha Huang, Siyu Li, Ruzhen Song
Link: https://arxiv.org/abs/2511.22151v1
Date: 2025-11-d
Summary:
Can machines think? This is a central question in artificial intelligence research. However, there is a substantial divergence of views on the answer to this question. Why do people have such significant differences of opinion, even when they are observing the same real world performance of artificial intelligence? The ability of logical reasoning like humans is often used as a criterion to assess whether a machine can think. This study explores whether human biases influence evaluations of the reasoning abilities of AI. An experiment was conducted where participants assessed two texts on the same topic, one AI generated and one human written,to test for perceptual biases in evaluating logical reasoning. Based on the experimental findings, a questionnaire was designed to quantify the attitudes toward AI.The results reveal a bias in perception. The evaluations of the logical reasoning ability of AI generated texts are significantly influenced by the preconceived views on the logical reasoning abilities of AI. Furthermore, frequent AI users were less likely to believe that AI usage undermines independent thinking.This study highlights the need to address perceptual biases to improve public understanding of AI's capabilities and foster better human AI interactions.
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Bridging Planning and Execution: Multi-Agent Path Finding Under Real-World Deadlines
Multi-Agent Path Finding seeks collision-free paths optimizing sum-of-costs or makespan objectives with applications in automated warehouses, manufacturing, and airport logistics. Most formulations assume simplified robot models, overlooking execution-time factors like kinodynamic constraints, communication latency, and controller variability—problematic for time-sensitive applications. REMAP introduces an execution-informed framework integrating ExecTimeNet for accurate execution time estimation based on planned paths, addressing MAPF with Real-world Deadlines where agents must reach goals before wall-clock times. Integrated with MAPF-LNS and CBS, REMAP achieves 20% solution quality improvement over baseline methods on benchmarks with up to 300 agents. Applications include warehouse robotics with tight delivery schedules, manufacturing coordination requiring precise timing, airport ground operations, automated guided vehicle systems, and dynamic logistics optimization under temporal constraints.
Authors: Jingtian Yan, Shuai Zhou, Stephen F. Smith, Jiaoyang Li
Link: https://arxiv.org/abs/2511.21886v1
Date: 2025-11-d
Summary:
The Multi-Agent Path Finding (MAPF) problem aims to find collision-free paths for multiple agents while optimizing objectives such as the sum of costs or makespan. MAPF has wide applications in domains like automated warehouses, manufacturing systems, and airport logistics. However, most MAPF formulations assume a simplified robot model for planning, which overlooks execution-time factors such as kinodynamic constraints, communication latency, and controller variability. This gap between planning and execution is problematic for time-sensitive applications. To bridge this gap, we propose REMAP, an execution-informed MAPF planning framework that can be combined with leading search-based MAPF planners with minor changes. Our framework integrates the proposed ExecTimeNet to accurately estimate execution time based on planned paths. We demonstrate our method for solving MAPF with Real-world Deadlines (MAPF-RD) problem, where agents must reach their goals before a predefined wall-clock time. We integrate our framework with two popular MAPF methods, MAPF-LNS and CBS. Experiments show that REMAP achieves up to 20% improvement in solution quality over baseline methods (e.g., constant execution speed estimators) on benchmark maps with up to 300 agents.
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LILAD: Learning In-context Lyapunov-stable Adaptive Dynamics Models
System identification aims to approximate dynamical systems from trajectory data. Neural networks demonstrate strong prediction but often fail preserving critical physical properties like stability and assume stationary dynamics, limiting applicability under distribution shifts. LILAD introduces a framework jointly guaranteeing adaptability and stability through simultaneously learning dynamics models and Lyapunov functions via in-context learning, explicitly accounting for parametric uncertainty. Trained across diverse tasks, LILAD produces stability-aware adaptive models with Lyapunov certificates adapting to new system instances using short trajectory prompts. State-dependent attenuators enforce sufficient Lyapunov function decrease conditions, extending stability guarantees under out-of-distribution scenarios. Applications include adaptive control for robotics, aerospace vehicle modeling, autonomous systems requiring safety guarantees, process control in chemical engineering, and model-based reinforcement learning requiring stable learned dynamics.
Authors: Amit Jena, Na Li, Le Xie
Link: https://arxiv.org/abs/2511.21846v1
Date: 2025-11-d
Summary:
System identification in control theory aims to approximate dynamical systems from trajectory data. While neural networks have demonstrated strong predictive accuracy, they often fail to preserve critical physical properties such as stability and typically assume stationary dynamics, limiting their applicability under distribution shifts. Existing approaches generally address either stability or adaptability in isolation, lacking a unified framework that ensures both. We propose LILAD (Learning In-Context Lyapunov-stable Adaptive Dynamics), a novel framework for system identification that jointly guarantees adaptability and stability. LILAD simultaneously learns a dynamics model and a Lyapunov function through in-context learning (ICL), explicitly accounting for parametric uncertainty. Trained across a diverse set of tasks, LILAD produces a stability-aware, adaptive dynamics model alongside an adaptive Lyapunov certificate. At test time, both components adapt to a new system instance using a short trajectory prompt, which enables fast generalization. To rigorously ensure stability, LILAD also computes a state-dependent attenuator that enforces a sufficient decrease condition on the Lyapunov function for any state in the new system instance. This mechanism extends stability guarantees even under out-of-distribution and out-of-task scenarios. We evaluate LILAD on benchmark autonomous systems and demonstrate that it outperforms adaptive, robust, and non-adaptive baselines in predictive accuracy.
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Through the telecom lens: Are all training samples important?
AI's rise in telecommunications—from Radio Access Network optimization to user experience management—has sharply increased data volumes and training demands. Telecom data is noisy, high-dimensional, costly to store, process, and label, yet standard workflows assume equal sample importance. This research questions this assumption through sample-level gradient analysis across epochs identifying influence patterns and redundancy. The proposed framework selectively prioritizes impactful data, reducing computation without compromising accuracy. Experiments on three real-world datasets demonstrate preserved performance with reduced data needs and computational overhead. Applications include efficient 5G/6G network optimization, sustainable AI reducing energy consumption in telecom operations, intelligent network management with streamlined training, edge computing deployment requiring efficient models, and scalable network analytics supporting next-generation telecommunications infrastructure development.
Authors: Shruti Bothe, Illyyne Saffar, Aurelie Boisbunon, Hasan Farooq, Julien Forgeat, Md Moin Uddin Chowdhury
Link: https://arxiv.org/abs/2511.21668v1
Date: 2025-11-d
Summary:
The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai's critical role, standard workflows still assume all training samples contribute equally. On the other hand, next generation systems require AI models that are accurate, efficient, and sustainable.The paper questions the assumptions of equal importance by focusing on applying and analyzing the roles of individual samples in telecom training and assessing whether the proposed model optimizes computation and energy use. we perform sample-level gradient analysis across epochs to identify patterns of influence and redundancy in model learning. Based on this, we propose a sample importance framework thats electively prioritizes impactful data and reduces computation without compromising accuracy. Experiments on three real-world telecom datasets show that our method [reserves performance while reducing data needs and computational overhead while advancing the goals of sustainable AI in telecommunications.
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This research examines generative AI's potential assisting internal review processes for UK higher education research quality evaluations, particularly Research Excellence Framework preparation. Using Viable Systems Model function substitution lens and experimental methodology with ChatGPT, researchers scored and ranked business and management papers from REF 2021, "reverse engineering" assessment by comparing AI scores with known results. Testing 822 papers across 11 institutions established scoring boundaries aligning with REF outcomes. AI provided consistent evaluations identifying borderline cases requiring human scrutiny while reducing substantial resource burden. Applications include streamlined research assessment processes, cost reduction in evaluation bureaucracy, consistent quality evaluation across institutions, hybrid human-AI review frameworks maintaining academic integrity, and scalable assessment approaches transforming research evaluation in higher education systems worldwide.
Authors: Gordon Fletcher, Saomai Vu Khan, Aldus Greenhill Fletcher
Link: https://arxiv.org/abs/2511.21790v1
Date: 2025-11-d
Summary:
This paper examines the potential for generative artificial intelligence (GenAI) to assist with internal review processes for research quality evaluations in UK higher education and particularly in preparation for the Research Excellence Framework (REF). Using the lens of function substitution in the Viable Systems Model, we present an experimental methodology using ChatGPT to score and rank business and management papers from REF 2021 submissions, "reverse engineering" the assessment by comparing AI-generated scores with known institutional results. Through rigourous testing of 822 papers across 11 institutions, we established scoring boundaries that aligned with reported REF outcomes: 49% between 1* and 2*, 59% between 2* and 3*, and 69% between 3* and 4*. The results demonstrate that AI can provide consistent evaluations that help identify borderline evaluation cases requiring additional human scrutiny while reducing the substantial resource burden of traditional internal review processes. We argue for application through a nuanced hybrid approach that maintains academic integrity while addressing the multi-million pound costs associated with research evaluation bureaucracy. While acknowledging these limitations including potential AI biases, the research presents a promising framework for more efficient, consistent evaluations that could transform current approaches to research assessment.
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SAM Guided Semantic and Motion Changed Region Mining for Remote Sensing Change Captioning
Remote sensing change captioning describes in natural language content changes between temporally different remote sensing images. Existing methods employ CNNs/Transformers for visual representation or incorporate auxiliary tasks, with weak region awareness and limited temporal alignment. This research leverages SAM foundation model extracting region-level representations and injecting region-of-interest knowledge into captioning frameworks. The method extracts global vision features, delineates semantic- and motion-level change regions through SAM, utilizes knowledge graphs providing object information, fuses heterogeneous information via cross-attention, and generates natural language descriptions through Transformer decoders. Achieving state-of-the-art performance across benchmarks, applications include environmental monitoring and documentation, urban development tracking, disaster assessment and reporting, agricultural change analysis, military intelligence, and automated satellite image analysis supporting decision-making.
Authors: Futian Wang, Mengqi Wang, Xiao Wang, Haowen Wang, Jin Tang
Link: https://arxiv.org/abs/2511.21420v1
Date: 2025-11-d
Summary:
Remote sensing change captioning is an emerging and popular research task that aims to describe, in natural language, the content of interest that has changed between two remote sensing images captured at different times. Existing methods typically employ CNNs/Transformers to extract visual representations from the given images or incorporate auxiliary tasks to enhance the final results, with weak region awareness and limited temporal alignment. To address these issues, this paper explores the use of the SAM (Segment Anything Model) foundation model to extract region-level representations and inject region-of-interest knowledge into the captioning framework. Specifically, we employ a CNN/Transformer model to extract global-level vision features, leverage the SAM foundation model to delineate semantic- and motion-level change regions, and utilize a specially constructed knowledge graph to provide information about objects of interest. These heterogeneous sources of information are then fused via cross-attention, and a Transformer decoder is used to generate the final natural language description of the observed changes. Extensive experimental results demonstrate that our method achieves state-of-the-art performance across multiple widely used benchmark datasets. The source code of this paper will be released on https://github.com/Event-AHU/SAM_ChangeCaptioning
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Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM
Rising AI inference demands, especially in higher education, necessitate novel solutions utilizing existing infrastructure. High-Performance Computing implementation has become prevalent, but classical HPC operating models don't adapt well to synchronous, user-facing dynamic AI application workloads. This research proposes integrating vLLM, Slurm, and Kubernetes on the RAMSES supercomputer for serving LLMs. Initial benchmarks indicate the architecture scales efficiently for 100, 500, and 1000 concurrent requests with approximately 500ms end-to-end latency overhead. Applications include educational AI services leveraging existing HPC infrastructure, research computing platforms offering LLM access, institutional AI deployment without dedicated inference hardware, dynamic workload management for varied AI services, and cost-effective AI democratization in academic institutions supporting teaching, research, and administrative functions through shared HPC resources.
Authors: Tim Trappen, Robert Keßler, Roland Pabel, Viktor Achter, Stefan Wesner
Link: https://arxiv.org/abs/2511.21413v1
Date: 2025-11-d
Summary:
Due to rising demands for Artificial Inteligence (AI) inference, especially in higher education, novel solutions utilising existing infrastructure are emerging. The utilisation of High-Performance Computing (HPC) has become a prevalent approach for the implementation of such solutions. However, the classical operating model of HPC does not adapt well to the requirements of synchronous, user-facing dynamic AI application workloads. In this paper, we propose our solution that serves LLMs by integrating vLLM, Slurm and Kubernetes on the supercomputer \textit{RAMSES}. The initial benchmark indicates that the proposed architecture scales efficiently for 100, 500 and 1000 concurrent requests, incurring only an overhead of approximately 500 ms in terms of end-to-end latency.
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Pygmalion Effect in Vision: Image-to-Clay Translation for Reflective Geometry Reconstruction
Understanding reflection remains a long-standing 3D reconstruction challenge due to appearance and geometry entanglement under view-dependent reflections. The Pygmalion Effect in Vision metaphorically "sculpts" reflective objects into clay-like forms through image-to-clay translation. Inspired by Pygmalion's myth, the method suppresses specular cues while preserving geometric consistency, enabling robust reconstruction from multi-view images containing complex reflections. A dual-branch network combines BRDF-based reflective branches with clay-guided branches stabilizing geometry and refining surface normals, trained jointly using synthesized clay-like images providing reflection-free supervision. Experiments demonstrate substantial normal accuracy and mesh completeness improvements. Applications include industrial quality inspection of reflective components, cultural heritage digitization of glossy artifacts, product design and virtual prototyping, augmented reality requiring accurate reflective object models, and autonomous vehicle perception.
Authors: Gayoung Lee, Junho Kim, Jin-Hwa Kim, Junmo Kim
Link: https://arxiv.org/abs/2511.21098v1
Date: 2025-11-d
Summary:
Understanding reflection remains a long-standing challenge in 3D reconstruction due to the entanglement of appearance and geometry under view-dependent reflections. In this work, we present the Pygmalion Effect in Vision, a novel framework that metaphorically "sculpts" reflective objects into clay-like forms through image-to-clay translation. Inspired by the myth of Pygmalion, our method learns to suppress specular cues while preserving intrinsic geometric consistency, enabling robust reconstruction from multi-view images containing complex reflections. Specifically, we introduce a dual-branch network in which a BRDF-based reflective branch is complemented by a clay-guided branch that stabilizes geometry and refines surface normals. The two branches are trained jointly using the synthesized clay-like images, which provide a neutral, reflection-free supervision signal that complements the reflective views. Experiments on both synthetic and real datasets demonstrate substantial improvement in normal accuracy and mesh completeness over existing reflection-handling methods. Beyond technical gains, our framework reveals that seeing by unshining, translating radiance into neutrality, can serve as a powerful inductive bias for reflective object geometry learning.
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Data-Driven Assessment of Concrete Slab Integrity via Impact-Echo Signals and Neural Networks
Subsurface defects—delamination, voids, honeycombing—critically affect concrete bridge deck durability but are difficult detecting reliably through visual inspection or manual sounding. This machine learning Impact Echo framework automates defect localization and multi-class classification. Raw signals undergo Fast Fourier Transform into peak-frequency features interpolated into spatial maps for visualization. Unsupervised k-means clustering highlights defect-prone regions; Ground Truth Masks validate spatial accuracy generating training labels. Spatially ordered peak-frequency sequences feed stacked LSTM networks classifying four defect types with 73% accuracy. Field validation demonstrates laboratory-trained model generalization under realistic conditions. Applications include automated bridge health monitoring, infrastructure maintenance prioritization, non-destructive evaluation at network scale, predictive maintenance scheduling, construction quality assurance, and intelligent transportation systems supporting data-driven decision-making for concrete structure management and public safety.
Authors: Yeswanth Ravichandran, Duoduo Liao, Charan Teja Kurakula
Link: https://arxiv.org/abs/2511.21080v1
Date: 2025-11-d
Summary:
Subsurface defects such as delamination, voids, and honeycombing critically affect the durability of concrete bridge decks but are difficult to detect reliably using visual inspection or manual sounding. This paper presents a machine learning based Impact Echo (IE) framework that automates both defect localization and multi-class classification of common concrete defects. Raw IE signals from Federal Highway Administration (FHWA) laboratory slabs and in-service bridge decks are transformed via Fast Fourier Transform (FFT) into dominant peak-frequency features and interpolated into spatial maps for defect zone visualization. Unsupervised k-means clustering highlights low-frequency, defect-prone regions, while Ground Truth Masks (GTMs) derived from seeded lab defects are used to validate spatial accuracy and generate high-confidence training labels. From these validated regions, spatially ordered peak-frequency sequences are constructed and fed into a stacked Long Short-Term Memory (LSTM) network that classifies four defect types shallow delamination, deep delamination, voids, and honeycombing with 73% overall accuracy. Field validation on the bridge deck demonstrates that models trained on laboratory data generalize under realistic coupling, noise, and environmental variability. The proposed framework enhances the objectivity, scalability, and repeatability of Non-Destructive Evaluation (NDE), supporting intelligent, data-driven bridge health monitoring at a network scale.
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