Tootfinder

Cost-aware Stopping for Bayesian Optimization
Qian Xie, Linda Cai, Alexander Terenin, Peter I. Frazier, Ziv Scully
https://arxiv.org/abs/2507.12453 https:/…

Cost-aware Stopping for Bayesian Optimization
In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive black-box functions is an important practical consideration. While several adaptive stopping rules have been proposed, in the cost-aware setting they lack guarantees ensuring they stop before incurring excessive function evaluation costs. We propose a cost-aware stopping rule for Bayesian optimization that adapts to varying evaluation costs and is free…

3D Aware Region Prompted Vision Language Model
An-Chieh Cheng, Yang Fu, Yukang Chen, Zhijian Liu, Xiaolong Li, Subhashree Radhakrishnan, Song Han, Yao Lu, Jan Kautz, Pavlo Molchanov, Hongxu Yin, Xiaolong Wang, Sifei Liu
https://arxiv.org/abs/2509.13317

3D Aware Region Prompted Vision Language Model
We present Spatial Region 3D (SR-3D) aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. SR-3D supports flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. We achieve this by enriching 2D visual features with 3D positional embeddings, which allows the 3D model to draw upon strong 2D priors…

FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health
Nobin Sarwar, Shubhashis Roy Dipta
https://arxiv.org/abs/2509.14275 https://

FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health
Privacy-preserving adaptation of Large Language Models (LLMs) in sensitive domains (e.g., mental health) requires balancing strict confidentiality with model utility and safety. We propose FedMentor, a federated fine-tuning framework that integrates Low-Rank Adaptation (LoRA) and domain-aware Differential Privacy (DP) to meet per-domain privacy budgets while maintaining performance. Each client (domain) applies a custom DP noise scale proportional to its data sensitivity, and the server adaptiv…

Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Ryan M. Bena, Gilbert Bahati, Blake Werner, Ryan K. Cosner, Lizhi Yang, Aaron D. Ames
https://arxiv.org/abs/2508.11129

Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular, legged robots typically possess manipulable asymmetric geometries which must be considered during safety-critical trajectory planning. This work proposes a predictive safety filter: a nonlinear model predictive control (MPC) algorithm for online trajectory generation with geometry-aware safety constraints based on con…

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
Yimian Ding, Jingzehua Xu, Guanwen Xie, Shuai Zhang, Yi Li
https://arxiv.org/abs/2506.15082

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental ad…

Adaptive Spiking with Plasticity for Energy Aware Neuromorphic Systems
Eduardo Calle-Ortiz, Hui Guan, Deepak Ganesan, Phuc Nguyen
https://arxiv.org/abs/2508.11689 https://

Adaptive Spiking with Plasticity for Energy Aware Neuromorphic Systems
This paper presents ASPEN, a novel energy-aware technique for neuromorphic systems that could unleash the future of intelligent, always-on, ultra-low-power, and low-burden wearables. Our main research objectives are to explore the feasibility of neuromorphic computing for wearables, identify open research directions, and demonstrate the feasibility of developing an adaptive spiking technique for energy-aware computation, which can be game-changing for resource-constrained devices in always-on a…

Retired School Teachers and Students: Be aware when traveling to the USA
#Math #Travel

Age of Semantic Information-Aware Wireless Transmission for Remote Monitoring Systems
Xue Han, Biqian Feng, Yongpeng Wu, Xiang-Gen Xia, Wenjun Zhang, Shengli Sun
https://arxiv.org/abs/2508.12248

Age of Semantic Information-Aware Wireless Transmission for Remote Monitoring Systems
Semantic communication is emerging as an effective means of facilitating intelligent and context-aware communication for next-generation communication systems. In this paper, we propose a novel metric called Age of Incorrect Semantics (AoIS) for the transmission of video frames over multiple-input multiple-output (MIMO) channels in a monitoring system. Different from the conventional age-based approaches, we jointly consider the information freshness and the semantic importance, and then formul…

Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
Lucien Heitz, Runze Li, Oana Inel, Abraham Bernstein
https://arxiv.org/abs/2508.13019

Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
Norm-aware recommender systems have gained increased attention, especially for diversity optimization. The recommender systems community has well-established experimentation pipelines that support reproducible evaluations by facilitating models' benchmarking and comparisons against state-of-the-art methods. However, to the best of our knowledge, there is currently no reproducibility framework to support thorough norm-driven experimentation at the pre-processing, in-processing, post-processing, …

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Yifei Li, Lingling Zhang, Hang Yan, Tianzhe Zhao, Zihan Ma, Muye Huang, Jun Liu
https://arxiv.org/abs/2508.11347

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several continual knowledge graph embedding (CKGE) methods have been developed to efficiently update KG embeddings to accommodate new facts while maintaining learned knowledge. As KGs grow at di…

Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models
Qiguang Chen, Dengyun Peng, Jinhao Liu, HuiKang Su, Jiannan Guan, Libo Qin, Wanxiang Che
https://arxiv.org/abs/2508.11582

Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models
Recent advancements in large language models (LLMs) have greatly improved their capabilities on complex reasoning tasks through Long Chain-of-Thought (CoT). However, this approach often results in substantial redundancy, impairing computational efficiency and causing significant delays in real-time applications. To improve the efficiency, current methods often rely on human-defined difficulty priors, which do not align with the LLM's self-awared difficulty, leading to inefficiencies. In this pa…

Temporally-Similar Structure-Aware Spatiotemporal Fusion of Satellite Images
Ryosuke Isono, Shunsuke Ono
https://arxiv.org/abs/2508.11259 https://arxiv.org…

Temporally-Similar Structure-Aware Spatiotemporal Fusion of Satellite Images
This paper proposes a novel spatiotemporal (ST) fusion framework for satellite images, named Temporally-Similar Structure-Aware ST fusion (TSSTF). ST fusion is a promising approach to address the trade-off between the spatial and temporal resolution of satellite images. In real-world scenarios, observed satellite images are severely degraded by noise due to measurement equipment and environmental conditions. Consequently, some recent studies have focused on enhancing the robustness of ST fusion…

Relation-Aware Slicing in Cross-Domain Alignment
Dhruv Sarkar, Aprameyo Chakrabartty, Anish Chakrabarty, Swagatam Das
https://arxiv.org/abs/2507.13194 http…

Relation-Aware Slicing in Cross-Domain Alignment
The Sliced Gromov-Wasserstein (SGW) distance, aiming to relieve the computational cost of solving a non-convex quadratic program that is the Gromov-Wasserstein distance, utilizes projecting directions sampled uniformly from unit hyperspheres. This slicing mechanism incurs unnecessary computational costs due to uninformative directions, which also affects the representative power of the distance. However, finding a more appropriate distribution over the projecting directions (slicing distributio…

Predicting ChatGPT Use in Assignments: Implications for AI-Aware Assessment Design
Surajit Das, Aleksei Eliseev
https://arxiv.org/abs/2508.12013 https://ar…

Predicting ChatGPT Use in Assignments: Implications for AI-Aware Assessment Design
The rise of generative AI tools like ChatGPT has significantly reshaped education, sparking debates about their impact on learning outcomes and academic integrity. While prior research highlights opportunities and risks, there remains a lack of quantitative analysis of student behavior when completing assignments. Understanding how these tools influence real-world academic practices, particularly assignment preparation, is a pressing and timely research priority. This study addresses this gap…

If you've always been vaguely aware of Edward Said but could do with a refresher, this episode of NPR's Throughline and his work on Palestine is a good listen
https://overcast.fm/ AAYs-wlLr8I

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
Elena Camuffo, Francesco Barbato, Mete Ozay, Simone Milani, Umberto Michieli
https://arxiv.org/abs/2509.14001 https…

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
We introduce MOCHA (Multi-modal Objects-aware Cross-arcHitecture Alignment), a knowledge distillation approach that transfers region-level multimodal semantics from a large vision-language teacher (e.g., LLaVa) into a lightweight vision-only object detector student (e.g., YOLO). A translation module maps student features into a joint space, where the training of the student and translator is guided by a dual-objective loss that enforces both local alignment and global relational consistency. Un…

PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Changhong Jing, Yan Liu, Shuqiang Wang, Bruce X. B. Yu, Gong Chen, Zhejing Hu, Zhi Zhang, Yanyan Shen
https://arxiv.org/abs/2508.11357

PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Cross-subject electroencephalography (EEG) decoding remains a fundamental challenge in brain-computer interface (BCI) research due to substantial inter-subject variability and the scarcity of subject-invariant representations. This paper proposed PTSM (Physiology-aware and Task-invariant Spatio-temporal Modeling), a novel framework for interpretable and robust EEG decoding across unseen subjects. PTSM employs a dual-branch masking mechanism that independently learns personalized and shared spat…

【ハク。Cover企画】MONO NO AWARE "かむかもしかもにどもかも！"
#music

A Severity-Aware Reliability Index for Risk-Informed Structural Design
Moussa Leblouba, Samer Barakat, Raghad Awad
https://arxiv.org/abs/2508.12068 https://

A Severity-Aware Reliability Index for Risk-Informed Structural Design
Classical measures of structural reliability, such as the probability of failure and the related reliability index, are still widely applied in practice. However, these measures are frequency-based only, and they do not give information about the severity of failure once it happens. This missing aspect can cause underestimation of risks, in particular when rare events produce very undesirable consequences. In this paper, a new reliability framework is proposed to address this issue. The framewo…

TENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge
Zhirui Huang, Rui Ma, Shijie Cao, Ran Shu, Ian Wang, Ting Cao, Chixiao Chen, Yongqiang Xiong
https://arxiv.org/abs/2509.13765

TENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge
Ternary quantization has emerged as a powerful technique for reducing both computational and memory footprint of large language models (LLM), enabling efficient real-time inference deployment without significantly compromising model accuracy. Conventional LLM inference platforms (e.g GPUs) cannot capitalize on its benefits, as they (i) lack native support for ternary arithmetic and memory specialization and (ii) remain severely under-utilized in low-batch, real-time scenarios. In this work, we …

Progrock listeners should be aware of Steven Wilson and/or his band Porcupine Tree (which is probably the best known of all his music activities). Here I have an interesting and entertaining discussion with Steven Wilson about music in general and his musical career in particular. Enjoy: https://www.youtube.com/watch?v=jOmpyJUPHpg

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio questuin answering
Jinghua Zhao, Hang Su, Lichun Fan, Zhenbo Luo, Jian Luan, Hui Wang, Haoqin Sun, Yong Qin
https://arxiv.org/abs/2509.12275

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio questuin answering
We propose Omni-CLST, an error-aware Curriculum Learning framework with guided Selective Chain-of-Thought for audio question answering. The framework efficiently leverages existing high-quality dataset through two key strategies: an error-aware curriculum that organizes samples by difficulty, and a guided thought dropout mechanism that focuses reasoning on challenging cases. Integrated with GRPO training, these strategies enable the model to learn more effectively from informative samples. Expe…

An Efficient Network-aware Direct Search Method for Influence Maximization
Matteo Bergamaschi, Sara Venturini, Francesco Tudisco, Francesco Rinaldi
https://arxiv.org/abs/2508.12164

An Efficient Network-aware Direct Search Method for Influence Maximization
Influence Maximization (IM) is a pivotal concept in social network analysis, involving the identification of influential nodes within a network to maximize the number of influenced nodes, and has a wide variety of applications that range from viral marketing and information dissemination to public health campaigns. IM can be modeled as a combinatorial optimization problem with a black-box objective function, where the goal is to select $B$ seed nodes that maximize the expected influence spread.…

Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
Anna Deichler, Siyang Wang, Simon Alexanderson, Jonas Beskow
https://arxiv.org/abs/2509.12880 https:/…

Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
Pointing is a key mode of interaction with robots, yet most prior work has focused on recognition rather than generation. We present a motion capture dataset of human pointing gestures covering diverse styles, handedness, and spatial targets. Using reinforcement learning with motion imitation, we train policies that reproduce human-like pointing while maximizing precision. Results show our approach enables context-aware pointing behaviors in simulation, balancing task performance with natural d…

Predictability-Aware Motion Prediction for Edge XR via High-Order Error-State Kalman Filtering
Ziyu Zhong, Hector A Caltenco, Bj\"orn Landfeldt, G\"unter Alce
https://arxiv.org/abs/2507.13179

Predictability-Aware Motion Prediction for Edge XR via High-Order Error-State Kalman Filtering
As 6G networks are developed and defined, offloading of XR applications is emerging as one of the strong new use cases. The reduced 6G latency coupled with edge processing infrastructure will for the first time provide a realistic offloading scenario in cellular networks where several computationally intensive functions, including rendering, can migrate from the user device and into the network. A key advantage of doing so is the lowering of the battery needs in the user devices and the possibi…

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park, Seungwoo Hwang, Seungwu Han, Sungwoo Kang, Youngho Kang
https://arxiv.org/abs/2506.15223

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient. However, this approach often induces catastrophic forgetting, undermining the generalizability that is a key advantage of pretrained MLI…

@… This comment to the video seems on spot:
It seems McKinsey aren't aware that "agentic AI" is just an LLM that can utter some magic incantations that do stuff. It's like a difference between a chimpanzee with a typewriter vs a chimpanzee with a typewriter and a gun.

DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis
Ye-Xin Lu, Yu Gu, Kun Wei, Hui-Peng Du, Yang Ai, Zhen-Hua Ling
https://arxiv.org/abs/2509.14684

DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis
This paper presents DAIEN-TTS, a zero-shot text-to-speech (TTS) framework that enables ENvironment-aware synthesis through Disentangled Audio Infilling. By leveraging separate speaker and environment prompts, DAIEN-TTS allows independent control over the timbre and the background environment of the synthesized speech. Built upon F5-TTS, the proposed DAIEN-TTS first incorporates a pretrained speech-environment separation (SES) module to disentangle the environmental speech into mel-spectrograms …

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Yifan Gao, Shaohao Rui, Haoyang Su, Jinyi Xiang, Lianming Wu, Xiaosong Wang
https://arxiv.org/abs/2507.11886

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Accurate segmentation of myocardial lesions from multi-sequence cardiac magnetic resonance imaging is essential for cardiac disease diagnosis and treatment planning. However, achieving optimal feature correspondence is challenging due to intensity variations across modalities and spatial misalignment caused by inconsistent slice acquisition protocols. We propose CAA-Seg, a composite alignment-aware framework that addresses these challenges through a two-stage approach. First, we introduce a sel…

MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval
Yifan Liu, Qianfeng Wen, Mark Zhao, Jiazhou Liang, Scott Sanner
https://arxiv.org/abs/2509.13562 https://

MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval
Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query-passage relevance in embedding space, which is effective when embeddings lie on a linear manifold. However, our experiments across DPR benchmarks suggest that embeddings often lie on lower-dimensional, non-linear manifolds, especially in out-of-distribution (OOD) settings, where cosine and Euclidean distance fail to capture semantic similarity. To address this limitation, we propose a manifold-aware …

Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models
Andela Ilic, Jiaxi Jiang, Paul Streli, Xintong Liu, Christian Holz
https://arxiv.org/abs/2506.15290

Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models
Motion capture using sparse inertial sensors has shown great promise due to its portability and lack of occlusion issues compared to camera-based tracking. Existing approaches typically assume that IMU sensors are tightly attached to the human body. However, this assumption often does not hold in real-world scenarios. In this paper, we present a new task of full-body human pose estimation using sparse, loosely attached IMU sensors. To solve this task, we simulate IMU recordings from an existing…

Distractor-Aware Memory-Based Visual Object Tracking
Jovana Videnovic, Matej Kristan, Alan Lukezic
https://arxiv.org/abs/2509.13864 https://arxiv.org/pdf/2…

Distractor-Aware Memory-Based Visual Object Tracking
Recent emergence of memory-based video segmentation methods such as SAM2 has led to models with excellent performance in segmentation tasks, achieving leading results on numerous benchmarks. However, these modes are not fully adjusted for visual object tracking, where distractors (i.e., objects visually similar to the target) pose a key challenge. In this paper we propose a distractor-aware drop-in memory module and introspection-based management method for SAM2, leading to DAM4SAM. Our design …

Importance-Aware Robust Semantic Transmission for LEO Satellite-Ground Communication
Hui Cao, Rui Meng, Xiaodong Xu, Shujun Han, Ping Zhang
https://arxiv.org/abs/2508.11457 http…

Importance-Aware Robust Semantic Transmission for LEO Satellite-Ground Communication
Satellite-ground semantic communication is anticipated to serve a critical role in the forthcoming 6G era. Nonetheless, task-oriented data transmission in such systems remains a formidable challenge, primarily due to the dynamic nature of signal-to-noise ratio (SNR) fluctuations and the stringent bandwidth limitations inherent to low Earth orbit (LEO) satellite channels. In response to these constraints, we propose an importance-aware robust semantic transmission (IRST) framework, specifically …

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
Zhanting Zhou, Jinshan Lai, Fengchun Zhang, Zeqin Wu, Fengli Zhang
https://arxiv.org/abs/2509.13895

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
Non-IID data and partial participation induce client drift and inconsistent local optima in federated learning, causing unstable convergence and accuracy loss. We present FedSSG, a stochastic sampling-guided, history-aware drift alignment method. FedSSG maintains a per-client drift memory that accumulates local model differences as a lightweight sketch of historical gradients; crucially, it gates both the memory update and the local alignment term by a smooth function of the observed/expected p…

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
S M Rafiuddin, Sadia Kamal, Mohammed Rakib, Arunkumar Bagavathi, Atriya Sen
https://arxiv.org/abs/2507.12695

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. Our model integrates dynamic modality weighting and context-adaptive attention, enhancing the extraction of sentiment and aspect-related information by focusing on how textual cues and visual context interact. We tested our approach against several baselines, i…

#Spearhead's #MichaelFranti played a concert last night at #SDSU as online rumblings grew about allegations from a female artist casting some unusually dark shadows over the positive vibes associated with ex-

I wasn't even aware you could buy cops on Vevor

DeAndre Hopkins returns to form with Ravens, impressing John Harbaugh, Lamar Jackson https://www.nfl.com/news/deandre-hopkins-ravens-returns-to-form-john-harbaugh-lamar-jackson

UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
Hanqi Zhu, Wuyang Zhang, Xinran Zhang, Ziyang Tao, Xinrui Lin, Yu Zhang, Jianmin Ji, Yanyong Zhang
https://arxiv.org/abs/2509.12207

UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary nature of these autonomous systems and closed-source GPU drivers hinder fine-grained control over GPU executions, often resulting in missed deadlines that compromise vehicle performance. To address this, we present UrgenGo, a non-intrusive, urgency-aware GPU scheduling system that operates without access to application sou…

Protocol-Aware Firmware Rehosting for Effective Fuzzing of Embedded Network Stacks
Moritz Bley, Tobias Scharnowski, Simon W\"orner, Moritz Schloegel, Thorsten Holz
https://arxiv.org/abs/2509.13740

Protocol-Aware Firmware Rehosting for Effective Fuzzing of Embedded Network Stacks
One of the biggest attack surfaces of embedded systems is their network interfaces, which enable communication with other devices. Unlike their general-purpose counterparts, embedded systems are designed for specialized use cases, resulting in unique and diverse communication stacks. Unfortunately, current approaches for evaluating the security of these embedded network stacks require manual effort or access to hardware, and they generally focus only on small parts of the embedded system. A pro…

'There were “media reports of people dying for lack of treatment” and on the “humanitarian impact of curfews affecting over 1 million people”. "There were Israeli soldiers indulging in “theft and looting from homes and shops and the vandalism of people’s homes”.
'And “many reports of the killing of unarmed Palestinians”.'
'But this is not Gaza in 2025. It was the occupied West Bank in 2002, described in an internal Foreign Office report'

UK aware of Israel’s ‘terror’ for over 20 years
Declassified files show Britain has long known of Israel’s criminality against Palestinians, as Whitehall has deepened its military, trade and diplomatic support.

A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
Kwassi Joseph Dzahini, Jeffrey M. Larson, Matt Menickelly, Stefan M. Wild
https://arxiv.org/abs/2507.10992

A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS leverages adaptive random subspace strategies to efficiently optimize the ansatz parameters of a QAOA circuit, in an effort to address challenges posed by a potentially large number of QAOA layers. ANASTAARS iteratively constructs random interpolation models within low-dimensional affine subspaces defined via Johnson--Lind…

SeqVLA: Sequential Task Execution for Long-Horizon Manipulation with Completion-Aware Vision-Language-Action Model
Ran Yang, Zijian An, Lifeng ZHou, Yiming Feng
https://arxiv.org/abs/2509.14138

SeqVLA: Sequential Task Execution for Long-Horizon Manipulation with Completion-Aware Vision-Language-Action Model
Long-horizon robotic manipulation tasks require executing multiple interdependent subtasks in strict sequence, where errors in detecting subtask completion can cascade into downstream failures. Existing Vision-Language-Action (VLA) models such as $π_0$ excel at continuous low-level control but lack an internal signal for identifying when a subtask has finished, making them brittle in sequential settings. We propose SeqVLA, a completion-aware extension of $π_0$ that augments the base architect…

Perspective-Aware AI in Extended Reality
Daniel Platnick, Matti Gruener, Marjan Alirezaie, Kent Larson, Dava J. Newman, Hossein Rahnama
https://arxiv.org/abs/2507.11479

Perspective-Aware AI in Extended Reality
AI-enhanced Extended Reality (XR) aims to deliver adaptive, immersive experiences-yet current systems fall short due to shallow user modeling and limited cognitive context. We introduce Perspective-Aware AI in Extended Reality (PAiR), a foundational framework for integrating Perspective-Aware AI (PAi) with XR to enable interpretable, context-aware experiences grounded in user identity. PAi is built on Chronicles: reasoning-ready identity models learned from multimodal digital footprints that ca…

Dynamic Quality-Latency Aware Routing for LLM Inference in Wireless Edge-Device Networks
Rui Bao, Nan Xue, Yaping Sun, Zhiyong Chen
https://arxiv.org/abs/2508.11291 https://

Dynamic Quality-Latency Aware Routing for LLM Inference in Wireless Edge-Device Networks
The integration of wireless communications and Large Language Models (LLMs) is poised to unlock ubiquitous intelligent services, yet deploying them in wireless edge-device collaborative environments presents a critical trade-off between inference quality and end-to-end latency. A fundamental mismatch exists between task complexity and resource allocation: offloading simple queries invites prohibitive latency, while on-device models lack the capacity for demanding computations. To address this c…

Traffic-Aware Pedestrian Intention Prediction
Fahimeh Orvati Nia, Hai Lin
https://arxiv.org/abs/2507.12433 https://arxiv.org/pdf/2507…

Traffic-Aware Pedestrian Intention Prediction
Accurate pedestrian intention estimation is crucial for the safe navigation of autonomous vehicles (AVs) and hence attracts a lot of research attention. However, current models often fail to adequately consider dynamic traffic signals and contextual scene information, which are critical for real-world applications. This paper presents a Traffic-Aware Spatio-Temporal Graph Convolutional Network (TA-STGCN) that integrates traffic signs and their states (Red, Yellow, Green) into pedestrian intenti…

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
Amin Masoumi, Mert Korkali
https://arxiv.org/abs/2507.13265

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The proposed method integrates a predictive deep learning (DL) model with information gap decision theory (IGDT) to create a risk-averse dispatch strategy. By reformulating the conventional virtual inertia scheduling (VIS) problem, the framework uses early predictions of post-fault dynamics to proactively redispatch resourc…

GLAD: Global-Local Aware Dynamic Mixture-of-Experts for Multi-Talker ASR
Yujie Guo, Jiaming Zhou, Yuhang Jia, Shiwan Zhao, Yong Qin
https://arxiv.org/abs/2509.13093 https://

GLAD: Global-Local Aware Dynamic Mixture-of-Experts for Multi-Talker ASR
End-to-end multi-talker automatic speech recognition (MTASR) faces significant challenges in accurately transcribing overlapping speech, especially under high-overlap conditions. To address these challenges, we proposed Global-Local Aware Dynamic (GLAD) Mixture-of-Experts, which dynamically fuse speaker-aware global information and fine-grained local features to guide expert selection. This mechanism enables speaker-specific routing by leveraging both global context and local acoustic cues. Exp…

Talks by Brian Kernighan are mostly entertaining, but rare. The most recent one I'm aware of was at this years VCF East (April 2025). It's about the history of UNIX and his time at Bell Labs. Enjoy: https://www.youtube.com/watch?v=WEb_YL1K1Qg

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu
https://arxiv.org/abs/2509.13790

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting i…

When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
Ming Tang, Xiaowen Huang, Jitao Sang
https://arxiv.org/abs/2508.11516

When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
Recommender systems increasingly suffer from echo chambers and user homogenization, systemic distortions arising from the dynamic interplay between algorithmic recommendations and human behavior. While prior work has studied these phenomena through the lens of algorithmic bias or social network structure, we argue that the psychological mechanisms of users and the closed-loop interaction between users and recommenders are critical yet understudied drivers of these emergent effects. To bridge th…

Secure, Scalable and Privacy Aware Data Strategy in Cloud
Vijay Kumar Butte, Sujata Butte
https://arxiv.org/abs/2509.13627 https://arxiv.org/pdf/2509.13627…

Secure, Scalable and Privacy Aware Data Strategy in Cloud
The enterprises today are faced with the tough challenge of processing, storing large amounts of data in a secure, scalable manner and enabling decision makers to make quick, informed data driven decisions. This paper addresses this challenge and develops an effective enterprise data strategy in the cloud. Various components of an effective data strategy are discussed and architectures addressing security, scalability and privacy aspects are provided.

RepCaM : Exploring Transparent Visual Prompt With Inference-Time Re-Parameterization for Neural Video Delivery
Rongyu Zhang, Xize Duan, Jiaming Liu, Li Du, Yuan Du, Dan Wang, Shanghang Zhang, Fangxin Wang
https://arxiv.org/abs/2509.14002

RepCaM++: Exploring Transparent Visual Prompt With Inference-Time Re-Parameterization for Neural Video Delivery
Recently, content-aware methods have been employed to reduce bandwidth and enhance the quality of Internet video delivery. These methods involve training distinct content-aware super-resolution (SR) models for each video chunk on the server, subsequently streaming the low-resolution (LR) video chunks with the SR models to the client. Prior research has incorporated additional partial parameters to customize the models for individual video chunks. However, this leads to parameter accumulation an…

TACR-YOLO: A Real-time Detection Framework for Abnormal Human Behaviors Enhanced with Coordinate and Task-Aware Representations
Xinyi Yin, Wenbo Yuan, Xuecheng Wu, Liangyu Fu, Danlei Huang
https://arxiv.org/abs/2508.11478

TACR-YOLO: A Real-time Detection Framework for Abnormal Human Behaviors Enhanced with Coordinate and Task-Aware Representations
Abnormal Human Behavior Detection (AHBD) under special scenarios is becoming increasingly crucial. While YOLO-based detection methods excel in real-time tasks, they remain hindered by challenges including small objects, task conflicts, and multi-scale fusion in AHBD. To tackle them, we propose TACR-YOLO, a new real-time framework for AHBD. We introduce a Coordinate Attention Module to enhance small object detection, a Task-Aware Attention Module to deal with classification-regression conflicts,…

ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting
Hyotaek Jeon, Hyunwook Lee, Juwon Kim, Sungahn Ko
https://arxiv.org/abs/2509.13753 https://…

ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting
Traffic forecasting represents a crucial problem within intelligent transportation systems. In recent research, Large Language Models (LLMs) have emerged as a promising method, but their intrinsic design, tailored primarily for sequential token processing, introduces notable challenges in effectively capturing spatial dependencies. Specifically, the inherent limitations of LLMs in modeling spatial relationships and their architectural incompatibility with graph-structured spatial data remain la…

INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems
Kaiyuan Li, Dongdong Mao, Yongxiang Tang, Yanhua Cheng, Yanxiang Zeng, Chao Wang, Xialong Liu, Peng Jiang
https://arxiv.org/abs/2508.11565

INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems
Feature interaction has long been a cornerstone of ranking models in large-scale recommender systems due to its proven effectiveness in capturing complex dependencies among features. However, existing feature interaction strategies face two critical challenges in industrial applications: (1) The vast number of categorical and sequential features makes exhaustive interaction computationally prohibitive, often resulting in optimization difficulties. (2) Real-world recommender systems typically in…

Towards Weight Distribution-Aware Polar Codes
Mohammad Rowshan, Vlad-Florin Dragoi
https://arxiv.org/abs/2506.15467 https://arxiv.org…

Towards Weight Distribution-Aware Polar Codes
Polar codes are constructed based on the reliability of sub-channels resulting from the polarization effect. However, this information-theoretic construction approach leads to a poor weight distribution. To address this issue, pre-transformed polar codes, such as CRC-polar codes and PAC codes, have been employed. In this paper, we focus on the structure of polar codes without applying any pre-transformations and explore methods, guided by the weight-contribution partial order, to design polar-l…

MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
Soheil Mahdizadeh, Elyas Oustad, Mohsen Ansari
https://arxiv.org/abs/2507.12028

MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
Task offloading in three-layer fog computing environments presents a critical challenge due to user equipment (UE) mobility, which frequently triggers costly service migrations and degrades overall system performance. This paper addresses this problem by proposing MOFCO, a novel Mobility- and Migration-aware Task Offloading algorithm for Fog Computing environments. The proposed method formulates task offloading and resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem and…

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
Pawe{\l} M\k{a}ka, Yusuf Can Semerci, Jan Scholtes, Gerasimos Spanakis
https://arxiv.org/abs/2509.14031

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
Achieving human-level translations requires leveraging context to ensure coherence and handle complex phenomena like pronoun disambiguation. Sparsity of contextually rich examples in the standard training data has been hypothesized as the reason for the difficulty of context utilization. In this work, we systematically validate this claim in both single- and multilingual settings by constructing training datasets with a controlled proportions of contextually relevant examples. We demonstrate a …

Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization
SaiKrishna Saketh Yellapragada, Esa Ollila, Mario Costa
https://arxiv.org/abs/2509.13786 https:/…

Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization
As wireless communication systems advance toward Sixth Generation (6G) Radio Access Networks (RAN), Deep Learning (DL)-based neural receivers are emerging as transformative solutions for Physical Layer (PHY) processing, delivering superior Block Error Rate (BLER) performance compared to traditional model-based approaches. Practical deployment on resource-constrained hardware, however, requires efficient quantization to reduce latency, energy, and memory without sacrificing reliability. We exten…

Enterprise AI Must Enforce Participant-Aware Access Control
Shashank Shreedhar Bhatt, Tanmay Rajore, Khushboo Aggarwal, Ganesh Ananthanarayanan, Ranveer Chandra, Nishanth Chandran, Suyash Choudhury, Divya Gupta, Emre Kiciman, Sumit Kumar Pandey, Srinath Setty, Rahul Sharma, Teijia Zhao
https://arxiv.org/abs/2509.14608

Enterprise AI Must Enforce Participant-Aware Access Control
Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data. While fine-tuning enhances performance by internalizing domain knowledge, it also introduces a critical security risk: leakage of confidential training data to unauthorized users. These risks are exacerbated when LLMs are combined with Retrieval-Augmented Generation (RAG) pipelines that dynamically fetch contextual docume…

GeoAware-VLA: Implicit Geometry Aware Vision-Language-Action Model
Ali Abouzeid, Malak Mansour, Zezhou Sun, Dezhen Song
https://arxiv.org/abs/2509.14117 https://

GeoAware-VLA: Implicit Geometry Aware Vision-Language-Action Model
Vision-Language-Action (VLA) models often fail to generalize to novel camera viewpoints, a limitation stemming from their difficulty in inferring robust 3D geometry from 2D images. We introduce GeoAware-VLA, a simple yet effective approach that enhances viewpoint invariance by integrating strong geometric priors into the vision backbone. Instead of training a visual encoder or relying on explicit 3D data, we leverage a frozen, pretrained geometric vision model as a feature extractor. A trainabl…

Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks
Yuzhen Zhan, Li Jin
https://arxiv.org/abs/2507.12975 https://…

Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks
We consider the cyber-physical security of parallel server systems, which is relevant for a variety of engineering applications such as networking, manufacturing, and transportation. These systems rely on feedback control and may thus be vulnerable to malicious attacks such as denial-of-service, data falsification, and instruction manipulations. In this paper, we develop a learning algorithm that computes a defensive strategy to balance technological cost for defensive actions and performance d…

NFLPA dispute emerges over lawsuit awareness https://www.espn.com/nfl/story/_/id/45762449/nflpa-members-disagree-knowledge-lloyd-howell-lawsuit

RegimeNAS: Regime-Aware Differentiable Architecture Search With Theoretical Guarantees for Financial Trading
Prathamesh Devadiga, Yashmitha Shailesh
https://arxiv.org/abs/2508.11338

RegimeNAS: Regime-Aware Differentiable Architecture Search With Theoretical Guarantees for Financial Trading
We introduce RegimeNAS, a novel differentiable architecture search framework specifically designed to enhance cryptocurrency trading performance by explicitly integrating market regime awareness. Addressing the limitations of static deep learning models in highly dynamic financial environments, RegimeNAS features three core innovations: (1) a theoretically grounded Bayesian search space optimizing architectures with provable convergence properties; (2) specialized, dynamically activated neural …

Collaborative Interest-aware Graph Learning for Group Identification
Rui Zhao, Beihong Jin, Beibei Li, Yiyuan Zheng
https://arxiv.org/abs/2506.14826 https:…

Collaborative Interest-aware Graph Learning for Group Identification
With the popularity of social media, an increasing number of users are joining group activities on online social platforms. This elicits the requirement of group identification (GI), which is to recommend groups to users. We reveal that users are influenced by both group-level and item-level interests, and these dual-level interests have a collaborative evolution relationship: joining a group expands the user's item interests, further prompting the user to join new groups. Ultimately, the two i…

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
Yuhui Shi, Yehan Yang, Qiang Sheng, Hao Mi, Beizhe Hu, Chaoxi Xu, Juan Cao
https://arxiv.org/abs/2506.15683

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
With the popularity of large language models (LLMs), undesirable societal problems like misinformation production and academic misconduct have been more severe, making LLM-generated text detection now of unprecedented importance. Although existing methods have made remarkable progress, a new challenge posed by text from privately tuned LLMs remains underexplored. Users could easily possess private LLMs by fine-tuning an open-source one with private corpora, resulting in a significant performanc…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Seongeun Ryu, Yunyong Ko, Sang-Wook Kim
https://arxiv.org/abs/2508.13064 https:/…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has improved interest matching through refined representations of news and users, the following time-related challenges remain underexplored: (C1) leveraging the age of clicked news to infer users' interest persistence, and (C2) modeling the varying lifetime of news across topics and us…

BEVUDA : Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection
Rongyu Zhang, Jiaming Liu, Xiaoqi Li, Xiaowei Chi, Dan Wang, Li Du, Yuan Du, Shanghang Zhang
https://arxiv.org/abs/2509.14151

BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection
Vision-centric Bird's Eye View (BEV) perception holds considerable promise for autonomous driving. Recent studies have prioritized efficiency or accuracy enhancements, yet the issue of domain shift has been overlooked, leading to substantial performance degradation upon transfer. We identify major domain gaps in real-world cross-domain scenarios and initiate the first effort to address the Domain Adaptation (DA) challenge in multi-view 3D object detection for BEV perception. Given the complexit…

CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping
Zijian An, Ran Yang, Yiming Feng, Lifeng Zhou
https://arxiv.org/abs/2509.14143 https://

CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping
Vision-language-action (VLA) models have recently emerged as a promising paradigm for robotic control, enabling end-to-end policies that ground natural language instructions into visuomotor actions. However, current VLAs often struggle to satisfy precise task constraints, such as stopping based on numeric thresholds, since their observation-to-action mappings are implicitly shaped by training data and lack explicit mechanisms for condition monitoring. In this work, we propose CLAW (CLIP-Languag…

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou, Peiyuan Liu, Yujin Tang, Rong Jin, Liang Sun
https://arxiv.org/abs/2509.14181

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because they have shown little performance advantage. We challenge this view and demonstrate that explicit representation alignment can supply critical information that bridges the distributional gap between input hist…

Multi-Satellite Cooperative MIMO Transmission: Statistical CSI-Aware RSMA Precoding Design
Sangwon Jo, Seok-Hwan Park
https://arxiv.org/abs/2508.11132 https://

Multi-Satellite Cooperative MIMO Transmission: Statistical CSI-Aware RSMA Precoding Design
We investigate inter-satellite cooperative transmission in a multiple low-Earth orbit (LEO) satellite communication system to enhance spectral efficiency. Specifically, we design multiple-input multipleoutput (MIMO) precoding at LEO satellites for cooperative rate-splitting multiple access (RSMA). Given the difficulty of acquiring instantaneous channel state information (iCSI) due to long delays and Doppler effects, we formulate an ergodic max-min fairness rate (MMFR) maximization problem based…

Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
Sudeshna Jana, Manjira Sinha, Tirthankar Dasgupta
https://arxiv.org/abs/2509.11330

Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
The widespread use of plastics and their persistence in the environment have led to the accumulation of micro- and nano-plastics across air, water, and soil, posing serious health risks including respiratory, gastrointestinal, and neurological disorders. We propose a novel framework that leverages large language models to extract relational metapaths, multi-hop semantic chains linking pollutant sources to health impacts, from scientific abstracts. Our system identifies and connects entities acr…

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Lianshuai Guo, Zhongzheng Yuan, Xunkai Li, Yinlin Zhu, Meixia Qu, Wenyu Wang
https://arxiv.org/abs/2508.11530

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant challenge arises as existing DFL optimization strategies, primarily designed for tasks such as computer vision, fail to address the unique topological information inherent in the local subgraph. Notably, while Federated Graph Learning (FGL) is tailored for graph data, it is predomi…

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
Jingyi Yuan, Jianxiong Ye, Wenkang Chen, Chenqiang Gao
https://arxiv.org/abs/2509.14084

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
Zero-Shot Anomaly Detection (ZSAD) seeks to identify anomalies from arbitrary novel categories, offering a scalable and annotation-efficient solution. Traditionally, most ZSAD works have been based on the CLIP model, which performs anomaly detection by calculating the similarity between visual and text embeddings. Recently, vision foundation models such as DINOv3 have demonstrated strong transferable representation capabilities. In this work, we are the first to adapt DINOv3 for ZSAD. However, …

Whole-body Motion Control of an Omnidirectional Wheel-Legged Mobile Manipulator via Contact-Aware Dynamic Optimization
Zong Chen, Shaoyang Li, Ben Liu, Min Li, Zhouping Yin, Yiqun Li
https://arxiv.org/abs/2509.14010

Whole-body Motion Control of an Omnidirectional Wheel-Legged Mobile Manipulator via Contact-Aware Dynamic Optimization
Wheel-legged robots with integrated manipulators hold great promise for mobile manipulation in logistics, industrial automation, and human-robot collaboration. However, unified control of such systems remains challenging due to the redundancy in degrees of freedom, complex wheel-ground contact dynamics, and the need for seamless coordination between locomotion and manipulation. In this work, we present the design and whole-body motion control of an omnidirectional wheel-legged quadrupedal robot…

Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
Yuxuan Xia, \'Angel F. Garc\'ia-Fern\'andez, Johan Karlsson, Yu Ge, Lennart Svensson, Ting Yuan
https://arxiv.org/abs/2506.15148

Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
This paper presents a generalization of the trajectory general optimal sub-pattern assignment (GOSPA) metric for evaluating multi-object tracking algorithms that provide trajectory estimates with track-level uncertainties. This metric builds on the recently introduced probabilistic GOSPA metric to account for both the existence and state estimation uncertainties of individual object states. Similar to trajectory GOSPA (TGOSPA), it can be formulated as a multidimensional assignment problem, and …

MAPS: A Mode-Aware Probabilistic Scheduling Framework for LPV-Based Adaptive Control
Taehun Kim, Guntae Kim, Cheolmin Jeong, Chang Mook Kang
https://arxiv.org/abs/2509.12695 htt…

MAPS: A Mode-Aware Probabilistic Scheduling Framework for LPV-Based Adaptive Control
This paper proposes Mode-Aware Probabilistic Scheduling (MAPS), a novel adaptive control framework tailored for DC motor systems experiencing varying friction. MAPS uniquely integrates an Interacting Multiple Model (IMM) estimator with a Linear Parameter-Varying (LPV) based control strategy, leveraging real-time mode probability estimates to perform probabilistic gain scheduling. A key innovation of MAPS lies in directly using the updated mode probabilities as the interpolation weights for onli…

PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
Li Fu, Yu Xin, Sunlu Zeng, Lu Fan, Youzheng Wu, Xiaodong He
https://arxiv.org/abs/2509.12647

PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
This paper presents a Pronunciation-Aware Contextualized (PAC) framework to address two key challenges in Large Language Model (LLM)-based Automatic Speech Recognition (ASR) systems: effective pronunciation modeling and robust homophone discrimination. Both are essential for raw or long-tail word recognition. The proposed approach adopts a two-stage learning paradigm. First, we introduce a pronunciation-guided context learning method. It employs an interleaved grapheme-phoneme context modeling …

Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments
Jeongyong Yang, KwangBin Lee, SooJean Han
https://arxiv.org/abs/2507.11920 …

Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments
Real-time path planning in dense, uncertain environments remains a challenging problem, as predicting the future motions of numerous dynamic obstacles is computationally burdensome and unrealistic. To address this, we introduce Hybrid Prediction-based Risk-Aware Planning (HyPRAP), a prediction-based risk-aware path-planning framework which uses a hybrid combination of models to predict local obstacle movement. HyPRAP uses a novel Prediction-based Collision Risk Index (P-CRI) to evaluate the ris…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/3]:
- Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Jantre, Wang, Park, Chopra, Jeon, Qian, Urban, Yoon

Preservation of Language Understanding Capabilities in Speech-aware Large Language Models
Marek Kubis, Pawe{\l} Sk\'orzewski, Iwona Christop, Mateusz Czy\.znikiewicz, Jakub Kubiak, {\L}ukasz Bondaruk, Marcin Lewandowski
https://arxiv.org/abs/2509.12171

Preservation of Language Understanding Capabilities in Speech-aware Large Language Models
The paper presents C3T (Cross-modal Capabilities Conservation Test), a new benchmark for assessing the performance of speech-aware large language models. The benchmark utilizes textual tasks and a voice cloning text-to-speech model to quantify the extent to which language understanding capabilities are preserved when the model is accessed via speech input. C3T quantifies the fairness of the model for different categories of speakers and its robustness across text and speech modalities.

Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
An-Lun Liu, Yu-Wei Chao, Yi-Ting Chen
https://arxiv.org/abs/2507.11287 https://…

Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
In this paper, we study task-oriented human grasp synthesis, a new grasp synthesis task that demands both task and context awareness. At the core of our method is the task-aware contact maps. Unlike traditional contact maps that only reason about the manipulated object and its relation with the hand, our enhanced maps take into account scene and task information. This comprehensive map is critical for hand-object interaction, enabling accurate grasping poses that align with the task. We propose…

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Xiucheng Wang, Qiming Zhang, Nan Cheng, Junting Chen, Zezhong Zhang, Zan Li, Shuguang Cui, Xuemin Shen
https://arxiv.org/abs/2507.12166

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fixed 2D plane, neglecting key parameters such as direction of arrival (DoA), time of arrival (ToA), and vertical spatial variations. Such a limitation is primarily due to the relian…

Positional Encoding via Token-Aware Phase Attention
Yu (Sid), Wang, Sheng Shen, R\'emi Munos, Hongyuan Zhan, Yuandong Tian
https://arxiv.org/abs/2509.12635 https://

Positional Encoding via Token-Aware Phase Attention
We prove under practical assumptions that Rotary Positional Embedding (RoPE) introduces an intrinsic distance-dependent bias in attention scores that limits RoPE's ability to model long-context. RoPE extension methods may alleviate this issue, but they typically require post-hoc adjustments after pretraining, such as rescaling or hyperparameters retuning. This paper introduces Token-Aware Phase Attention (TAPA), a new positional encoding method that incorporates a learnable phase function into …

PaiP: An Operational Aware Interactive Planner for Unknown Cabinet Environments
Chengjin Wang, Zheng Yan, Yanmin Zhou, Runjie Shen, Zhipeng Wang, Bin Cheng, Bin He
https://arxiv.org/abs/2509.11516

PaiP: An Operational Aware Interactive Planner for Unknown Cabinet Environments
Box/cabinet scenarios with stacked objects pose significant challenges for robotic motion due to visual occlusions and constrained free space. Traditional collision-free trajectory planning methods often fail when no collision-free paths exist, and may even lead to catastrophic collisions caused by invisible objects. To overcome these challenges, we propose an operational aware interactive motion planner (PaiP) a real-time closed-loop planning framework utilizing multimodal tactile perception. …

E2-BKI: Evidential Ellipsoidal Bayesian Kernel Inference for Uncertainty-aware Gaussian Semantic Mapping
Junyoung Kim, Minsik Jeon, Jihong Min, Kiho Kwak, Junwon Seo
https://arxiv.org/abs/2509.11964

E2-BKI: Evidential Ellipsoidal Bayesian Kernel Inference for Uncertainty-aware Gaussian Semantic Mapping
Semantic mapping aims to construct a 3D semantic representation of the environment, providing essential knowledge for robots operating in complex outdoor settings. While Bayesian Kernel Inference (BKI) addresses discontinuities of map inference from sparse sensor data, existing semantic mapping methods suffer from various sources of uncertainties in challenging outdoor environments. To address these issues, we propose an uncertainty-aware semantic mapping framework that handles multiple sources…

Travel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Framework
Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler
https://arxiv.org/abs/2509.12043 h…

Travel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Framework
Traffic flow forecasting is essential for managing congestion, improving safety, and optimizing various transportation systems. However, it remains a prevailing challenge due to the stochastic nature of urban traffic and environmental factors. Better predictions require models capable of accommodating the traffic variability influenced by multiple dynamic and complex interdependent factors. In this work, we propose a Graph Neural Network (GNN) framework to address the stochasticity by leveragin…

CATVis: Context-Aware Thought Visualization
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
https://arxiv.org/abs/2507.11522 https://

CATVis: Context-Aware Thought Visualization
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) captio…

Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translation
Ziyu Ge, Gabriel Chua, Leanne Tan, Roy Ka-Wei Lee
https://arxiv.org/abs/2507.11966 …

Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translation
As online communication increasingly incorporates under-represented languages and colloquial dialects, standard translation systems often fail to preserve local slang, code-mixing, and culturally embedded markers of harmful speech. Translating toxic content between low-resource language pairs poses additional challenges due to scarce parallel data and safety filters that sanitize offensive expressions. In this work, we propose a reproducible, two-stage framework for toxicity-preserving translat…

Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification
Md. Mithun Hossain, Sanjara, Md. Shakil Hossain, Sudipto Chaki
https://arxiv.org/abs/2509.11604

Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification
Entity-level sentiment classification involves identifying the sentiment polarity linked to specific entities within text. This task poses several challenges: effectively modeling the subtle and complex interactions between entities and their surrounding sentiment expressions; capturing dependencies that may span across sentences; and ensuring consistent sentiment predictions for multiple mentions of the same entity through coreference resolution. Additionally, linguistic phenomena such as nega…

Describe Anything Model for Visual Question Answering on Text-rich Images
Yen-Linh Vu, Dinh-Thang Duong, Truong-Binh Duong, Anh-Khoi Nguyen, Thanh-Huy Nguyen, Le Thien Phuc Nguyen, Jianhua Xing, Xingjian Li, Tianyang Wang, Ulas Bagci, Min Xu
https://arxiv.org/abs/2507.12441

Describe Anything Model for Visual Question Answering on Text-rich Images
Recent progress has been made in region-aware vision-language modeling, particularly with the emergence of the Describe Anything Model (DAM). DAM is capable of generating detailed descriptions of any specific image areas or objects without the need for additional localized image-text alignment supervision. We hypothesize that such region-level descriptive capability is beneficial for the task of Visual Question Answering (VQA), especially in challenging scenarios involving images with dense tex…

GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
Jizhuo Chen, Diwen Liu, Jiaming Wang, Harold Soh
https://arxiv.org/abs/2509.11594 https://

GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refines the model to match true grasp outcomes. A PointNet++ style point cloud encoder with an MLP scores dense grids of candidate poses, enabling rapid online selection without full task-and-motion optimization.…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation
Ray, Pan, Gu, Du, Feng, Ananthanarayanan, Netravali, Jiang

KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding
Luohe Shi, Zuchao Li, Lefei Zhang, Guoming Liu, Baoyuan Qi, Hai Zhao
https://arxiv.org/abs/2507.11273

KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding
Large language models (LLMs) based on Transformer Decoders have become the preferred choice for conversational generative AI. Despite the overall superiority of the Decoder architecture, the gradually increasing Key-Value (KV) cache during inference has emerged as a primary efficiency bottleneck, both in aspects of memory consumption and data transfer bandwidth limitations. To address these challenges, we propose a paradigm called KV-Latent. By down-sampling the Key-Value vector dimensions into…

Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
Junsu Kim, Naeun Kim, Jaeho Lee, Incheol Park, Dongyoon Han, Seungryul Baek
https://arxiv.org/abs/2507.13314

Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
The reasoning-based pose estimation (RPE) benchmark has emerged as a widely adopted evaluation standard for pose-aware multimodal large language models (MLLMs). Despite its significance, we identified critical reproducibility and benchmark-quality issues that hinder fair and consistent quantitative evaluations. Most notably, the benchmark utilizes different image indices from those of the original 3DPW dataset, forcing researchers into tedious and error-prone manual matching processes to obtain…

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta, Jovin D'sa, Faizan M. Tariq, David Isele, Sangjae Bae
https://arxiv.org/abs/2507.11940

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems from the AVs' limited ability to anticipate and respond to the interactive behavior of surrounding agents. Traditional decoupled prediction and planning pipelines rely on non-interactive predictions that overlook the fact that agents often adapt their behavior in response to the AV's actions. To address this, we propose …

Prominence-aware automatic speech recognition for conversational speech
Julian Linke, Barbara Schuppler
https://arxiv.org/abs/2509.10116 https://arxiv.org/…

Prominence-aware automatic speech recognition for conversational speech
This paper investigates prominence-aware automatic speech recognition (ASR) by combining prominence detection and speech recognition for conversational Austrian German. First, prominence detectors were developed by fine-tuning wav2vec2 models to classify word-level prominence. The detector was then used to automatically annotate prosodic prominence in a large corpus. Based on those annotations, we trained novel prominence-aware ASR systems that simultaneously transcribe words and their prominen…

SAMP: Spatial Anchor-based Motion Policy for Collision-Aware Robotic Manipulators
Kai Chen, Zhihai Bi, Guoyang Zhao, Chunxin Zheng, Yulin Li, Hang Zhao, Jun Ma
https://arxiv.org/abs/2509.11185

SAMP: Spatial Anchor-based Motion Policy for Collision-Aware Robotic Manipulators
Neural-based motion planning methods have achieved remarkable progress for robotic manipulators, yet a fundamental challenge lies in simultaneously accounting for both the robot's physical shape and the surrounding environment when generating safe and feasible motions. Moreover, existing approaches often rely on simplified robot models or focus primarily on obstacle representation, which can lead to incomplete collision detection and degraded performance in cluttered scenes. To address these li…

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
Chenye Zou, Xingyue Wen, Tianyi Hu, Qian Janice Wang, Daniel Hershcovich
https://arxiv.org/abs/2509.12961

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
Recent advances in large language models (LLMs) have opened the door to culture-aware language tasks. We introduce the novel problem of adapting wine reviews across Chinese and English, which goes beyond literal translation by incorporating regional taste preferences and culture-specific flavor descriptors. In a case study on cross-cultural wine review adaptation, we compile the first parallel corpus of professional reviews, containing 8k Chinese and 16k Anglophone reviews. We benchmark both ne…