Tootfinder

Finished “Orbital” by Samantha Harvey, the short novel and 2024 Booker Prize winner. I am at a loss to understand how that could possibly be so. I hated it. Because it is short and in hope there was something redeeming, I saw it through, regrettably.
There are moments of beautifully lyrical prose, but my god… just endless lists of things. The book is plotless, which can work in an essay structure. But it needs to be compelling. This wasn’t.
1/5 stars ⭐️

Orbital
A slender novel of epic power and the winner of the Boo…

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Hans-Arno Jacobsen
https://arxiv.org/abs/2508.12472

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasonin…

A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari, Mohamed Chahine Ghanem
https://arxiv.org/abs/2508.12470 …

A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
The increased Internet of Medical Things IoMT and the Industrial Internet of Things IIoT interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces a novel transformer-based intrusion detection system IDS, termed BiGAT-ID a hybrid model that combines bidirectional gated recurrent units BiGRU, long short-term memory LSTM networks, and multi-head att…

An Improved Solution to the Two Normal Means Problem via Regularization
Yang Liu, Jonathan P. Williams
https://arxiv.org/abs/2508.13012 https://arxiv.org/p…

An Improved Solution to the Two Normal Means Problem via Regularization
The many-normal-means problem is a classic example that motivates the development of many important inferential procedures in the history of statistics. In this short note, we consider a further special case of the problem, which involves only two normally distributed data points with a constraint that the pair of means are not too far apart from one another. Starting with a regularized ML estimator, we construct a novel possibilistic IM for marginal inference on one of the two means. Not only …

Time-Varying Heterogeneous Treatment Effects in Event Studies
Irene Botosaru, Laura Liu
https://arxiv.org/abs/2509.13698 https://arxiv.org/pdf/2509.13698…

Time-Varying Heterogeneous Treatment Effects in Event Studies
This paper examines the identification and estimation of heterogeneous treatment effects in event studies, emphasizing the importance of both lagged dependent variables and treatment effect heterogeneity. We show that omitting lagged dependent variables can induce omitted variable bias in the estimated time-varying treatment effects. We develop a novel semiparametric approach based on a short-T dynamic linear panel model with correlated random coefficients, where the time-varying heterogeneous …

Day 23: Thi Bui
Indirect CW: parental neglect, war, intergenerational trauma
Bui is the author of "The Best We Could Do", a graphic memoir which explores her relationship with her parents and unpacks some of the intergenerational trauma coming out of the Vietnam War. It has a lot of wisdom to offer about both dealing with troubled parents as a 1.5th-generation immigrant, and it delves deeply into her parents' histories in Vietnam and the complexities of the situation there both in the north and in the south. It's beautifully illustrated and very nicely plotted together given all the disparate threads it is working with.
I haven't read any of Bui's other work, but it looks like she's published a picture book for kids as well as a series of short comics during the pandemic. Besides Oseman who also writes non-illustrated fiction and the two manga artists Ice mentioned, Bui is the first graphic novel author I've included here, but I've actually got quite a few of them in my longer list, one of whom may make it into the 30 I'll include in this thread. These days I'm reading a bunch of graphic novels since they're easy to get through, and the variety of stories and perspectives in that space is wonderful these days, with a huge array of indie stuff that probably never would have gotten off the ground in traditional publishing/comics spaces.
#30AuthorsNoMen

CWT-LSTM Autoencoder: A Novel Approach for Gravitational Wave Detection in Synthetic Data
Jericho Cain
https://arxiv.org/abs/2509.10505 https://arxiv.org/p…

CWT-LSTM Autoencoder: A Novel Approach for Gravitational Wave Detection in Synthetic Data
Gravitational wave detection requires sophisticated signal processing to identify weak astrophysical signals buried in instrumental noise. Traditional matched filtering approaches face computational challenges with diverse signal morphologies and non-stationary noise. This work presents a deep learning approach combining Continuous Wavelet Transform (CWT) preprocessing with Long Short-Term Memory (LSTM) autoencoder architecture for gravitational wave detection in synthetic data. The CWT provide…

Adaptive Temporal Fusion Transformers for Cryptocurrency Price Prediction
Arash Peik, Mohammad Ali Zare Chahooki, Amin Milani Fard, Mehdi Agha Sarram
https://arxiv.org/abs/2509.10542

Adaptive Temporal Fusion Transformers for Cryptocurrency Price Prediction
Precise short-term price prediction in the highly volatile cryptocurrency market is critical for informed trading strategies. Although Temporal Fusion Transformers (TFTs) have shown potential, their direct use often struggles in the face of the market's non-stationary nature and extreme volatility. This paper introduces an adaptive TFT modeling approach leveraging dynamic subseries lengths and pattern-based categorization to enhance short-term forecasting. We propose a novel segmentation method…

Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
https://arxiv.org/abs/2508.08454

Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Accurately modeling user preferences is crucial for improving the performance of content-based recommender systems. Existing approaches often rely on simplistic user profiling methods, such as averaging or concatenating item embeddings, which fail to capture the nuanced nature of user preference dynamics, particularly the interactions between long-term and short-term preferences. In this work, we propose LLM-driven Temporal User Profiling (LLM-TUP), a novel method for user profiling that explic…

PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
Dawei Xiang, Wenyan Xu, Kexin Chu, Zixu Shen, Tianqi Ding, Wei Zhang
https://arxiv.org/abs/2509.12446 https:/…

PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context-often through multiple rounds of refinement. We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process. Our system decomposes the task into four specialized agents that work collaboratively to transform a short, …

Challenge: "Name 20 female authors you admire, 1 per day"
Day 17: Vicki Jarrett
So far she's published a couple of really good novels and a collection of short stories. She strikes me as the type of author who aims for quality, not quantity.
I adored her first novel "Nothing is Heavy", based on real life experiences in Edinburgh. Her other novel, "Always North" is an eco-thriller set in Svalbard, a Norwegian arctic island.
Both great…

DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
Taeyeop Lee, Gyuree Kang, Bowen Wen, Youngho Kim, Seunghyeok Back, In So Kweon, David Hyunchul Shim, Kuk-Jin Yoon
https://arxiv.org/abs/2510.05662

DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic grasping capabilities.Although some methods have partially addressed these issues, most of them have limitations in generalizability to novel objects and are insufficient for precise long-horizon robot manipulation. To address this limitation, we propose DeLTa (Demonstration and Language-Guided Novel Transparent Object Manip…

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Stefano Riva, Carolina Introini, Jos\`e Nathan Kutz, Antonio Cammi
https://arxiv.org/abs/2510.12368

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including unobservable fields, which is agnostic to sensor positions and able to handle noisy data through an ensemble strategy, leveraging the short training times and without the need for …

Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement
Ruirui Lin, Guoxi Huang, Nantheera Anantrasirichai
https://arxiv.org/abs/2510.09450 https://

Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement
Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradations. Learning-based approaches offer fast inference but still struggle with heavy noise in real low-light scenes, primarily due to limitations in effectively leveraging temporal information. In this paper, we address this issue with DWTA-Net, a novel two-stage framework that jointly exploits short- and long-term temporal cues. Stage I employs Visual State-Space blocks for multi-frame alignment, reco…

Online Dose Verification in VHEE Radiotherapy Using Bremsstrahlung Radiation
Francesco Urso, Pietro Carra, Esther Ciarrocchi, Matteo Morrocchia, Maria Giuseppina Bisogni
https://arxiv.org/abs/2509.13056

Online Dose Verification in VHEE Radiotherapy Using Bremsstrahlung Radiation
Very high energy electrons (VHEE) in the 50-250 MeV range, delivered in short pulses at ultra-high dose rates, are proposed for clinical FLASH radiotherapy (RT) targeting deep-seated tumors. The clinical implementation of VHEE-FLASH RT requires online verification to optimize dose delivery. In this study we propose a novel online dose verification technique based on the detection of bremsstrahlung photons during VHEE interactions with matter. A polymethyl methacrylate (PMMA) phantom was simulat…

Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare
Susanta Mitra
https://arxiv.org/abs/2509.11944 https://

Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare
Healthcare and medicine are multimodal disciplines that deal with multimodal data for reasoning and diagnosing multiple diseases. Although some multimodal reasoning models have emerged for reasoning complex tasks in scientific domains, their applications in the healthcare domain remain limited and fall short in correct reasoning for diagnosis. To address the challenges of multimodal medical reasoning for correct diagnosis and assist the healthcare professionals, a novel temporal graph-based rea…

Toward Efficient and Privacy-Aware eHealth Systems: An Integrated Sensing, Computing, and Semantic Communication Approach
Yinchao Yang, Yahao Ding, Zhaohui Yang, Chongwen Huang, Zhaoyang Zhang, Dusit Niyato, Mohammad Shikh-Bahaei
https://arxiv.org/abs/2510.11514

Toward Efficient and Privacy-Aware eHealth Systems: An Integrated Sensing, Computing, and Semantic Communication Approach
Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and se…

Chord: Chain of Rendering Decomposition for PBR Material Estimation from Generated Texture Images
Zhi Ying, Boxiang Rong, Jingyu Wang, Maoyuan Xu
https://arxiv.org/abs/2509.09952

Chord: Chain of Rendering Decomposition for PBR Material Estimation from Generated Texture Images
Material creation and reconstruction are crucial for appearance modeling but traditionally require significant time and expertise from artists. While recent methods leverage visual foundation models to synthesize PBR materials from user-provided inputs, they often fall short in quality, flexibility, and user control. We propose a novel two-stage generate-and-estimate framework for PBR material generation. In the generation stage, a fine-tuned diffusion model synthesizes shaded, tileable texture…

#WritersCoffeeClub
13. Talk about the joy writing has brought you.
14. Why do you write in the form you do? Why not in another format? (poem, short story, novel, etc)
15. How do your immediate surroundings influence your work?
---
13. A usual mix on neuromediators, I believe. Not sure if there's anything special about it. Hard to describe.
14. Trivial: that&…

Large Language Model Data Generation for Enhanced Intent Recognition in German Speech
Theresa Pekarek Rosin, Burak Can Kaplan, Stefan Wermter
https://arxiv.org/abs/2508.06277 ht…

Large Language Model Data Generation for Enhanced Intent Recognition in German Speech
Intent recognition (IR) for speech commands is essential for artificial intelligence (AI) assistant systems; however, most existing approaches are limited to short commands and are predominantly developed for English. This paper addresses these limitations by focusing on IR from speech by elderly German speakers. We propose a novel approach that combines an adapted Whisper ASR model, fine-tuned on elderly German speech (SVC-de), with Transformer-based language models trained on synthetic text d…

#FantasticPromptsEnglish 27.08.2025
Choose a scene that has stuck in your mind & rewrite it from memory.
I did once steal a scene outline from someone else's short story which I thought was really badly written, and built a whole novel around it which I'm on the whole pretty pleased with; but it's a novel I'll never share with anyone and definite…

In-situ characterisation and data-driven crystal plasticity analysis of short-to-long crack transition in a ductile aluminium alloy
Abdalrhaman Koko, Bemin Sheen, Caitlin Green, Fionn Dunne
https://arxiv.org/abs/2510.03713

In-situ characterisation and data-driven crystal plasticity analysis of short-to-long crack transition in a ductile aluminium alloy
Crack arrest in ductile alloys plays a critical role in damage-tolerant design for aerospace and structural applications, yet the transition from microstructure-sensitive short cracks to load-controlled long cracks remains poorly understood. Here, we present an in-situ, high-resolution experimental study of crack propagation in cold-worked 5052 aluminium alloy using scanning electron microscopy digital image correlation (SEM-DIC), electron backscatter diffraction (EBSD), and novel data-driven c…

LiDO: Discovery of a 10:1 Resonator with a Novel Libration State
Rosemary E. Pike, Ruth Murray-Clay, Kathryn Volk, Mike Alexandersen, Mark Comte, Samantha M. Lawler, Ying-Tung Chen, Arcelia Hermosillo Ruiz, Cameron Semenchuck, Cameron Collyer, J. J. Kavelaars, Lowell Peltier
https://arxiv.org/abs/2508.05627…

LiDO: Discovery of a 10:1 Resonator with a Novel Libration State
The Large inclination Distant Objects LiDO survey has discovered the first securely classified object in the 10:1 mean motion resonance of Neptune. This object, 2020 VN40, is short-term stable in the 10:1 resonance, but not stable on Gyr timescales. 2020 VN40 is likely part of the scattering sticking population, and temporarily resides in the 10:1 resonance at ~139.5 au. This discovery confirms that this distant resonance is populated, as a single detection is likely to be indicative of a large…

IOCC: Aligning Semantic and Cluster Centers for Few-shot Short Text Clustering
Jixuan Yin, Zhihao Yao, Wenshuai Huo, Xinmiao Yu, Xiaocheng Feng, Bo Li
https://arxiv.org/abs/2508.06126

IOCC: Aligning Semantic and Cluster Centers for Few-shot Short Text Clustering
In clustering tasks, it is essential to structure the feature space into clear, well-separated distributions. However, because short text representations have limited expressiveness, conventional methods struggle to identify cluster centers that truly capture each category's underlying semantics, causing the representations to be optimized in suboptimal directions. To address this issue, we propose IOCC, a novel few-shot contrastive learning method that achieves alignment between the cluster ce…

A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network
Bilal Dalgic (Manisa Celal Bayar University, Turkey), Betul Sen (Manisa Celal Bayar University, Turkey), Muge Erel-Ozcevik (Manisa Celal Bayar University, Turkey)
https://arxiv.org/abs/2509.20068

A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network
Secure monitoring and dynamic control in an IIoT environment are major requirements for current development goals. We believe that dynamic, secure monitoring of the IIoT environment can be achieved through integration with the Software-Defined Network (SDN) and Digital Twin (DT) paradigms. The current literature lacks implementation details for SDN-based DT and time-aware intelligent model training for short-term anomaly detection against IIoT threats. Therefore, we have proposed a novel framew…

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Muhammad Usama, Haris Koutsopoulos
https://arxiv.org/abs/2510.03121

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway…

AZRA: Extending the Affective Capabilities of Zoomorphic Robots using Augmented Reality
Shaun Macdonald, Salma ElSayed, Mark McGill
https://arxiv.org/abs/2508.08507 https://

AZRA: Extending the Affective Capabilities of Zoomorphic Robots using Augmented Reality
Zoomorphic robots could serve as accessible and practical alternatives for users unable or unwilling to keep pets. However, their affective interactions are often simplistic and short-lived, limiting their potential for domestic adoption. In order to facilitate more dynamic and nuanced affective interactions and relationships between users and zoomorphic robots we present AZRA, a novel augmented reality (AR) framework that extends the affective capabilities of these robots without physical modi…

BPI: A Novel Efficient and Reliable Search Structure for Hybrid Storage Blockchain
Xinkui Zhao, Rengrong Xiong, Guanjie Cheng, Xinhao Jin, Shawn Shi, Xiubo Liang, Gongsheng Yuan, Xiaoye Miao, Jianwei Yin, Shuiguang Deng
https://arxiv.org/abs/2509.00480

BPI: A Novel Efficient and Reliable Search Structure for Hybrid Storage Blockchain
Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SPs) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness. Although ADS can verify the existence of individual query results, they fall short of preventing SPs f…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration ...
Minwoo Oh, Minsu Park, Eunil Park

MuMTAffect: A Multimodal Multitask Affective Framework for Personality and Emotion Recognition from Physiological Signals
Meisam Jamshidi Seikavandi, Fabricio Batista Narcizo, Ted Vucurevich, Andrew Burke Dittberner, Paolo Burelli
https://arxiv.org/abs/2509.04254

MuMTAffect: A Multimodal Multitask Affective Framework for Personality and Emotion Recognition from Physiological Signals
We present MuMTAffect, a novel Multimodal Multitask Affective Embedding Network designed for joint emotion classification and personality prediction (re-identification) from short physiological signal segments. MuMTAffect integrates multiple physiological modalities pupil dilation, eye gaze, facial action units, and galvanic skin response using dedicated, transformer-based encoders for each modality and a fusion transformer to model cross-modal interactions. Inspired by the Theory of Constructe…

Using LLMs to Capture Users' Temporal Context for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
https://arxiv.org/abs/2508.08512 https://

Using LLMs to Capture Users' Temporal Context for Recommendation
Effective recommender systems demand dynamic user understanding, especially in complex, evolving environments. Traditional user profiling often fails to capture the nuanced, temporal contextual factors of user preferences, such as transient short-term interests and enduring long-term tastes. This paper presents an assessment of Large Language Models (LLMs) for generating semantically rich, time-aware user profiles. We do not propose a novel end-to-end recommendation architecture; instead, the c…

Just finished "Tall Water" written by SJ Sindu and illustrated by Dion MBD. An excellent semi-autobiographical graphic novel about the 2004 Indian Ocean tsunami, the Sri Lankan civil war, and parental estrangement.
Absolutely beautiful illustrations and a gripping plot had me tear through it in just an hour or so, though it's by no means short for a graphic novel. If my list of authors I deeply respect grows too fast, I'll just have to keep posting in my other thread indefinitely.
#AmReading

Challenge: "Name 20 female authors you admire, 1 per day"
Day 14:
Anne Donovan
Her novel "BuddhaDa" is one of the most clever, hilarious, uplifting and yet very serious works of literature I've ever read. It sits on my favourites shelf. As she writes the dialogues the way people talk, you will learn some Scots Glasgow dialect along the way. I had the pleasure of attending a reading with her at a local library in Edinburgh and she signed this much-love…

In-space manufacturing of optical lenses: Fluidic Shaping aboard the International Space Station
Omer Luria, Mor Elgarisi, Eytan Stibbe, Michael Lopez-Alegria, Crissy Canerday, Alexey Razin, Sivan Perl, Valeri Frumkin, Jonathan Ericson, Khaled Gommed, Daniel Widerker, Israel Gabay, Ruslan Belikov, Edward Balaban, Moran Bercovici
https://ar…

In-space manufacturing of optical lenses: Fluidic Shaping aboard the International Space Station
In-space manufacturing technologies are vital for enabling advanced space missions and addressing logistical limitations of space exploration. While additive manufacturing has progressed rapidly, it still falls short of delivering the ultra-smooth surfaces required for optical elements. Fluidic Shaping is a novel method that harnesses surface tension under microgravity to form optical components with exceptionally smooth surfaces. This study demonstrates the feasibility and potential of Fluidic…

Native Hybrid Attention for Efficient Sequence Modeling
Jusen Du, Jiaxi Hu, Tao Zhang, Weigao Sun, Yu Cheng
https://arxiv.org/abs/2510.07019 https://arxiv.…

Native Hybrid Attention for Efficient Sequence Modeling
Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recall accuracy over long contexts. In this work, we introduce Native Hybrid Attention (NHA), a novel hybrid architecture of linear and full attention that integrates both intra \& inter-layer hybridization into a unified layer design. NHA maintains long-term context in key-value slots updated by a linear RNN, and augments them with short-term tokens fro…

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
Ali Abu-Nada, Subhashish Banerjee
https://arxiv.org/abs/2509.22758

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
We present a \emph{novel} and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory using only local ancilla measurements. A system qubit is coherently coupled to an ancilla via a symmetric XY Hamiltonian; the ancilla interacts with a noisy environment and is the only qubit we measure. A feedforward neural network, trained on short sliding windows of supplementary data from the past, forecasts the observable system $\langle Z_{(S)…

Hyperbolic Mass in 2 1 Dimensions
Raphaela Wutte
https://arxiv.org/abs/2509.00994 https://arxiv.org/pdf/2509.00994

Hyperbolic Mass in 2+1 Dimensions
This short review surveys mass for two-dimensional asymptotically locally hyperbolic initial data sets. I explain the difficulties in defining mass in spatial dimension two, which are resolved via minimisation using a positive energy theorem, and review how gluing theorems can be used to construct novel initial data sets with controlled mass.

Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Kevin Bradley Dsouza, Graham Alexander Watt, Yuri Leonenko, Juan Moreno-Cruz
https://arxiv.org/abs/2508.06386

Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically opt…

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Xunzhi Xiang, Yabo Chen, Guiyu Zhang, Zhongyu Wang, Zhe Gao, Quanming Xiang, Gonghu Shang, Junqi Liu, Haibin Huang, Yang Gao, Chi Zhang, Qi Fan, Xuelong Li
https://arxiv.org/abs/2508.03334

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Current autoregressive diffusion models excel at video generation but are generally limited to short temporal durations. Our theoretical analysis indicates that the autoregressive modeling typically suffers from temporal drift caused by error accumulation and hinders parallelization in long video synthesis. To address these limitations, we propose a novel planning-then-populating framework centered on Macro-from-Micro Planning (MMPL) for long video generation. MMPL sketches a global storyline f…

Emulating Global 21 cm Cosmology Observations from the Lunar Far Side to Achieve Quick and Reliable Physical Constraints
J. Dorigo Jones, J. O. Burns, D. Rapetti, Shah Mohammad Bahauddin, B. Reyes, D. W. Barker
https://arxiv.org/abs/2509.00976

Emulating Global 21 cm Cosmology Observations from the Lunar Far Side to Achieve Quick and Reliable Physical Constraints
Efforts are underway to measure the global 21 cm signal from neutral hydrogen, which is a powerful probe of the early universe, using NASA radio telescopes on the far side of the Moon. Physics-based models of the signal are computationally expensive to perform Bayesian multi-parameter inferences, for which we have developed novel, publicly-available neural network emulators utilizing a Long Short-Term Memory (LSTM) network and a Kolmogorov-Arnold Network (KAN). $\texttt{21cmLSTM}$ is currently …

VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Andr\'e Hottung, Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, Daniel Wetzel, Michael R\"omer, Haoran Ye, Davide Zago, Michael Poli, Stefano Massaroli, Jinkyoo Park, Kevin Tierney
https://arxiv.org/abs/2510.07073

VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based code generation has recently shown promise across many domains, but it still falls short of producing heuristics that rival those crafted by human experts. In this paper, we propose VRPAgent, a framework that integrates LLM-generated components into a metaheuristic and refines them through a novel genetic search. By u…

When Collaborative Maintenance Falls Short: The Persistence of Retracted Papers on Wikipedia
Haohan Shi, Yulin Yu, Daniel M. Romero, Em\H{o}ke-\'Agnes Horv\'at
https://arxiv.org/abs/2509.18403 …

Designing a Layered Framework to Secure Data via Improved Multi Stage Lightweight Cryptography in IoT Cloud Systems
Hojjat Farshadinia, Ali Barati, Hamid Barati
https://arxiv.org/abs/2509.01717

Designing a Layered Framework to Secure Data via Improved Multi Stage Lightweight Cryptography in IoT Cloud Systems
This paper presents a novel multi-layered hybrid security approach aimed at enhancing lightweight encryption for IoT-Cloud systems. The primary goal is to overcome limitations inherent in conventional solutions such as TPA, Blockchain, ECDSA and ZSS which often fall short in terms of data protection, computational efficiency and scalability. Our proposed method strategically refines and integrates these technologies to address their shortcomings while maximizing their individual strengths. By d…

A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion
Yalda Zafari, Roaa Elalfy, Mohamed Mabrok, Somaya Al-Maadeed, Tamer Khattab, Essam A. Rashed
https://arxiv.org/abs/2507.16955

A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion
Early and accurate interpretation of screening mammograms is essential for effective breast cancer detection, yet it remains a complex challenge due to subtle imaging findings and diagnostic ambiguity. Many existing AI approaches fall short by focusing on single view inputs or single-task outputs, limiting their clinical utility. To address these limitations, we propose a novel multi-view, multitask hybrid deep learning framework that processes all four standard mammography views and jointly pr…

Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
Saeideh Bakhshi, Phuong Mai Nguyen, Robert Schiller, Tiantian Xu, Pawan Kodandapani, Andrew Levine, Cayman Simpson, Qifan Wang
https://arxiv.org/abs/2510.07621

Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
Recommendation systems have traditionally relied on short-term engagement signals, such as clicks and likes, to personalize content. However, these signals are often noisy, sparse, and insufficient for capturing long-term user satisfaction and retention. We introduce Retentive Relevance, a novel content-level survey-based feedback measure that directly assesses users' intent to return to the platform for similar content. Unlike other survey measures that focus on immediate satisfaction, Retenti…

A laser with instability reaching $4 \times 10^{-17}$ based on a 10-cm-long silicon cavity at sub-5-K temperatures
Zhi-Ang Chen, Hao-Ran Zeng, Wen-Wei Wang, Han Zhang, Run-Qi Lei, Jian-Zhang Li, Cai-Yin Pang, She-Song Huang, Xibo Zhang
https://arxiv.org/abs/2510.06636

A laser with instability reaching $4 \times 10^{-17}$ based on a 10-cm-long silicon cavity at sub-5-K temperatures
The realization of ultra-stable lasers with $10^{-17}$-level frequency stability has enabled a wide range of researches on precision metrology and fundamental science, where cryogenic single-crystalline cavities constitute the heart of such ultra-stable lasers. For further improvements in stability, increasing the cavity length at few-kelvin temperatures provides a promising alternative to utilizing relatively short cavities with novel coating, but has yet to be demonstrated with state-of-the-a…

Scenario-Agnostic Deep-Learning-Based Localization with Contrastive Self-Supervised Pre-training
Lingyan Zhang, Yuanfeng Qiu, Dachuan Li, Shaohua Wu, Tingting Zhang, Qinyu Zhang
https://arxiv.org/abs/2508.03084

Scenario-Agnostic Deep-Learning-Based Localization with Contrastive Self-Supervised Pre-training
Wireless localization has become a promising technology for offering intelligent location-based services. Although its localization accuracy is improved under specific scenarios, the short of environmental dynamic vulnerability still hinders this approach from being fully practical applications. In this paper, we propose CSSLoc, a novel framework on contrastive self-supervised pre-training to learn generic representations for accurate localization in various scenarios. Without the location info…

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Xinzhu Li, Juepeng Zheng, Yikun Chen, Xudong Mao, Guanghui Yue, Wei Zhou, Chenlei Lv, Ruomei Wang, Fan Zhou, Baoquan Zhao
https://arxiv.org/abs/2508.03397

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait r…

An Approach to Grounding AI Model Evaluations in Human-derived Criteria
Sasha Mitts
https://arxiv.org/abs/2509.04676 https://arxiv.org/pdf/2509.04676

An Approach to Grounding AI Model Evaluations in Human-derived Criteria
In the rapidly evolving field of artificial intelligence (AI), traditional benchmarks can fall short in attempting to capture the nuanced capabilities of AI models. We focus on the case of physical world modeling and propose a novel approach to augment existing benchmarks with human-derived evaluation criteria, aiming to enhance the interpretability and applicability of model behaviors. Grounding our study in the Perception Test and OpenEQA benchmarks, we conducted in-depth interviews and large…

Edge Universality for Inhomogeneous Random Matrices
Dang-Zheng Liu, Guangyi Zou
https://arxiv.org/abs/2508.17838 https://arxiv.org/pdf/2508.17838

Edge Universality for Inhomogeneous Random Matrices
We consider symmetric and Hermitian random matrices whose entries are independent and symmetric random variables with an arbitrary variance pattern. Under a novel Short-to-Long Mixing condition, which is sharp in the sense that it precludes a corrected shift at the spectral edge, we establish GOE/GUE edge universality for such inhomogeneous random matrices. This condition effectively reduces the universality problem to verifying the mixing properties of a random walk governed by the variance pr…

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Mehdi Khalaj, Shahrzad Golestani Najafabadi, Julita Vassileva
https://arxiv.org/abs/2507.15460

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Personalized News Recommendation systems (PNR) have emerged as a solution to information overload by predicting and suggesting news items tailored to individual user interests. However, traditional PNR systems face several challenges, including an overreliance on textual content, common neglect of short-term user interests, and significant privacy concerns due to centralized data storage. This paper addresses these issues by introducing a novel multimodal federated learning-based approach for n…

Index-MSR: A high-efficiency multimodal fusion framework for speech recognition
Jinming Chen, Lu Wang, Zheshu Song, Wei Deng
https://arxiv.org/abs/2509.22744 https://

Index-MSR: A high-efficiency multimodal fusion framework for speech recognition
Driven by large scale datasets and LLM based architectures, automatic speech recognition (ASR) systems have achieved remarkable improvements in accuracy. However, challenges persist for domain-specific terminology, and short utterances lacking semantic coherence, where recognition performance often degrades significantly. In this work, we present Index-MSR, an efficient multimodal speech recognition framework. At its core is a novel Multimodal Fusion Decoder (MFD), which effectively incorporate…

A QoS Framework for Service Provision in Multi-Infrastructure-Sharing Networks
Quang Minh Nguyen, Eytan Modiano
https://arxiv.org/abs/2509.01694 https://ar…

A QoS Framework for Service Provision in Multi-Infrastructure-Sharing Networks
We propose a framework for resource provisioning with QoS guarantees in shared infrastructure networks. Our novel framework provides tunable probabilistic service guarantees for throughput and delay. Key to our approach is a Modified Dirft-plus-Penalty (MDP) policy that ensures long-term stability while capturing short-term probabilistic service guarantees using linearized upper-confidence bounds. We characterize the feasible region of service guarantees and show that our MDP procedure achieves…

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li, Xiaoxiao Xu, Jiaqi Yu, Zixiu Wang, Lantao Hu, Han Li, Kun Gai
https://arxiv.org/abs/2508.05093

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-tru…

Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs
Lokesh Antony Kadiyala, Amir Mirzaeinia
https://arxiv.org/abs/2510.01203 https://

Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs
The stock market is extremely difficult to predict in the short term due to high market volatility, changes caused by news, and the non-linear nature of the financial time series. This research proposes a novel framework for improving minute-level prediction accuracy using semantic sentiment scores from top ten different large language models (LLMs) combined with minute interval intraday stock price data. We systematically constructed a time-aligned dataset of AAPL news articles and 1-minute Ap…

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime
Hongzhi Zhu, Wei Xu, Xiaohu You
https://arxiv.org/abs/2507.14951 https:/…

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime
Transformer architectures have emerged as promising deep learning (DL) tools for modeling complex sequence-to-sequence interactions in channel decoding. However, current transformer-based decoders for error correction codes (ECCs) demonstrate inferior performance and generalization capabilities compared to conventional algebraic decoders, especially in short-code regimes. In this work, we propose a novel latent-attention based transformer (LAT) decoder for polar codes that addresses the limitat…

Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Pierrick Lorang, Hong Lu, Johannes Huemer, Patrik Zips, Matthias Scheutz
https://arxiv.org/abs/2508.21501

Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large datasets, and struggle to solve long-horizon tasks or generalize across task variations and distribution shifts. We propose a novel neuro-symbolic framework that jointly learns continuous control policies and symbolic domain abstractions from a few skill demonstrations. Our method abstracts high-level task structures i…

In any case, day 2: Ursula K Le Guin.
As I've said elsewhere, part of her science fiction thesis is that "human" can encompass much more than what we mere Terrans think of it as, and that moral standing extends broadly throughout the universe. This is the antithesis of Tokens fantasy, wherein "race" is real and determines moral standing. For Le Guin, it's barely okay to intervene in complex alien politics unless you carefully ensure you're not causing systemic harms; for Tolkien, it's okay to ambush and murder orc children, because they are by nature evil.
Add to her excellent politics Le Guin's masterful worldbuilding and unparalleled range of plots, and you have the one author I loved as a decidedly liberal and naïve teen and love even more now that I'm an adult. She's an absolute legend and deserves a very high place on any list of women authors (or list of authors, period.).
For a short story, try "The Ones Who Walk Away From Omelas" which you can read here: https://www.utilitarianism.com/nu/omelas.pdf
For fantasy "A Wizard of Earthsea" (also has a nice graphic novel adaptation), or for science fiction, "The Left Hand of Darkness" or if you want a more anarchist flavor, "The Dispossessed."
I'll close this with an amazing quote from her:
"""
We live in capitalism. Its power seems inescapable. So did the divine right of kings. Any human power can be resisted and changed by human beings. Resistance and change often begin in art, and very often in our art, the art of words.
"""

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
Maciej Wysocki, Pawe{\l} Sakowski
https://arxiv.org/abs/2508.14999 https://

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use …

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Qi Peng, Jialin Cui, Jiayuan Xie, Yi Cai, Qing Li
https://arxiv.org/abs/2508.03038

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Large language models (LLMs) have shown great potential in the medical domain. However, existing models still fall short when faced with complex medical diagnosis task in the real world. This is mainly because they lack sufficient reasoning depth, which leads to information loss or logical jumps when processing a large amount of specialized medical data, leading to diagnostic errors. To address these challenges, we propose Tree-of-Reasoning (ToR), a novel multi-agent framework designed to handl…

ReflectivePrompt: Reflective evolution in autoprompting algorithms
Viktor N. Zhuravlev, Artur R. Khairullin, Ernest A. Dyagin, Alena N. Sitkina, Nikita I. Kulin
https://arxiv.org/abs/2508.18870

ReflectivePrompt: Reflective evolution in autoprompting algorithms
Autoprompting is the process of automatically selecting optimized prompts for language models, which has been gaining popularity with the rapid advancement of prompt engineering, driven by extensive research in the field of large language models (LLMs). This paper presents ReflectivePrompt - a novel autoprompting method based on evolutionary algorithms that employs a reflective evolution approach for more precise and comprehensive search of optimal prompts. ReflectivePrompt utilizes short-term …

SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting
Di Li, Jie Feng, Jiahao Chen, Weisheng Dong, Guanbin Li, Yuhui Zheng, Mingtao Feng, Guangming Shi
https://arxiv.org/abs/2507.23772

SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting
3D affordance reasoning, the task of associating human instructions with the functional regions of 3D objects, is a critical capability for embodied agents. Current methods based on 3D Gaussian Splatting (3DGS) are fundamentally limited to single-object, single-step interactions, a paradigm that falls short of addressing the long-horizon, multi-object tasks required for complex real-world applications. To bridge this gap, we introduce the novel task of Sequential 3D Gaussian Affordance Reasonin…

Remaining Time Prediction in Outbound Warehouse Processes: A Case Study (Short Paper)
Erik Penther, Michael Grohs, Jana-Rebecca Rehse
https://arxiv.org/abs/2509.18986 https://…

Hybrid FIM and STAR-BD-RIS-Aided Wireless Communications with Short Packet Length: A Meta-TD3 Approach
Ayla Eftekhari, Maryam Cheraghy, Armin Farhadi, Mohammad Robat Mili, Qingqing Wu
https://arxiv.org/abs/2509.16417

Hybrid FIM and STAR-BD-RIS-Aided Wireless Communications with Short Packet Length: A Meta-TD3 Approach
Reconfigurable intelligent surfaces (RIS) and flexible intelligent metasurfaces (FIM) have been widely adopted in multi-user wireless communication systems to enhance channel quality through simultaneous transmission and reflection of signals and three-dimensional reconfiguration of antennas. In this paper, we propose a novel system architecture that integrates the benefits of both technologies by deploying an FIM antenna at the base station (BS) and a simultaneously transmitting and reflecting…

ILRe: Intermediate Layer Retrieval for Context Compression in Causal Language Models
Manlai Liang, Mandi Liu, Jiangzhou Ji, Huaijun Li, Haobo Yang, Yaohan He, Jinlong Li
https://arxiv.org/abs/2508.17892

ILRe: Intermediate Layer Retrieval for Context Compression in Causal Language Models
Large Language Models (LLMs) have demonstrated success across many benchmarks. However, they still exhibit limitations in long-context scenarios, primarily due to their short effective context length, quadratic computational complexity, and high memory overhead when processing lengthy inputs. To mitigate these issues, we introduce a novel context compression pipeline, called Intermediate Layer Retrieval (ILRe), which determines one intermediate decoder layer offline, encodes context by streamin…

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting
Yuxi Hu, Jun Zhang, Kuangyi Chen, Zhe Zhang, Friedrich Fraundorfer
https://arxiv.org/abs/2508.20754

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting
Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization. In particular, recent advancements utilize feed-forward networks to predict per-pixel Gaussian parameters, enabling high-quality synthesis from sparse input views. However, existing approaches fall short in encoding discriminative, multi-view consistent features for Gaussian predictions, which struggle to construct accurate geometry with sparse views. To address this, we propose $\m…

PreSem-Surf: RGB-D Surface Reconstruction with Progressive Semantic Modeling and SG-MLP Pre-Rendering Mechanism
Yuyan Ye, Hang Xu, Yanghang Huang, Jiali Huang, Qian Weng
https://arxiv.org/abs/2508.13228

PreSem-Surf: RGB-D Surface Reconstruction with Progressive Semantic Modeling and SG-MLP Pre-Rendering Mechanism
This paper proposes PreSem-Surf, an optimized method based on the Neural Radiance Field (NeRF) framework, capable of reconstructing high-quality scene surfaces from RGB-D sequences in a short time. The method integrates RGB, depth, and semantic information to improve reconstruction performance. Specifically, a novel SG-MLP sampling structure combined with PR-MLP (Preconditioning Multilayer Perceptron) is introduced for voxel pre-rendering, allowing the model to capture scene-related information…

Session-Level Spoken Language Assessment with a Multimodal Foundation Model via Multi-Target Learning
Hong-Yun Lin, Jhen-Ke Lin, Chung-Chun Wang, Hao-Chien Lu, Berlin Chen
https://arxiv.org/abs/2509.16025

Session-Level Spoken Language Assessment with a Multimodal Foundation Model via Multi-Target Learning
Spoken Language Assessment (SLA) estimates a learner's oral proficiency from spontaneous speech. The growing population of L2 English speakers has intensified the demand for reliable SLA, a critical component of Computer Assisted Language Learning (CALL). Existing efforts often rely on cascaded pipelines, which are prone to error propagation, or end-to-end models that often operate on a short audio window, which might miss discourse-level evidence. This paper introduces a novel multimodal found…

Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks
Dimitrios Rontogiannis, Maxime Peyrard, Nicolas Baldwin, Martin Josifoski, Robert West, Dimitrios Gunopulos
https://arxiv.org/abs/2508.18905

Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks
Standard single-turn, static benchmarks fall short in evaluating the nuanced capabilities of Large Language Models (LLMs) on complex tasks such as software engineering. In this work, we propose a novel interactive evaluation framework that assesses LLMs on multi-requirement programming tasks through structured, feedback-driven dialogue. Each task is modeled as a requirement dependency graph, and an ``interviewer'' LLM, aware of the ground-truth solution, provides minimal, targeted hints to an `…

DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou
Qinyao Li, Xiaoyang Zheng, Qihang Zhao, Ke Xu, Zhongbo Sun, Chao Wang, Chenyi Lei, Han Li, Wenwu Ou
https://arxiv.org/abs/2508.17754

DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou
Personalized search ranking systems are critical for driving engagement and revenue in modern e-commerce and short-video platforms. While existing methods excel at estimating users' broad interests based on the filtered historical behaviors, they typically under-exploit explicit alignment between a user's real-time intent (represented by the user query) and their past actions. In this paper, we propose DiffusionGS, a novel and scalable approach powered by generative models. Our key insight is t…

Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
Congchi Yin, Tianyi Wu, Yankai Shu, Alex Gu, Yunhan Wang, Jun Shao, Xun Jiang, Piji Li
https://arxiv.org/abs/2508.19035

Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
Existing tasks fall short in evaluating reasoning ability of Large Language Models (LLMs) in an interactive, unknown environment. This deficiency leads to the isolated assessment of deductive, inductive, and abductive reasoning, neglecting the integrated reasoning process that is indispensable for humans discovery of real world. We introduce a novel evaluation paradigm, \textit{black-box interaction}, to tackle this challenge. A black-box is defined by a hidden function that maps a specific set…

CSIT-Free Downlink Transmission for mmWave MU-MISO Systems in High-Mobility Scenario
Jeongjae Lee, Wonseok Choi, Songnam Hong
https://arxiv.org/abs/2509.15564 https://

CSIT-Free Downlink Transmission for mmWave MU-MISO Systems in High-Mobility Scenario
This paper investigates the downlink (DL) transmission in millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems especially focusing on a high speed mobile scenario. To complete the DL transmission within an extremely short channel coherence time, we propose a novel DL transmission framework that eliminates the need for channel state information at the transmitter (CSIT), of which acquisition process requires a substantial overhead, instead fully exploiting the given…

Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty
Zhichao Yang, Zhaoxin Fan, Gen Li, Yuanze Hu, Xinyu Wang, Ye Qiu, Xin Wang, Yifan Sun, Wenjun Wu
https://arxiv.org/abs/2508.19069

Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty
Structured, procedural reasoning is essential for Large Language Models (LLMs), especially in mathematics. While post-training methods have improved LLM performance, they still fall short in capturing deep procedural logic on complex tasks. To tackle the issue, in this paper, we first investigate this limitation and uncover a novel finding: a Scaling Law by Difficulty, which reveals that model performance follows a U-shaped curve with respect to training data complexity -- excessive low-difficu…