Trending Papers (based on social & online metrics):
1, TITLE: InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields
AUTHORS: HAO YU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper introduces InfiniDepth, which represents depth as neural implicit fields. [Save to Library]
2, TITLE: LTX-2: Efficient Joint Audio-Visual Foundation Model
AUTHORS: YOAV HACOHEN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We introduce LTX-2, an open-source foundational model capable of generating high-quality, temporally synchronized audiovisual content in a unified manner. [Save to Library]
3, TITLE: From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence
AUTHORS: MARC FINZI et. al.
CATEGORY: cs.LG [cs.LG, stat.ML]
HIGHLIGHT: In this work, we identify and exemplify three seeming paradoxes in information theory: (1) information cannot be increased by deterministic transformations; (2) information is independent of the order of data; (3) likelihood modeling is merely distribution matching. [Save to Library]
4, TITLE: MemRL: Self-Evolving Agents Via Runtime Reinforcement Learning on Episodic Memory
AUTHORS: SHENGTAO ZHANG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: While Large Language Models possess strong reasoning capabilities, they struggle to emulate this self-evolution: fine-tuning is computationally expensive and prone to catastrophic forgetting, while existing memory-based methods rely on passive semantic matching that often retrieves noise. To address these challenges, we propose MemRL, a framework that enables agents to self-evolve via non-parametric reinforcement learning on episodic memory. [Save to Library]
5, TITLE: UniCorn: Towards Self-Improving Unified Multimodal Models Through Self-Generated Supervision
AUTHORS: RUIYAN HAN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis. To address this, we propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. [Save to Library]
Industry News:
1, Creators of Tailwind laid off 75% of their engineering team
2, LaTeX Coffee Stains (2021) [pdf]
3, Tailscale state file encryption no longer enabled by default
4, Firefox extension to redirect x.com to xcancel.com
5, "Stop Designing Languages. Write Libraries Instead" (2016)
Relevant Posts (Reddit):
1, [R] DeepSeek-R1's paper was updated 2 days ago, expanding from 22 pages to 86 pages and adding a substantial amount of detail.
2, AI isn't "just predicting the next word" anymore
3, Biggest successes (and failures) of computer vision in the last few years -- for course intro
4, [D] ICLR new ACs - how's it going?
5, It's been a big week for AI ; Here are 10 massive developments you might've missed:
Tracking Results:
1*, TITLE: Current Agents Fail to Leverage World Model As Tool for Foresight
AUTHORS: CHENG QIAN et. al.
CATEGORY: cs.AI [cs.AI, cs.CL, cs.LG]
HIGHLIGHT: Agents built on vision-language models increasingly face tasks that demand anticipating future states rather than relying on short-horizon reasoning. Generative world models offer a promising remedy: agents could use them as external simulators to foresee outcomes before acting. [Save to Library]
2*, TITLE: From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
AUTHORS: YINGJIAN CHEN et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: We propose Self-Graph Reasoning (SGR), a framework that enables LLMs to explicitly represent their reasoning process as a structured graph before producing the final answer. [Save to Library]
3*, TITLE: Autonomous Maneuvering Decision-Making Method for Unmanned Aerial Vehicle Based on Soft Actor-Critic Algorithm
AUTHORS: Shiming Quan ; Su Cao ; Chang Wang ; Huangchao Yu
SOURCE: Drones
HIGHLIGHT: Autonomous Maneuvering Decision-Making Method for Unmanned Aerial Vehicle Based on Soft Actor-Critic Algorithm [Save to Library]
4*, TITLE: DisastQA: A Comprehensive Benchmark for Evaluating Question Answering in Disaster Management
AUTHORS: ZHITONG CHEN et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We introduce DisastQA, a large-scale benchmark of 3,000 rigorously verified questions (2,000 multiple-choice and 1,000 open-ended) spanning eight disaster types. [Save to Library]
5*, TITLE: Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search
AUTHORS: YU GUO et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: However, existing pruning methods typically rely on sequential revisions driven by unreliable critique signals, often failing to detect the loss of answer-critical data. To address this limitation, we propose TabTrim, a novel table pruning framework which transforms table pruning from sequential revisions to gold trajectory-supervised parallel search. [Save to Library]
6*, TITLE: EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering for Enhanced Alignment and Reasoning
AUTHORS: MINGYANG WEI et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: We present EpiQAL, the first diagnostic benchmark for epidemiological question answering across diverse diseases, comprising three subsets built from open-access literature. [Save to Library]
7*, TITLE: PALM-Bench: A Comprehensive Benchmark for Personalized Audio-Language Models
AUTHORS: YUWEN WANG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Large Audio-Language Models (LALMs) have demonstrated strong performance in audio understanding and generation. [Save to Library]
8*, TITLE: SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems
AUTHORS: Andreea-Elena Bodea ; Stephen Meisenbacher ; Alexandra Klymenko ; Florian Matthes
CATEGORY: cs.CR [cs.CR, cs.CL]
HIGHLIGHT: Numerous recent works have explored various aspects of privacy risks in RAG systems, from adversarial attacks to proposed mitigations. With the goal of surveying and unifying these works, we ask one simple question: What are the privacy risks in RAG, and how can they be measured and mitigated? [Save to Library]
9*, TITLE: FLEx: Language Modeling with Few-shot Language Explanations
AUTHORS: Adar Avsian ; Christopher Richardson ; Anirudh Sundar ; Larry Heck
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: Natural language explanations can help correct these errors, but collecting them at scale may be infeasible, particularly in domains where expert annotators are required. To address this issue, we introduce FLEx ($\textbf{F}$ew-shot $\textbf{L}$anguage $\textbf{Ex}$planations), a method for improving model behavior using a small number of explanatory examples. [Save to Library]
10*, TITLE: Doc-PP: Document Policy Preservation Benchmark for Large Vision-Language Models
AUTHORS: Haeun Jang ; Hwan Chang ; Hwanhee Lee
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we introduce Doc-PP (Document Policy Preservation Benchmark), a novel benchmark constructed from real-world reports requiring reasoning across heterogeneous visual and textual elements under strict non-disclosure policies. [Save to Library]
11*, TITLE: An Algorithmic Framework for Systematic Literature Reviews: A Case Study for Financial Narratives
AUTHORS: Gabin Taibi ; Joerg Osterrieder
CATEGORY: q-fin.GN [q-fin.GN, cs.AI]
HIGHLIGHT: This paper introduces an algorithmic framework for conducting systematic literature reviews (SLRs), designed to improve efficiency, reproducibility, and selection quality assessment in the literature review process. [Save to Library]
12*, TITLE: When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question-Answering
AUTHORS: Hugh Mee Wong ; Rick Nouwen ; Albert Gatt
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Multiple-choice question answering (MCQA) is easy to evaluate but adds a meta-task: models must both solve the problem and output the symbol that *represents* the answer, conflating reasoning errors with symbol-binding failures. [Save to Library]
13*, TITLE: PCoA: A New Benchmark for Medical Aspect-Based Summarization With Phrase-Level Context Attribution
AUTHORS: BOHAO CHU et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Verifying system-generated summaries remains challenging, as effective verification requires precise attribution to the source context, which is especially crucial in high-stakes medical domains. To address this challenge, we introduce PCoA, an expert-annotated benchmark for medical aspect-based summarization with phrase-level context attribution. [Save to Library]
14*, TITLE: SegNSP: Revisiting Next Sentence Prediction for Linear Text Segmentation
AUTHORS: JOSÉ ISIDRO et. al.
CATEGORY: cs.CL [cs.CL, cs.AI, cs.IR]
HIGHLIGHT: In this work, we introduce SegNSP, framing linear text segmentation as a next sentence prediction (NSP) task. [Save to Library]
15*, TITLE: Challenges and Mitigating Strategies for Sustainable Development of Digitally Transformed Community 5.0: Insights from The Higher Education Leadership
AUTHORS: Muhammad Mujtaba Asad ; Amjad Ali Rind ; Norah Mansour Almusharraf
SOURCE: Asian Education and Development Studies
HIGHLIGHT: Purpose The purpose of this study is to understand the challenges and opportunities for Higher Education Leaders for developing digital Community 5.0 in higher education institutions (HEIs). [Save to Library]
16*, TITLE: MARVEL: A Multi Agent-based Research Validator and Enabler Using Large Language Models
AUTHORS: Nikhil Mukund ; Yifang Luo ; Fan Zhang ; Lisa Barsotti ; Erik Katsavounidis
CATEGORY: astro-ph.IM [astro-ph.IM, cs.AI]
HIGHLIGHT: We present MARVEL (https://ligogpt.mit.edu/marvel), a locally deployable, open-source framework for domain-aware question answering and assisted scientific research. [Save to Library]
17*, TITLE: Role of AI Recommendation on Consumer Behavior
AUTHORS: Vineesh A R, Mr. Rahul K R, Reshma S
SOURCE: International Journal of Advanced Research in Science Communication and Technology
HIGHLIGHT: The present study aims to examine the role of AI recommendation systems in influencing consumer behavior, with special emphasis on consumer decision-making, purchase intention, perceived usefulness, satisfaction, personalization, and trust in digital marketplaces. [Save to Library]
18*, TITLE: The Influence of Institutional Constraints on Artificial Intelligence-Driven Innovation and Its Impacts on Academic and Organisational Performance in Higher Learning Institutions
AUTHORS: Salum A. Msoka ; Evance E. Sanga ; Eveline Kusaga ; Eliakimu Tweve
SOURCE: International Journal of Innovative Science and Research Technology
HIGHLIGHT: This paper examines how institutional constraints influence artificial intelligence (AI)-driven innovation and how such innovation affects academic and organisational performance in higher learning institutions. [Save to Library]
19*, TITLE: IntroLM: Introspective Language Models Via Prefilling-Time Self-Evaluation
AUTHORS: Hossein Hosseini Kasnavieh ; Gholamreza Haffari ; Chris Leckie ; Adel N. Toosi
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: We propose IntroLM, a method that enables causal language models to predict their own output quality during the prefilling phase without affecting generation using introspective tokens. [Save to Library]
20*, TITLE: Literary Language Mashup: Curating Fictions with Large Language Models
AUTHORS: Gerardo Aleman Manzanarez ; Raul Monroy ; Jorge Garcia Flores ; Hiram Calvo
SOURCE: Mathematics
HIGHLIGHT: In this paper, we propose an alternative approach by employing LLMs themselves as evaluators within the GrAImes framework. [Save to Library]
21*, TITLE: EVALUATING THE ROLE OF DIGITISATION IN SHAPING MANPOWER PLANNING STRATEGIES IN INDIAN FINANCIAL INSTITUTIONS
AUTHORS: Supriya Paul ; Mahua Pal ; Aryan Narayan Das ; Rama Prosad Banerjee
SOURCE: International Journal of Leadership and Management
HIGHLIGHT: This study proposes a strategic framework for implementing sustainable, digitally enabled manpower planning models to support the long-term competitiveness and resilience of Indian financial institutions. [Save to Library]
Daily Papers (sorted by potential impact and then category):
22, TITLE: Training-Free Adaptation of New-Generation LLMs Using Legacy Clinical Models
AUTHORS: SASHA RONAGHI et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: We propose Cross-Architecture Proxy Tuning (CAPT), a model-ensembling approach that enables training-free adaptation of state-of-the-art general-domain models using existing clinical models. [Save to Library]
23, TITLE: Deep Learning Guided Design of Protease Substrates
AUTHORS: CARMEN MARTIN-ALONSO et. al.
SOURCE: Nature Communications
HIGHLIGHT: We present CleaveNet, an end-to-end AI pipeline for the design of protease substrates. [Save to Library]
24, TITLE: A Multi-Domain Machine Learning Framework for Intelligent Condition Monitoring of Marine Diesel Engines
AUTHORS: MUHAMMAD BILAL ASIF et. al.
SOURCE: Engineering Research Express
HIGHLIGHT: The proposed approach, tested and validated on the Sulzer 6AL20/24 test engine, achieves classification accuracies and AUC values exceeding 98%, demonstrating robustness to noise, class imbalance, and limited fault data. [Save to Library]
25, TITLE: Vat Photopolymerization‐based Bioprinting: Shaping Next‐generation Tissues with Light
AUTHORS: Wei Long Ng ; Carlos T. B. Paula ; Arménio C. Serra ; Jorge F. J. Coelho ; Paulo Bartolo
SOURCE: Interdisciplinary Medicine
HIGHLIGHT: This review presents a comprehensive overview of recent advances in VP‐based bioprinting, organized around core themes of photopolymerization chemistry, printing modalities, bio‐ink design, and biomedical applications. [Save to Library]
26, TITLE: Correlating Structure Loss and Operational Conditions in Czochralski Silicon Ingot Growth Using Machine Learning
AUTHORS: Alfredo Sanchez Garcia ; Rania Hendawi ; Hendrik Schön ; Marisa Di Sabatino
SOURCE: SiliconPV Conference Proceedings
HIGHLIGHT: This work investigates the relationships between process parameters and the occurrence of structure loss in Czochralski silicon ingots using machine learning. [Save to Library]
27, TITLE: Automated Retinal Disease Classification Using Deep Learning and AlexNet with Statistical Models Analysis
AUTHORS: EL-SAYED M. ELKENAWY et. al.
SOURCE: PLOS One
HIGHLIGHT: This study proposes a deep learning-based framework for the automated classification of retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. [Save to Library]
28, TITLE: CPGPrompt: Translating Clinical Guidelines Into LLM-Executable Decision Support
AUTHORS: RUIQI DENG et. al.
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: Previous approaches, such as rule-based systems, face significant limitations, including poor interpretability, inconsistent adherence to guidelines, and narrow domain applicability. To address this, we develop and validate CPGPrompt, an auto-prompting system that converts narrative clinical guidelines into large language models (LLMs). [Save to Library]
29, TITLE: HearSay Benchmark: Do Audio LLMs Leak What They Hear?
AUTHORS: JIN WANG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: This paper takes the first step to investigate whether ALLMs inadvertently leak user privacy solely through acoustic voiceprints and introduces $\textit{HearSay}$, a comprehensive benchmark constructed from over 22,000 real-world audio clips. [Save to Library]
30, TITLE: ODE‐Driven Neural Networks for Trajectory Tracking of Autonomous Vehicles Under Periodic Noise Suppressed
AUTHORS: Siyuan Bai ; Longqi Liu
SOURCE: Computational Intelligence
HIGHLIGHT: This paper establishes a kinematic model for the controlled vehicle and formulates the control problem as an optimization task using model predictive control (MPC). [Save to Library]
31, TITLE: Unveiling The Potential of Spin–orbit Torque in A Magnetic Single Layer for Advancing Spintronics Application
AUTHORS: ZEYU HAN et. al.
SOURCE: Applied Physics Reviews
HIGHLIGHT: However, conventional SOT devices face efficiency constraints like interfacial spin scattering, limited spin-diffusion lengths, and complexity, driving interest in single-layer SOT switching. Given that research on single-layer SOT systems is still in its early stages and the underlying physical mechanisms remain complex and not fully understood, this review aims to consolidate recent key advances in the field. [Save to Library]
32, TITLE: A Rapid Review of Using AI-generated Instructional Videos in Higher Education
AUTHORS: Tran Trieu Hai ; Duong Thi Thuy Mai ; Nguyen Van Hanh
SOURCE: Frontiers in Computer Science
HIGHLIGHT: Methods This study conducted a rapid review following PRISMA principles. [Save to Library]
33, TITLE: Strategic Management of Urban Services Using Artificial Intelligence in The Development of Sustainable Smart Cities—Managerial and Legal Challenges
AUTHORS: Tomáš Peráček ; Michal Kaššaj
SOURCE: Sustainability
HIGHLIGHT: At the same time, the question arises as to how legal and strategic frameworks can support the use of artificial intelligence in a way that contributes to environmental, social and economic sustainability in line with the objectives of the European Union. The aim of this scientific study is to examine the interdisciplinary use of artificial intelligence, data management and sustainability at the European Union level, including support instruments such as regulatory initiatives and funding programs, and to assess their implementation in relation to smart cities. [Save to Library]
34, TITLE: E5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings
AUTHORS: Haonan Chen ; Sicheng Gao ; Radu Timofte ; Tetsuya Sakai ; Zhicheng Dou
CATEGORY: cs.CL [cs.CL, cs.AI, cs.CV]
HIGHLIGHT: In practice, this causes three common issues: (i) similarity logits have modality-dependent sharpness, so scores are not on a consistent scale; (ii) in-batch negatives become less effective over time because mixed-modality batches create an imbalanced hardness distribution; as a result, many negatives quickly become trivial and contribute little gradient; and (iii) embeddings across modalities show mismatched first- and second-order statistics, which makes rankings less stable. To tackle these problems, we propose e5-omni, a lightweight explicit alignment recipe that adapts off-the-shelf VLMs into robust omni-modal embedding models. [Save to Library]
35, TITLE: Quantifying The Effect of Test Set Contamination on Generative Evaluations
AUTHORS: RYLAN SCHAEFFER et. al.
CATEGORY: cs.LG [cs.LG, cs.CL]
HIGHLIGHT: In this work, we quantitatively assess the effect of test set contamination on generative evaluations through the language model lifecycle. [Save to Library]
36, TITLE: Improving Robustness in X-ray Image Classification Through Attention Mechanisms in Convolutional Neural Networks
AUTHORS: ZAENAB ALAMMAR et. al.
SOURCE: PeerJ Computer Science
HIGHLIGHT: However, interpreting these images reliably is challenging due to a lack of labelled data, inherent image noise, and the lack of explainable artificial intelligence (AI). This research aims to improve the robustness against noise, accuracy, and interpretability of musculoskeletal radiograph classification by addressing these key challenges. [Save to Library]
37, TITLE: Understanding Reward Hacking in Text-to-Image Reinforcement Learning
AUTHORS: Yunqi Hong ; Kuei-Chun Kao ; Hengguang Zhou ; Cho-Jui Hsieh
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we systematically analyze reward hacking behaviors in text-to-image (T2I) RL post-training. [Save to Library]
38, TITLE: Retrieval Heads Are Dynamic
AUTHORS: YUPING LIN et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we investigate retrieval heads from a dynamic perspective. [Save to Library]
39, TITLE: From Language to Action: A Review of Large Language Models As Autonomous Agents and Tool Users
AUTHORS: SADIA SULTANA CHOWA et. al.
SOURCE: Artificial Intelligence Review
HIGHLIGHT: From Language to Action: A Review of Large Language Models As Autonomous Agents and Tool Users [Save to Library]
40, TITLE: Soft Contextualized Encoder For User Defined Text Classification
AUTHORS: Charu Maheshwari ; Vyas Raina
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: We propose a soft-contextualized encoder architecture for UDTC which contextualizes each candidate label with the label set and a static soft prompt representation of the input query. [Save to Library]
41, TITLE: Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
AUTHORS: Magnus Bühler ; Lennart Purucker ; Frank Hutter
CATEGORY: cs.LG [cs.LG]
HIGHLIGHT: We propose CausalMixFT, a method that enhances fine-tuning robustness and downstream performance by generating structurally consistent synthetic samples using Structural Causal Models (SCMs) fitted on the target dataset. [Save to Library]
42, TITLE: CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos
AUTHORS: CHUBIN ZHANG et. al.
CATEGORY: cs.RO [cs.RO, cs.CV]
HIGHLIGHT: Existing Latent Action Models attempt to leverage video data but often suffer from visual entanglement, capturing noise rather than manipulation skills. To address this, we propose Contrastive Latent Action Pretraining (CLAP), a framework that aligns the visual latent space from videos with a proprioceptive latent space from robot trajectories. [Save to Library]
43, TITLE: Sustainability in The Energy Sector: A Systematic Literature Review of Energy Transitions, Technologies, and Policy Instruments
AUTHORS: Sofik Handoyo
SOURCE: Energy Reports
HIGHLIGHT: Sustainability in The Energy Sector: A Systematic Literature Review of Energy Transitions, Technologies, and Policy Instruments [Save to Library]
44, TITLE: LinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesis
AUTHORS: Yayati Jadhav ; Amir Barati Farimani
CATEGORY: cs.LG [cs.LG]
HIGHLIGHT: We introduce an autoregressive diffusion framework that exploits the dyadic nature of linkage assembly by representing mechanisms as sequentially constructed graphs, where nodes correspond to joints and edges to rigid links. [Save to Library]
45, TITLE: ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesis
AUTHORS: HAITAO LI et. al.
CATEGORY: eess.AS [eess.AS, cs.AI, cs.SD]
HIGHLIGHT: We propose ReStyle-TTS, a framework that enables continuous and reference-relative style control in zero-shot TTS. [Save to Library]
46, TITLE: Unlocking The Pre-Trained Model As A Dual-Alignment Calibrator for Post-Trained LLMs
AUTHORS: Beier Luo ; Cheng Wang ; Hongxin Wei ; Sharon Li ; Xuefeng Du
CATEGORY: cs.LG [cs.LG]
HIGHLIGHT: In particular, we show that calibration errors arise from two regimes: (i) confidence drift, where final confidence inflates despite largely consistent intermediate decision processes, and (ii) process drift, where intermediate inference pathways diverge. Guided by this diagnosis, we propose Dual-Align, an unsupervised post-hoc framework for dual alignment in confidence calibration. [Save to Library]
47, TITLE: NeuronScope: A Multi-Agent Framework for Explaining Polysemantic Neurons in Language Models
AUTHORS: Weiqi Liu ; Yongliang Miao ; Haiyan Zhao ; Yanguang Liu ; Mengnan Du
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: In this work, we propose NeuronScope, a multi-agent framework that reformulates neuron interpretation as an iterative, activation-guided process. [Save to Library]
48, TITLE: IndexTTS 2.5 Technical Report
AUTHORS: YUNPEI LI et. al.
CATEGORY: cs.SD [cs.SD, cs.AI]
HIGHLIGHT: In prior work, we introduced IndexTTS 2, a zero-shot neural text-to-speech foundation model comprising two core components: a transformer-based Text-to-Semantic (T2S) module and a non-autoregressive Semantic-to-Mel (S2M) module, which together enable faithful emotion replication and establish the first autoregressive duration-controllable generative paradigm. [Save to Library]
49, TITLE: O-Researcher: An Open Ended Deep Research Model Via Multi-Agent Distillation and Agentic RL
AUTHORS: YI YAO et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: The performance gap between closed-source and open-source large language models (LLMs) is largely attributed to disparities in access to high-quality training data. To bridge this gap, we introduce a novel framework for the automated synthesis of sophisticated, research-grade instructional data. [Save to Library]
50, TITLE: Artificial Intelligence and Machine Learning: Shaping The Future of Food Safety, Quality Control, Traceability Systems, and Nutrition
AUTHORS: MUHAMMAD ADIL et. al.
SOURCE: Food Reviews International
HIGHLIGHT: Artificial Intelligence and Machine Learning: Shaping The Future of Food Safety, Quality Control, Traceability Systems, and Nutrition [Save to Library]
51, TITLE: Simulated Students in Tutoring Dialogues: Substance or Illusion?
AUTHORS: Alexander Scarlatos ; Jaewook Lee ; Simon Woodhead ; Andrew Lan
CATEGORY: cs.CL [cs.CL, cs.CY]
HIGHLIGHT: Surprisingly, little work has been done to ensure or even measure the quality of simulated students. In this work, we formally define the student simulation task, propose a set of evaluation metrics that span linguistic, behavioral, and cognitive aspects, and benchmark a wide range of student simulation methods on these metrics. [Save to Library]
52, TITLE: Advances in Forecasting Realized Volatility: A Review of Methodologies
AUTHORS: Radmir Mishelevich Leushuis ; Nicolai Petkov
SOURCE: Financial Innovation
HIGHLIGHT: Traditionally, academic research has emphasized linear models for forecasting realized volatility. [Save to Library]
53, TITLE: A Systematic Review of Intelligent and Robot Tutoring Systems: Evolution, Pedagogical Design, and AI-driven Classification
AUTHORS: Ehsan Latif ; Vincent Liu ; Xiaoming Zhai
SOURCE: Smart Learning Environments
HIGHLIGHT: Abstract This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS), in addressing global educational challenges through advanced technologies. [Save to Library]
54, TITLE: Local Gradient Regulation Stabilizes Federated Learning Under Client Heterogeneity
AUTHORS: Ping Luo ; Jiahuan Wang ; Ziqing Wen ; Tao Sun ; Dongsheng Li
CATEGORY: cs.LG [cs.LG, cs.DC]
HIGHLIGHT: Here, we show that client heterogeneity destabilizes FL primarily by distorting local gradient dynamics during client-side optimization, causing systematic drift that accumulates across communication rounds and impedes global convergence. [Save to Library]
55, TITLE: Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control
AUTHORS: CHANGHAO JIANG et. al.
CATEGORY: cs.SD [cs.SD, cs.CL]
HIGHLIGHT: We train Muse via single-stage supervised finetuning of a Qwen-based language model extended with discrete audio tokens using MuCodec, without task-specific losses, auxiliary objectives, or additional architectural components. [Save to Library]
56, TITLE: ImLoc: Revisiting Visual Localization with Image-based Representation
AUTHORS: Xudong Jiang ; Fangjinhua Wang ; Silvano Galliani ; Christoph Vogel ; Marc Pollefeys
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we revisit visual localization with a 2D image-based representation and propose to augment each image with estimated depth maps to capture the geometric structure. [Save to Library]
57, TITLE: Enhancing Predictive Modeling of Interrill and Rill Erosion Susceptibility in The Eastern Mediterranean Using Stacking Ensemble Machine Learning Algorithms
AUTHORS: Hazem Ghassan Abdo ; Sahar Mohammed Richi ; Hoang Thi Hang ; Jasem A. Albanai ; Javed Mallick
SOURCE: Soil and Tillage Research
HIGHLIGHT: Enhancing Predictive Modeling of Interrill and Rill Erosion Susceptibility in The Eastern Mediterranean Using Stacking Ensemble Machine Learning Algorithms [Save to Library]
58, TITLE: Machine Learning for Renewable Energy Advancements: Prospects and Emerging Techniques
AUTHORS: SAFIULLAH KHAN et. al.
SOURCE: Energy Reports
HIGHLIGHT: Machine Learning for Renewable Energy Advancements: Prospects and Emerging Techniques [Save to Library]
59, TITLE: Role of Deep Learning in Battery Management System (BMS) for Electric Vehicles – A Review
AUTHORS: RASEL AHMED et. al.
SOURCE: Energy Reports
HIGHLIGHT: Role of Deep Learning in Battery Management System (BMS) for Electric Vehicles – A Review [Save to Library]
60, TITLE: R$^3$L: Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification
AUTHORS: WEIJIE SHI et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: To this end, we propose R$^3$L, Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification. [Save to Library]
61, TITLE: Sustainable Modeling of The Urban Air Quality in Abu Dhabi Using Machine Learning and Open-Source Satellite Data
AUTHORS: Maria Iruj ; Danish Mustafa Khan ; Saima Yaqoob ; Zunaira Iqbal
SOURCE: Sustainable Processes Connect
HIGHLIGHT: This research develops a predictive AI model to monitor and forecast air quality in Abu Dhabi using publicly available, satellite-based environmental datasets. [Save to Library]
62, TITLE: AI-Driven Big Data Analytics for Precision Medicine and Healthcare Intelligence: A Unified Framework for Cancer, Chronic Disease, and Clinical Decision Optimization
AUTHORS: Md Minzamul Hasan ; Md. Ishtiaque Alam ; Md Amjad Hossain Chowdhury ; Md Mazharul Anwar
SOURCE: Frontiers in Computer Science and Artificial Intelligence
HIGHLIGHT: By combining cross-domain evidence and proposing a unified analytical architecture, this work is designed to provide researchers, practitioners, and authorities with practical and useful knowledge by responding to the demand to operationalize at scale AI-powered precision medicine. [Save to Library]
63, TITLE: Towards A Mechanistic Understanding of Propositional Logical Reasoning in Large Language Models
AUTHORS: Danchun Chen ; Qiyao Yan ; Liangming Pan
CATEGORY: cs.AI [cs.AI, cs.LG]
HIGHLIGHT: While prior mechanistic studies focus on identifying taskspecific circuits, they leave open the question of what computational strategies LLMs employ for propositional reasoning. We address this gap through comprehensive analysis of Qwen3 (8B and 14B) on PropLogic-MI, a controlled dataset spanning 11 propositional logic rule categories across one-hop and two-hop reasoning. [Save to Library]
64, TITLE: VisionSpeak Object Detection and Narration System
AUTHORS: Prof. Plasin Francis Dias ; K P Chinmayi ; Mahima Hanchinal ; Anurag Dindalkopp ; Neha Khan
SOURCE: International Journal of Scientific Research in Engineering and Management
HIGHLIGHT: Using a laptop webcam and the pre trained YOLOv8s model, our system achieves 22 FPS on consumer hardware (Intel Core i3) with 81% average precision across seven common indoor objects. [Save to Library]
65, TITLE: Cancer Neuroscience: Linking Neuronal Plasticity with Brain Tumor Growth and Resistance
AUTHORS: DOAA S. R. KHAFAGA et. al.
SOURCE: Biology
HIGHLIGHT: In this review, we summarize fundamental principles of neuronal plasticity, contrasting physiological roles with pathological reprogramming in brain tumors. [Save to Library]
66, TITLE: Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction
AUTHORS: JIAXIN HUANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present Gen3R, a method that bridges the strong priors of foundational reconstruction models and video diffusion models for scene-level 3D generation. [Save to Library]
67, TITLE: RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection
AUTHORS: SONG-DUO MA et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a retrieval-augmented detector with adversarial refinement for robust fake news detection. [Save to Library]
68, TITLE: Image‐Based Deep Learning Models for Stock Predictions: Combining Line, Candlestick, and Bar Charts
AUTHORS: Wei‐Chao Lin ; Ming‐Chang Wang ; Chih‐Fong Tsai ; Jui‐Pin Hsu
SOURCE: Journal of Forecasting
HIGHLIGHT: In this paper, three types of image patterns are compared, specifically, line charts with trading volume information represented by a bar chart, candlestick charts with trading volume information, and a mixed type of image with two other related technical indicators, that is, MACD and RSI. [Save to Library]
69, TITLE: Machine Learning Analysis of Iran’s Wildfire Landscape and Anthropogenic Influences
AUTHORS: NASIM SADRA et. al.
SOURCE: Scientific Reports
HIGHLIGHT: This study analyzes wildfire occurrences in Iran from 2001 to 2022, using NASA FIRMS’ active fire detections MCD14DL data. [Save to Library]
70, TITLE: CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval
AUTHORS: ZHIPENG QIAN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this work, we propose CSMCIR, a unified representation framework that achieves efficient query-target alignment through three synergistic components. [Save to Library]
71, TITLE: Multi-source Transfer Learning with Limited Data Access: A Neural Networks and Fuzzy Rules Approach for Regression Problems
AUTHORS: Masoume Gholizade ; Hadi Soltanizadeh ; Mohammad Rahmanimanesh
SOURCE: Cluster Computing
HIGHLIGHT: Multi-source Transfer Learning with Limited Data Access: A Neural Networks and Fuzzy Rules Approach for Regression Problems [Save to Library]
72, TITLE: AirNav: A Large-Scale Real-World UAV Vision-and-Language Navigation Dataset with Natural and Diverse Instructions
AUTHORS: HENGXING CAI et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Existing Unmanned Aerial Vehicle (UAV) Vision-Language Navigation (VLN) datasets face issues such as dependence on virtual environments, lack of naturalness in instructions, and limited scale. To address these challenges, we propose AirNav, a large-scale UAV VLN benchmark constructed from real urban aerial data, rather than synthetic environments, with natural and diverse instructions. [Save to Library]
73, TITLE: PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation
AUTHORS: WENLONG HUANG et. al.
CATEGORY: cs.RO [cs.RO, cs.AI, cs.CV]
HIGHLIGHT: We introduce PointWorld, a large pre-trained 3D world model that unifies state and action in a shared 3D space as 3D point flows: given one or few RGB-D images and a sequence of low-level robot action commands, PointWorld forecasts per-pixel displacements in 3D that respond to the given actions. [Save to Library]
74, TITLE: RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models
AUTHORS: Quy-Anh Dang ; Chris Ngo ; Truong-Son Hy
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: However, existing red teaming datasets suffer from inconsistent risk categorizations, limited domain coverage, and outdated evaluations, hindering systematic vulnerability assessments. To address these challenges, we introduce RedBench, a universal dataset aggregating 37 benchmark datasets from leading conferences and repositories, comprising 29,362 samples across attack and refusal prompts. [Save to Library]
75, TITLE: ComfySearch: Autonomous Exploration and Reasoning for ComfyUI Workflows
AUTHORS: JINWEI SU et. al.
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: However, the large number of components in ComfyUI and the difficulty of maintaining long-horizon structural consistency under strict graph constraints frequently lead to low pass rates and workflows of limited quality. To tackle these limitations, we present ComfySearch, an agentic framework that can effectively explore the component space and generate functional ComfyUI pipelines via validation-guided workflow construction. [Save to Library]
76, TITLE: PaperAudit-Bench: Benchmarking Error Detection in Research Papers for Critical Automated Peer Review
AUTHORS: SONGJUN TU et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we introduce PaperAudit-Bench, which consists of two components: (1) PaperAudit-Dataset, an error dataset covering both errors identifiable within individual sections and those requiring cross-section reasoning, designed for controlled evaluation under long-context settings; and (2) PaperAudit-Review, an automated review framework that integrates structured error detection with evidence-aware review generation to support critical assessment. [Save to Library]
77, TITLE: From Laboratory to Real-World Applications: Benchmarking Agentic Code Reasoning at The Repository Level
AUTHORS: Jia Li ; Yuxin Su ; Michael R. Lyu
CATEGORY: cs.SE [cs.SE, cs.AI]
HIGHLIGHT: We present RepoReason, a white-box diagnostic benchmark centered on abductive assertion verification. [Save to Library]
78, TITLE: Numerical Investigation of Double Diffusion of NEPCM Around Oscillating Cylinders in A Curved Cavity Using ISPH and Machine Learning
AUTHORS: Munirah Aali Alotaibi ; Weaam Alhejaili ; Samiyah Almalki ; Abdelraheem M. Aly
SOURCE: International Journal of Numerical Methods for Heat & Fluid Flow
HIGHLIGHT: Purpose This paper aims to investigate transient double-diffusive convection and phase change of nano-encapsulated phase-change materials (NEPCM) in a porous curved cavity with two oppositely oscillating cylinders and to quantify how oscillatory actuation and boundary conditions govern heat and mass transfer. [Save to Library]
79, TITLE: How Do Large Language Models Learn Concepts During Continual Pre-Training?
AUTHORS: BARRY MENGLONG YAO et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we study how individual concepts are acquired and forgotten, as well as how multiple concepts interact through interference and synergy. [Save to Library]
80, TITLE: Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers
AUTHORS: Tian Tian ; Yinyin Bao
SOURCE: Macromolecular Rapid Communications
HIGHLIGHT: Yet this multiscale flexibility also creates a vast and complex design space, where the interplay of monomer choice, polymer architecture, and processing methods makes it impossible to exhaustively map their structure–property relationships by empirical means. In this perspective, we review the development of recent design strategies in LEPs, highlighting the key experimental challenges they reveal, and discuss how data‐driven approaches, particularly machine learning, can help navigate this complexity and accelerate the discovery and optimization of next‐generation LEPs. [Save to Library]
81, TITLE: Visual Merit or Linguistic Crutch? A Close Look at DeepSeek-OCR
AUTHORS: YUNHAO LIANG et. al.
CATEGORY: cs.CL [cs.CL, cs.CV]
HIGHLIGHT: We release all data, results and scripts used in this study at https://github.com/dududuck00/DeepSeekOCR. [Save to Library]
82, TITLE: Rational Design of Multifunctional Nano–Architected Coatings for Bipolar Plates in Proton Exchange Membrane Fuel Cells
AUTHORS: SHIJIE LI et. al.
SOURCE: Rare Metals
HIGHLIGHT: This review presents a systematic framework for evaluating and designing bipolar plate coatings, integrating material selection, performance metrics, deposition techniques, and future innovation strategies. [Save to Library]
83, TITLE: More Than An Algorithm: Mental Health Professionals Confront The Promise and Ethical Perils of Artificial Intelligence
AUTHORS: Mehmet Demir ; Fuat Aydoğdu ; Mine Begümhan Alabay ; Hatice Kübra Yaşar ; Aras Bozkurt
SOURCE: Universal Access in the Information Society
HIGHLIGHT: Abstract This study explores the advantages and disadvantages of Artificial Intelligence (AI) in mental health, professionals' interactions with AI, and future predictions. [Save to Library]
84, TITLE: IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting
AUTHORS: WEI LONG et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: Existing methods typically rely solely on a single warp to estimate depth probability, which hinders their ability to fully leverage cross-view geometric cues, resulting in unstable and coarse depth maps. To address this limitation, we propose IDESplat, which iteratively applies warp operations to boost depth probability estimation for accurate Gaussian mean prediction. [Save to Library]
85, TITLE: Neural Network–based Approach for Improving The Evaluation of Antibody–antigen Docking Poses
AUTHORS: Alessandro Meta ; Giancarlo Ruocco ; Edoardo Milanetti
SOURCE: Frontiers in Physics
HIGHLIGHT: Here, we present a protocol based on multiple minimal neural network (NN)–based approaches, trained on a set of carefully selected physicochemical features, to discriminate docking decoy poses (structurally distant from the experimental complex) from native-like poses (structurally close to the native conformation) within a specific class of biologically relevant protein–protein complexes, namely antibody–antigen systems in which the antigen is a protein. [Save to Library]
86, TITLE: SOO-YOLO: An Efficient Small Object Detection Model for UAV Images
AUTHORS: RENJIE CHEN et. al.
SOURCE: Cluster Computing
HIGHLIGHT: SOO-YOLO: An Efficient Small Object Detection Model for UAV Images [Save to Library]
87, TITLE: Large Language Models for Computer-Aided Design: A Survey
AUTHORS: Licheng Zhang ; Bach Le ; Naveed Akhtar ; Siew-Kei Lam ; Duc Ngo
SOURCE: ACM Computing Surveys
HIGHLIGHT: This article presents the first systematic survey exploring the intersection of LLMs and CAD. [Save to Library]
88, TITLE: I2E: From Image Pixels to Actionable Interactive Environments for Text-Guided Image Editing
AUTHORS: JINGHAN YU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paradigm is severely limited by 1) the implicit coupling of planning and execution, 2) the lack of object-level control granularity, and 3) the reliance on unstructured, pixel-centric modeling. To address these limitations, we propose I2E, a novel "Decompose-then-Action" paradigm that revisits image editing as an actionable interaction process within a structured environment. [Save to Library]
89, TITLE: LECITE: LoRA-Enhanced and Consistency-Guided Iterative Knowledge Graph Construction
AUTHORS: Donghao Xiao ; Quan Qian
SOURCE: Future Internet
HIGHLIGHT: This paper proposes an innovative, efficient, and locally deployable knowledge graph construction framework that leverages low-rank adaptation (LoRA) to fine-tune large language models (LLMs) in order to reduce noise. [Save to Library]
90, TITLE: OLA: Output Language Alignment in Code-Switched LLM Interactions
AUTHORS: Juhyun Oh ; Haneul Yoo ; Faiz Ghifari Haznitrama ; Alice Oh
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We introduce OLA, a benchmark to evaluate LLMs' Output Language Alignment in code-switched interactions. [Save to Library]
91, TITLE: Physics-Informed Gaussian Process Regression for The Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models
AUTHORS: CHENYANG LI et. al.
CATEGORY: cs.LG [cs.LG, cond-mat.mtrl-sci]
HIGHLIGHT: Understanding and modeling the constitutive behavior of concrete is crucial for civil and defense applications, yet widely used phenomenological models such as Karagozian \& Case concrete (KCC) model depend on empirically calibrated failure surfaces that lack flexibility in model form and associated uncertainty quantification. This work develops a physics-informed framework that retains the modular elastoplastic structure of KCC model while replacing its empirical failure surface with a constrained Gaussian Process Regression (GPR) surrogate that can be learned directly from experimentally accessible observables. [Save to Library]
92, TITLE: Analyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation
AUTHORS: David Stap
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this thesis, we study cross-lingual knowledge transfer in neural models and develop methods to improve robustness and generalization in multilingual settings, using machine translation as a central testbed. [Save to Library]
93, TITLE: Performance Analysis of Explainable Deep Learning-Based Intrusion Detection Systems for IoT Networks: A Systematic Review
AUTHORS: Taiwo Blessing Ogunseyi ; Gogulakrishan Thiyagarajan ; Honggang He ; Vinay Bist ; Zhengcong Du
SOURCE: Sensors
HIGHLIGHT: Although explainable artificial intelligence (XAI) has been increasingly adopted to enhance interpretability, its impact on detection performance and computational efficiency in resource-constrained IoT environments remains insufficiently understood. This systematic review investigates the performance of an explainable deep learning-based IDS for IoT networks by analyzing trade-offs among detection accuracy, computational overhead, and explanation quality. [Save to Library]
94, TITLE: Determining Critical Factors for The Success of Machine Learning Libraries Considering Fuzzy Interrelationships
AUTHORS: İsmail Enes Parlak ; Miraç Tuba Çelik ; Gürkan Işık ; Aytaç Yildiz
SOURCE: Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi
HIGHLIGHT: This study examines the direct and indirect impacts of various factors on the success of machine learning libraries, leveraging expert evaluations. [Save to Library]
95, TITLE: Computational Learning Theories: A Mixed-Methods Framework for AI Enhanced Educational Research
AUTHORS: David Gibson ; Dirk Ifenthaler
SOURCE: International Journal of Technology in Teaching and Learning
HIGHLIGHT: This article proposes a computational mixed-methods approach as a necessary evolution in educational research methodology, encompassing a three-level hierarchical framework that integrates individual, social, and cultural learning processes through network-based modeling. [Save to Library]
96, TITLE: Predicting Carbonation Depth in Fiber-reinforced Ultra-high Performance Concrete (FR-UHPC) Using State-of-the-art Machine Learning Techniques
AUTHORS: ARSALAN MAHMOODZADEH et. al.
SOURCE: Scientific Reports
HIGHLIGHT: Predicting Carbonation Depth in Fiber-reinforced Ultra-high Performance Concrete (FR-UHPC) Using State-of-the-art Machine Learning Techniques [Save to Library]
97, TITLE: FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning
AUTHORS: YUJIE FENG et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL]
HIGHLIGHT: Motivated by recent findings that LLM forgetting mirrors the Ebbinghaus human forgetting curve, we propose FOREVER (FORgEtting curVe-inspired mEmory Replay), a novel CL framework that aligns replay schedules with a model-centric notion of time. [Save to Library]
98, TITLE: Using Machine Learning, Positive Matrix Factorization and Kernel Density Estimation to Assess Source-oriented Health Risk of Groundwater from A Coal-mining Area of Southwestern China
AUTHORS: YUNHUI ZHANG et. al.
SOURCE: Journal of Hydrology: Regional Studies
HIGHLIGHT: Using Machine Learning, Positive Matrix Factorization and Kernel Density Estimation to Assess Source-oriented Health Risk of Groundwater from A Coal-mining Area of Southwestern China [Save to Library]
99, TITLE: GAMBIT: A Gamified Jailbreak Framework for Multimodal Large Language Models
AUTHORS: Xiangdong Hu ; Yangyang Jiang ; Qin Hu ; Xiaojun Jia
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: If a model can think like a human, can we influence its cognitive-stage decisions so that it proactively completes a jailbreak? To validate this idea, we propose GAMBI} (Gamified Adversarial Multimodal Breakout via Instructional Traps), a novel multimodal jailbreak framework that decomposes and reassembles harmful visual semantics, then constructs a gamified scene that drives the model to explore, reconstruct intent, and answer as part of winning the game. [Save to Library]
100, TITLE: The Psychology of Learning from Machines: Anthropomorphic AI and The Paradox of Automation in Education
AUTHORS: Junaid Qadir ; Muhammad Mumtaz
CATEGORY: cs.CY [cs.CY, cs.AI]
HIGHLIGHT: We identify three persistent challenges intensified by Generative AI's conversational fluency. [Save to Library]
This message was intended for "john.smith@paperdigest.org".
You are receiving daily digests for the following categories: [cs.AI, cs.CL, cs.CV, cs.IR, cs.LG, cs.NE]
© 2026 Paper Digest | New York
