Carlos's Debrief

May 14, 2026 09:00
39ArXiv Papers
8Web Findings
47Total Sources
Topics searched: Artificial Intelligence Machine Learning Large Language Models Security & Cybersecurity Cryptography Zero Knowledge Quantum Computing Crypto & Blockchain AI Agents & Reasoning AI Safety & Alignment
May 14, 2026 09:00 — Curated by Hermes Research Scout

⚡ Quick Summary

📰 News Headlines

📄 ArXiv Papers

🧠 LLMs 23

PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents [1]
Mikhail Menschikov, Matvey Iskornev, Alexander Kharitonov, Alina Bogdanova, Mikhail Belkin
We introduce PersonalAI 2.0 (PAI-2), a novel framework, designed to enhance large language model (LLM) based systems through integration of external knowledge graphs (KG). The proposed approach addresses key limitations of existing Graph Retrieval-Augmented Generation…
cs.CLcs.ROcs.AI
Learning Agentic Policy from Action Guidance [6]
Yuxiang Ji, Zengbin Wang, Yong Wang, Shidong Yang, Ziyu Ma
Agentic reinforcement learning (RL) for Large Language Models (LLMs) critically depends on the exploration capability of the base policy, as training signals emerge only within its in-capability region. For tasks where the base policy cannot…
cs.CLcs.AIcs.LG
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking [8]
Rohan Surana, Gagan Mundada, Junda Wu, Xintong Li, Yizhu Jiao
Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models (LLMs) broaden this into a generative process: given a candidate pool, an…
cs.CLcs.CVcs.AI
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling [9]
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In such interactions, the counterpart's LLM, prompts, control logic,…
cs.CLcs.ROcs.AI
SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety [11]
Zhe Liu, Zonghao Ying, Wenxin Zhang, Quanchen Zou, Deyue Zhang
With the rapid evolution of foundation models, Large Language Model (LLM) agents have demonstrated increasingly powerful tool-use capabilities. However, this proficiency introduces significant security risks, as malicious actors can manipulate agents into executing tools to…
cs.CLcs.AIcs.LG
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation [13]
Jiashuo Sun, Jimeng Shi, Yixuan Xie, Saizhuo Wang, Jash Rajesh Parekh
Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that…
cs.CLcs.AIcs.LG
HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution [14]
Dongming Jiang, Yi Li, Guanpeng Li, Qiannan Li, Bingzhe Li
Memory retrieval in agentic large language model (LLM) systems is often treated as a static lookup problem, relying on flat vector search or fixed binary relational graphs. However, fixed graph structures cannot capture the varying…
cs.CLcs.AIcs.LG
Orthrus: Memory-Efficient Parallel Token Generation via Dual-View Diffusion [15]
Chien Van Nguyen, Chaitra Hegde, Van Cuong Pham, Ryan A. Rossi, Franck Dernoncourt
We introduce Orthrus, a simple and efficient dual-architecture framework that unifies the exact generation fidelity of autoregressive Large Language Models (LLMs) with the high-speed parallel token generation of diffusion models. The sequential nature of standard…
cs.CLcs.CVcs.AI
MinT: Managed Infrastructure for Training and Serving Millions of LLMs [16]
Mind Lab, Song Cao, Vic Cao, Andrew Chen, Kaijie Chen
We present MindLab Toolkit (MinT), a managed infrastructure system for Low-Rank Adaptation (LoRA) post-training and online serving. MinT targets a setting where many trained policies are produced over a small number of expensive base-model deployments.…
cs.CLcs.CVcs.AI
Many-Shot CoT-ICL: Making In-Context Learning Truly Learn [17]
Tsz Ting Chung, Lemao Liu, Mo Yu, Dit-Yan Yeung
In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve…
cs.CLcs.LGcs.IR
ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes [18]
Shivam Kumar
We introduce ShapeCodeBench, a synthetic benchmark for perception-to-program reconstruction: given a rendered raster image, a model must emit an executable drawing program that a deterministic evaluator re-renders and compares with the target. The v1 DSL…
cs.CLcs.CVcs.LG
Qwen-Image-VAE-2.0 Technical Report [19]
Zekai Zhang, Deqing Li, Kuan Cao, Yujia Wu, Chenfei Wu
We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To address the reconstruction bottlenecks of high compression, we adopt an improved architecture featuring Global…
cs.CLcs.CVcs.AI
RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data [20]
Harold Haodong Chen, Sirui Chen, Yingjie Xu, Wenhang Ge, Ying-Cong Chen
The scalability of robotic manipulation is fundamentally bottlenecked by the scarcity of task-aligned physical interaction data. While vision-language models (VLMs) and video generation models (VGMs) hold promise for autonomous data synthesis, they suffer from semantic-spatial…
cs.CLcs.CVcs.RO
Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context [21]
Zhaowei Wang, Lishu Luo, Haodong Duan, Weiwei Liu, Sijin Wu
Long-context modeling is becoming a core capability of modern large vision-language models (LVLMs), enabling sustained context management across long-document understanding, video analysis, and multi-turn tool use in agentic workflows. Yet practical training recipes remain insufficiently…
cs.CLcs.CVcs.AI
Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty? [22]
Yichen Feng, Yuetai Li, Chunjiang Liu, Yuanyuan Chen, Fengqing Jiang
Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of these applications require an explicit aesthetic judgment. Most existing solutions reduce this judgment to predicting a…
cs.CLcs.CVcs.RO
MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning [24]
Yuxin Liu, Ziang Ye, Yueqing Sun, Mingye Zhu, Jinwei Xiao
Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforehand. This temporal inversion leads to Delayed Environmental Perception: agents must infer environmental constraints through…
cs.CLcs.ROcs.AI
Context Training with Active Information Seeking [25]
Zeyu Huang, Adhiguna Kuncoro, Qixuan Feng, Jiajun Shen, Lucio Dery
Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or niche domain knowledge. Recent work has shown that, by manipulating and optimizing their context,…
cs.CLcs.AIcs.LG
WriteSAE: Sparse Autoencoders for Recurrent State [28]
Jack Young
We introduce WriteSAE, the first sparse autoencoder that decomposes and edits the matrix cache write of state-space and hybrid recurrent language models, where residual SAEs cannot reach. Existing SAEs read residual streams, but Gated DeltaNet,…
cs.CLcs.LG
The Extrapolation Cliff in On-Policy Distillation of Near-Deterministic Structured Outputs [29]
Xin Li, Hao Jiang, Annan Wang, Yichi Zhang, Chau Yuen
On-policy distillation (OPD) is widely used for LLM post-training. When pushed with a reward-extrapolation coefficient lambda > 1, the student can lift past the teacher in domain, but past a threshold lambda* the same step…
cs.CLcs.LG
Revisiting DAgger in the Era of LLM-Agents [30]
Changhao Li, Rushi Qiang, Jiawei Huang, Chenxiao Gao, Chao Zhang
Long-horizon LM agents learn from multi-turn interaction, where a single early mistake can alter the subsequent state distribution and derail the whole trajectory. Existing recipes fall short in complementary ways: supervised fine-tuning provides dense teacher…
cs.CLcs.CVcs.AI
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production [33]
Xiang Liu, Shimiao Yuan, Zhenheng Tang, Peijie Dong, Kaiyong Zhao
LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the relevant output is a quality-conditioned token produced under joint constraints…
cs.CLcs.AIcs.LG
Useful Memories Become Faulty When Continuously Updated by LLMs [38]
Dylan Zhang, Yanshan Lin, Zhengkun Wu, Yihang Sun, Bingxuan Li
Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated abstractions distilled across many episodes into reusable, schema-like lessons. Recent agentic-memory systems pursue…
cs.CLcs.ROcs.AI
Covering Human Action Space for Computer Use: Data Synthesis and Benchmark [39]
Miaosen Zhang, Xiaohan Zhao, Zhihong Tan, Zhou Huoshen, Yijia Fan
Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions is still poor, limiting user trust. Our analysis of failure cases from advanced models suggests a…
cs.CLcs.CVcs.AI

🧠 Vision & Gen AI 10

From Pixels to Concepts: Do Segmentation Models Understand What They Segment? [2]
Shuang Liang, Zeqing Wang, Yuxian Li, Xihui Liu, Han Wang
Segmentation is a fundamental vision task underlying numerous downstream applications. Recent promptable segmentation models, such as Segment Anything Model 3 (SAM3), extend segmentation from category-agnostic mask prediction to concept-guided localization conditioned on high-level textual prompts.…
cs.CVcs.ROcs.LG
FrameSkip: Learning from Fewer but More Informative Frames in VLA Training [3]
Bin Yu, Shijie Lian, Xiaopeng Lin, Zhaolong Shen, Yuliang Wei
Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal…
cs.CVcs.ROcs.AI
Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs [4]
Yunhong Lu, Qichao Wang, Hengyuan Cao, Xiaoyin Xu, Min Zhang
Existing preference datasets for text-to-image models typically store only the final winner/loser images. This representation is insufficient for rectified flow (RF) models, whose generation is naturally indexed by a specific prior noise sample and follows…
cs.CVcs.AIcs.LG
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image [5]
Alan Arazi, Eilam Shapira, Shoham Grunblat, Mor Ventura, Elad Hoffer
Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numerical and categorical structured data. However, they lack native support for unstructured…
cs.CVcs.AIcs.LG
TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking [12]
Jisu Nam, Jahyeok Koo, Soowon Son, Jaewoo Jung, Honggyu An
Dense 3D tracking from monocular video is fundamental to dynamic scene understanding. While recent 3D foundation models provide reliable per-frame geometry, recovering object motion in this geometry remains challenging and benefits from strong motion priors…
cs.CVcs.LG
Asymmetric Flow Models [26]
Hansheng Chen, Jan Ackermann, Minseo Kim, Gordon Wetzstein, Leonidas Guibas
Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We present Asymmetric Flow Modeling (AsymFlow), a rank-asymmetric velocity parameterization that restricts noise prediction…
cs.CVcs.LG
AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation [27]
Yuchao Gu, Guian Fang, Yuxin Jiang, Weijia Mao, Song Han
Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion. This…
cs.CVcs.LG
PresentAgent-2: Towards Generalist Multimodal Presentation Agents [31]
Wei Wu, Ziyang Xu, Zeyu Zhang, Yang Zhao, Hao Tang
Presentation generation is moving beyond static slide creation toward end-to-end presentation video generation with research grounding, multimodal media, and interactive delivery. We introduce PresentAgent-2, an agentic framework for generating presentation videos from user queries. Given…
cs.CVcs.AIcs.LG
The DAWN of World-Action Interactive Models [32]
Hongbo Lu, Liang Yao, Chenghao He, Haoyu Wang, Xiang Gu
A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action Models (WAMs) largely miss this reciprocity, treating world prediction and action…
cs.CVcs.ROcs.LG
Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling [34]
Xuehai Bai, Yang Shi, Yi-Fan Zhang, Xuanyu Zhu, Yuran Wang
Recent image editing models have achieved remarkable progress in instruction following, multimodal understanding, and complex visual editing. However, existing benchmarks often fail to faithfully reflect human judgment, especially for strong frontier models, due to limited…
cs.CVcs.AIcs.LG

🧠 Robotics & Control 3

Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition [7]
Kush Juvekar, Kavya Manohar, Aditya Srinivas Menon, Arghya Bhattacharya, Kumarmanas Nethil
Fine-tuning multilingual ASR models like Whisper for low-resource languages often improves read speech but degrades spontaneous audio performance, a phenomenon we term studio-bias. To diagnose this mismatch, we introduce Vividh-ASR, a complexity-stratified benchmark for Hindi…
cs.ROcs.LGcs.SD
MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading [10]
Baibei Ji, Xiaoyang Weng, Juntao Li, Zecheng Tang, Yihang Lou
To tackle long-context reasoning tasks without the quadratic complexity of standard attention mechanisms, approaches based on agent memory have emerged, which typically maintain a dynamically updated memory when linearly processing document chunks. To mitigate the…
cs.ROcs.AIcs.LG
Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge [35]
Dhaval Patel, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula, Ling Yue, Shuxin Lin
Competition retrospectives are useful when they explain what a leaderboard measured, how hidden evaluation changed conclusions, and which design patterns were rewarded. We revisit the CODS 2025 challenge, a privacy-aware Codabench competition on industrial multi-agent…
cs.ROcs.AIcs.LG

🧠 Agents 2

AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation [23]
Priyam Sahoo, Gaurav Mittal, Xiaomin Li, Shengjie Ma, Benjamin Steenhoek
Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view treats a principled solution and a chaotic trial-and-error process as equivalent. We show…
cs.AIcs.LGcs.IR
EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory [37]
Yuyang Li, Yime He, Zeyu Zhang, Dong Gong
Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi-hop questions. Existing iterative methods refine queries via generated content or document-level signals, but none explicitly diagnoses the…
cs.AIcs.LGcs.IR

🧠 Machine Learning 1

Frequency Bias and OOD Generalization in Neural Operators under a Variable-Coefficient Wave Equation [36]
Runlong Xie, An Luo
Neural operators learn to map initial conditions to the terminal solution of partial differential equations (PDEs), providing a surrogate for the full operator mapping. This enables rapid prediction across different input configurations. While recent neural…
cs.LG

🌐 Web Findings

🔗 Lobsters 2

Sculpt OS release 26.04 [40]
Sculpt OS 26.04 gives the user unprecedented transparency and interactive control over all components underpinning the operating system.
Lobsters
ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It [41]
Edit April 2, 2026: I've been getting inbound interest from researchers wanting to run their own queries. The MCP integration I use for my own research lets you analyze live…
Lobsters

🔗 Google Scholar 6

Early identification of breakthrough technologies: Insights from science-driven innovations [42]
… Building on this premise, this paper presents a framework for identifying breakthrough … potential to trigger technological breakthroughs. Next, a machine learning-based link prediction …
Google Scholar
Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements [43]
Rising levels of atmospheric carbon dioxide (CO 2 ) and methane (CH 4 ) have sparked the interest of researchers in resolving this issue. Various technologies have been utilized such…
Google Scholar
Large language models (LLM) in computational social science: prospects, current state, and challenges [44]
… The advent of large language models (LLMs) has marked a new … LLM usage. We further present the challenges associated with data bias, privacy, and the integration of these…
Google Scholar
Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms [45]
… in adversarial AI, automated threat intelligence, and AI-driven security orchestration, this … AI’s role in cybersecurity. Figure 1 shows the key areas where Artificial intelligence (AI) and …
Google Scholar
Securing the future: exploring post-quantum cryptography for authentication and user privacy in IoT devices [46]
Cluster Computing - With the emergence of quantum computers, traditional cryptographic methods are vulnerable to attacks, emphasizing the need for post-quantum cryptography to secure devices and...
Google Scholar
Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements [47]
… in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-… It varies from applications in optimization, drug discovery, and quantum-secured communications, …
Google Scholar

🔗 All Sources

  1. [1] PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents (HuggingFace/arXiv)
  2. [2] From Pixels to Concepts: Do Segmentation Models Understand What They Segment? (HuggingFace/arXiv)
  3. [3] FrameSkip: Learning from Fewer but More Informative Frames in VLA Training (HuggingFace/arXiv)
  4. [4] Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs (HuggingFace/arXiv)
  5. [5] MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image (HuggingFace/arXiv)
  6. [6] Learning Agentic Policy from Action Guidance (HuggingFace/arXiv)
  7. [7] Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition (HuggingFace/arXiv)
  8. [8] F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking (HuggingFace/arXiv)
  9. [9] Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling (HuggingFace/arXiv)
  10. [10] MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading (HuggingFace/arXiv)
  11. [11] SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety (HuggingFace/arXiv)
  12. [12] TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking (HuggingFace/arXiv)
  13. [13] Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation (HuggingFace/arXiv)
  14. [14] HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution (HuggingFace/arXiv)
  15. [15] Orthrus: Memory-Efficient Parallel Token Generation via Dual-View Diffusion (HuggingFace/arXiv)
  16. [16] MinT: Managed Infrastructure for Training and Serving Millions of LLMs (HuggingFace/arXiv)
  17. [17] Many-Shot CoT-ICL: Making In-Context Learning Truly Learn (HuggingFace/arXiv)
  18. [18] ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes (HuggingFace/arXiv)
  19. [19] Qwen-Image-VAE-2.0 Technical Report (HuggingFace/arXiv)
  20. [20] RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data (HuggingFace/arXiv)
  21. [21] Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context (HuggingFace/arXiv)
  22. [22] Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty? (HuggingFace/arXiv)
  23. [23] AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation (HuggingFace/arXiv)
  24. [24] MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning (HuggingFace/arXiv)
  1. [25] Context Training with Active Information Seeking (HuggingFace/arXiv)
  2. [26] Asymmetric Flow Models (HuggingFace/arXiv)
  3. [27] AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation (HuggingFace/arXiv)
  4. [28] WriteSAE: Sparse Autoencoders for Recurrent State (HuggingFace/arXiv)
  5. [29] The Extrapolation Cliff in On-Policy Distillation of Near-Deterministic Structured Outputs (HuggingFace/arXiv)
  6. [30] Revisiting DAgger in the Era of LLM-Agents (HuggingFace/arXiv)
  7. [31] PresentAgent-2: Towards Generalist Multimodal Presentation Agents (HuggingFace/arXiv)
  8. [32] The DAWN of World-Action Interactive Models (HuggingFace/arXiv)
  9. [33] Position: LLM Inference Should Be Evaluated as Energy-to-Token Production (HuggingFace/arXiv)
  10. [34] Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling (HuggingFace/arXiv)
  11. [35] Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge (HuggingFace/arXiv)
  12. [36] Frequency Bias and OOD Generalization in Neural Operators under a Variable-Coefficient Wave Equation (HuggingFace/arXiv)
  13. [37] EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory (HuggingFace/arXiv)
  14. [38] Useful Memories Become Faulty When Continuously Updated by LLMs (HuggingFace/arXiv)
  15. [39] Covering Human Action Space for Computer Use: Data Synthesis and Benchmark (HuggingFace/arXiv)
  16. [40] Sculpt OS release 26.04 (Lobsters)
  17. [41] ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It (Lobsters)
  18. [42] Early identification of breakthrough technologies: Insights from science-driven innovations (Google Scholar)
  19. [43] Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements (Google Scholar)
  20. [44] Large language models (LLM) in computational social science: prospects, current state, and challenges (Google Scholar)
  21. [45] Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms (Google Scholar)
  22. [46] Securing the future: exploring post-quantum cryptography for authentication and user privacy in IoT devices (Google Scholar)
  23. [47] Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements (Google Scholar)
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