Carlos's Debrief

May 27, 2026 09:07
0ArXiv Papers
39Web Findings
39Total 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 27, 2026 09:07 — Curated by Hermes Research Scout

⚡ Quick Summary

📰 News Headlines

📄 ArXiv Papers

🧠 LLMs 0

🤖 Agents 0

🔬 Machine Learning 0

🛡️ AI Safety 0

🔐 Security & Crypto 0

🌐 Web Findings

🧠 LLMs & Foundation Models 21

D^2-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing [2]
Aoxi Liu, Yupeng Chen, James Oldfield, Guanzhe Hong, Junchi Yu
Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs…
HuggingFace
Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments [3]
Yuxin Chen, Xiaodong Cai, Junfeng Fang, Zhuowen Han, Yu Wang
Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning, and tool use. Despite strong performance on existing benchmarks,…
HuggingFace
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions [4]
Yuxin Chen, Yi Zhang, Zhengzhou Cai, Yaorui Shi, Zhiyuan Yao
Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on understanding the user beyond what is…
HuggingFace
Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals [5]
Federico Torrielli, Peter Schneider-Kamp, Lukas Galke Poech
Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white-box interpretability techniques. However, uncertainty quantification (UQ) for the…
HuggingFace
Rethinking VLM Representation for VLA Initialization [6]
Weifeng Lin, Siyuan Huang, Hao Li, Tingwei Chen, Ruichuan An
Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is useful as a VLA initialization. In this…
HuggingFace
Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling [8]
Xinglin Wang, Hao Lin, Shaoxiong Feng, Peiwen Yuan, Yiwei Li
Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution space. However, existing parallel TTS methods typically keep…
HuggingFace
DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs [9]
Yi Li, Songtao Wei, Dongming Jiang, Zhichun Guo, Qiannan Li
Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead. When agents exchange raw responses or…
HuggingFace
Understanding Data Temporality Impact on Large Language Models Pre-training [10]
Pilchen Hippolyte, Fabre Romain, Signe Talla Franck, Perez Patrick, Grave Edouard
Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understood. In this work, we study the impact…
HuggingFace
SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent [11]
Yuyang Hu, Hongjin Qian, Shuting Wang, Jiongnan Liu, Ziliang Zhao
Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The challenge is not merely that these…
HuggingFace
LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence [12]
Xiang An, Yin Xie, Feilong Tang, Yunyao Yan, Huajie Tan
We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of multimodal benchmarks. The model builds…
HuggingFace
MobileMoE: Scaling On-Device Mixture of Experts [13]
Yanbei Chen, Hanxian Huang, Ernie Chang, Jacob Szwejbka, Digant Desai
Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales for on-device deployment remain largely unexplored. To close this…
HuggingFace
Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows [15]
Harshada Badave, Santosh Borse, Andrea Gomez, Harshitha Narahari, Sara Carter
Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination benchmarks still evaluate only the final output, missing failures…
HuggingFace
LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV [17]
Tengfei Liu, Yang Shi, Xuanyu Zhu, Jiafu Tang, Liu Yang
Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to short-form settings. Existing benchmarks primarily focus on 5--10…
HuggingFace
Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement [18]
Dingwei Chen, Zefang Zong, Zhipeng Ma, Leo Luo, Yang Li
Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that agentic RL training induces increasing redundant tool calls and blurs…
HuggingFace
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence [19]
MiniMax, Aili Chen, Aonian Li, Baichuan Zhou, Bangwei Gong
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The flagship M2 contains 229.9B total…
HuggingFace
Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models [21]
Mingze Wang, Shuchen Zhu, Yuxin Fang, Binghui Li, Kai Shen
Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the normalization operation has been extensively studied, the scale…
HuggingFace
Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation [22]
Shuhong Zheng, Aashish Kumar Misraa, Yu-Teng Li, Yu-Jhe Li, Igor Gilitschenski
Subject-driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Existing approaches often encode text and reference images separately.…
HuggingFace
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding [24]
Shihao Wang, Shilong Liu, Yuanguo Kuang, Xinyu Wei, Yangzhou Liu
Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are learned and decoded largely…
HuggingFace
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation [25]
Huawei Lin, Peng Li, Jie Song, Fuxin Jiang, Tieying Zhang
Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and…
HuggingFace
EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation [27]
Songlin Yang, Haobin Zhong, Ruilin Zhang, Xiaotong Zhao, Shuai Li
The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community transitions towards Reinforcement…
HuggingFace
Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents [30]
Yeonjun In, Wonjoong Kim, Sangwu Park, Kanghoon Yoon, Chanyoung Park
Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are different across users. This misalignment…
HuggingFace

🤖 Agents & Tool Use 15

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments [3]
Yuxin Chen, Xiaodong Cai, Junfeng Fang, Zhuowen Han, Yu Wang
Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning, and tool use. Despite strong performance on existing benchmarks,…
HuggingFace
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions [4]
Yuxin Chen, Yi Zhang, Zhengzhou Cai, Yaorui Shi, Zhiyuan Yao
Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on understanding the user beyond what is…
HuggingFace
Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling [8]
Xinglin Wang, Hao Lin, Shaoxiong Feng, Peiwen Yuan, Yiwei Li
Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution space. However, existing parallel TTS methods typically keep…
HuggingFace
DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs [9]
Yi Li, Songtao Wei, Dongming Jiang, Zhichun Guo, Qiannan Li
Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead. When agents exchange raw responses or…
HuggingFace
SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent [11]
Yuyang Hu, Hongjin Qian, Shuting Wang, Jiongnan Liu, Ziliang Zhao
Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The challenge is not merely that these…
HuggingFace
MobileMoE: Scaling On-Device Mixture of Experts [13]
Yanbei Chen, Hanxian Huang, Ernie Chang, Jacob Szwejbka, Digant Desai
Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales for on-device deployment remain largely unexplored. To close this…
HuggingFace
MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research [14]
Dingbang Wu, Rui Hao, Haiyang Wang, Shuzhe Wu, Han Xiao
We present MobileGym, a browser-hosted, lightweight, fully controllable environment for everyday mobile use, targeting interaction fidelity without replicating proprietary backends. It enables two capabilities…
HuggingFace
Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows [15]
Harshada Badave, Santosh Borse, Andrea Gomez, Harshitha Narahari, Sara Carter
Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination benchmarks still evaluate only the final output, missing failures…
HuggingFace
Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement [18]
Dingwei Chen, Zefang Zong, Zhipeng Ma, Leo Luo, Yang Li
Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that agentic RL training induces increasing redundant tool calls and blurs…
HuggingFace
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence [19]
MiniMax, Aili Chen, Aonian Li, Baichuan Zhou, Bangwei Gong
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The flagship M2 contains 229.9B total…
HuggingFace
Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration [20]
Yiren Song, Huilin Zhong, Kevin Qinghong Lin, Haofan Wang, Mike Zheng Shou
We study series-level cinematic remaking, a long-horizon video-to-video generation problem that localizes full episodes or films via stylization or actor replacement while strictly preserving narrative structure, motion…
HuggingFace
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation [25]
Huawei Lin, Peng Li, Jie Song, Fuxin Jiang, Tieying Zhang
Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and…
HuggingFace
SpatialBench: Is Your Spatial Foundation Model an All-Round Player? [26]
Haosong Peng, Hao Li, Jiaqi Chen, Yuhao Pan, Runmao Yao
While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing robustly across diverse downstream…
HuggingFace
EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation [27]
Songlin Yang, Haobin Zhong, Ruilin Zhang, Xiaotong Zhao, Shuai Li
The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community transitions towards Reinforcement…
HuggingFace
Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents [30]
Yeonjun In, Wonjoong Kim, Sangwu Park, Kanghoon Yoon, Chanyoung Park
Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are different across users. This misalignment…
HuggingFace

🔬 Machine Learning & Vision 20

D^2-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing [2]
Aoxi Liu, Yupeng Chen, James Oldfield, Guanzhe Hong, Junchi Yu
Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs…
HuggingFace
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions [4]
Yuxin Chen, Yi Zhang, Zhengzhou Cai, Yaorui Shi, Zhiyuan Yao
Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on understanding the user beyond what is…
HuggingFace
Rethinking VLM Representation for VLA Initialization [6]
Weifeng Lin, Siyuan Huang, Hao Li, Tingwei Chen, Ruichuan An
Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is useful as a VLA initialization. In this…
HuggingFace
Learning High-Frequency Continuous Action Chunks in Latent Space [7]
Kunyun Wang, Yuhang Zheng, Yupeng Zheng, Jieru Zhao, Wenchao Ding
Modern robotic policies increasingly rely on action chunking to execute complex tasks in the physical world. While action chunking improves temporal consistency at moderate action frequencies, it becomes insufficient…
HuggingFace
SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent [11]
Yuyang Hu, Hongjin Qian, Shuting Wang, Jiongnan Liu, Ziliang Zhao
Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The challenge is not merely that these…
HuggingFace
LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence [12]
Xiang An, Yin Xie, Feilong Tang, Yunyao Yan, Huajie Tan
We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of multimodal benchmarks. The model builds…
HuggingFace
MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale [16]
Zhicong Tang, Zhao Zhang, Jingye Chen, Mohan Zhou, Yifan Pu
Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, analogous to word-level editing in natural language. Despite its…
HuggingFace
LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV [17]
Tengfei Liu, Yang Shi, Xuanyu Zhu, Jiafu Tang, Liu Yang
Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to short-form settings. Existing benchmarks primarily focus on 5--10…
HuggingFace
Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration [20]
Yiren Song, Huilin Zhong, Kevin Qinghong Lin, Haofan Wang, Mike Zheng Shou
We study series-level cinematic remaking, a long-horizon video-to-video generation problem that localizes full episodes or films via stylization or actor replacement while strictly preserving narrative structure, motion…
HuggingFace
Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation [22]
Shuhong Zheng, Aashish Kumar Misraa, Yu-Teng Li, Yu-Jhe Li, Igor Gilitschenski
Subject-driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Existing approaches often encode text and reference images separately.…
HuggingFace
Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning [23]
Fanhu Zeng, Zhicong Luo, Zefan Wang, You Li, Chi Chen
Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-source inputs, existing approaches tend to treat them as a mere…
HuggingFace
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding [24]
Shihao Wang, Shilong Liu, Yuanguo Kuang, Xinyu Wei, Yangzhou Liu
Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are learned and decoded largely…
HuggingFace
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation [25]
Huawei Lin, Peng Li, Jie Song, Fuxin Jiang, Tieying Zhang
Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and…
HuggingFace
SpatialBench: Is Your Spatial Foundation Model an All-Round Player? [26]
Haosong Peng, Hao Li, Jiaqi Chen, Yuhao Pan, Runmao Yao
While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing robustly across diverse downstream…
HuggingFace
EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation [27]
Songlin Yang, Haobin Zhong, Ruilin Zhang, Xiaotong Zhao, Shuai Li
The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community transitions towards Reinforcement…
HuggingFace
RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models [28]
Xing Cong, Hanlin Tang, Kan Liu, Lan Tao, Lin Qu
Diffusion Transformers (DiT) achieve strong performance in image generation but incur substantial inference costs. While prior work has reduced this cost via quantization and distillation, semi-structured sparsity,…
HuggingFace
Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction [29]
Jin Hyeon Kim, Jaeeun Lee, Claire Kim, Kyoungjin Oh, Paul Hyunbin Cho
Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typically trained and evaluated under ideal, degradation-free imaging…
HuggingFace
Recursive Flow Matching [31]
Jiahe Huang, Sihan Xu, Sharvaree Vadgama, Rose Yu
Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without incurring high computational cost remains…
HuggingFace
PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design [32]
Runtian Wang, Renhao Xue, Baige Chen, Hao Wu
The inverse problem of multilayer thin-film optical coatings design represents a complex combinatorial-continuous optimization challenge. We present PRISM (Position-encoded Regressive Inverse Spectral Model), a unified…
HuggingFace
Cross-scale Aligned Supervision for Training GANs [33]
Sangeek Hyun, MinKyu Lee, Jae-Pil Heo
Modern GANs often introduce adversarial supervision on intermediate generator outputs and interpret the resulting multi-stage synthesis as coarse-to-fine hierarchical generation. In this work, we challenge this…
HuggingFace

🛡️ Safety, Monitoring & Alignment 4

D^2-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing [2]
Aoxi Liu, Yupeng Chen, James Oldfield, Guanzhe Hong, Junchi Yu
Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs…
HuggingFace
Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals [5]
Federico Torrielli, Peter Schneider-Kamp, Lukas Galke Poech
Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white-box interpretability techniques. However, uncertainty quantification (UQ) for the…
HuggingFace
DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs [9]
Yi Li, Songtao Wei, Dongming Jiang, Zhichun Guo, Qiannan Li
Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead. When agents exchange raw responses or…
HuggingFace
Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows [15]
Harshada Badave, Santosh Borse, Andrea Gomez, Harshitha Narahari, Sara Carter
Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination benchmarks still evaluate only the final output, missing failures…
HuggingFace

🔐 Security, Privacy & Cryptography 7

ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It [1]
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 mobile telemetry continuously collected from…
Lobste.rs
Early identification of breakthrough technologies: Insights from science-driven innovations [34]
… 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 [35]
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 [36]
… 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 models …
Google Scholar
Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms [37]
… 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 [38]
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 [39]
… 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] ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It (lobsters)
  2. [2] D^2-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing (huggingface_papers)
  3. [3] Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments (huggingface_papers)
  4. [4] VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions (huggingface_papers)
  5. [5] Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals (huggingface_papers)
  6. [6] Rethinking VLM Representation for VLA Initialization (huggingface_papers)
  7. [7] Learning High-Frequency Continuous Action Chunks in Latent Space (huggingface_papers)
  8. [8] Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling (huggingface_papers)
  9. [9] DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs (huggingface_papers)
  10. [10] Understanding Data Temporality Impact on Large Language Models Pre-training (huggingface_papers)
  11. [11] SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent (huggingface_papers)
  12. [12] LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence (huggingface_papers)
  13. [13] MobileMoE: Scaling On-Device Mixture of Experts (huggingface_papers)
  14. [14] MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research (huggingface_papers)
  15. [15] Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows (huggingface_papers)
  16. [16] MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale (huggingface_papers)
  17. [17] LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV (huggingface_papers)
  18. [18] Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement (huggingface_papers)
  19. [19] The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence (huggingface_papers)
  20. [20] Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration (huggingface_papers)
  1. [21] Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models (huggingface_papers)
  2. [22] Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation (huggingface_papers)
  3. [23] Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning (huggingface_papers)
  4. [24] LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding (huggingface_papers)
  5. [25] MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation (huggingface_papers)
  6. [26] SpatialBench: Is Your Spatial Foundation Model an All-Round Player? (huggingface_papers)
  7. [27] EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation (huggingface_papers)
  8. [28] RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models (huggingface_papers)
  9. [29] Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction (huggingface_papers)
  10. [30] Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents (huggingface_papers)
  11. [31] Recursive Flow Matching (huggingface_papers)
  12. [32] PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design (huggingface_papers)
  13. [33] Cross-scale Aligned Supervision for Training GANs (huggingface_papers)
  14. [34] Early identification of breakthrough technologies: Insights from science-driven innovations (google_scholar)
  15. [35] Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements (google_scholar)
  16. [36] Large language models (LLM) in computational social science: prospects, current state, and challenges (google_scholar)
  17. [37] Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms (google_scholar)
  18. [38] Securing the future: exploring post-quantum cryptography for authentication and user privacy in IoT devices (google_scholar)
  19. [39] Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements (google_scholar)
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