Carlos's Debrief

May 09, 2026 09:00
38ArXiv Papers
9Web 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 09, 2026 09:00 — Curated by Hermes Research Scout

⚡ Quick Summary

📰 News Headlines

📄 ArXiv Papers

🧠 LLMs 11

Verifier-Backed Hard Problem Generation for Mathematical Reasoning [1]
Yuhang Lai, Jiazhan Feng, Yee Whye Teh, Ning Miao
Large Language Models (LLMs) demonstrate strong capabilities for solving scientific and mathematical problems, yet they struggle to produce valid, challenging, and novel problems…
cs.LGcs.AIcs.CL
Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less [2]
Yuxing Liu, Jianyu Wang, Tong Zhang
Optimizers play an important role in both pretraining and finetuning stages when training large language models (LLMs). In this paper, we present an observation that full…
cs.LGcs.AImath.OC
When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels [3]
Sushant Gautam, Finn Schwall, Annika Willoch Olstad, Fernando Vallecillos Ruiz, Birk Torpmann-Hagen, Sunniva Maria Stordal Bjørklund, Leon Moonen, Klas Pettersen, Michael A. Riegler
Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this…
cs.LGcs.AIcs.CL
StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction [4]
Xiangyuan Xue, Yifan Zhou, Zidong Wang, Shengji Tang, Philip Torr, Wanli Ouyang, Lei Bai, Zhenfei Yin
Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are…
cs.CLcs.AI
Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML [5]
Jai Moondra, Ayela Chughtai, Bhargavi Lanka, Swati Gupta
Ranking LLMs via pairwise human feedback underpins current leaderboards for open-ended tasks, such as creative writing and problem-solving. We analyze ~89K comparisons in 116…
cs.LGcs.DMcs.ETmath.OC
EMO: Pretraining Mixture of Experts for Emergent Modularity [6]
Ryan Wang, Akshita Bhagia, Sewon Min
Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of capabilities, e.g., code, math, or…
cs.CL
FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning [7]
Su Zhang, Junfeng Guo, Heng Huang
Watermark radioactivity testing type of methods can detect whether a model was trained on watermarked documents, and have become key tools for protecting data ownership in the…
cs.CRcs.LG
PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization [8]
Murat Bilgehan Ertan, Xiaochen Zhu, Phuong Ha Nguyen, Marten van Dijk, Srinivas Devadas
We introduce PACZero, a family of PAC-private zeroth-order mechanisms for fine-tuning large language models that delivers usable utility at $I(S^*; Y_{1:T})=0$. This privacy…
cs.LGcs.AIcs.CR
Autonomous Adversary: Red-Teaming in the age of LLM [9]
Mohammad Mamun, Mohamed Gaber, Scott Buffett, Sherif Saad
Language Model Agents (LMAs) are emerging as a powerful primitive for augmenting red-team operations. They can support attack planning, adversary emulation, and the orchestration…
cs.CR
Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models [10]
Zeyuan Chen, Yihan Ma, Xinyue Shen, Michael Backes, Yang Zhang
Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data raises serious privacy concerns.…
cs.CR
Task-Aware Answer Preservation under Audio Compression for Large Audio Language Models [11]
Amir Ivry
Large audio language models (LALMs) are increasingly used to reason over long audio clips, yet deployment often compresses audio before inference to reduce memory and latency. The…
eess.AS

⚙️ Machine Learning 7

UniPool: A Globally Shared Expert Pool for Mixture-of-Experts [12]
Minbin Huang, Han Shi, Chuanyang Zheng, Yimeng Wu, Guoxuan Chen, Xintong Yu, Yichun Yin, Hong Cheng
Modern Mixture-of-Experts (MoE) architectures allocate expert capacity through a rigid per-layer rule: each transformer layer owns a separate expert set. This convention couples…
cs.LGcs.AI
Inductive Venn-Abers and related regressors [13]
Ivan Petej, Vladimir Vovk
Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary…
cs.LG
Rational homology disk degenerations of elliptic surfaces [14]
Marcos Canedo, Giancarlo Urzúa
In this paper, a $\mathbb{Q}$HD singularity is a weighted homogeneous normal surface singularity admitting a rational homology disk ($\mathbb{Q}$HD) smoothing. These singularities…
math.AGmath.GNmath.SG
Multi-Robot Coordination in V2X Environments [15]
John Pravin Arockiasamy, Alexey Vinel
This paper presents a Vehicle-to-Everything (V2X) communication framework that enables decentralized cooperation among social robots operating in complex urban traffic…
cs.RO
Relational Dualities and Bisimulation [16]
Piotr Kozicki, Alex Kavvos
The Kripke semantics of various logics arises via categorical dualities between a category of relational frames and their maps, and a category of algebras and logical…
cs.LO
Improving Variance Estimation for Covariate Adjustment with Binary Outcomes [17]
Kaitlyn Lee, Alex Ocampo, Courtney Schiffman, Michael Friesenhahn, Christina Rabe, Michael Rosenblum
Covariate adjustment is a general method for improving precision when estimating treatment effects in randomized trials and is recommended by the FDA in its 2023 guidance when…
stat.ME
On the plasticity of the unit spheres of $\ell_1$, $\ell_{\infty}$, $c$, and Hilbert spaces [18]
Maksym Levchenko, Olesia Zavarzina
This paper demonstrates the expand-contract plasticity of the unit spheres of $\ell_1$, $\ell_{\infty}$, and $c$. Furthermore, it establishes the strong plasticity of the unit…
math.FA

🤖 Agents 6

BAMI: Training-Free Bias Mitigation in GUI Grounding [19]
Borui Zhang, Bo Zhang, Bo Wang, Wenzhao Zheng, Yuhao Cheng, Liang Tang, Yiqiang Yan, Jie Zhou, Jiwen Lu
GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. However, in complex scenarios like the ScreenSpot-Pro benchmark,…
cs.CVcs.AI
AI Co-Mathematician: Accelerating Mathematicians with Agentic AI [20]
Daniel Zheng, Ingrid von Glehn, Yori Zwols, Iuliya Beloshapka, Lars Buesing, Daniel M. Roy, Martin Wattenberg, Bogdan Georgiev, Tatiana Schmidt, Andrew Cowie, Fernanda Viegas, Dimitri Kanevsky, Vineet Kahlon, Hartmut Maennel, Sophia Alj, George Holland, Alex Davies, Pushmeet Kohli
We introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents to pursue open-ended research. The AI co-mathematician is optimized to…
cs.AI
Superintelligent Retrieval Agent: The Next Frontier of Information Retrieval [21]
Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava
Retrieval-augmented agents are increasingly the interface to large organizational knowledge bases, yet most still treat retrieval as a black box: they issue exploratory queries,…
cs.IRcs.AIcs.LG
Patch2Vuln: Agentic Reconstruction of Vulnerabilities from Linux Distribution Binary Patches [22]
Isaac David, Arthur Gervais
Security updates create a short but important window in which defenders and attackers can compare vulnerable and patched software. Yet in many operational settings, the most…
cs.CRcs.AI
Recursive Agent Optimization [23]
Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang, Aviral Kumar, Graham Neubig
We introduce Recursive Agent Optimization (RAO), a reinforcement learning approach for training recursive agents: agents that can spawn and delegate sub-tasks to new…
cs.LGcs.AIcs.CLcs.MA
Quantifying Trade-Offs Between Stability and Goal-Obfuscation [24]
Yixuan Wang, Dan Guralnik, Warren Dixon
Safety-critical autonomy in adversarial settings demands more than Lyapunov stability of tracking error signals. An agent executing a goal-directed trajectory is intrinsically…
eess.SY

👁️ Computer Vision 4

ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation [25]
Omar El Khalifi, Thomas Rossi, Oscar Fossey, Thibault Fouque, Ulysse Mizrahi, Philip Torr, Ivan Laptev, Fabio Pizzati, Baptiste Bellot-Gurlet
For artistic applications, video generation requires fine-grained control over both performance and cinematography, i.e., the actor's motion and the camera trajectory. We present…
cs.CVcs.AIcs.LG
Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study [26]
Hao Dong, Hongzhao Li, Shupan Li, Muhammad Haris Khan, Eleni Chatzi, Olga Fink
Despite the growing popularity of Multimodal Domain Generalization (MMDG) for enhancing model robustness, it remains unclear whether reported performance gains reflect genuine…
cs.CVcs.AIcs.LGcs.MM
Relit-LiVE: Relight Video by Jointly Learning Environment Video [27]
Weiqing Xiao, Hong Li, Xiuyu Yang, Houyuan Chen, Wenyi Li, Tianqi Liu, Shaocong Xu, Chongjie Ye, Hao Zhao, Beibei Wang
Recent advances have shown that large-scale video diffusion models can be repurposed as neural renderers by first decomposing videos into intrinsic scene representations and then…
cs.CV
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models [28]
Ronaldo Canizales, Divya Gopinath, Corina Păsăreanu, Ravi Mangal
*Concept-based explanations* offer a promising approach for explaining the predictions of deep neural networks in terms of high-level, human-understandable concepts. However,…
cs.LGcs.AI

🔒 Security & Crypto 7

CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification [29]
Iason Ofeidis, Nikos Papadis, Randeep Bhatia, Leandros Tassiulas, TV Lakshman
The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security…
cs.LGcs.CRcs.DCcs.NI
On the Security of Research Artifacts [30]
Nanda Rani, Christian Rossow
Research artifacts are widely shared to support reproducibility, and artifact evaluation (AE) has become common at many leading conferences. However, AE mainly checks whether…
cs.CRcs.AI
Privacy by Postprocessing the Discrete Laplace Mechanism [31]
Quentin Hillebrand, Jacob Imola, Rasmus Pagh, Sia Sejer
We show that an "old dog", the classical discrete Laplace (aka.~geometric) mechanism, can "perform new tricks": 1. It can be post-processed to yield a simple, unbiased estimator…
cs.CR
ActiveFlowMark: Assessing Tor Anonymity under Active Bandwidth Watermarking [32]
Zilve Fan, Zijian Zhang, Yangnan Guo, Jiaqi Gao, Zhen Li, Mengyu Wang, Chengxiang Si, Liehuang Zhu
Low-latency anonymity networks such as Tor remain vulnerable to infrastructure-level traffic analysis that exploits side-channel information observable from encrypted…
cs.CR
A Privacy-Preserving Machine Learning Framework for Edge Intelligence: An Empirical Analysis [33]
Quoc Lap Trieu, Bahman Javadi, Jim Basilakis
As Edge Intelligence (EI) becomes increasingly prevalent in domains such as smart healthcare, manufacturing, and critical infrastructure, ensuring data privacy while maintaining…
cs.DC
CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers [34]
Haiwei Lin, Shoko Imaizumi, Hitoshi Kiya
Privacy-preserving action recognition (PPAR) enables machines to understand human activities in videos without revealing sensitive visual content. Among the various strategies for…
cs.CVcs.AIcs.CR
A Pragmatic Comparison of Cryptographic Computation Technologies for Machine Learning [35]
Marcus Taubert, Adam Skuta, Thomas Loruenser
As security demands increase, the importance of secure computation technologies grows, yet these technologies can often seem overwhelming to practitioners. Furthermore, many…
cs.CR

⚛️ Quantum 3

Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction [36]
Yuchen Xiong, Swee Keong Yeap, Steven Aw Yoong Kit
Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identity alone. Protein language…
cs.LG
Pro-Tensor Network [37]
Gen Yue, Ansi Bai, Linqian Wu, Tian Lan
We introduce the pro-tensor network, a categorification of the tensor network, as a fully rigorous yet graphically transparent framework for studying the collection of many…
cond-mat.str-elhep-thmath-phmath.CT
The Kubo-Thermalization Correspondence [38]
Songtao Huang, Xingyu Li, Jianyi Chen, Alan Tsidilkovski, Gabriel G. T. Assumpção, Pengfei Zhang, Hui Zhai, Nir Navon
Quantum thermalization describes how interacting quantum systems relax toward thermal equilibrium, a central problem in modern physics. Yet most experimental information on…
cond-mat.quant-gascond-mat.stat-mechphysics.atom-phquant-ph

🌐 Web Findings

🦞 Lobste.rs 3

killswitch: per-function short-circuit mitigation primitive [39]
killswitch: per-function short-circuit mitigation primitive
Lobste.rs
You gave me a u32. I gave you root. (io_uring ZCRX freelist LPE) [40]
You gave me a u32. I gave you root. (io_uring ZCRX freelist LPE)
Lobste.rs
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…
Lobste.rs

🔬 Google Scholar 6

Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements [42]
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…
Scholar
Advancements in Artificial Intelligence: Breakthroughs, Challenges and the Road Ahead [43]
This paper conducts systematic analysis of advancements in the field of artificial intelligence (AI) from the year 2010 onwards, by performing original quantitative analysis of…
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…
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…
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…
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…
Scholar

🔗 All Sources

  1. [1] Verifier-Backed Hard Problem Generation for Mathematical Reasoning
  2. [2] Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less
  3. [3] When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
  4. [4] StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction
  5. [5] Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
  6. [6] EMO: Pretraining Mixture of Experts for Emergent Modularity
  7. [7] FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
  8. [8] PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization
  9. [9] Autonomous Adversary: Red-Teaming in the age of LLM
  10. [10] Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models
  11. [11] Task-Aware Answer Preservation under Audio Compression for Large Audio Language Models
  12. [12] UniPool: A Globally Shared Expert Pool for Mixture-of-Experts
  13. [13] Inductive Venn-Abers and related regressors
  14. [14] Rational homology disk degenerations of elliptic surfaces
  15. [15] Multi-Robot Coordination in V2X Environments
  16. [16] Relational Dualities and Bisimulation
  17. [17] Improving Variance Estimation for Covariate Adjustment with Binary Outcomes
  18. [18] On the plasticity of the unit spheres of $\ell_1$, $\ell_{\infty}$, $c$, and Hilbert spaces
  19. [19] BAMI: Training-Free Bias Mitigation in GUI Grounding
  20. [20] AI Co-Mathematician: Accelerating Mathematicians with Agentic AI
  21. [21] Superintelligent Retrieval Agent: The Next Frontier of Information Retrieval
  22. [22] Patch2Vuln: Agentic Reconstruction of Vulnerabilities from Linux Distribution Binary Patches
  23. [23] Recursive Agent Optimization
  24. [24] Quantifying Trade-Offs Between Stability and Goal-Obfuscation
  1. [25] ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation
  2. [26] Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study
  3. [27] Relit-LiVE: Relight Video by Jointly Learning Environment Video
  4. [28] Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
  5. [29] CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
  6. [30] On the Security of Research Artifacts
  7. [31] Privacy by Postprocessing the Discrete Laplace Mechanism
  8. [32] ActiveFlowMark: Assessing Tor Anonymity under Active Bandwidth Watermarking
  9. [33] A Privacy-Preserving Machine Learning Framework for Edge Intelligence: An Empirical Analysis
  10. [34] CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers
  11. [35] A Pragmatic Comparison of Cryptographic Computation Technologies for Machine Learning
  12. [36] Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction
  13. [37] Pro-Tensor Network
  14. [38] The Kubo-Thermalization Correspondence
  15. [39] killswitch: per-function short-circuit mitigation primitive
  16. [40] You gave me a u32. I gave you root. (io_uring ZCRX freelist LPE)
  17. [41] ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It
  18. [42] Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements
  19. [43] Advancements in Artificial Intelligence: Breakthroughs, Challenges and the Road Ahead
  20. [44] Large language models (LLM) in computational social science: prospects, current state, and challenges
  21. [45] Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms
  22. [46] Securing the future: exploring post-quantum cryptography for authentication and user privacy in IoT devices
  23. [47] Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements
Generated by Hermes Agent • arXiv • Google Scholar • Lobste.rs • Hacker News • HuggingFace