Carlos's Debrief

⚡ Quick Summary

Post-training for reasoning models is getting cheaper and more principled: Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe and Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation argue that offline or better-understood on-policy distillation can preserve useful teacher behavior without the full cost of live teacher serving.
Instruction-following remains surprisingly brittle: One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness shows that even tiny lexical constraints can trigger large drops in helpfulness, exposing how fragile current alignment still is under small prompt perturbations.
AI assurance is becoming a measurable bottleneck: The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime and LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software push beyond anecdotes by formalizing rare-error auditing limits and building more systematic evaluations for logical vulnerability repair.
Agent papers are separating deliberation, execution, and grounding: Toward Autonomous Long-Horizon Engineering for ML Research; See, Point, Refine: Multi-Turn Approach to GUI Grounding with Visual Feedback reflect a shift toward agents that reason longer, act more carefully, and use iterative feedback instead of single-shot tool use.
Quantum work spans both algorithms and deployment: Classical and Quantum Speedups for Non-Convex Optimization via Energy Conserving Descent and Quantum-safe IPsec in the banking industry show the field pushing simultaneously on optimization primitives, photonic hardware, and practical migration concerns such as quantum-safe networking.

📰 News Headlines

GUI agents: web paper feeds clustered around full-stack GUI-agent infrastructure, humanization/anti-detection benchmarks, and better vision-to-action training pipelines rather than simple demo agents.
Reasoning under scrutiny: several findings question whether stronger reasoning automatically yields better behavior, emphasizing instruction hierarchy, bounded reflection, and correctness introspection instead.
Multimodal expansion: new work is pushing beyond text into audio grounding, vector graphics generation, and rule-following failures in vision-language models.
Quantum + post-quantum: scholar and news sources were heavy on PQC migration, ZK-friendly hashing, and the narrative that quantum hardware may become a real competitor to AI-centric compute stacks.
AI product privacy: reverse-engineering attention is shifting from model outputs to browser instrumentation and anti-abuse layers surrounding consumer AI applications.

📄 ArXiv Papers

🧠 LLMs 10

SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis [1]

Kathakoli Sengupta, Kai Ao, Paola Cascante-Bonilla

Large Language Models (LLMs) and Vision-Language Models (VLMs) increasingly generate indoor scenes through intermediate structures such as layouts and scene graphs, yet evaluation still relies on LLM or VLM judges that score rendered views, making judgments sensitive to viewpoint, prompt phrasing, and hallucination…

cs.CVcs.CL

Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe [2]

Yaxuan Li, Yuxin Zuo, Bingxiang He, Jinqian Zhang, et al.

On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly understood. This paper provides a systematic investigation of OPD dynamics and mechanisms. It matters because post-training efficiency and robustness now directly shape the…

cs.LGcs.AIcs.CL

ROSE: An Intent-Centered Evaluation Metric for NL2SQL [3]

Wenqi Pei, Shizheng Hou, Boyan Li, Han Chen, et al.

Execution Accuracy (EX), the widely used metric for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions, is becoming increasingly unreliable. It is sensitive to syntactic variation, ignores that questions may admit multiple interpretations, and is easily misled by erroneous ground-truth SQL. It…

cs.DBcs.AI

Parcae: Scaling Laws For Stable Looped Language Models [4]

Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick, Daniel Y. Fu

Traditional fixed-depth architectures scale quality by increasing training FLOPs, typically through increased parameterization, at the expense of a higher memory footprint, or data. A potential alternative is looped architectures, which instead increase FLOPs by sending activations through a block of layers in a loop…

cs.LG

Obscured at the Core: Evidence for Nuclear Dust in Reddened Type-1 AGN [5]

Miguel A. Montalvo Hernandez, Andy D. Goulding, Jenny E. Greene

Reddened Type-1 quasars offer a unique window into the structure and evolution of active galactic nuclei (AGN), yet their physical origin and the source of their reddening remain uncertain. Optical surveys often miss these dust-obscured objects, resulting in an incomplete view of the quasar population. It matters…

astro-ph.GAastro-ph.HE

Lyra 2.0: Explorable Generative 3D Worlds [6]

Tianchang Shen, Sherwin Bahmani, Kai He, Sangeetha Grama Srinivasan, et al.

Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via feed-forward reconstruction techniques. This generative reconstruction approach combines the visual fidelity and creative capacity of video…

cs.CV

Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation [7]

Yecheng Wu, Song Han, Hai Cai

On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teacher inference server throughout training, resulting in substantial infrastructure overhead. It matters because reliable long-horizon agents depend on better grounding…

cs.LGcs.AI

Generative Refinement Networks for Visual Synthesis [8]

Jian Han, Jinlai Liu, Jiahuan Wang, Bingyue Peng, et al.

While diffusion models dominate the field of visual generation, they are computationally inefficient, applying a uniform computational effort regardless of different complexity. In contrast, autoregressive (AR) models are inherently complexity-aware, as evidenced by their variable likelihoods, but are often hindered…

cs.CV

DySkew: Dynamic Data Redistribution for Skew-Resilient Snowpark UDF Execution [9]

Chenwei Xie, Urjeet Shrestha, Corbin McElhanney, Lukas Lorimer, et al.

Snowflake revolutionized data warehousing with an elastic architecture that decouples compute and storage, enabling scalable solutions for diverse data analytics needs. Building on this foundation, Snowflake has advanced its AI Data Cloud vision by introducing Snowpark, a managed turnkey solution that supports data…

cs.DCcs.DB

Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns [10]

Baris Sarper Tezcan, Hrishikesh Viswanath, Rubab Saher, Daniel Aliaga

Urban areas are increasingly vulnerable to thermal extremes driven by rapid urbanization and climate change. Traditionally, thermal extremes have been monitored using Earth-observing satellites and numerical modeling frameworks. It matters because reliable long-horizon agents depend on better grounding, control, and…

cs.CV

🎨 Machine Learning 5

Visual Preference Optimization with Rubric Rewards [11]

Ya-Qi Yu, Fangyu Hong, Xiangyang Qu, Hao Wang, et al.

The effectiveness of Direct Preference Optimization (DPO) depends on preference data that reflect the quality differences that matter in multimodal tasks. Existing pipelines often rely on off-policy perturbations or coarse outcome-based signals, which are not well suited to fine-grained visual reasoning. It matters…

cs.CVcs.AI

PAL: Personal Adaptive Learner [12]

Megha Chakraborty, Darssan L. Eswaramoorthi, Madhur Thareja, Het Riteshkumar Shah, et al.

AI-driven education platforms have made some progress in personalisation, yet most remain constrained to static adaptation--predefined quizzes, uniform pacing, or generic feedback--limiting their ability to respond to learners' evolving understanding. This shortfall highlights the need for systems that are both…

cs.AIcs.HC

Nonparametric efficient inference for network quantile causal effects under partial interference [13]

Chao Cheng, Fan Li

Interference arises when the treatment assigned to one individual affects the outcomes of other individuals. Commonly, individuals are naturally grouped into clusters, and interference occurs only among individuals within the same cluster, a setting referred to as partial interference. It matters because improved…

stat.MEstat.APstat.ML

Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data [14]

Farbod Alinezhad, Jianfei Cao, Gary J. Young, Brady Post

Predicting counterfactual outcomes in longitudinal data, where sequential treatment decisions heavily depend on evolving patient states, is critical yet notoriously challenging due to complex time-dependent confounding and inadequate uncertainty quantification in existing methods. It matters because it quantifies…

stat.MLcs.LGecon.EM

An Optimal Sauer Lemma Over $k$-ary Alphabets [15]

Steve Hanneke, Qinglin Meng, Shay Moran, Amirreza Shaeiri

The Sauer-Shelah-Perles Lemma is a cornerstone of combinatorics and learning theory, bounding the size of a binary hypothesis class in terms of its Vapnik-Chervonenkis (VC) dimension. For classes of functions over a $k$-ary alphabet, namely the multiclass setting, the Natarajan dimension has long served as an analogue…

cs.LGmath.COstat.ML

🔐 Security & Crypto 13

Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory [16]

Shaopeng Fu, Di Wang

Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To improve the efficiency of AT for LLMs, recent studies propose continuous AT (CAT) that searches for adversarial inputs within the continuous embedding space of LLMs…

cs.LGcs.CRstat.ML

Toward Autonomous Long-Horizon Engineering for ML Research [17]

Guoxin Chen, Jie Chen, Lei Chen, Jiale Zhao, et al.

Autonomous AI research has advanced rapidly, but long-horizon ML research engineering remains difficult: agents must sustain coherent progress across task comprehension, environment setup, implementation, experimentation, and debugging over hours or days. We introduce AiScientist, a system for autonomous long-horizon…

cs.CL

SpanKey: Dynamic Key Space Conditioning for Neural Network Access Control [18]

WenBin Yan

SpanKey is a lightweight way to gate inference without encrypting weights or chasing leaderboard accuracy on gated inference. The idea is to condition activations on secret keys. It matters because reliable long-horizon agents depend on better grounding, control, and separation between reasoning and execution…

cs.CRcs.AI

Recursive Completion in Higher K-Models: Front-Seed Semantics, Proof-Relevant Witnesses, and the K-Infinity Model [19]

Daniel O. Martinez-Rivillas, Arthur F. Ramos, Ruy J. G. B. de Queiroz

Martinez-Rivillas and de Queiroz gave extensional Kan semantics for the untyped lambda-calculus and later constructed the concrete K-infinity homotopy-model. The two main mathematical results of the present paper are these. It matters because reliable long-horizon agents depend on better grounding, control, and…

cs.LO

Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting [20]

Mingxi Zhang, Renjie Xie, Jincheng Wang, Guyue Li, et al.

Radio frequency fingerprints (RFFs) enable secure wireless authentication but struggle in open-set scenarios with unknown devices and varying channels. Existing methods face challenges in generalization and incur high computational costs. It matters because reliable long-horizon agents depend on better grounding…

eess.SPcs.CRcs.LG

Parallax: Why AI Agents That Think Must Never Act [21]

Joel Fokou

Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applications will embed AI copilots by the end of 2026. As agents gain the ability to execute real-world actions (reading files, running commands, making network requests…

cs.CRcs.AI

On a new theory of models for formal mathematical systems [22]

Matthias Kunik

We study a new model theory for formal mathematical systems that we developed in a previous paper. We introduce isomorphic and homomorphic structures for formal languages, present some results and examples and conclude our paper with a discussion about the reduced set theory RST adapted to our new theory. It matters…

math.LO

LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software [23]

Syed Md Mukit Rashid, Abdullah Al Ishtiaq, Kai Tu, Yilu Dong, et al.

Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. Although existing automated program repair techniques primarily focus on repairing memory corruption vulnerabilities, they struggle with logical vulnerabilities because of their…

cs.CRcs.AI

Improving Network Clock Synchronization by Marking Congestion [24]

Yash Deshpande, Quirin Vogel, Laura Becker, Kaan Aykurt, et al.

Achieving consistent time across devices in distributed systems often involves exchanging timestamped messages over a network. Precise time synchronization is crucial for applications such as cellular networks, industrial automation, and transactional databases. It matters because reliable long-horizon agents depend…

cs.NI

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations [25]

Tong Zhang, Jiangning Zhang, Zhucun Xue, Juntao Jiang, et al.

Balancing convergence speed, generalization capability, and computational efficiency remains a core challenge in deep learning optimization. First-order gradient descent methods, epitomized by stochastic gradient descent (SGD) and Adam, serve as the cornerstone of modern training pipelines. It matters because reliable…

cs.LGcs.CV

Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge [26]

Gustavo de Carvalho Bertoli

While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs). This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism against MIAs in FL, leveraging the environment of the 2025 NIST Genomics…

cs.CRcs.LG

Distinguishers for Skew and Linearized Reed-Solomon Codes [27]

Felicitas Hörmann, Anna-Lena Horlemann

Generalized Reed-Solomon (GRS) and Gabidulin codes have been proposed for various code-based cryptosystems, though most such schemes without elaborate disguising techniques have been successfully attacked. Both code classes are prominent examples of the isometric families of (generalized) skew and linearized…

cs.CRcs.IT

CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference [28]

Qiang Zhang, Zhongnian Li

Binary decompilation is a critical reverse engineering task aimed at reconstructing high-level source code from stripped executables. Although Large Language Models (LLMs) have recently shown promise, they often suffer from "logical hallucinations" and "semantic misalignment" due to the irreversible semantic loss…

cs.SEcs.AIcs.CR

🕵️ Zero Knowledge 4

XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios [29]

Junming Wang, Teng Pu, Wingmun Fung, Jindong Wang, et al.

The acquisition of high-quality, action-aligned demonstration data remains a fundamental bottleneck in scaling foundation models for dexterous robot manipulation. Although robot-free human demonstrations (e.g., the UMI paradigm) offer a scalable alternative to traditional teleoperation, current systems are constrained…

cs.RO

Sparse Contrastive Learning for Content-Based Cold Item Recommendation [30]

Gregor Meehan, Johan Pauwels

Item cold-start is a pervasive challenge for collaborative filtering (CF) recommender systems. Existing methods often train cold-start models by mapping auxiliary item content, such as images or text descriptions, into the embedding space of a CF model. It matters because it quantifies failure modes that current…

cs.IR

PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models [31]

Han Bao, Penghao Zhang, Yue Huang, Zhengqing Yuan, et al.

Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason about policy-related content remains underexplored. It matters because post-training efficiency and robustness now directly shape the cost and…

cs.CLcs.CY

Modeling Co-Pilots for Text-to-Model Translation [32]

Serdar Kadioglu, Karthik Uppuluri, Akash Singirikonda

There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance this line of research by introducing extsc{Text2Model} and extsc{Text2Zinc}. It matters because reliable long-horizon agents depend on better grounding, control, and…

cs.AI

⚛️ Quantum 12

Zeno Blockade Enabling Photonic Quantum Optimization [33]

Mohammad-Ali Miri, Uchenna Chukwu, Nicholas Chancellor

In this work we explore the potential of implementing an optical quantum optimizer using non-linear optics, specifically using sum-frequency generation and/or two photon absorption. This proposal uses Zeno effects to enforce independence constraints and then a linear protocol to find a maximum independent set in a way…

quant-ph

Ternary Quantum Eraser Cryptography [34]

Ahmed Halawani, Yahya Meshalwi Khabrani, Abdulaziz Al-Mogheeth, Zheng-Hong Li, et al.

Quantum key distribution protocols based on the quantum eraser phenomenon offer an operational advantage: automatic identification of matching and mismatching encoding choices through interference, eliminating basis reconciliation over public channels. However, security analysis reveals that binary quantum eraser…

quant-ph

Representation geometry shapes task performance in vision-language modeling for CT enterography [35]

Cristian Minoccheri, Emily Wittrup, Kayvan Najarian, Ryan Stidham

Computed tomography (CT) enterography is a primary imaging modality for assessing inflammatory bowel disease (IBD), yet the representational choices that best support automated analysis of this modality are unknown. We present the first study of vision-language transfer learning on abdominal CT enterography and…

cs.CVcs.AI

Quantum-safe IPsec in the banking industry [36]

Rafael J. Vicente, Jaime Gómez García, Juan P. Brito, Yorlandy Lobaina, et al.

The emergence of Cryptographically Relevant Quantum Computers (CRQCs) presents a critical threat to classical cryptographic systems, particularly widely adopted protocols such as RSA, Diffie-Hellman (DH), and Elliptic Curve Cryptography (ECC). Given their extensive use in the financial sector, the advent of quantum…

quant-ph

Practical Evaluation of the Crypto-Agility Maturity Model [37]

Leonie Wolf, Samson Umezulike, Gurur Öndarö, Sebastian Schinzel, et al.

Cryptographic agility is a key prerequisite for maintaining the long-term security of digital communication, particularly in light of the transition to post-quantum cryptography. To systematically assess this capability, Hohm et al. It matters because reliable long-horizon agents depend on better grounding, control…

cs.CR

Partial majorization and Schur concave functions on the sets of quantum and classical states [38]

M. E. Shirokov

We construct for a Schur concave function $f$ on the set of quantum states a tight upper bound on the difference $f(ρ)-f(σ)$ for a quantum state $ρ$ with finite $f(ρ)$ and any quantum state $σ$ $m$-partially majorized by the state $ρ$ in the sense described in [1]. It matters because stronger security and…

quant-phcs.ITmath-ph

Floquet Many-Body Cages [39]

Tom Ben-Ami, Roderich Moessner, Markus Heyl

Many-body cages have very recently emerged as a general route for nonergodic behaviour in quantum matter. Here, we show that new types of many-body cages can be engineered in Floquet circuits with the potential to realize novel nonequilibrium quantum states. It matters because stronger security and quantum-aware…

quant-phcond-mat.quant-gas

EXTree: Towards Supporting Explainability in Attribute-based Access Control [40]

Shanampudi Pranaya Chowdary, Shamik Sural

With increasing emphasis on transparency in digital governance, users expect more than silence when their access requests are denied by a system. However, authorization methods are notorious for their inability to provide any form of meaningful feedback under such situations. It matters because reliable long-horizon…

cs.CR

Classical and Quantum Speedups for Non-Convex Optimization via Energy Conserving Descent [41]

Yihang Sun, Huaijin Wang, Patrick Hayden, Jose Blanchet

The Energy Conserving Descent (ECD) algorithm was recently proposed (De Luca & Silverstein, 2022) as a global non-convex optimization method. Unlike gradient descent, appropriately configured ECD dynamics escape strict local minima and converge to a global minimum, making it appealing for machine learning…

quant-phcs.LGmath.OCstat.ML

CLAD: Efficient Log Anomaly Detection Directly on Compressed Representations [42]

Benzhao Tang, Shiyu Yang

The explosive growth of system logs makes streaming compression essential, yet existing log anomaly detection (LAD) methods incur severe pre-processing overhead by requiring full decompression and parsing. We introduce CLAD, the first deep learning framework to perform LAD directly on compressed byte streams. It…

cs.LGcs.DB

Asymptotically faster algorithms for recognizing $(k,\ell)$-sparse graphs [43]

Bence Deák, Péter Madarasi

The family of $(k,\ell)$-sparse graphs, introduced by Lorea, plays a central role in combinatorial optimization and has a wide range of applications, particularly in rigidity theory. A key algorithmic problem is to decide whether a given graph is $(k,\ell)$-sparse and, if not, to produce a vertex set certifying the…

cs.DScs.DMmath.CO

A complexity phase transition at the EPR Hamiltonian [44]

Kunal Marwaha, James Sud

We study the computational complexity of 2-local Hamiltonian problems generated by a positive-weight symmetric interaction term, encompassing many canonical problems in statistical mechanics and optimization. We show these problems belong to one of three complexity phases: QMA-complete, StoqMA-complete, and reducible…

quant-phcond-mat.stat-mechcs.CC

🛡️ AI Safety 2

The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime [45]

Jason Z Wang

The most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 (Guo et al., 2017) -- is below the statistical noise floor. We prove this is not a failure of the experiment but a law: the minimax rate for estimating calibration error with model error rate epsilon is…

cs.LG

One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness [46]

Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram

Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of…

cs.CLcs.AI

🤖 Agents 2

See, Point, Refine: Multi-Turn Approach to GUI Grounding with Visual Feedback [47]

Himangi Mittal, Gaurav Mittal, Nelson Daniel Troncoso, Yu Hu

Computer Use Agents (CUAs) fundamentally rely on graphical user interface (GUI) grounding to translate language instructions into executable screen actions, but editing-level grounding in dense coding interfaces, where sub-pixel accuracy is required to interact with dense IDE elements, remains underexplored. Existing…

cs.CV

Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem [48]

Yinghao Qin, Mosab Bazargani, Edmund K. Burke, Carlos A. Coello Coello, et al.

This paper tackles the Electric Capacitated Vehicle Routing Problem (E-CVRP) through a bilevel optimization framework that handles routing and charging decisions separately or jointly depending on the search stage. By analyzing their interaction, we introduce a surrogate objective at the upper level to guide the…

cs.AImath.OC

🌐 Web Findings

🦞 Lobste.rs 1

ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It [49]

Lobste.rs • privacy • 2026-03-30

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 real devices in the wild, directly from Claude…

Lobste.rsprivacy

🎓 Google Scholar 11

Status report on the fourth round of the nist post-quantum cryptography standardization process [50]

Google Scholar • cryptography post-quantum

… NIST is also grateful for the efforts of those in the cryptographic … NIST would not be able to select post-quantum digital … that calls for NIST-approved post-quantum keyestablishment. …

Google Scholarcryptography post-quantum

Securing the future: exploring post-quantum cryptography for authentication and user privacy in IoT devices [51]

Google Scholar • cryptography post-quantum

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... It matters because quantum and cryptographic migration work is moving from theory…

Google Scholarcryptography post-quantum

Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements [52]

Google Scholar • quantum computing algorithms

… in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-… It varies from applications in optimization, drug discovery, and quantum-secured communications, … It matters because quantum and cryptographic migration work is moving from theory into concrete…

Google Scholarquantum computing algorithms

Large language models (LLM) in computational social science: prospects, current state, and challenges [53]

Google Scholar • large language models LLM

… 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 Scholarlarge language models LLM

Gotta Hash'Em All! Speeding Up Hash Functions for Zero-Knowledge Proof Applications [54]

Google Scholar • zero knowledge proofs ZK

Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, traditional CRHs like SHA-2 or SHA-3, while optimized for CPUs, generate large circuits, rendering them…

Google Scholarzero knowledge proofs ZK

Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs [55]

Google Scholar • machine learning breakthroughs

… This study introduces a novel committee machine-learning Gaussian Process Regression (CML-GPR) model, which integrates three well-established machine-learning algorithms—…

Google Scholarmachine learning breakthroughs

Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements [56]

Google Scholar • machine learning breakthroughs

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 Scholarmachine learning breakthroughs

Can new energy demonstration cities break through the multiple carbon lock-in? Evidence based on double machine learning [57]

Google Scholar • machine learning breakthroughs

… This paper employs a double machine learning model to examine the influence of NEDCPP on urban CLI. The results show that: (1) The NEDCPP has markedly mitigated urban CLI, …

Google Scholarmachine learning breakthroughs

Authentication protocol for vehicular networks using zero-knowledge proofs and elliptic curve cryptography [58]

Google Scholar • zero knowledge proofs ZK

… establishment protocol using Zero-Knowledge Proofs (zk-SNARKs) … zk-SNARK computations introduce additional verification overhead, our optimizations, such as precomputed proof … It matters because quantum and cryptographic migration work is moving from theory into concrete…

Google Scholarzero knowledge proofs ZK

Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms [59]

Google Scholar • AI security cybersecurity

… 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 ScholarAI security cybersecurity

A survey of post-quantum cryptography migration in vehicles [60]

Google Scholar • cryptography post-quantum

The advent of quantum computers makes asymmetric cryptographic algorithms insecure and vulnerable. A promising solution to ensure the continued security attributes of confidentiality, integrity, and availability is the use of Post-Quantum Cryptography (PQC) algorithms…

Google Scholarcryptography post-quantum

🧑‍💻 Hacker News 2

Nvidia should be 'shaking in their boots' as quantum computing battles AI GPUs [61]

Hacker News • quantum computing

This hackernews item highlights Nvidia should be 'shaking in their boots' as quantum computing battles AI GPUs in the context of quantum computing. It matters because quantum and cryptographic migration work is moving from theory into concrete deployment choices…

Hacker Newsquantum computing

D Wave Ceo Says Nvidia Should Be Shaking in Their Boots as Quantum Computing Battles AI Gpus [62]

Hacker News • quantum computing

This hackernews item highlights D Wave Ceo Says Nvidia Should Be Shaking in Their Boots as Quantum Computing Battles AI Gpus in the context of quantum computing. It matters because quantum and cryptographic migration work is moving from theory into concrete deployment choices…

Hacker Newsquantum computing

🤗 HuggingFace 23

VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization [63]

HuggingFace • daily curated papers • 2026-04-14

Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what information is preserved and how it is organized…