Every paper checked — nothing hidden

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Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
A$^3$PreCore: Robust Rigid Registration for Native FLAIR-Space Brain-Mask Transfer This paper presents A3PreCore, a six-degree-of-freedom rigid-registration method for robust T1-to-FLAIR brain-mask transfer alongside quantitative quality control metrics. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Specific medical image registration technique, not multimodal language-vision models.
A$^3$PreCore: Robust Rigid Registration for Native FLAIR-Space Brain-Mask Transfer This paper presents A3PreCore, a six-degree-of-freedom rigid-registration method for robust T1-to-FLAIR brain-mask transfer alongside quantitative quality control metrics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Medical brain MRI registration, not factory production visual inspection.
Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation This paper introduces Any Time, Any Space, a world-state memory framework enabling consistent multi-shot long-form agentic video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Screenplay-to-video generation, not industrial manufacturing vision.
Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on mesh PDE solving, not language model efficiency.
Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates fluid dynamics surrogate models, not language model benchmarks.
Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with fluid dynamics simulation accuracy, not AI deployment safety.
Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Numerical PDE surrogate architecture, not multimodal reasoning models.
Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Surrogate PDE solver for fluid dynamics, not computer vision.
Feed-Forward Multi-View Feature Coding for 3D Gaussian Splatting This paper presents a feed-forward framework that encodes multi-view image features into a compact bitstream for 3D Gaussian Splatting scene reconstruction. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Novel view 3D Gaussian splatting, not industrial defect detection.
Feed-Forward Multi-View Feature Coding for 3D Gaussian Splatting This paper presents a feed-forward framework that encodes multi-view image features into a compact bitstream for 3D Gaussian Splatting scene reconstruction. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant 3D scene reconstruction and compression, not multimodal text-image-audio integration.
GeoThinker-$F$: Endogenous Predictive World Representations for Fast Spatial Intelligence This paper proposes GeoThinker-F, an efficient architecture that predicts spatial representations directly from multimodal hidden states without dedicated 3D encoders. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant General 3D MLLM representation, not manufacturing anomaly detection.
DynThinker: Unifying Static 3D and Dynamic 4D Spatial Reasoning This paper presents DynThinker, a framework using parameter averaging and spatial-dynamic fusion to balance static 3D and dynamic 4D reasoning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant General 3D/4D spatial reasoning, not industrial quality control.
CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation This paper introduces CommerceGen-Studio, an end-to-end framework using specialized agents and diffusion models to generate e-commerce product images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant E-commerce marketing image generation, not industrial manufacturing inspection.
VITAL: From Visual Motion to Precise Action This paper presents VITAL, an action tokenizer coupling visual motion scaffolding with residual error encoding for autonomous driving decision-making. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Autonomous driving trajectory prediction, not industrial visual inspection.
Representation Conditioning for Generation This paper proposes representation conditioning to feed vision encoder features into later layers of diffusion transformers for improved generation semantics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion training, not industrial inspection or defect detection.
Can Learning to Answer Spatial Questions Improve Novel-View Generation? This paper introduces Ask4NV, combining multimodal 3D language understanding with camera geometry to guide novel-view image generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on novel-view image generation, not industrial inspection or defect detection.
An Initial Study of Physiological Markers for Deepfake Speech Detection This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Explores speech feature engineering, not model compression or efficient inference.
An Initial Study of Physiological Markers for Deepfake Speech Detection This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Targets audio deepfake detection rather than AI deployment guardrails, safety, or robustness.
An Initial Study of Physiological Markers for Deepfake Speech Detection This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech audio deepfake detection, not industrial vision inspection.
An Initial Study of Physiological Markers for Deepfake Speech Detection This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies physiological speech markers, not LLM evaluation or benchmark methodology.
An Initial Study of Physiological Markers for Deepfake Speech Detection This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies audio speech production markers exclusively, not multimodal vision-language models.
Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model This paper proposes an adaptive Gaussian mixture model framework to detect physical-layer channel state information spoofing attacks in wireless localization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on wireless signal physical-layer spoofing, not AI deployment safety.
Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model This paper proposes an adaptive Gaussian mixture model framework to detect physical-layer channel state information spoofing attacks in wireless localization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Applies standard GMMs to wireless signals, not efficient neural model serving.
Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model This paper proposes an adaptive Gaussian mixture model framework to detect physical-layer channel state information spoofing attacks in wireless localization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes physical wireless CSI anomaly detection, not LLM evaluation benchmarks.
Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries This paper analyzes the mathematical limits of statistical safety certification for embodied and agentic AI facing adaptive adversaries. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical adversary testing bounds, not efficient serving or model compression.
Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries This paper analyzes the mathematical limits of statistical safety certification for embodied and agentic AI facing adaptive adversaries. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of safety bounds under adaptive adversaries, not benchmark evaluation methodology.
Combining Post-Quantum Cryptography and Quantum Key Distribution: Taxonomy, Constructions, Proofs, Standards, Deployments and Open Problems This survey provides an eight-dimension taxonomy and comparative analysis of hybrid protocols combining post-quantum cryptography with quantum key distribution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Discusses post-quantum cryptography key distribution protocols, not AI safety or deployment.
TrackShield: An Efficient Runtime Defense Against Availability Attacks on High-Performance Real-Time Multi-Object Tracking This paper presents TrackShield, a runtime resource-management layer that protects multi-object tracking pipelines from availability attacks by enforcing deterministic admission control. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Protects classical multi-object tracking pipelines from availability attacks, not AI safety.
Did the LLM Answer the Question? A Semantic Reconstruction Game for Jailbreak Evaluation This paper proposes a semantic reconstruction game to accurately evaluate jailbreak success by filtering out harmless repetitions and refusals. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on jailbreak evaluation accuracy, not model compression or efficient serving.
AutoPoly-HE: Autonomous Design of Polynomial Activations for Privacy-Preserving Encrypted Inference The authors propose AutoPoly-HE, a framework that automatically selects dataset-tailored polynomial activations for homomorphic encrypted neural network inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on homomorphic encryption activations rather than AI deployment safety and robustness.
Differentially Private Learning of Markov Chains: Near-Optimal Sample Complexity for Item and User Level Privacy This paper establishes sample complexity bounds for differentially private estimation of Markov chain transition matrices under item and user privacy. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical differential privacy for Markov chains, not deployment AI safety.
Quantum-Resilient Secure Protocol for Decentralized Edge-IoT Peer-to-Peer Energy Trading Systems The authors present a post-quantum authenticated key agreement protocol for decentralized edge peer-to-peer energy trading systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with quantum key distribution for IoT energy trading, not AI safety.
What You Sign Is Not What You Run: The Verifiability Gap in Quantized LLM Supply Chains This paper examines the verifiability gap in quantized model supply chains, showing that common GGUF artifacts lack necessary build environment disclosures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on artifact reproducibility in supply chains, not LLM benchmark evaluation.
Mission (Im)Possible: Towards Autonomous Discovery of Autopilot Logic Flaws in Open-Source Drone Frameworks The authors present an approach using domain-informed language models to discover semantic logic flaws in open-source drone autopilot software. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on testing drone software rather than AI model safety, guardrails, or robustness.
A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference This paper constructs an empirical graph of 62 private transformer inference systems to evaluate comparability, cryptographic progress, and scaling behavior. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes cryptographic inference protocols, not LLM capabilities or evaluation benchmarks.
A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference This paper constructs an empirical graph of 62 private transformer inference systems to evaluate comparability, cryptographic progress, and scaling behavior. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on cryptographic secure computation, not model compression, distillation, or quantization.
No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation The authors present IDGuardian, an adversarial perturbation framework designed to defend facial images against unauthorized personalized diffusion models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on personal facial privacy in diffusion models, not industrial manufacturing vision.
No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation The authors present IDGuardian, an adversarial perturbation framework designed to defend facial images against unauthorized personalized diffusion models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses image diffusion personalization defense, not multimodal reasoning or vision-language models.
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection This paper analyzes latent backdoor poisoning in flow-based intrusion detection and introduces a perturbation-based diagnostic and mitigation methodology. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Uses pruning solely for backdoor defense in intrusion detection, not efficient inference.
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection This paper analyzes latent backdoor poisoning in flow-based intrusion detection and introduces a perturbation-based diagnostic and mitigation methodology. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on network intrusion detection systems rather than AI deployment safety and robustness.
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection This paper analyzes latent backdoor poisoning in flow-based intrusion detection and introduces a perturbation-based diagnostic and mitigation methodology. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies network backdoor attacks, not LLM evaluation benchmarks or methodologies.
Adaptive and Proximal Federated Optimization for Transferable Network Intrusion Detection The authors study whether adaptive and proximal federated optimizers improve the detection of unseen attack classes in network intrusion detection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on federated optimization for network intrusion detection, not AI model deployment safety.
DarKnight: Retrieval-Guided Agentic Framework for Breaking Dark Web CAPTCHA Gates This paper introduces DarKnight, a framework that uses locally hosted vision-language models to solve dark web CAPTCHAs during threat intelligence gathering. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Solves CAPTCHAs for dark web crawling, not industrial manufacturing inspection.
DarKnight: Retrieval-Guided Agentic Framework for Breaking Dark Web CAPTCHA Gates This paper introduces DarKnight, a framework that uses locally hosted vision-language models to solve dark web CAPTCHAs during threat intelligence gathering. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Targets automated CAPTCHA cracking rather than AI model safety, guardrails, or robustness.
Global and Local Entropy Tests for STL Files Under IEEE-754 Validity Constraints The authors derive statistical entropy tests under IEEE-754 validity constraints to detect non-randomness in encrypted stereolithography CAD files. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on cryptographic entropy statistics of 3D file formats, not AI systems.
Bilinear Compressive Security with Finitely Many Samples This paper investigates finite-sample key-recovery attacks against bilinear compressive security using a covariance-based estimation approach. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on mathematical cryptography for compressed sensing, not AI safety or deployment.
ESAU: Expeditious Swarm Remote Attestation for Pub-Sub IoT Networks with Untrusted Cloud-based Proxy Verifier This paper proposes ESAU, a lightweight swarm remote attestation framework for internet-of-things devices using untrusted cloud proxies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers remote attestation for IoT networks, not AI system safety.
FreeSBee: A Constant-Time Compilation Pipeline The authors propose FreeSBee, a compiler pipeline combining taint analysis and automated control-flow transformations to defend against timing side-channels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with compiler hardening against timing attacks, not AI system safety.
Secret Sharing from Non-Extractable Randomness This paper proves that two-out-of-two secret sharing is achievable from non-extractable randomness sources, answering an open cryptographic question. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Pure cryptographic theory on secret sharing, unrelated to AI safety.
Towards Efficient Threat Intelligence Instruction Tuning via Quality-Aware and Optimal Transport-Guided Data Selection The authors present CTI-TDS, a data selection method using quality filtering and optimal transport to improve instruction tuning for threat intelligence. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on instruction tuning for cyber threat intelligence, not AI safety.
Building Sound Evaluation Practices for Rust-Based Security Frameworks This paper examines evaluation practices for Rust security testing tools and proposes a methodology to support scientifically sound assessments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on Rust security testing frameworks, not AI model deployment safety.
Phantomware: a Data Augmentation Framework for the Detection of Emerging Malware Campaigns The authors propose Phantomware, a data augmentation framework designed to generate realistic malware variants for detecting emerging malicious campaigns. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers malware detection data augmentation, not AI deployment safety or LLMs.
Tulva: Practical Asynchronous Threshold FHE This paper presents Tulva, a modular asynchronous threshold fully homomorphic encryption scheme evaluated for privacy-preserving smart contract applications. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with threshold fully homomorphic encryption, not AI safety or models.
Quantum-Aware Key Rank for Side-Channel Attacks evaluation The authors introduce a cost model and metric for evaluating side-channel key rank against an adversary equipped with a quantum computer. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on cryptographic key side-channel attacks, not AI safety or LLMs.
Prioritizing Microservice Requests via Retries This paper introduces DiffTry, a stateless retry mechanism that transforms retries into request prioritization to prevent microservice overload storms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Presents cloud microservice networking retry policies, not AI deployment safety.
Can LLMs Access their World Knowledge for Event Prediction? The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on formatting event descriptions, not retrieval-augmented generation systems.
Can LLMs Access their World Knowledge for Event Prediction? The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates natural language event forecasting, not multimodal vision-language reasoning.
Can LLMs Access their World Knowledge for Event Prediction? The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Examines temporal event forecasting, not software programming or code tasks.
Can LLMs Access their World Knowledge for Event Prediction? The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates prompt interface design for forecasting, not multi-step agents.
Can LLMs Access their World Knowledge for Event Prediction? The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies knowledge elicitation interfaces, not safety, hallucination, or guardrails.

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