Every paper checked — nothing hidden

All papers

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
Scaling Online Optimal Transport Minibatches for Flow Matching This paper develops an online entropic optimal transport approximation to scale minibatch coupling for stable flow matching. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on generative flow optimization, not LLM evaluation benchmarks.
Scaling Online Optimal Transport Minibatches for Flow Matching This paper develops an online entropic optimal transport approximation to scale minibatch coupling for stable flow matching. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with optimal transport training stability, not deployment safety or guardrails.
Scaling Online Optimal Transport Minibatches for Flow Matching This paper develops an online entropic optimal transport approximation to scale minibatch coupling for stable flow matching. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Theoretical generative modeling paper with no multimodal language reasoning focus.
RepProbe: Can Video Language Models Count Repeated Actions? A Diagnostic Benchmark This paper introduces a benchmark to diagnose the ability of video language models to count repeated action cycles. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on video action counting benchmarks, not industrial visual inspection.
WorldRecon: Generative Multi-View Mesh Reconstruction for Large-Scale 3D World This paper proposes a generative framework to reconstruct complete, large-scale 3D scene meshes from unposed multi-view RGB images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D scene reconstruction from photos, not industrial manufacturing.
WorldRecon: Generative Multi-View Mesh Reconstruction for Large-Scale 3D World This paper proposes a generative framework to reconstruct complete, large-scale 3D scene meshes from unposed multi-view RGB images. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses purely on 3D geometric mesh reconstruction, not multimodal text-image reasoning.
PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts This paper applies mixture-of-experts architectures to scale capacity in pixel-space diffusion models without needing learned tokenizers. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies pixel-space image generation with MoE, not multimodal reasoning models.
PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts This paper applies mixture-of-experts architectures to scale capacity in pixel-space diffusion models without needing learned tokenizers. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on image generation diffusion architectures, not industrial defect inspection.
Visual Tokens on Demand: Visual Budgeting for Scalable and Efficient Multi-Image Reasoning in Multimodal LLMs The paper discusses visual budgeting strategies to manage token redundancy for scalable multi-image reasoning in multimodal language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-image reasoning efficiency, not industrial quality control.
PLAYGROUND: Progressive Layout Generation with Render-Grounded Planning This paper proposes a progressive layout generation framework that dynamically adapts graphic design compositions to the rendered canvas. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on graphic design layout, not manufacturing defect detection.
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion This paper proposes a dimensionless prior to complete dense mesh motion from sparse observations across different geometry families. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with geometric motion priors, not LLM compression or inference.
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion This paper proposes a dimensionless prior to complete dense mesh motion from sparse observations across different geometry families. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses geometric motion completion, not safety or deployment reliability guardrails.
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion This paper proposes a dimensionless prior to complete dense mesh motion from sparse observations across different geometry families. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on mesh motion estimation rather than LLM benchmarking methods.
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion This paper proposes a dimensionless prior to complete dense mesh motion from sparse observations across different geometry families. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D mesh motion completion, not manufacturing or inspection.
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion This paper proposes a dimensionless prior to complete dense mesh motion from sparse observations across different geometry families. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Concerns 3D meshes and point tracking, not multimodal language reasoning.
Dog-Breed Text-to-image Flow Matching from Pretraining to Fine-Tuning on 4 Consumer GPUs This paper presents a framework to pretrain and fine-tune text-to-image diffusion transformers using four consumer GPUs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Covers text-to-image diffusion training on dog images, not industrial inspection.
EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ This paper introduces a 100-hour multimodal dataset capturing egocentric video, body motion, and touch for human loco-manipulation tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies human loco-manipulation capture, not industrial visual inspection or defect detection.
LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts This paper introduces a closed-loop world-action model that improves generalist robot policies using deployment rollouts of mixed quality. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general robot manipulation policies, not industrial inspection or defect detection.
TrustPaper for forged PDF with Issuer Fingerprints This paper presents an issuer-aware forensic method that uses learned reference fingerprints to detect unauthorized alterations in PDF documents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on PDF metadata forensics for certificates, not AI system safety.
RoboFake: Are Deepfake Detectors Ready for Humanoid Face Robots? This paper evaluates whether existing deepfake detection models can identify physical humanoid face robots performing speech-synchronized lip movements. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates facial deepfake detection, not industrial manufacturing or inspection.
RoboFake: Are Deepfake Detectors Ready for Humanoid Face Robots? This paper evaluates whether existing deepfake detection models can identify physical humanoid face robots performing speech-synchronized lip movements. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Tests deepfake detectors on physical robots rather than multimodal models.
ARMOR: Adversary Cost Amplification via a Schema-Preserving Password-Hardening Retrofit for Resource-Constrained Systems This paper introduces a verifier-side transform to harden password databases against offline cracking in resource-constrained systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers IoT password hashing defenses, not AI or LLM safety.
DICE: Tilting the Odds through Interface Attackson System One Model This paper analyzes vulnerabilities in system interfaces where adapter substitutions alter model decisions without modifying the underlying model itself. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Analyzes pipeline interface attacks, not code generation or coding assistants.
ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models This paper presents a byte-level watermarking method that allows a single detector to verify text across models with different tokenizers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a watermarking method rather than LLM benchmarks or evaluation methods.
ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models This paper presents a byte-level watermarking method that allows a single detector to verify text across models with different tokenizers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on watermarking across models, not model compression or efficiency.
DS and AMDS Symbol-Pair Codes from Three Families of Simple Root Cyclic Codes The paper constructs three families of cyclic error-correcting codes and calculates their symbol-pair distance metrics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical error-correcting codes, not AI safety or reliability.
Declared but Unaccountable: Measuring the Seller-Identity Gap in Advertising Supply-Chain Transparency The paper measures gaps between publisher ads.txt declarations and seller registries across millions of online advertising transparency records. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers digital advertising transparency records, not AI safety or deployment reliability.
(Implicitly) Characterizing Probabilistic Polynomial Time as Used in Cryptography The paper formalizes probabilistic polynomial-time cryptographic computation and adversary reductions using an implicit lambda calculus framework. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Theoretical lambda-calculus for cryptography, not software engineering with LLMs.
(Implicitly) Characterizing Probabilistic Polynomial Time as Used in Cryptography The paper formalizes probabilistic polynomial-time cryptographic computation and adversary reductions using an implicit lambda calculus framework. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical cryptographic complexity, not AI safety or deployment.
KedgeFlow: A Programmable Control Plane for Enterprise AI Agents The paper proposes a control plane enforcing structured contracts and single-use allowances to prevent unauthorized actions by enterprise AI agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a control plane architecture, not an evaluation benchmark.
KedgeFlow: A Programmable Control Plane for Enterprise AI Agents The paper proposes a control plane enforcing structured contracts and single-use allowances to prevent unauthorized actions by enterprise AI agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Not focused on model compression, quantization, or efficient inference.
StateSync-GKR: Design and Evaluation of a Reusable Sparse GKR Engine The paper evaluates a reusable Rust-based cryptographic proof engine designed for efficient state transitions and parallel execution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Zero-knowledge proof cryptographic engine, unrelated to AI deployment safety.
SEEK: Agentic Knowledge Graph Retrieval for Secure Code Generation The paper introduces an agentic knowledge graph retrieval framework that uses vulnerability taxonomies to generate secure code. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a generation framework rather than a general evaluation benchmark.
SEEK: Agentic Knowledge Graph Retrieval for Secure Code Generation The paper introduces an agentic knowledge graph retrieval framework that uses vulnerability taxonomies to generate secure code. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression or inference acceleration techniques.
Only What You've Signed up For: Secure Workflows with Constrained Diffusion Agents The paper presents a framework enforcing security policies during the denoising steps of diffusion language models to prevent prompt injection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, or serving efficiency.
Only What You've Signed up For: Secure Workflows with Constrained Diffusion Agents The paper presents a framework enforcing security policies during the denoising steps of diffusion language models to prevent prompt injection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on a security defense mechanism, not LLM evaluation benchmarks.
On the Effectiveness of NetFlow-Based Deanonymization of Tor Onion Services (Extended Version) The paper demonstrates active traffic analysis attacks that deanonymize Tor onion services using coarse NetFlow packet statistics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies Tor network traffic deanonymization, not AI safety.
Asymptotics of Random-Lattice Enumeration: Beyond HKZ via Dense Sublattices The paper analyzes the asymptotic tree sizes of random lattice enumeration algorithms to establish bounds beyond quasi-HKZ bases. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical lattice cryptography math paper, not AI deployment safety.
From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents The paper surveys security vulnerabilities and defenses in edge-deployed audio language model agents across the sensing-to-action pipeline. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focus is on security and physical action risks, not multimodal modeling.
From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents The paper surveys security vulnerabilities and defenses in edge-deployed audio language model agents across the sensing-to-action pipeline. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines edge security risks, not model compression or efficiency.
From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents The paper surveys security vulnerabilities and defenses in edge-deployed audio language model agents across the sensing-to-action pipeline. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on audio LLMs rather than industrial vision inspection.
From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents The paper surveys security vulnerabilities and defenses in edge-deployed audio language model agents across the sensing-to-action pipeline. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Survey of audio agent security, not an evaluation benchmark.
Poisoned into Action: Context Attacks on Typed Decision Workflows The paper studies how context poisoning triggers unauthorized state changes in typed decision workflows and evaluates an execution-aware defense protocol. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Designs an attack evaluation protocol for workflows, not general LLM benchmarking.
Poisoned into Action: Context Attacks on Typed Decision Workflows The paper studies how context poisoning triggers unauthorized state changes in typed decision workflows and evaluates an execution-aware defense protocol. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not cover model compression or inference efficiency.
Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata The paper evaluates the real-world deployment and effectiveness of Fetch Metadata security headers across the top one million websites. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes standard web browser HTTP security headers, not AI systems.
Riemannian Suppression of Cross-Session Identity Leakage in Motor-Imagery EEG The paper introduces a Riemannian transformation to suppress cross-session subject identity leakage in EEG brain-computer interfaces without training a privacy model. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers EEG brain-computer interface privacy, not AI deployment safety.
Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an adversarial detector for vision, not an LLM benchmark.
Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, pruning, or efficient inference.
Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical radiology imaging rather than industrial manufacturing inspection.
Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Evaluates unimodal medical imaging classification, not multimodal models.
To Cache or Not to Cache: Understanding and Mitigating Safety Incompatibility in Cross-Model KV Cache Reuse The paper studies safety behavioral deviations caused by cross-model key-value cache reuse and proposes an alignment technique to mitigate them. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Not introducing a general benchmark or evaluation methodology for LLMs.
To Cache or Not to Cache: Understanding and Mitigating Safety Incompatibility in Cross-Model KV Cache Reuse The paper studies safety behavioral deviations caused by cross-model key-value cache reuse and proposes an alignment technique to mitigate them. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Investigates KV cache reuse across models, not software engineering.
LLM-based Code Auditing by Inferring and Evaluating Developer Assumptions The paper presents a code auditing method that uses language models to infer developer beliefs and flag contradictions as potential vulnerabilities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on static code analysis rather than deployed AI safety.
Penetrating the Reality Wall: Long-Range and Through-Wall AR/VR Privacy Eavesdropping via Wireless Power Side Channel The paper presents an attack reconstructing AR and VR user behaviors through non-intrusive wireless power side-channel eavesdropping. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on physical wireless side-channel eavesdropping on AR/VR devices, not AI safety.
The Role of Generative Artificial Intelligence in Testing and Automation of Open RAN Systems: GNG-OTA Framework The paper proposes a framework combining generative AI and graph neural networks for automated testing in Open Radio Access Networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on Open RAN telecommunications testing, not AI deployment safety risks.
Forensic Text Recovery from Document Thumbnails Using Text-Aware U-Net The paper develops a text-aware U-Net guided by optical character recognition loss to restore readable text from degraded document thumbnails. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on forensic document image restoration, not AI safety or reliability.
Adversarial Robustness of Camera-Radar Sensor Fusion for Autonomous Driving The paper evaluates the adversarial robustness of five camera-radar fusion algorithms against sensor perturbations in autonomous driving settings. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates autonomous driving sensor fusion rather than industrial quality control.
Adversarial Robustness of Camera-Radar Sensor Fusion for Autonomous Driving The paper evaluates the adversarial robustness of five camera-radar fusion algorithms against sensor perturbations in autonomous driving settings. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on adversarial sensor fusion for autonomous vehicles, not multimodal foundation models.
HDKEM: Authentication Allocation for Deniable Hybrid Key Exchange The paper formalizes authentication allocation in deniable hybrid key exchange and presents a concrete protocol combining ML-KEM-768 and X25519. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on cryptographic key exchange protocols, not AI deployment safety.
Resource-constrained Remote Attestation of Smart Meters Using Merkle Trees in a Trusted Execution Environment The paper introduces a remote attestation method for smart meters that uses Merkle trees inside a trusted execution environment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on smart meter hardware attestation and TEEs, not AI safety.

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