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
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	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	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	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	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.
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.
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.
Risk-aware Road-network Trajectory Synthesis under Local Differential Privacy The paper presents a framework for road-network trajectory synthesis under local differential privacy to protect user mobility patterns and transition choices. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on road-network trajectory privacy, not AI system deployment safety.
Not All Challenges Are Equal: Evaluating Interactive Liveness Detection Against Real-Time Face Swaps The paper evaluates the effectiveness of randomized interactive gesture challenges in detecting real-time face-swap attacks during remote identity verification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates deepfake liveness detection rather than industrial inspection or defect detection.
Not All Challenges Are Equal: Evaluating Interactive Liveness Detection Against Real-Time Face Swaps The paper evaluates the effectiveness of randomized interactive gesture challenges in detecting real-time face-swap attacks during remote identity verification. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies interactive face liveness detection instead of general multimodal models.
Revisiting Fast Tendermint: An Agreement Violation and Two Repairs The paper identifies an agreement violation in Fast Tendermint's published round-change rule and proposes two protocol repairs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses distributed consensus protocol correctness, not AI deployment safety.
Digital-Twin-Referenced Belief-Guided Q-Learning for Jammer Identification and Beam-Power Control in a mmWave Model The paper introduces a digital-twin-referenced belief-guided Q-learning method for jammer identification and beam-power control in millimeter-wave networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with RF wireless communications and jammer mitigation, not AI system deployment safety.
Beyond User-Side Defenses: Image Protection in Open-Weight Diffusion Models The paper proposes design requirements and evaluates defenses against model-side protection removal in open-weight image diffusion models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on image protection in diffusion models, not industrial inspection.
Beyond User-Side Defenses: Image Protection in Open-Weight Diffusion Models The paper proposes design requirements and evaluates defenses against model-side protection removal in open-weight image diffusion models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses image generation model security rather than multimodal understanding or reasoning.
Security and robustness analysis of encrypted communications for local home automation: ESPHome Native API and MQTT over TLS 1.3 The paper reviews security and performance trade-offs between MQTT over TLS 1.3 and the ESPHome Native API for local home automation. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant IoT communication security review, not related to AI safety or reliability.
Think Locally Before Computing Jointly: Efficient Secure Multi-Party Computation via Maximizing Local Processing The paper optimizes garbled-circuit secure multi-party computation by restructuring Boolean circuits to maximize local, non-cryptographic evaluations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Cryptographic multi-party computation optimization, not related to AI systems.
The Right to Be Forgotten under Correlation: Privacy-Utility Tradeoffs This paper derives the optimal tradeoff between privacy leakage and utility when redacting information from correlated datasets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Information-theoretic privacy for data redaction, not AI system safety/reliability.
SEAL-DT: An End-to-End Security Engineering and Assurance Framework for Cross-Domain Digital Twins This paper introduces an end-to-end security engineering framework for specifying, simulating, and assuring cross-domain digital twins. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Framework for digital twin security engineering, not AI safety or reliability.
Can Rényi Accountants Beat Lossless Accountants for DP-SGD? This paper demonstrates that Renyi differential privacy accountants can provide tighter privacy bounds for DP-SGD than lossless accountants. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical differential privacy analysis, not an LLM benchmark.
Can Rényi Accountants Beat Lossless Accountants for DP-SGD? This paper demonstrates that Renyi differential privacy accountants can provide tighter privacy bounds for DP-SGD than lossless accountants. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical DP-SGD privacy bounds, outside deployed AI safety and reliability.
Can Rényi Accountants Beat Lossless Accountants for DP-SGD? This paper demonstrates that Renyi differential privacy accountants can provide tighter privacy bounds for DP-SGD than lossless accountants. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates DP-SGD accounting, not model compression or efficiency.
Tight End-to-End Accounting for Differentially Private Synthetic Data This paper develops accounting techniques to maintain tight differential privacy guarantees across complex synthetic data generation pipelines. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on differential privacy mathematics for synthetic data, not deployed AI safety/reliability.
Tight End-to-End Accounting for Differentially Private Synthetic Data This paper develops accounting techniques to maintain tight differential privacy guarantees across complex synthetic data generation pipelines. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical privacy accounting framework, not an LLM benchmark.
Tight End-to-End Accounting for Differentially Private Synthetic Data This paper develops accounting techniques to maintain tight differential privacy guarantees across complex synthetic data generation pipelines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on differential privacy accounting, not small or efficient models.
SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy This paper proposes using state space models to guide black-box image perturbations that disrupt facial recognition systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces an adversarial attack method rather than an evaluation benchmark.
SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy This paper proposes using state space models to guide black-box image perturbations that disrupt facial recognition systems. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with face recognition image perturbations, not multimodal reasoning models.
SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy This paper proposes using state space models to guide black-box image perturbations that disrupt facial recognition systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on adversarial attacks against computer vision face recognition, not deployed AI safety/reliability.

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