Every paper checked — nothing hidden

All papers

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

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Does Client Diversity Protect the Lightning Network? Evidence from the 2026 Core Lightning Shutdown Studies the resilience of the Lightning Network during a client node shutdown to measure the practical value of client diversity. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Analyzes cryptocurrency Lightning Network node diversity and availability, not AI safety.
PAGE404: Breaking GPU Isolation Through Hardware-assisted Page Migration Demonstrates side-channel and covert-channel security risks in hardware-assisted page migration on unified CPU-GPU superchips. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on hardware and driver side channels in GPU memory architecture, not AI safety.
Practical wavelength selection for quantum-augmented networks over shared optical fiber Develops a wavelength selection framework for running quantum-augmented error-corrected networks over optical fiber shared with classical traffic. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses physical quantum optical networking, entirely unrelated to AI deployment safety.
Blind Watermarking of Software Repositories via Module Placement Introduces a software watermarking technique that embeds identifiers by altering module placement rather than modifying source text. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on repository module watermarking for traditional software provenance, not AI safety.
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy Proves theoretical sample bounds and presents algorithms for learning univariate probability distributions under local differential privacy. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Theoretical statistical work on density estimation, not language model evaluation.
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy Proves theoretical sample bounds and presents algorithms for learning univariate probability distributions under local differential privacy. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Covers theoretical distribution learning algorithms rather than small, efficient language models.
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy Proves theoretical sample bounds and presents algorithms for learning univariate probability distributions under local differential privacy. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Theoretical density estimation under differential privacy, unrelated to operational AI deployment safety.
NOMAI: Scalable Federated Learning with Private Weights Presents a secure multiparty computation system for federated learning that protects model weights, user data, and system integrity at scale. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on multi-party computation protocols for federated learning rather than AI safety or reliability.
Reaction Attack on CKKS Deployments with Asymptotically Optimal Query Complexity Presents a reaction leakage attack that recovers secret keys in homomorphic encryption deployments using client validity responses. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies cryptographic reaction attacks against CKKS encryption deployments, not AI safety.
Optimal Reduced Representatives of Quadratic APN Permutations in Odd Dimension Proves existence conditions for optimal reduced polynomial representatives of quadratic almost perfect nonlinear permutations in odd dimensions. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Pure mathematics concerning APN permutations and finite fields, not AI safety.
Amora: Communication-Efficient Private Approximate Nearest Neighbors Search Presents a communication-efficient private approximate nearest neighbors search system for vector databases using partitioned graph indices and private information retrieval. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses cryptographic private nearest neighbor search in vector databases, not AI system reliability.
Explicit reduced APN permutations: ramification and optimal Galois-ring lifts Derives explicit reduced almost perfect nonlinear permutations and analyzes their ramification and lifts over Galois rings. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Mathematical work on APN permutations in finite fields, not AI safety.
Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse Introduces a clean-data backdoor method that persists through standard knowledge distillation by targeting naturally occurring feature subspaces. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on creating backdoor attacks resistant to distillation, not on LLM evaluation benchmarks.
Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse Introduces a clean-data backdoor method that persists through standard knowledge distillation by targeting naturally occurring feature subspaces. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Investigates backdoor attacks surviving distillation rather than designing efficient or small models.
On the Complexity of Symmetric Boolean Functions in Multiparty Private Simultaneous Messages Analyzes the communication complexity and lower bounds of symmetric Boolean functions in multiparty private simultaneous message protocols. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with theoretical multiparty computation and Boolean function complexity, not AI safety.
Combining LLM and Dynamic Analysis for Universal Testing of Static Application Security Testing Tools Uses large language models and dynamic feedback to generate code variants and test static application security analysis tools. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Evaluates static analysis tools for traditional software rather than addressing AI deployment safety.
Evaluating and Improving Static Application Security Testing Tools for Rust Evaluates static analysis security testing tools on real-world Rust vulnerabilities and presents an improved framework for vulnerability detection. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on static security testing of Rust programs, not AI safety or deployment risks.
Benchmarking Post-Quantum Key Encapsulation in S3-Compatible Object Storage under Client Concurrency Measures the performance impact of post-quantum key encapsulation algorithms during per-object client-side encryption in concurrent cloud storage systems. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on post-quantum cryptography in cloud object storage, not AI safety or reliability.
Smoothness of Shortest Isogenies to Frobenius Conjugates Proves the smoothness of minimum degree isogenies to Frobenius conjugates, confirming an attack complexity for supersingular elliptic curves. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Pure mathematical cryptography paper unrelated to AI safety.
NEST: Stale-Robust Tokenized-Stock Measurement for a Crypto-Treasury LETF Stack Proposes a measurement framework to evaluate the informational value of off-hours tokenized stock prices relative to listed market moves. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Financial econometrics for tokenized stocks unrelated to AI safety.
On the Robustness of Gossip Protocols in Modern Blockchains Evaluates network topology robustness and adversary resistance in gossip protocols used by modern committee-based blockchains. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Blockchain gossip network protocol study unrelated to AI safety.
Pricing the Unprovable: Ambiguity, Turnstile Runs, and the Verifiability Discount in Crypto Protocols Develops a framework and empirical model to measure market valuation discounts following unverified vulnerability repairs in privacy-preserving crypto protocols. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Financial crypto-protocol market analysis unrelated to AI deployment safety.
Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning Studies label privacy leakage and data poisoning risks during head-only federated fine-tuning of vision models across distributed clients. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies FL privacy attacks rather than LLM evaluation benchmarks.
Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning Studies label privacy leakage and data poisoning risks during head-only federated fine-tuning of vision models across distributed clients. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies federated learning attacks rather than efficient model inference.
SMT-Boosted Security Types and Correctness for Low-Level MPC Develops a verification type theory using SMT solvers to enforce security and correctness in low-level secure multi-party computation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies cryptography and MPC verification, not AI safety deployment.
MAppPrint: Robust and Real-time Mobile App Fingerprinting via Traffic Behavior Correlation Graphs Introduces a real-time mobile app fingerprinting method based on traffic behavior correlation graphs to identify apps from encrypted network flows. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Network traffic fingerprinting paper unrelated to AI system safety.
Subgraph Counting with Local Node Differential Privacy Develops an algorithm with tight error bounds for counting subgraphs under local node differential privacy constraints. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Theoretical graph privacy paper unrelated to AI deployment safety.
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Audits cascade routing integrity rather than standard LLM benchmark evaluation.
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Not focused on model compression, pruning, or efficient inference design.
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not study code generation or software engineering models.
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on routing and cache integrity in LLM cascades, not agentic planning.
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on routing and resource audit rather than document retrieval systems.
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses strictly on language model cascades without multimodal inputs.
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address AI safety, jailbreaking, or guardrail systems.
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on architecture efficiency rather than LLM benchmarking methodologies.
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Not about autonomous multi-step planning or tool use agents.
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not examine retrieval-augmented generation architectures.
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not focused on code generation or software engineering tasks.
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses purely on text language modeling architecture.
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to code generation or software engineering tasks.
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Studies text-only conversational benchmarks without multimodal inputs.
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not evaluate model compression, distillation, or inference efficiency.
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on social awareness benchmarking rather than safety or guardrails.
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not study retrieval-augmented generation or document grounding.
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Evaluates social conversation response rather than autonomous tool-using agents.
PRISM: Transfer and Limits of Activation Interventions in Tool-Calling Language Models Studies the transferability of internal activation interventions to reduce unsafe tool execution across model precisions and languages. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not focus on code generation or software engineering.
PRISM: Transfer and Limits of Activation Interventions in Tool-Calling Language Models Studies the transferability of internal activation interventions to reduce unsafe tool execution across model precisions and languages. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Uses quantization only as an auxiliary tool, not efficient model design.
PRISM: Transfer and Limits of Activation Interventions in Tool-Calling Language Models Studies the transferability of internal activation interventions to reduce unsafe tool execution across model precisions and languages. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Does not address multimodal models across text, image, or audio.
PRISM: Transfer and Limits of Activation Interventions in Tool-Calling Language Models Studies the transferability of internal activation interventions to reduce unsafe tool execution across model precisions and languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation or document retrieval.
PRISM: Transfer and Limits of Activation Interventions in Tool-Calling Language Models Studies the transferability of internal activation interventions to reduce unsafe tool execution across model precisions and languages. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes an alignment framework rather than general LLM evaluation benchmarks.
Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on RL training algorithms rather than deployment safety or guardrails.
Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Does not study multimodal models combining text with vision or audio.
Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not specifically study code generation or software engineering tasks.
Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation or database retrieval.
Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a training algorithm rather than an LLM evaluation benchmark.
Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on RL optimization theory rather than agentic planning or tools.
Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on RL optimization rather than small models or efficient inference.
PEDBench: Can Speech Models Detect Phonetic Errors? Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation pipelines.
PEDBench: Can Speech Models Detect Phonetic Errors? Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on speech audio processing rather than multimodal cross-modality models.
PEDBench: Can Speech Models Detect Phonetic Errors? Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not evaluate model compression, distillation, or serving efficiency.

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