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System For Privacy-Preserving Cryptographic Auditing Using Zero-Knowledge Proofs, N/A Jun 2026

System For Privacy-Preserving Cryptographic Auditing Using Zero-Knowledge Proofs, N/A

Defensive Publications Series

Systems for enterprise security may rely on high (fidelity) logging, which can involve collecting and storing large volumes of sensitive event metadata, creating security and privacy liabilities. A cryptographic auditing framework may be utilized where, instead of storing raw event data, a system can generate and store a succinct cryptographic proof, such as a zero-knowledge proof. This proof can serve as a mathematical attestation that a permitted action occurred in compliance with a governing policy, potentially without revealing the specific details of the user or the action. This approach can be used to generate a tamper-evident, privacy-preserving, and space-efficient audit …

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Autonomous Stress Testing In A Network Digital Twin For Predictive Remediation, Mohit Chandra, Amrita Mukherjee Jun 2026

Autonomous Stress Testing In A Network Digital Twin For Predictive Remediation, Mohit Chandra, Amrita Mukherjee

Defensive Publications Series

The management of complex telecommunications networks can be challenged by difficulties in predicting the impact of component failures and the risks of testing automations on a live system. Systems and methods are described that can address these challenges by creating and maintaining a graph-based digital representation, such as a digital twin, of an operational network that can be synchronized with real-time data. Within this digital twin, for example, an autonomous agent can perform synthetic fault injections to simulate a wide range of potential failures and stressors. The system can analyze the effects of these simulated faults to predict potential service-level …

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Speculative Data Prefetching Based On Predicted Agent Intent In Machine Learning Systems, Anonymous Jun 2026

Speculative Data Prefetching Based On Predicted Agent Intent In Machine Learning Systems, Anonymous

Defensive Publications Series

Techniques are described for speculative data prefetching in systems in which an LLM agent dynamically invokes retrieval backends whose candidates require embedding lookups from SSD-backed embedding tables. A user memory state with tiered memory data is obtained and converted into features. An intent prediction model produces, for each backend, a probability that the agent will invoke the backend. A prefetch controller computes a backend-specific prefetch decision using a value metric that combines probability, expected embedding lookups, SSD latency, and prefetch cost, and selects backends subject to a bandwidth budget based on SSD throughput and a Phase 1 time window. Speculative …

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Coordinated Load-Aware Contention Domain Assignment, Peiman Amini, Navid Reyhanian, Ardalan Alizadeh, Matt Silverman Jun 2026

Coordinated Load-Aware Contention Domain Assignment, Peiman Amini, Navid Reyhanian, Ardalan Alizadeh, Matt Silverman

Defensive Publications Series

A system is proposed herein through which a set of techniques can be utilized to create a coordinated, latency-aware allocation policy for Dynamic Sub-band Operation (DSO).  DSO can be used to create multiple parallel contention domains.  For example, an access point (AP) can use fine-grained per-sub-band measurements, including overlapping basic service set (OBSS) frame ratio, jitter variance, airtime fairness, clear channel assessment (CCA), and retry metrics, to compute a condition score per sub-band. The AP can also use neighbor coordination pressure to prevent overlapping basic service sets (BSSs) from converging on the same sub-band and apply traffic-class-aware admission limits with …

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Multi-Lane Concurrent Specification Evolution System With Configurable Ai Governance For Distributed Engineering Organizations, Anonymous Jun 2026

Multi-Lane Concurrent Specification Evolution System With Configurable Ai Governance For Distributed Engineering Organizations, Anonymous

Defensive Publications Series

A multi-lane concurrent specification evolution system maintains a hierarchical specification across three lanes: isolated team experimentation branches, an integration lane, and a low-velocity consensus lane. Per-domain governance policies define permission levels for AI autonomy and human involvement, including human-only, human-review, AI-propose with an objection window, and AI-auto subject to a confidence threshold. An AI agent proposes and/or executes merges from team branches to the integration lane using conflict detection, contract compatibility checking, invariant validation, and cross-reference impact analysis for inter-domain accept/produce contracts, and notifies affected owners. When stabilization criteria are met, the integrated state is promoted to a versioned baseline …

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Hierarchical Multi-Level Knowledge Graph With Compositional Contracts For Ai Agent Navigation Of Distributed Software Systems, Anonymous Jun 2026

Hierarchical Multi-Level Knowledge Graph With Compositional Contracts For Ai Agent Navigation Of Distributed Software Systems, Anonymous

Defensive Publications Series

Systems and methods store a hierarchical knowledge graph for navigating and changing distributed software systems. A product layer defines product identity, system bindings, and an end-to-end dataflow across system domains. A system knowledge graph includes a four-level specification hierarchy: domain specifications with intent phrases and ownership metadata; feature specifications with typed accepts/produces compositional contracts; behavior specifications with given/when/then invariants; and technical specifications with implementation anchors and referenced skills. An agent follows a six-phase change protocol: understand a request via semantic discovery over intent phrases, plan by traversing bindings and dataflow to identify impacted specs and code locations for approval, execute …

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Persistent Hierarchical Context System With Cloud-Synchronized Memory For Ai Agent Workspace Awareness, Anonymous Jun 2026

Persistent Hierarchical Context System With Cloud-Synchronized Memory For Ai Agent Workspace Awareness, Anonymous

Defensive Publications Series

Systems and methods provide persistent, hierarchical, project-aware context for an AI agent using a dual-use file hierarchy. A cloud-synchronized workspace contains embedded, machine-parseable context files at multiple directory levels. When the agent operates with respect to a directory, a context loader automatically loads a chain of context files from that directory and parent directories to form layered context including user identity, preferences, and project-specific information. Because the context resides as files within the cloud-synchronized workspace, the context persists across sessions and follows the user across devices. An ingestion mechanism accepts arbitrary inputs such as links or notes, analyzes content to …

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Hierarchical Multi-Resolution Columnar Encoding With Trait-Specific Aggregation For User Engagement Sequence Feature Stores, Anonymous Jun 2026

Hierarchical Multi-Resolution Columnar Encoding With Trait-Specific Aggregation For User Engagement Sequence Feature Stores, Anonymous

Defensive Publications Series

A self-contained columnar record format stores a user engagement sequence across multiple traits using multiple tiers having different time windows and granularities. The record includes a header that specifies tier descriptors (including entry counts, byte offsets, and encoder identifiers) and trait descriptors (including data types and trait-specific aggregation functions). A rolling compaction pipeline moves data from finer tiers to coarser tiers by bucketizing by the coarser granularity and applying the stored aggregation function per trait (e.g., SUM, MAX, MIN, MODE, OR, SAMPLE_TOP_K, WEIGHTED_AVG). A resolution-aware decode API receives a resolution budget selecting a subset of tiers and optional per-tier entry …

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Closed-Loop Profile-Guided Kernel Optimization With Bottleneck-Shift Detection And Cross-Kernel Transfer Learning, Anonymous Jun 2026

Closed-Loop Profile-Guided Kernel Optimization With Bottleneck-Shift Detection And Cross-Kernel Transfer Learning, Anonymous

Defensive Publications Series

A closed-loop GPU kernel optimization technique collects hardware performance counters to form a normalized counter-profile vector and classifies the kernel into a bottleneck class. Candidate structured transforms are selected using probabilistic ranking based on stored successes and failures for transforms conditioned on bottleneck class and hardware. After each transform, the kernel is re-profiled (in addition to benchmarking), re-classified, and a bottleneck shift is detected when the bottleneck class changes. Upon a shift, remaining transforms are re-ranked for the new bottleneck class. Optimization trajectories are recorded as sequences of transforms, bottleneck-shift pairs, and speedups, enabling multi-step lookahead scoring. Cross-kernel transfer is …

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Ai-Powered Autonomous Agent For Enterprise Platform Configuration With Live Introspection And Dependency-Aware Deployment, Anonymous Jun 2026

Ai-Powered Autonomous Agent For Enterprise Platform Configuration With Live Introspection And Dependency-Aware Deployment, Anonymous

Defensive Publications Series

An autonomous agent configures a configurable enterprise platform to onboard a new business vertical. The agent performs live environment introspection using parallel administrative API calls to build a snapshot and a graph of configuration components and their relationships. Existing verticals are detected using naming patterns and relationship traversal with confidence scoring, and a baseline vertical template is extracted. A three-phase requirement analysis maps requirement patterns to component clusters, matches required components against the snapshot using five-tier classifications (exact, sibling, partial, misconfigured, missing) with risk metadata, and enriches results using a machine-learning model that identifies dependencies and contradictions and outputs structured …

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Systems And Methods For Automated Recipe Extraction From Multimedia Content With Visual Pantry Inventory Detection And Differential Shopping List Generation, Anonymous Jun 2026

Systems And Methods For Automated Recipe Extraction From Multimedia Content With Visual Pantry Inventory Detection And Differential Shopping List Generation, Anonymous

Defensive Publications Series

Systems and methods are described for transforming unstructured cooking-related multimedia into actionable outputs. A client provides a video link, web link, screenshot, or photo. One or more models extract ingredients and preparation actions using combinations of speech recognition, optical character recognition, and visual recognition. A structured recipe is generated with ingredients and ordered steps, and additional attributes such as serving size, nutrition or macronutrients, and dietary tags are derived. A meal plan may be produced over a time window, and required ingredients are aggregated. Optionally, the user captures images of pantry and/or refrigerator contents via a guided flow. Inventory items …

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Systems And Methods For Asymmetric Propagation Constraints With Partitioned Memory Architecture In Collaborative Recommendation, Anonymous Jun 2026

Systems And Methods For Asymmetric Propagation Constraints With Partitioned Memory Architecture In Collaborative Recommendation, Anonymous

Defensive Publications Series

Techniques are described for collaborative recommendation that constrain cross-user propagation of user-derived signals using asymmetric, type-dependent rules. A signal classification layer assigns each generated insight to a psychological signal type including positive preference, negative preference, behavioral pattern, and vulnerability indicator. An asymmetric propagation engine applies type-specific parameters such as hop depth, evidence thresholds, confidence discounting, and temporal decay, including higher friction for negative, behavioral, and vulnerability-related signals than for positive preferences. Negative preference propagation is constrained to a demotion-only effect in ranking, reducing scores without excluding candidates. A partitioned memory architecture stores ranking-accessible preferences in a first partition and stores …

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Systems And Methods For Automated Code Review Quality Assessment Using Hybrid Classification And Logarithmic Scoring With Anti-Gaming Safeguards, Anonymous Jun 2026

Systems And Methods For Automated Code Review Quality Assessment Using Hybrid Classification And Logarithmic Scoring With Anti-Gaming Safeguards, Anonymous

Defensive Publications Series

Systems and methods are described for automated assessment of code review quality using review comments, reviewer actions, reactions, and diff context. A pipeline identifies human reviewers, extracts per-reviewer comment threads, and executes parallel sub-agent analyses to produce structured signals including impact and relevance scores and comment attributes. A deterministic ruleset classifies each comment into HIGH, MEDIUM, LOW, or OUT_OF_SCOPE. A reviewer score is computed using weighted counts of classified comments with a logarithmic normalization term to provide diminishing returns, and may include additive bonuses such as code suggestions, reactions, and request-changes actions. Multi-layer integrity safeguards may include concentration ratio thresholds, …

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Autonomous Multi-Agent System For Social Discovery And Interpersonal Compatibility Evaluation, Anonymous Jun 2026

Autonomous Multi-Agent System For Social Discovery And Interpersonal Compatibility Evaluation, Anonymous

Defensive Publications Series

Autonomous agent-mediated social discovery is disclosed. Users configure personalized AI agents with personality models, tiered values hierarchies, and conversational behavior parameters. Agents publish anonymized personality embeddings to a discovery service that performs compatibility pre-filtering and a handshake protocol to initiate contact. Matched agents conduct a structured, multi-stage, multi-turn compatibility conversation managed by a conversation orchestrator, with optional early termination based on non-negotiable value misalignment. A compatibility scoring engine generates a multi-dimensional compatibility report derived from conversation signals, including sub-scores for personality alignment, values congruence, and behavioral compatibility. A human supervisory interface enables observation of agent-to-agent interactions, private guidance injection invisible …

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Product Ownership Inference From Conversion Signals For Advertising Optimization, Anonymous Jun 2026

Product Ownership Inference From Conversion Signals For Advertising Optimization, Anonymous

Defensive Publications Series

A digital advertising platform constructs a per-user product ownership graph by ingesting third-party conversion signals from multiple advertisers, including purchase events and registration-related events, and resolving each signal to a canonical product entity using catalog matching, fuzzy attribute matching, and cross-merchant deduplication. The platform assigns an ownership confidence score and stores owned product nodes with acquisition metadata, taxonomy, attributes, lifecycle state, and relationship edges such as complementary, successor, substitute, and sequence relationships. During ad selection and auction, the platform suppresses ads for owned products and for satisfied categories, and adjusts ranking to promote complementary products, timed replacements, version upgrades, similarity-based …

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Cryptographic Attestation Of Tool Responses For Gating Autonomous Agent Actions, Anonymous Jun 2026

Cryptographic Attestation Of Tool Responses For Gating Autonomous Agent Actions, Anonymous

Defensive Publications Series

Tool services generate cryptographically signed tool-call receipts that bind a tool-call request and a tool-call response to metadata including a caller identifier, timestamp, and nonce. The receipt includes a request hash and a response hash, and is signed (e.g., Ed25519) over receipt fields. An autonomous agent forwards the signed receipt with a subsequent action request to a downstream gate service. The gate service verifies the signature and enforces gating conditions including request binding to the targeted resource, freshness via the timestamp, and non-replay via nonce consumption. Consequential actions are authorized only when required receipt prerequisites validate; missing receipts, mismatched request …

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Distributed Ledger-Based Provenance Tracking And Verification For Multi-Agent Ai Knowledge Systems, Anonymous Jun 2026

Distributed Ledger-Based Provenance Tracking And Verification For Multi-Agent Ai Knowledge Systems, Anonymous

Defensive Publications Series

A blockchain-based provenance verification layer is described for text-based knowledge artifacts shared among multiple AI agents. Middleware intercepts creation and consumption of artifacts in shared knowledge stores and associates each artifact or claim with a provenance record on a distributed ledger, including content hash, author identifier, timestamp, verification status, parent links, propagation depth, and confidence. Independent verification agents generate cryptographically signed attestations using techniques such as command help checks, API test calls, cross-references, and consistency checks, and ledger status is updated accordingly, optionally blocking unverified writes. During reads, a trust score is computed from verification status, attestation density, propagation depth, …

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Agent Preference Memory Integrity Verification System For Detecting And Recovering From Preference Poisoning Attacks, Anonymous Jun 2026

Agent Preference Memory Integrity Verification System For Detecting And Recovering From Preference Poisoning Attacks, Anonymous

Defensive Publications Series

Systems and methods are described for integrity verification of AI agent preference memory. Preference entries include semantic content, confidence, timestamp, source interaction identifier, and reinforcement history, and are associated with cryptographic provenance signatures and interaction context hashes. A preference consistency graph computes embedding-based consistency weights between preferences and produces anomaly scores for candidate preferences based on contradictions with stored high-confidence preferences. Confidence values may decay over time and be re-verified using subsequent behavior and a multi-source corroboration ladder. The system creates cryptographically signed checkpoints and performs targeted rollback to surgically remove unverifiable or anomalous preference entries while preserving verified entries, …

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Recommendation Circuit Breaker System For Agent-Speed Feedback Loop Stability, Anonymous Jun 2026

Recommendation Circuit Breaker System For Agent-Speed Feedback Loop Stability, Anonymous

Defensive Publications Series

Systems and methods are disclosed for stabilizing recommendation rankings subject to fast feedback-loop dynamics. A circuit breaker layer monitors ranking position time series to compute ranking velocity and a catalog-level ranking volatility index, detects oscillations via frequency-domain analysis, and measures exposure concentration using concentration indices. The system may additionally detect absolute quality drift using calibration probe items and a probe-to-organic engagement gap. A composite instability score selects among graduated intervention tiers, including diversity-weight adjustment, ranking velocity capping, ranking freezes using cached stable rankings, and fallback rankings based on longer-horizon popularity. The system may classify sessions as agent-originated or human-originated using …

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Position-Dependent Psychological Risk Management For Recommendation Ranking, Anonymous Jun 2026

Position-Dependent Psychological Risk Management For Recommendation Ranking, Anonymous

Defensive Publications Series

A recommendation system constructs a ranked slate using position-dependent psychological risk management. The system partitions slate positions into contiguous zones including a Trust zone, an Engagement zone, and a Discovery zone, with zone boundaries adapted per user based on a user trust score and optionally cognitive mode or session progress. A base ranking model produces base value scores and an uncertainty measure. For each target position, the system computes a position-modified score by applying a zone-specific risk tolerance function conditioned on user state. In the Trust zone, items are penalized as a function of prediction uncertainty scaled by user distrust; …

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Feedback Loop Intensity Monitoring And Automatic Intervention For Recommendation Systems, Anonymous Jun 2026

Feedback Loop Intensity Monitoring And Automatic Intervention For Recommendation Systems, Anonymous

Defensive Publications Series

Systems and methods are described for monitoring recommendation feedback loops and automatically intervening in live ranking. A feedback loop intensity (FLI) for a user and topic is computed as a ratio of a user-conditional topic serving proportion to a global topic serving proportion over a rolling window. An organic interest score is computed from non-recommendation-channel evidence including one or more of search, direct navigation, creator following, or external engagement. An adjusted FLI is generated by dampening the raw FLI as a function of organic interest. When adjusted FLI exceeds configurable thresholds, a controller applies topic-specific, tiered interventions in the ranking …

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Uncovering App Activity Patterns Behind E-Commerce App Usage: An Explainable Ai Framework, Myounggu Lee, Hye-Jin Kim Jun 2026

Uncovering App Activity Patterns Behind E-Commerce App Usage: An Explainable Ai Framework, Myounggu Lee, Hye-Jin Kim

Asia Marketing Journal

In today's competitive mobile ecosystem, understanding how general app activity influences e-commerce app usage is crucial for marketers. This study introduces an explainable AI (XAI) framework using SHapley Additive exPlanations (SHAP) to identify non-e-commerce apps associated with e-commerce app usage. Drawing on mobile app launch data from 1,000 Android users in South Korea, we train a machine learning model to predict usage and apply SHAP to quantify the marginal contribution of each app. Results show that apps such as YouTube, Naver, and KakaoTalk are strongly linked to e-commerce activity at the user level. By visualizing user–app associations and clustering users …

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Can Ai-Generated Ads With Future Predictions Drive Higher Donations Than Human-Created Ads?, Chaewon Song, Sujin Song Jun 2026

Can Ai-Generated Ads With Future Predictions Drive Higher Donations Than Human-Created Ads?, Chaewon Song, Sujin Song

Asia Marketing Journal

The use of AI in generating ads offers benefits for NPOs, such as enhanced cost efficiency and personalized content. However, consumers often exhibit algorithm aversion when AI performs subjective tasks, like ad creation. According to mind perception theory, AI is seen as high in agency but low in experience. Given this, we hypothesized that including future predictions in AI-generated ads would mitigate this aversion. Across four studies, we found that AI-generated donation ads were generally perceived as less credible and elicited lower donation intentions than human-created ones. Crucially, when future predictions were included, the credibility and donation gap between AI …

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Improving Body Protocoling To Reduce Patient Dose And Optimize Patient Care, Rafael Herrera Jun 2026

Improving Body Protocoling To Reduce Patient Dose And Optimize Patient Care, Rafael Herrera

Graduate Medical Education Research Journal

NA

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From Visual Field To Visual Field: 10-2 Visual Field Map Prediction From 24-2 Visual Field Using Machine Learning And Detection Of Central Visual Field Progression, Golnoush Mahmoudinezhad Jun 2026

From Visual Field To Visual Field: 10-2 Visual Field Map Prediction From 24-2 Visual Field Using Machine Learning And Detection Of Central Visual Field Progression, Golnoush Mahmoudinezhad

Graduate Medical Education Research Journal

NA

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Primary Tracheal Small Cell Carcinoma: A Systematized Review, Tony Satroplus Jun 2026

Primary Tracheal Small Cell Carcinoma: A Systematized Review, Tony Satroplus

Graduate Medical Education Research Journal

NA

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Associations Of Insurance Status, Frailty, And American Society Of Anesthesiologists On Endoscopic Transnasal Transsphenoidal Surgery, Steven Goicoechea Jun 2026

Associations Of Insurance Status, Frailty, And American Society Of Anesthesiologists On Endoscopic Transnasal Transsphenoidal Surgery, Steven Goicoechea

Graduate Medical Education Research Journal

NA

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A Rare Case Of Haemophilus Influenzae Shoulder Septic Arthritis In An Elderly Patient, Annemarie Leonard Jun 2026

A Rare Case Of Haemophilus Influenzae Shoulder Septic Arthritis In An Elderly Patient, Annemarie Leonard

Graduate Medical Education Research Journal

NA

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Inspiring Multilingual Children To Explore The Natural World Using Nature-Based Literature, Jennifer Hill, Deborah Wheeler Jun 2026

Inspiring Multilingual Children To Explore The Natural World Using Nature-Based Literature, Jennifer Hill, Deborah Wheeler

Journal of English Learner Education

Nature-based learning has become more popular in the United States since COVID-19, and incorporating nature-based learning including books about the natural world aligns with children’s natural curiosity, and desire to actively explore through play. Indeed, nature-based learning has many benefits ranging from supporting child development, promoting social-emotional well-being, and creating an inclusive learning environment for multilingual students. Reading nature-based literature and providing access to nature-based literature is a holistic methodology teachers should include in their classroom. This paper highlights the benefits of nature-based learning and provides a variety of topics and resources teacher can utilize.

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Sustained Reading: Multilingual Learners Exploring The Language Of A Text, Priscila J.B.M. Costa, Luciana C. De Oliveira, Meghan Love Jun 2026

Sustained Reading: Multilingual Learners Exploring The Language Of A Text, Priscila J.B.M. Costa, Luciana C. De Oliveira, Meghan Love

Journal of English Learner Education

In US schools, multilingual learners (MLs) are expected to meet the same rigorous disciplinary standards as their English-speaking peers while also developing proficiency in English. This article demonstrates how sustained reading, as part of the Teaching and Learning Cycle, can support MLs' access to disciplinary literacy through the explicit attention to language features. Grounded on the Language-based Approach to Content Instruction (LACI), the article presents a classroom vignette from a 5th-grade social studies lesson in which MLs dissected a mentor text. The lesson illustrates how code-breaking within sustained reading makes disciplinary language visible, helping students notice the linguistic choices common …

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