Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 18061 - 18090 of 5150034

Full-Text Articles in Entire DC Network

Sampling-Based Ground Truth Discovery For Data Asset Compliance Verification, Anonymous Jun 2026

Sampling-Based Ground Truth Discovery For Data Asset Compliance Verification, Anonymous

Defensive Publications Series

Sampling-based ground truth discovery for data asset compliance verification is disclosed. Regulated user identifiers are sampled from one or more monitored populations, including production sampling and/or controlled test users. For multiple target data assets across heterogeneous data store types, scannable columns are identified using heuristics, machine-learning predictions, and/or semantic annotations. Join queries are executed between sampled identifiers and target assets, including nested structures such as maps, arrays, and JSON, to obtain empirical observations of user data presence. Observations are evaluated against privacy expectations for asset groups using states including ALLOW, DISALLOW, ALLOW_EXCLUSIVE, and EXIST_DEFINITE, optionally incorporating dynamic carve-out and disallow …

Go to article

Privacy-Preserving Structured Logging Architecture For Semantic Artifacts In Machine Learning Inference Systems, Anonymous Jun 2026

Privacy-Preserving Structured Logging Architecture For Semantic Artifacts In Machine Learning Inference Systems, Anonymous

Defensive Publications Series

Structured logging techniques are described for natural-language reasoning artifacts produced during inference in LLM-based recommendation systems. A reasoning chain may be decomposed into structured fields at multiple sensitivity levels, including non-sensitive numerical fields, low-sensitivity categorical fields, a scrubbed rationale, and a raw rationale. Sensitivity classification may include a first layer detecting explicit personal identifiers using pattern matching and a second layer detecting semantically sensitive categories using a sensitive-category dictionary. Scrubbing replaces sensitive sentences with category-level abstractions. The structured fields are routed to different storage tiers with differential access controls, including audit-logged access for restricted tiers. A retention policy engine enforces …

Go to article

Semantic Profile Version-Based Cache Invalidation For Machine Learning Inference Systems, Anonymous Jun 2026

Semantic Profile Version-Based Cache Invalidation For Machine Learning Inference Systems, Anonymous

Defensive Publications Series

Systems and methods are described for caching large language model (LLM) reasoning outputs in recommendation services using semantic profile versioning. Cache entries map a key including user identifier, user profile version, and candidate identifier to a reasoning output that includes a score, tier, and rationale. A profile version increments in one embodiment when a profile update delta is classified as a semantic change, including preference additions/removals or profile regeneration, and optionally confidence updates that cross configured thresholds, while session and timestamp updates are treated as non-semantic. A two-level cache may be used with a session cache and a cross-session cache. …

Go to article

Runtime-Constructed Execution Graphs With Agent-Directed Path Selection And Constraint Enforcement For Serving Systems, Anonymous Jun 2026

Runtime-Constructed Execution Graphs With Agent-Directed Path Selection And Constraint Enforcement For Serving Systems, Anonymous

Defensive Publications Series

A serving system constructs and executes a directed acyclic graph (DAG) at runtime for each request based on an agent model’s tool-call decisions. The DAG is incrementally formed from base tools and intent-selected tools chosen using request intent, conversational context, and intermediate results. Runtime safety constraints are enforced, including a maximum path length, per-tool repetition bounds to avoid cycles, allowlist enforcement, and a token budget. A tool registry provides per-tool description, capability tags, and estimated token and latency metadata that may be used to filter or prioritize tool selection under remaining budgets. Execution may include multiple phases, with later phases …

Go to article

Write Amplification Management For Machine Learning-Based Collaborative Propagation Systems, Anonymous Jun 2026

Write Amplification Management For Machine Learning-Based Collaborative Propagation Systems, Anonymous

Defensive Publications Series

Techniques are described for managing write amplification in recommendation systems that use large language models (LLMs) to semantically extract preference insights from user interactions and to propagate insights to similar users. A propagation controller applies hierarchical quotas including interaction-level confidence gating, per-user limits, and a system-wide budget. Neighbor selection uses graph-topology-aware fan-out reduction based on user out-degree in a similarity graph, with neighbors ranked by similarity and top-K selected. Work is scheduled into a two-tier priority queue in which fresh interaction processing is handled with strict priority over propagated updates. Low-priority propagation tasks may be temporally batched for coalescing. A …

Go to article

Hybrid Serving Architecture With Cost-Benefit Routing For Llm-Based Recommendation Systems, Anonymous Jun 2026

Hybrid Serving Architecture With Cost-Benefit Routing For Llm-Based Recommendation Systems, Anonymous

Defensive Publications Series

A hybrid online serving architecture for recommendation systems selectively applies large language model (LLM) reasoning at a late ranking stage. A request is processed through retrieval and early-stage ranking to reduce candidates and produce confidence statistics. A cost-benefit routing orchestrator computes a routing score using traditional-model confidence, user value tier, budget utilization, latency constraints, and surface priority, and compares the routing score to a dynamically adapted threshold responsive to budget pressure, quality gap, and tail-latency pressure. Prior to LLM invocation, an ROI estimate is computed from expected quality lift and token cost, and the LLM is invoked only when ROI …

Go to article

Shadow Scoring System For Cost-Effective Llm Recommendation Experimentation, Anonymous Jun 2026

Shadow Scoring System For Cost-Effective Llm Recommendation Experimentation, Anonymous

Defensive Publications Series

A shadow scoring framework evaluates LLM-based recommendation ranking without serving LLM-ranked results to users. For enrolled requests, a production ranker serves a primary ranking while an LLM ranker computes a shadow ranking asynchronously off the serving critical path. The system logs paired observations including the primary ranking, the shadow ranking, and observed engagement with the served slate. Multi-level enrollment controls cost using persistent user hashing at a configurable rate, per-user request subsampling, and a daily token budget cap that pauses shadow scoring when exceeded. Counterfactual metrics are computed from logs, including NDCG comparison using observed engagement as relevance, promoted item …

Go to article

Per-User Kv Cache With Active User Windowing And Tiered Memory Placement For Billion-Scale Llm Recommendation, Anonymous Jun 2026

Per-User Kv Cache With Active User Windowing And Tiered Memory Placement For Billion-Scale Llm Recommendation, Anonymous

Defensive Publications Series

Techniques manage per-user attention key-value (KV) caches for large language model (LLM) recommendation serving at very large user scale. A cache manager maintains activity windows to bound the number of users whose KV cache tensors are retained. KV caches for users active within a hot window are stored in GPU high-bandwidth memory at higher precision, while KV caches for users active within a warm window are stored in host RAM in a more compact quantized format. Entries are demoted from the hot tier to the warm tier after a hot inactivity threshold using quantization, promoted back to the hot tier …

Go to article

Token-Aware Capacity Management With Multi-Level Budget Hierarchy For Llm-Based Recommendation Systems, Anonymous Jun 2026

Token-Aware Capacity Management With Multi-Level Budget Hierarchy For Llm-Based Recommendation Systems, Anonymous

Defensive Publications Series

Token-aware capacity management is described for LLM-based recommendation serving. A request token cost is estimated as a sum of token components including profile, candidate, context, and output tokens. Admission control evaluates the estimated cost against a four-level hierarchical token budget comprising a global budget, per-surface budgets, per-user budgets derived from user tiers, and a per-request cap. For sub-100 ms serving paths, serving instances perform budget checks and deductions using local leased counters obtained asynchronously from a central token pool, enabling constant-time decisions without network round-trips and non-blocking lease renewal. When budgets are insufficient, a graceful degradation orchestrator selects among full …

Go to article

Computer Vision Camera Sensor With Embedded Ai Features And Imu For Ultra-Low Power Adaptive Device Management, Anonymous Jun 2026

Computer Vision Camera Sensor With Embedded Ai Features And Imu For Ultra-Low Power Adaptive Device Management, Anonymous

Defensive Publications Series

A computer vision camera sensor module includes an image sensing element, embedded AI processing, and an inertial measurement unit (IMU) to support ultra-low power always-on operation and adaptive device management. The sensor executes AI algorithms locally to analyze visual data and IMU motion data, detect user activity and environmental change, and select among tracking modes including 3-DoF tracking for sedentary activity and 6-DoF tracking for standing or walking. Based on detected meaningful change, the sensor autonomously maintains other device sensors or cameras in standby or triggers their activation, while avoiding wake-up of a backend processor for decision-making. The module may …

Go to article

Cross-Platform Agent Contamination Detection And Containment, Anonymous Jun 2026

Cross-Platform Agent Contamination Detection And Containment, Anonymous

Defensive Publications Series

Cross-platform agent contamination detection and containment is disclosed for autonomous agents that operate across multiple recommendation platforms while sharing preference memory. Preferences are stored with structured provenance metadata identifying an origin platform and interaction context, optionally including cryptographic signatures, confidence tiers, influence tracking, and quarantine status. Cross-platform monitoring computes temporal and categorical correlation metrics, including influence scoring and causality-oriented tests, to determine whether acquisition on one platform predicts behavior change on another. When suspected contamination is detected, affected preferences are quarantined with platform-differentiated influence control, optionally including recursive quarantine of influenced preferences, while enabling independent local verification and cross-verification to …

Go to article

Modular Protocol Bridge Band For Enabling Certified Smartphone Communication In Neural Interface Wearables, Anonymous Jun 2026

Modular Protocol Bridge Band For Enabling Certified Smartphone Communication In Neural Interface Wearables, Anonymous

Defensive Publications Series

A replaceable wearable band includes an embedded protocol bridge module that enables certified smartphone communication for a wearable core device. The band carries a wired interface that provides power and data between the wearable core device and the protocol bridge module. The protocol bridge module includes an authentication coprocessor and a dual-mode Bluetooth system-on-chip and translates between a wired-device protocol and a certified accessory communication protocol carried over Bluetooth Classic to a smartphone. The wearable core device interacts with the protocol bridge module as a wired peripheral without implementing the certified accessory protocol. In some embodiments, the protocol bridge module …

Go to article

Dynamic Multi-Space Agent Rotation For Persistent Ai Assistants In Rotating Operational Roles, Anonymous Jun 2026

Dynamic Multi-Space Agent Rotation For Persistent Ai Assistants In Rotating Operational Roles, Anonymous

Defensive Publications Series

A single persistent AI agent operates across multiple operator-dedicated communication spaces and switches which space it actively serves according to external rotation schedule data. The agent maintains a unified domain knowledge base that persists across rotations and separate per-user profiles that customize behavior per operator. At rotation boundaries, the agent automatically generates a shift summary for an outgoing operator, stores open issue context into the unified domain knowledge base, switches active output routing to an incoming operator’s space, loads the incoming operator’s profile, sends a welcome briefing including open issues and recent patterns, and activates monitoring in the new space. …

Go to article

Convergent Memory Architecture - Unified Tiered Store For Embedding Vectors And Natural Language User Profiles, Anonymous Jun 2026

Convergent Memory Architecture - Unified Tiered Store For Embedding Vectors And Natural Language User Profiles, Anonymous

Defensive Publications Series

A unified tiered store serves both fixed-size embedding vectors and variable-size natural-language user profiles from a shared hierarchy including GPU HBM, host DRAM, and SSD-backed persistence. A single database instance maintains separate logical partitions for embeddings and profiles with different table formats and caching representations. Cache capacity is dynamically partitioned between data types based on observed workload rates and expected value sizes with smoothing and minimum reservations. A heterogeneous I/O scheduler prioritizes embedding SSD reads and reserves SSD service for embeddings while remaining work-conserving for profile reads. Cross-type cache coherence is provided by tracking associations between user identifiers and derived …

Go to article

Write-Amplification-To-Cache-Warming Conversion For Collaborative Memory Propagation In Recommendation Systems, Anonymous Jun 2026

Write-Amplification-To-Cache-Warming Conversion For Collaborative Memory Propagation In Recommendation Systems, Anonymous

Defensive Publications Series

Systems and methods are described for converting collaborative memory propagation write activity into selective cache warming for recommendation-serving data. Upon a propagation event that writes preference information for a target user, a controller estimates a warming value based on a return probability for the target user, cache hit probability, storage latency, and request frequency. The return probability may be elevated using social clustering signals including co-activity, tie strength, and temporal patterns. Subject to a storage bandwidth warming budget (e.g., token-bucket limited and traffic-health adaptive), the controller issues an asynchronous warming read that loads the target user embedding or profile data …

Go to article

Workload-Parameterized Performance Prediction Framework For Embedding Cache Optimization Triage, Anonymous Jun 2026

Workload-Parameterized Performance Prediction Framework For Embedding Cache Optimization Triage, Anonymous

Defensive Publications Series

A workload-parameterized performance prediction framework is described for triaging embedding cache optimizations across a fleet of machine-learning models. Workload parameters including a distribution parameter (alpha), reuse, table size N, batch size, and embedding dimension are input to a closed-form causal-chain model that predicts unique indices, cache misses across multiple cache levels, persistent-store reads, and storage I/O operations. A predicted latency is computed as a weighted combination of predicted access counts and latency terms, and a bottleneck class is identified. Each optimization is associated with a targeted segment of the causal chain and a maximum efficiency, enabling estimation of predicted speedups …

Go to article

Workload-Parameterized Performance Prediction Framework For Embedding Cache Optimization Triage, Anonymous Jun 2026

Workload-Parameterized Performance Prediction Framework For Embedding Cache Optimization Triage, Anonymous

Defensive Publications Series

A workload-parameterized performance prediction framework is described for triaging embedding cache optimizations across a fleet of machine-learning models. Workload parameters including a distribution parameter (alpha), reuse, table size N, batch size, and embedding dimension are input to a closed-form causal-chain model that predicts unique indices, cache misses across multiple cache levels, persistent-store reads, and storage I/O operations. A predicted latency is computed as a weighted combination of predicted access counts and latency terms, and a bottleneck class is identified. Each optimization is associated with a targeted segment of the causal chain and a maximum efficiency, enabling estimation of predicted speedups …

Go to article

Dual-Mode Cache Observability With Feedback-Driven Optimization For Machine Learning Embedding Stores, Anonymous Jun 2026

Dual-Mode Cache Observability With Feedback-Driven Optimization For Machine Learning Embedding Stores, Anonymous

Defensive Publications Series

Techniques are described for cache observability in machine-learning embedding stores using dual-mode measurement with non-interfering consumers. A monotonic, non-resetting vector of counters tracks total lookups and tier-specific hits. Monitoring derives cumulative hit rates via non-mutating reads of the counters. Profiling derives windowed hit rates by capturing counter snapshots at boundaries and computing deltas, enabling virtual resets and overlapping windows without resetting shared state. The same counters are exposed across multiple software layers (e.g., native code, graph execution, and scripting) using scalar loads and simple arithmetic. A feedback controller maintains a ring buffer of per-batch deltas to compute sliding-window hit rates, …

Go to article

Pipelined Asynchronous Prefetch With Decoupled Fill Queues And Completion-Order Processing For Storage-Backed Embedding Tables, Anonymous Jun 2026

Pipelined Asynchronous Prefetch With Decoupled Fill Queues And Completion-Order Processing For Storage-Backed Embedding Tables, Anonymous

Defensive Publications Series

Techniques are described for prefetching embeddings from SSD-backed storage using a decoupled pipeline. Cache misses are identified by a caller thread and enqueued to a miss queue. A dedicated reader stage dequeues misses, issues asynchronous multi-key storage reads, and forwards completed results individually to a fill queue in completion order rather than request order. A dedicated cache filler stage dequeues fill entries and inserts embedding values into one or more caches, optionally selecting fill work using a priority policy that accounts for urgency (e.g., embeddings awaited by a forward pass), access frequency, and batch position. The reader stage may adapt …

Go to article

Zipf-Aware Adaptive Cache Hierarchy Configuration For Storage-Backed Embedding Systems, Anonymous Jun 2026

Zipf-Aware Adaptive Cache Hierarchy Configuration For Storage-Backed Embedding Systems, Anonymous

Defensive Publications Series

Techniques are described for configuring a multi-tier cache hierarchy for SSD-backed embedding stores based on measured or predicted Zipf access skew. Live embedding index accesses are monitored to estimate a Zipf exponent using a sliding window and a Hill estimator, and temporal reuse may be estimated from inter-batch overlap. Cache sizes for multiple tiers are derived using closed-form Zipf working-set relationships for target hit rates, including handling for exponents greater than one and approximations near one. Cache sizes may be dynamically reconfigured at runtime using smoothed exponent estimates and a deviation threshold, without restarting processing; evicted entries from an upper …

Go to article

Low-Latency Pointwise Language Model Ranker With Token-Probability Normalization And Coordinated Batch Inference For Online Content Ranking, Anonymous Jun 2026

Low-Latency Pointwise Language Model Ranker With Token-Probability Normalization And Coordinated Batch Inference For Online Content Ranking, Anonymous

Defensive Publications Series

Techniques are described for low-latency pointwise content ranking using a fine-tuned student language model. A ranking service constructs per-candidate language-model inputs that include user-context signals and candidate item context, and sends the inputs to an inference server in coordinated batches. The inference server returns output values for designated positive and negative label tokens, reducing accelerator-to-host transfer. A continuous relevance score is computed using token-probability normalization, score = P(pos)/(P(pos)+P(neg)), yielding a calibrated value in [0,1] for thresholding and ranking. Batch processing may include reuse of cached key/value states for shared user-context prefixes. The techniques enable scoring hundreds of candidates within tight …

Go to article

Multi-Organ Living Donor Matching System Using Social Graph Embedding With Donation-Aware Bias And Heuristic Medical Compatibility Scoring, Anonymous Jun 2026

Multi-Organ Living Donor Matching System Using Social Graph Embedding With Donation-Aware Bias And Heuristic Medical Compatibility Scoring, Anonymous

Defensive Publications Series

A computer-implemented platform matches living organ donors with recipients using self-reported profile data and social-network data. A request is routed by organ type to kidney, liver, or bone marrow logic. A Friend2Vec graph embedding model constructs a weighted social graph, performs biased random walks, and trains a Skip-Gram network to produce user embeddings. Friend2Vec adds a donation-aware bias parameter that preferentially traverses edges associated with successful donation events. Embeddings are fused with medical and geographic attribute vectors using a learned fusion layer with layer normalization. A multi-layer perceptron predicts P(Donation | Donor, Recipient) from concatenated donor and recipient embeddings, their …

Go to article

Dual-Mode Adaptive Cache Management For Storage-Backed Embedding Systems, Anonymous Jun 2026

Dual-Mode Adaptive Cache Management For Storage-Backed Embedding Systems, Anonymous

Defensive Publications Series

Techniques are described for adaptive cache management in SSD-backed embedding table systems that serve both training and inference. A mode detection engine monitors access pattern features including access multiplicity, write frequency, batch sequentiality, and request concurrency, and classifies operation as training, inference, or hybrid. Based on the classification, a cache manager switches among mode-specific policies for locking, prefetch, eviction, and write-back. Training mode may use generation-based locking, DataLoader lookahead prefetch, distance-based eviction, and write-back of dirty entries on eviction. Inference mode may use reference-count locking to block eviction during active requests, request coalescing with index deduplication for prefetch, and frequency-weighted …

Go to article

Gpu-Storage I/O Isolation For Distributed Embedding Training Systems, Anonymous Jun 2026

Gpu-Storage I/O Isolation For Distributed Embedding Training Systems, Anonymous

Defensive Publications Series

A GPU-storage I/O isolation architecture is described for distributed embedding training with SSD-backed embedding tables. Storage-related callbacks are registered using a host function launch mechanism that holds a GPU driver mutex only during enqueue, allowing blocking key-value store reads to execute on a CPU thread without stalling other CUDA streams. SSD read completions are delivered through a condition-variable-backed fill queue that wakes a filler thread without polling. Cache eviction is overlapped with prefetch using a double-buffer eviction manager that alternates buffers across training steps while a background thread writes dirty entries back to SSD. A stream isolation scheduler assigns dedicated …

Go to article

Distributed Cache Coherence Protocol For Tiered Embedding Storage Across Multi-Node Training Systems, Anonymous Jun 2026

Distributed Cache Coherence Protocol For Tiered Embedding Storage Across Multi-Node Training Systems, Anonymous

Defensive Publications Series

A multi-node training system uses tiered embedding storage with a high-bandwidth memory cache and local persistent storage on each node. Each node maintains a probabilistic cache directory, such as a Bloom filter, that encodes identifiers of cached embedding rows and periodically exchanges the directory with peer nodes, optionally using delta updates. Upon a local cache miss, a fetch decision engine consults received directories to identify candidate peers and selects between a one-sided RDMA read from a peer’s cache and a local persistent-store read based on estimated costs and load limits. Remotely retrieved embeddings are accepted subject to a staleness threshold …

Go to article

Pipelined Double-Buffer Eviction Scheduling With Gpudirect Storage For Zero-Copy Tiered Embedding Management, Anonymous Jun 2026

Pipelined Double-Buffer Eviction Scheduling With Gpudirect Storage For Zero-Copy Tiered Embedding Management, Anonymous

Defensive Publications Series

Techniques are described for tiered embedding management in which embedding rows are evicted from GPU memory to NVMe SSDs and prefetched from SSDs into GPU memory using GPU-direct storage DMA that bypasses CPU memory. Two page-aligned GPU-resident buffers are registered with a GPU-direct storage interface and are alternated by a double-buffer scheduler such that one buffer serves embedding access for model computation while the other buffer performs I/O. Eviction uses GPU-direct writes from GPU memory to SSD, and prefetch uses GPU-direct reads into a GPU-resident buffer followed by scatter to target GPU addresses. A batch coalescing layer groups small per-row …

Go to article

Object Redaction For High-Resolution Images Using Geometrically Aligned Generative Patches, Mira Leung Jun 2026

Object Redaction For High-Resolution Images Using Geometrically Aligned Generative Patches, Mira Leung

Defensive Publications Series

Redacting objects from high-resolution, wide-aspect-ratio images, such as panoramas, can be challenging, as certain automated methods may introduce resolution degradation, unpredictable cropping, or visual artifacts. Systems and methods can utilize an iterative pipeline that combines object detection with constrained generative inpainting and geometric alignment. A process may, for example, identify and mask target objects, provide a version of the image with blurred masked areas to a generative model for inpainting, and then geometrically align the generative model's output to correct for distortions. The aligned output can be used as a patch, selectively compositing the generated content into the masked regions …

Go to article

Ai-Driven Runtime Software Adaptation With Ai Judge Validation And Sandboxing, Mira Leung Jun 2026

Ai-Driven Runtime Software Adaptation With Ai Judge Validation And Sandboxing, Mira Leung

Defensive Publications Series

Static, pre-compiled software applications can be rigid, limiting their ability to provide dynamic, personalized user experiences and often requiring slow, resource-intensive update cycles. A system is described that can enable software to adapt its functionality or user interface at runtime. The system may utilize an architecture of artificial intelligence (AI) agents to generate code or configuration modifications based on high-level goals. A feature of this system can be an automated validation loop where an AI-based judge evaluates these modifications against a governance corpus of rules for security and correctness. Approved modifications can then be executed within a sandboxed environment in …

Go to article

Multi-Agent Ai System For Human-In-The-Loop Document Packet Verification, Bhavani Sankar Sikakolli, Kalyan Konidala, Pinkesh Badjatiya, Yogesh Indoria, Gourabdip Ghosh, Siddhant Kurmi, Aryan Singh, Tilottama Basu, Farhan Rawani, Sangram Mohite Jun 2026

Multi-Agent Ai System For Human-In-The-Loop Document Packet Verification, Bhavani Sankar Sikakolli, Kalyan Konidala, Pinkesh Badjatiya, Yogesh Indoria, Gourabdip Ghosh, Siddhant Kurmi, Aryan Singh, Tilottama Basu, Farhan Rawani, Sangram Mohite

Defensive Publications Series

Verification of document packets for regulatory compliance may rely on manual review, which can be inefficient and susceptible to error. Some automated tools may not adequately compare data across diverse structured forms and unstructured evidence within a packet. A multi-agent artificial intelligence system can use specialized agents, which may leverage large language models, to automate processes such as the ingestion, classification, extraction, and validation of information. The system can employ a configurable source-of-truth hierarchy and multi-tiered matching logic to validate data. A human-in-the-loop mechanism can present flagged discrepancies or unverified fields to a human specialist for review and adjudication. This …

Go to article

The Substrate Pushback Principle: Replacing Particle Dark Matter With Holographic Confinement Fields, Christopher L. Eckes Jun 2026

The Substrate Pushback Principle: Replacing Particle Dark Matter With Holographic Confinement Fields, Christopher L. Eckes

Defensive Publications Series

This paper introduces the Substrate Pushback Principle, a theoretical framework that replaces particle-centric dark matter models with an emergent informational cosmology. Instead of introducing hypothetical ultra-light bosons, we demonstrate that galactic dark matter halos can be modeled as non-local confinement fields generated by the holographic limits of spacetime. By mapping the baryonic stress-energy tensor to an information-load metric bounded by Bekenstein limits, we show that high-density galactic cores trigger an inward, geometric stabilization pressure from the spacetime substrate. This computational surface tension inherits the exact macroscopic wave mechanics of contemporary Fuzzy Dark Matter (FDM) models. Furthermore, by defining the substrate's …

Go to article

First Prev Next Last