AI4OS: AI-Driven Enhancements in OS

• AI4OS is a paradigm that integrates AI techniques—like ML, deep learning, and LLMs—into OS design to achieve adaptive resource management, hierarchical memory, and secure interfaces.
• Key methodologies include agent-driven kernel re-architectures, real-time scheduling with ML predictors, and decentralized system protocols that deliver significant performance gains.
• This approach enables personalized user experiences through advanced memory retention, reduced latency in process scheduling, and transparent ethical frameworks for open science.

AI-Driven Enhancements for OS (AI4OS) denote the systematic integration of artificial intelligence—encompassing classical machine learning, deep learning models, LLMs, agents, and AI-specific memory primitives—into the foundational layers and ecosystem interfaces of operating systems. This paradigm is characterized by the transformation of traditional resource management, memory, scheduling, user interaction, security, and knowledge translation into adaptive, self-optimizing, and personalized subsystems, frequently assessed via rigorous empirical benchmarks and formal mathematical abstractions. Key research directions include hierarchical memory management for agents, LLM/agent-centric OS re-architectures, AI-powered kernel interfaces, composable execution of distributed AI workloads, agentic UI control, protocol-level decentralization, and open science-oriented multi-agent ethics.

1. Hierarchical Memory and Personalization for AI Agents

One fundamental challenge in agentized operating systems is effective management of memory across both short-lived context and long-term personalization. MemoryOS (Kang et al., 30 May 2025) demonstrates an OS-inspired, three-tier hierarchical memory:

• Short-Term Memory (STM): Implements a FIFO queue (e.g. length L=7) of interaction pages $(Q_i, R_i, T_i, \text{meta}^{chain}_i)$, supporting turn-by-turn dialogue coherence.
• Mid-Term Memory (MTM): Maintains segments via semantic–keyword similarity score

$$\mathcal{F}_\text{score}(p,s) = \cos(e_s,e_p) + \frac{|K_s\cap K_p|}{|K_s\cup K_p|}$$

and promotes segments with "heat" exceeding threshold $\tau$, formalized as

$$Heat = \alpha N_\text{visit} + \beta L_\text{interaction} + \gamma R_\text{recency}$$

where $R_\text{recency} = \exp(-\Delta t/\mu)$.

• Long-Term Personal Memory (LPM): Encodes user and agent persona traits, dynamic factual KBs, and static profiles, supporting up to 90-dimensional trait vectors for nuanced personalization.

Retrieval interfaces fuse STM, MTM, and LPM via competitive similarity and embedding mechanisms. Experimental results on the LoCoMo benchmark indicate +49.11% F1 and +46.18% BLEU-1 improvements over SOTA baselines, with superior contextual coherence and memory retention. This advances agent-based OS memory far beyond naive text history caches.

2. Operating System Redesigns: LLMs, Agent Ecosystems, and AIOS

Recent frameworks reconceptualize the OS kernel and app layers as LLM-driven or agent-driven substrates. The AIOS "LLM as OS" formalism (Ge et al., 2023) defines

$\text{AIOS} = (K, M, F, T, UI, A)$

where the kernel $K$ is a parametrized LLM, $M$ a context window memory, $F$ long-term indexed storage, $T$ hardware/software tools, $UI$ a prompt-driven interface, and $A$ a set of Agent Applications (AAPs). This abstraction supports natural language as the core programming interface (NLProg), delegating fine-grained tool invocation, memory extension, and ecosystem governance (SDK, AppStore-style registries) to LLM planning agents. Agents may interact physically (robotics), digitally (APIs), or collaboratively/adversarially within multi-agent societies. The roadmap emphasizes dynamic memory management, agent DSLs, security (sandboxed tool calls), and human-centered interfaces.

Prompt-to-OS (P2OS) (Tolomei et al., 2023) extends this vision with a model-orchestrator architecture: user prompts are parsed, dispatched to generative models (LLMs, diffusion) and rendered as text, speech, or synthesized GUIs; the boundary between applications and OS services is dissolved. Personalization is achieved by updating user preference vectors

$$P_{u,\mathrm{new}} = (1-\eta) P_{u,\mathrm{old}}+\eta F_\mathrm{interaction}$$

and multi-modal neural datastores. This raises new challenges of data privacy, provenance, and explainable trust scores in end-user OS workflows.

3. Kernel-Level AI Integration and Adaptive Scheduling

Research on kernel-level ML (Safarzadeh et al., 2021, Zhang et al., 2024, Singh et al., 1 Aug 2025) details the embedding of AI modules into process scheduling, memory management, I/O handling, and security. Techniques include:

• I/O scheduler ML: Support Vector Machine predictors, lightweight block-layer neural nets (LinnOS) embedded in kernel space, yielding tail latency reductions up to 60% with sub-50µs inference overhead.
• CPU scheduling: MLP-based load balancing and RL (Q-learning, DQN, OSML) for resource allocation, achieving 12–18% turnaround reductions and 40% improvement in QoS compliance under microservice loads.
• Memory/cache: RL agents (RLCache) and LSTM predictors for adaptive replacement, boosting hit rates by 5–15%.
• Security: LSTM and CNN classifiers for run-time anomaly detection and malware suppression, yielding 95–97% detection at minimal syscall latency cost.

Composable kernel architectures (Singh et al., 1 Aug 2025) treat Loadable Kernel Modules (LKMs) as first-class AI computation units, integrate AVX-512 floating-point math, DMA zero-copy buffers, ML-aware dynamic scheduling, and a neurosymbolic logic layer formalized with category theory and homotopy type theory, supporting sub-ms module compute and scheduler jitter reduction.

Table: Kernel-Space ML Performance (from (Singh et al., 1 Aug 2025))

Task	User-space	AI-Kernel	Speedup
Matrix Mult (256³)	2.30 ms	0.68 ms	3.4×
CNN (ResNet-18)	25.4 ms	8.1 ms	3.1×
GPU Offload Latency	1.2 ms	0.4 ms	3.0×

4. Agent-Based User Interaction with General Computing Environments

OS Agents, particularly those built atop multi-modal LLMs (M)LLMs (Hu et al., 6 Aug 2025, Zhang et al., 2024), automate user requests across GUI-centric operating environments. The mathematical formalism adopts a POMDP framework

$M = \langle S, A, T, R, \Omega, O, \gamma \rangle$

with multimodal observations (screenshots, DOM trees), high-dimensional action spaces, and step-wise/iterative planning strategies (chain-of-thought, ReAct, CoAT). Grounding mechanisms map text/vision to GUI controls for robust execution; retrieval of memories and planning are fused for compositional and personalized actions.

UFO (Zhang et al., 2024) implements a dual-agent system for Windows OS control, with application selection and action specification grounded via GPT-Vision and the Windows UI Automation API. Empirical results report an 86% success rate over nine principal Windows applications, far surpassing GPT-3.5 (24%) and GPT-4 (42%) surrogates. Limitations include dependency on supported controls and vision model OCR stability.

Advanced agents such as ColorAgent (Li et al., 22 Oct 2025) introduce multi-agent frameworks, step-wise RL with GRPO gradient optimization, and personalized intent models (dual-branch Transformers, low-rank deployment adaptation), achieving 77.2% and 50.7% SOTA success rates on AndroidWorld and AndroidLab, respectively.

5. AI4OS for Autonomous, Distributed, and Decentralized Systems

AI-driven OSes expand to edge robotics, aviation, and decentralized cloud fabrics:

• CyberCortex.AI (Grigorescu et al., 2024): A distributed OS for autonomous robots executes functional units ("Filters") within a DataBlock, each with temporal addressable memory (TAM), supporting zero-copy data sharing and adaptive, urgency-predictor DNN-based scheduling. Field deployments yield semantic segmentation latency reductions from 29ms to 16ms, CPU/GPU load reductions, and 20% tracking accuracy improvement in autonomous vehicles.
• OrinFlight OS (Tan et al., 2024): Aviation OS with distributed kernel/middleware; features mixed-integer CPU/GPU optimization, real-time preemptive scheduler (rate monoticity, EDF), hardware-accelerated AES-256/ECDH security, and modular vision/navigation/fusion/coordination services. Benchmarked at 50 FPS vision pipelines, 15ms real-time path-planning, and <2s recovery from faults with system availability A > 0.999998.
• Ratio1 (Damian et al., 5 Sep 2025): A meta-OS protocol for global AI model orchestration, using blockchain-backed authentication (dAuth, ERC-721 Node Deeds), distributed CRDT key–value stores (CSTORE), IPFS-derived content addressing (R1FS), decentralized PBFT-based container orchestration (Deeploy), and homomorphic federated learning (EDIL). The token-economic model incentivizes node availability (PoA) and validated AI workflow completion (PoAI), supporting near-linear throughput scaling and sub-10% overhead for encrypted pipelines.

6. Methodologies, Toolchains, and Evaluation Pipelines

Integration of AI with OS necessitates unified toolchains, modular interfaces, and methodical evaluation protocols (Zhang et al., 2024). Real-time kernel inference uses eBPF hooks, optimized quantized model libraries, and CUDA offloading. AI modules are deployed through pipelines

1. Train → Quantize → Compile → Deploy → Infer → Monitor → Retrain

with continuous performance monitoring, audit logging, and federated learning strategies. Metrics include throughput, latency (mean/P₉₉), predictive accuracy, resource overhead, and model drift.

Benchmarks span web (MiniWoB, WebArena), mobile (AndroidWorld), desktop (OfficeBench), and system agent contexts, emphasizing step-level accuracy and task completion.

7. Knowledge Translation, Open Science, and Ethical Paradigms

AI4OS also figures in epistemic frameworks for open knowledge translation (Yakaboski et al., 2023). AI4OS systems are defined as multi-agent, provenance-rich knowledge translators, with stages for data curation, information extraction, and pattern labeling. Openness is formalized by the metric

$$\max\,\Bigl[\bigl\|\bigcup_{i,j,\ell} k_{ij\ell} \cap K\bigr\|-\bigl\|\bigcup_{i,j,\ell} k_{ij\ell}\cap K^c\bigr\|\Bigr]$$

mandating maximal net true knowledge yield. Ethical imperatives involve transparent datasheets, open algorithmic provenance, and inclusive participation to mitigate siloing, bias, and audit failures.

Conclusion

AI-driven enhancements for OS (AI4OS) span hierarchical memory architectures, LLM/agent kernels, kernel-embedded ML, multimodal agentic user control, decentralized scheduling, composable kernel structures, and ethical multi-agent open science. Quantitative evaluation across benchmarks confirms empirical gains in memory retention, task automation accuracy, latency, and throughput. The ongoing evolution toward AI-native, agent-driven, and privacy-preserving operating systems is governed by principles of modularity, auditability, extensibility, and personalization, drawing upon a rigorously formalized and empirically validated multi-disciplinary foundation.