Kairos: Adaptive, Time-Critical Systems

Updated 3 July 2026

Kairos is a multifaceted framework characterized by dynamic resource allocation across real-time hardware controllers, distributed storage, machine learning models, and security analytics.
It leverages precise synchronization and adaptive scheduling to achieve sub-100 ns interrupt latencies, throughput gains up to 3.1×, and state-of-the-art forecasting performance.
Kairos enables efficient resource management and robust security analytics through just-in-time inference, real-time intrusion detection, and context-driven control mechanisms.

Kairos refers to a diverse set of systems, frameworks, hardware prototypes, and methodologies across computing, machine learning, systems design, and infrastructure management, unified by a focus on time-critical, context-aware, or dynamic resource allocation and control. Below, the key instances and conceptual underpinnings of "Kairos"—as reflected by contemporary research—are systematically described.

1. Hardware Systems: Kairos as Real-Time RISC-V Controller for 2.5D Chiplet-Based SoCs

Kairos is the silicon demonstrator of ControlPULPlet, a purpose-built real-time multi-core RISC-V controller supporting both monolithic and 2.5D chiplet-based system integration (Ottaviano et al., 2024). Fabricated in TSMC’s 65nm CMOS process, it manifests a 32-bit CV32RT manager core (max 290 MHz, 30 mW) and an optimized microarchitectural pipeline:

Manager Domain: Features the CV32RT core with context save/restore, shadow-register banking, and tail-chaining for sub-100 ns interrupt latency, all interrupts dispatched via a Core-Local Interrupt Controller (CLIC).
Scratchpad Memory & DMA: 448 KiB SPM directly accessible to the manager; multi-dimensional DMA engine (up to 4 nested loops) for automated, periodic off-chip and on-chip data movement.
Die-to-Die (D2D) Link: A source-synchronous, DDR-signaled PHY providing up to 128-bit packetized AXI4 transfer at up to 51.2 Gb/s duplex (f=200 MHz, α≈0.83 bus utilization), with only 16.5 kGE area overhead per channel.
Predictive Control: Deterministically schedules MPC loops, utilizing DMA for sensor sampling and hard-real-time solver execution, completing workloads such as a 10-state MPC within a 500 μs cycle with >90% slack.

Physical integration allows Kairos to serve as a real-time chiplet controller in heterogeneous, high-bandwidth system environments (Ottaviano et al., 2024).

2. Distributed Systems: Kairos for Scale-Out Transactional Key-Value Stores

KAIROS in distributed transactional storage denotes a system that uses precise synchronized clocks to facilitate inter-transaction cache self-invalidation and sharded transaction validation for scalable throughput (Misra et al., 2020). Core principles:

Inter-Transaction Caching: Utilizes client-side value/version/lease-expiry triads for each cached item, coupled with precise physical clocks (e.g., PTP, <1 μs skew) to bound staleness via TTL leases.
Self-Invalidating Leases: Lease duration d is dynamically chosen per key to balance expected hit rate and probability of staleness, leveraging global write rates and client-local read rates.
Sharded Validation: Each client co-locates validators, adapting Centiman’s watermarking protocol, scaling validation load independently of data storage tier.
Performance: Delivers 2.35× throughput over intra-transaction-only caching, 3.1× with sharded validation, and 1.46× over explicit invalidation-based schemes under skewed, read-heavy workloads.

Unlike traditional callback/invalidation approaches, KAIROS achieves high cache hit rates and scalable serializability without sharer lists or invalidation messages, bounded by synchronously set lease durations (Misra et al., 2020).

3. Machine Learning: Kairos for Time Series Foundation Models and Non-Autoregressive Forecasting

Two major frameworks leverage the KAIROS name in time series machine learning:

Unified Non-Autoregressive Time Series Forecasting: KAIROS is a foundation model that predicts segment-wise, multi-modal distributions without left-to-right autoregressive loops, using a sparse mixture-of-experts decoder and exogenous latent vectors for scenario diversity (Ding et al., 2 Oct 2025).
- Architecture: Adaptive grained patch encoder, segment-level MoE, segment causal residuals, allowing just-in-time (JIT) inference with inference time constant in forecast horizon.
- Empirical Results: Achieves state-of-the-art or near-SOTA results on zero-shot benchmarks at a lower inference cost than autoregressive baselines, with wall-clock time decoupled from output length.
Adaptive TSFM with Instance-Adaptive Tokenization and Position Embedding: KAIROS implements mixture-of-size dynamic patching and instance-adaptive RoPE, using a large-scale, predictability-stratified pretraining corpus (PreSTS, 300+ billion points) (Feng et al., 30 Sep 2025).
- Features: Instance-tailored granularity in patching and positional encoding, multi-patch forecasting for horizon flexibility, with robustness across domains and time scales.
- Performance: Surpasses larger TSFM models on GIFT-Eval and TSLib with far fewer parameters, evidencing that adaptive temporal representation is a key to generalization.

Both frameworks establish KAIROS as a paradigm for scalable, adaptive, and highly parallelizable foundation models suitable for real-world, heterogeneous time series (Ding et al., 2 Oct 2025, Feng et al., 30 Sep 2025).

4. Systems and Network Infrastructure: Kairos for SLO-Aware Scheduling and Multi-Agent Serving

Kairos designates multiple distinct systems in modern inference serving, telecommunication, and resource orchestration:

SLO-Aware Scheduling in Disaggregated LLM Inference: Employs urgency-based priority scheduling during prefill and slack-guided adaptive batching during decode to minimize head-of-line blocking and maximize SLO (Time-to-First-Token, TPOT) attainment (Wang et al., 4 May 2026). At QPS 3.0, yields +33.8 pp gain in end-to-end SLO attainment over strong baselines.
Multi-Agent Workflow Serving in LLM Applications: Features workflow-aware latency profiling, Wasserstein/MDS-embedded agent-level prioritization, and memory-packing dispatch to minimize GPU OOM and preemptions under excessive load and agent heterogeneity (Chen et al., 9 Aug 2025). Reduces end-to-end latency by 17.8–72.8%.
Timing-Induced Failure Testing in LTE/5G Control Planes: Lightweight testbed that injects timing perturbations to trigger state machine anomalies (interleaving, nested, incomplete) without reference to specification documents (Guo et al., 29 May 2026). Identified 20 novel vulnerabilities and 34 known bugs across 4 cores.

These systems exhibit a central Kairos principle: explicit, context-driven scheduling or orchestration in environments characterized by non-uniform request profiles, stringent time constraints, and resource contention (Wang et al., 4 May 2026, Chen et al., 9 Aug 2025, Guo et al., 29 May 2026).

5. Optimization and Resource Management: Kairos for Power, Cost, and Data Valuation

Kairos is associated with several resource optimization frameworks:

Cost-Efficient ML Inference on Heterogeneous Cloud Hardware: KAIROS runtime separates offline pool optimization (closed-form bounds on QPS under a cost budget) from per-arrival, QoS-aware bipartite matching; achieves up to 2× higher throughput than homogeneous pooling and up to 70% better than state-of-the-art scheduling algorithms (Li et al., 2022).
Power-Efficient Agentic AI Serving: Context-aware runtime that tracks per-agent memory, throughput, and context growth to adapt GPU frequency, per-instance concurrency, and multi-instance placement, maximizing power savings while meeting strict token throughput SLOs; achieves average power reductions of 27% and up to 46% in multi-instance settings (Yuan et al., 17 Apr 2026).
Data Valuation via MMD Influence: KAIROS (Kernel-Agnostic Influence for Robust Outlier Scoring) ranks data points by their influence on distributional divergence between training and a clean reference, using closed-form MMD influence. Supports $O(n^2)$ offline and $O(mn)$ online updates, delivering $>80\%$ detection of poisoning/mislabel/corruption within $<10\%$ inspection (Zhu et al., 30 Jun 2025).

Each instance implements analytically tractable, model-agnostic, or context-sensitive strategies for cost, power, or data valuation, establishing KAIROS as a universal framework for dynamic, large-scale resource control (Li et al., 2022, Yuan et al., 17 Apr 2026, Zhu et al., 30 Jun 2025).

6. Specialized Application Domains: Physical AI, World Models, and Auction Market Structures

Specialized Kairos deployments span physical intelligence and cryptoeconomic systems:

Physical AI Serving and World Modeling: Kairos is both a scalable serving system tailored for physical AI—integrating execution-aware scheduling and horizon adaptation for robots—and a native world model stack that combines cross-embodiment curriculum, hybrid temporal attention, and deployment-aware inference (Dai et al., 12 May 2026, Team et al., 15 Jun 2026). Achieves up to 2.67× longer planning horizons at the same accuracy and state-of-the-art results on long-horizon embodied benchmarks, with strong efficiency-capability trade-offs.
O-RAN Radio Unit Energy Control: Joint ASM (Advanced Sleep Mode) and radio scheduling policy, optimized via dimensionality-invariant and distributional-critic-based RL in an xApp, produces empirically proven energy savings of 15%–72% on commercial RUs while keeping QoS violation rates minimal (Lozano et al., 27 Jan 2025).
Just-in-Time Auction Markets: In Arbitrum’s Timeboost mechanism, Kairos functions as a secondary-market intermediary, winning bulk access and reselling it in micro-auctions, which collapses primary auction competitiveness (capture ratio $\rho$ drops from 62.7% to 14.8%) and shifts surplus away from the protocol (Öz et al., 20 Mar 2026).

These domains exemplify Kairos as a system for context-adaptive control, learning, or market equilibrium in real-world, time-sensitive, or adversarial settings (Dai et al., 12 May 2026, Team et al., 15 Jun 2026, Lozano et al., 27 Jan 2025, Öz et al., 20 Mar 2026).

7. Intrusion Detection, Provenance Analysis, and Explainability

KAIROS also denotes practical, attack-agnostic, real-time provenance-based intrusion detection systems (Cheng et al., 2023, Dhanuka et al., 20 Dec 2025):

Stream-Based Provenance IDS: Temporal GNN encoder–decoder scoring edge-level anomaly in dynamic provenance graphs, with automated attack reconstruction (community detection yields compact attack DAGs). Demonstrates 100% recall and near-zero false positives across DARPA and real-world datasets.
Explainability Layer (PROVEX): Integrates post-hoc explainers (GraphMask, GNNExplainer, VA-TGExplainer) on KAIROS outputs, surfacing concise subgraph explanations with uncertainty estimates, at negligible overhead (3–5 s per event), improving SOC analyst trust and triage without altering detection performance.

This KAIROS instance meets all four PIDS desiderata (scope, attack-agnosticity, timeliness, reconstruction), combining streaming GNN inference and explainability for operational-scale forensics (Cheng et al., 2023, Dhanuka et al., 20 Dec 2025).

Kairos embodies a unifying theme of timely, context- or state-aware adaptation—spanning real-time hardware, storage consistency, ML forecasting, inference serving, network robustness, power and cost optimization, data valuation, market design, and security analytics. Across domains, Kairos systems typically outperform prior solutions by exploiting synchrony, multi-granular state tracking, and provable guarantees in regimes of contention, resource heterogeneity, or dynamism.