KRONOS: A Polysemous Technical Benchmark

• KRONOS is a polysemous technical label defined differently across fields—ranging from a benchmark star in astrophysics to engineered systems in finance and biomedical modeling.
• In stellar studies, Kronos (HD 240430) is a chemically anomalous benchmark star whose detailed differential analysis reveals significant refractory enhancements indicative of planetary engulfment.
• Engineered KRONOS systems underpin foundation models in quantitative finance, proteomics, embedded dependability, and sharding blockchain consensus, demonstrating innovative multi-domain applications.

KRONOS is a recurrent research designation rather than a single object. In recent literature it denotes, among other things, a chemically anomalous solar-type star in a benchmark wide binary, a financial time-series foundation model, a patient-specific graph-LLM for oncology, a foundation model for spatial proteomics, a JWST program on young sub-Neptune systems and stellar surface heterogeneity, a FreeRTOS fault-injection framework, a sharding blockchain consensus, and an atomic-data resource for charge-exchange X-ray modeling. The term is therefore best understood as a polysemous technical label whose meaning is fixed by disciplinary context.

1. Taxonomic scope of the name

The usages of KRONOS span observational astrophysics, machine learning, biomedical modeling, dependable systems, distributed consensus, and X-ray atomic-data infrastructure. In some cases the term is a proper name or nickname, as for the star HD 240430; in others it is an acronym, as in “Knowledge Representation of patient Omics Networks in Oncology via Structured tuning” and “Keys to Revealing the Origin and Nature Of sub-neptune Systems”; elsewhere it names a software framework or data package (Miquelarena et al., 2024, Adam et al., 26 Sep 2025, Murphy et al., 2 Jun 2026, Mannella et al., 26 Mar 2026, Liu et al., 2024, Mullen et al., 2017).

Domain	Referent	Core role
Stellar astrophysics	HD 240430	Metal-rich component of the Krios–Kronos binary
Financial ML	Kronos	Token-based foundation model for K-line data
Oncology and proteomics	KRONOS	Graph-LLM and spatial-proteomics foundation models
Exoplanet science	KRONOS program	JWST program on young sub-Neptune systems
Systems and infrastructure	KRONOS	FI framework, sharding consensus, and CX data resource

A useful distinction is between referential and architectural uses. In stellar work, Kronos is a particular star. In finance, oncology, spatial proteomics, RTOS dependability, and blockchain research, KRONOS is an engineered system. In exoplanet spectroscopy, it is a coordinated observing program. In X-ray astrophysics, it is a curated backend for charge-exchange cross sections and line ratios.

2. Kronos as an astrophysical benchmark star

In stellar astrophysics, Kronos is HD 240430, the more metal-rich component of the wide binary HD 240429/30, whose companion is HD 240429, nicknamed Krios. The system is a wide binary of G-type dwarfs with projected separation $\approx 11{,}300\,\mathrm{au}$, and the 2024 high-precision differential study derives for Kronos $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$, $\log g = 4.44 \pm 0.06\ \mathrm{dex}$, $[\mathrm{Fe/H}] = +0.220 \pm 0.007$, and $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$. For Krios the corresponding parameters are $T_\mathrm{eff} = 5892 \pm 52\ \mathrm{K}$, $\log g = 4.49 \pm 0.08\ \mathrm{dex}$, $[\mathrm{Fe/H}] = -0.010 \pm 0.010$, and $$v_\mathrm{turb} = 1.18 \pm 0.06\ \mathrm{km\,s^{-1}}$$, giving a differential metallicity $$\Delta[\mathrm{Fe/H}] = -0.230 \pm 0.005\ \mathrm{dex}$$ for Krios minus Kronos (Miquelarena et al., 2024).

This abundance difference is astrophysically remarkable because the stars are near twins in $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$0, $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$1, and age, yet Kronos is enriched in Fe and many refractory elements relative to Krios. The 2024 analysis measured 26 elements with high-S/N, high-resolution MAROON-X spectra using a full line-by-line differential technique, ATLAS12 non-solar-scaled opacities, and FUNDPAR + MOOG. The abundance pattern shows a strong condensation-temperature trend, with $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$2 and $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$3, and with slopes of $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$4 for all elements and $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$5 for refractories (Miquelarena et al., 2024).

Lithium is the second defining anomaly. The 2024 study gives $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$6 for Kronos and $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$7 for Krios, so $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$8. The earlier discovery paper reported the same system as the largest metallicity difference found in a wide binary pair yet, with HD 240430 enhanced by $T_\mathrm{eff} = 5895 \pm 66\ \mathrm{K}$9 dex in refractory elements and by $\log g = 4.44 \pm 0.06\ \mathrm{dex}$0 dex in lithium relative to HD 240429, and interpreted the nickname “Kronos” as a metaphor for a star that appears to have “eaten” rocky planetary material (Oh et al., 2017).

The current interpretation is that diffusion and primordial chemical differences are insufficient, and that planetary engulfment is a plausible explanation. The 2017 work suggested accretion of $\log g = 4.44 \pm 0.06\ \mathrm{dex}$1 of rocky material; the 2024 analysis, using the terra code and a present-day convection-zone mass $\log g = 4.44 \pm 0.06\ \mathrm{dex}$2, found a best-fit engulfment mass of $\log g = 4.44 \pm 0.06\ \mathrm{dex}$3, decomposed as $\log g = 4.44 \pm 0.06\ \mathrm{dex}$4 Earth-masses of terrestrial material plus $\log g = 4.44 \pm 0.06\ \mathrm{dex}$5 Earth-masses of meteoritic/chondritic material (Oh et al., 2017, Miquelarena et al., 2024). TESS photometry found no transiting planets around either star, so Kronos remains primarily a chemical benchmark: a stringent test case for planet ingestion signatures and for the limits of chemical tagging in co-natal binaries.

3. KRONOS as a foundation-model label in quantitative finance and biomedicine

In quantitative finance, Kronos is a decoder-only Transformer TSFM tailored to financial K-line data. It tokenizes each OHLCVA bar with Binary Spherical Quantization into coarse and fine subtokens and trains autoregressively on a corpus of over 12 billion K-line records from 45 global exchanges. The model is evaluated in zero-shot settings for price forecasting, return forecasting, realized volatility forecasting, synthetic K-line generation, and portfolio backtesting; on benchmark datasets it boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline, achieves a 9% lower MAE in volatility forecasting, and improves generative fidelity for synthetic K-line sequences by 22% (Shi et al., 2 Aug 2025).

A separate MNQ study clarifies the data-scale assumptions behind that result. It implements a deliberately simplified, Kronos-inspired tokenization-plus-sequential-modeling pipeline on 72,604 five-minute OHLCV bars and 944 sessions of a single futures instrument, using gradient boosting and a one-layer LSTM with 16 hidden units. No configuration produces statistically significant out-of-sample accuracy above the 51.80% base rate; the best gradient boosting model reaches $\log g = 4.44 \pm 0.06\ \mathrm{dex}$6 in one fold but has permutation-test $\log g = 4.44 \pm 0.06\ \mathrm{dex}$7, while the LSTM has $\log g = 4.44 \pm 0.06\ \mathrm{dex}$8. The paper’s central conclusion is that four years of single-instrument five-minute OHLCV data are insufficient for reliable sequential ML-based intraday forecasting, so the Kronos design does not transfer without the multi-million-bar scale and diversity of the foundation-model regime (Mesfin, 18 May 2026).

In oncology, KRONOS denotes a graph-LLM that integrates patient-specific proteomics with protein-protein interaction structure. The framework constructs, for each patient, a graph $\log g = 4.44 \pm 0.06\ \mathrm{dex}$9 on STRING v11 topology, encodes it with a GNN such as GraphSAGE, GAT, or GIN, maps the graph embedding into the Vicuna 7B v1.5 token space via a dense connector, and then performs two-stage structured instruction tuning. The associated CPTAC-PROTSTRUCT dataset contains 354,812 schema-alignment QA pairs and 26,157 clinical-reasoning QA pairs derived from CPTAC proteomics. On benchmark tasks, the best configuration, Vicuna7b + GAT PPI graph encoder, achieves mortality-prediction AUC $[\mathrm{Fe/H}] = +0.220 \pm 0.007$0, cancer-type-classification AUC $[\mathrm{Fe/H}] = +0.220 \pm 0.007$1, overall-survival C-index $[\mathrm{Fe/H}] = +0.220 \pm 0.007$2, and stage-classification AUC $[\mathrm{Fe/H}] = +0.220 \pm 0.007$3 (Adam et al., 26 Sep 2025).

In spatial proteomics, KRONOS is a ViT-S/16 foundation model trained self-supervised on over 47 million image patches covering 175 protein markers, 16 tissue types, and 8 fluorescence-based imaging platforms. Its architecture uses shared convolutional token embedding across channels, sinusoidal marker encodings, spatial positional encodings, and DINO-v2–style self-distillation plus masked image modeling. The model is explicitly designed for segmentation-free patch-level processing and is evaluated across 11 independent cohorts, where it achieves state-of-the-art performance in cell phenotyping, treatment-response prediction, and multi-scale retrieval; in DLBCL-2 cell phenotyping, for example, KRONOS reaches balanced accuracy $[\mathrm{Fe/H}] = +0.220 \pm 0.007$4 (Shaban et al., 3 Jun 2025).

4. KRONOS as a JWST exoplanet and stellar-surface program

In exoplanet science, KRONOS is the JWST GO 5959 program “Keys to Revealing the Origin and Nature Of sub-neptune Systems.” Its stated aim is to connect young, inflated planets to the compact super-Earth/sub-Neptune population by measuring primordial atmospheric composition, atmospheric escape signatures, and the evolution of the mass–metallicity relation with age. KRONOS I presents the $[\mathrm{Fe/H}] = +0.220 \pm 0.007$5 NIRISS/SOSS transmission spectrum of the $[\mathrm{Fe/H}] = +0.220 \pm 0.007$6 Myr planet V1298 Tau c and reports an H$[\mathrm{Fe/H}] = +0.220 \pm 0.007$7O detection with $[\mathrm{Fe/H}] = +0.220 \pm 0.007$8 volume mixing ratio $[\mathrm{Fe/H}] = +0.220 \pm 0.007$9, together with an inferred atmospheric metallicity $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$0 the Solar value. The paper emphasizes that this metallicity is similar to literature measurements for other young planets, including V1298 Tau b, and systematically lower than the metallicities of mature planets of similar mass and temperature (Murphy et al., 2 Jun 2026).

KRONOS II uses the same V1298 Tau JWST dataset for stellar physics. From NIRISS/SOSS transit observations of the 20–30 Myr planets V1298 Tau bcd, it identifies 14 starspot crossing events across two visits and derives $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$1 spot-contrast spectra. The contrasts can only be explained when umbral and penumbral components are modeled separately, yielding $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$2, $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$3, $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$4, and an umbral area fraction of $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$5. The study also combines JWST with long-baseline LCOGT photometry to infer at least 5 additional large unocculted active regions (Murphy et al., 15 Jun 2026).

Taken together, KRONOS I and II define the program in two coupled senses. First, KRONOS is a comparative exoplanet survey of young systems. Second, it is a stellar-heterogeneity program, because starspot substructure is directly relevant to transmission spectroscopy through the transit light source effect. The V1298 Tau analyses therefore use the same observations to constrain both planetary atmospheres and stellar surface morphology.

5. KRONOS in dependable systems and blockchain consensus

In dependable embedded systems, KRONOS is a software-based, non-intrusive post-propagation Fault Injection framework for FreeRTOS. It operates in the hosted Linux and Windows ports, injects transient and permanent faults into scheduler-related global variables, kernel pointers, lists, and current-task TCB fields, and does so without specialized hardware or debug interfaces. The reported campaign uses 666 injections per location for a total of 83,916 injections. Overall outcome distributions are similar for transient and permanent faults: 70.16% benign and 20.43% crash for transient faults, and 69.66% benign and 20.82% crash for permanent faults. The corruption of pxCurrentTCB is especially severe, with 100% of injections leading to crashes, whereas many TCB fields have only limited impact on system availability (Mannella et al., 26 Mar 2026).

In blockchain systems, Kronos is a sharding consensus framework for secure and efficient cross-shard transactions. Its central mechanism is a buffer managed jointly by shard members: valid transactions are transferred to the payee via the buffer, while invalid ones are rejected through happy or unhappy paths. The protocol is proved to achieve atomicity under malicious clients with optimal intra-shard overhead $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$6, where $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$7 is the involved shard number and $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$8 is a Byzantine fault tolerance cost, and to achieve reliable cross-shard transfer with overhead $$v_\mathrm{turb} = 1.18 \pm 0.04\ \mathrm{km\,s^{-1}}$$9, where $T_\mathrm{eff} = 5892 \pm 52\ \mathrm{K}$0 is shard size, $T_\mathrm{eff} = 5892 \pm 52\ \mathrm{K}$1 is the number of transactions, and $T_\mathrm{eff} = 5892 \pm 52\ \mathrm{K}$2 is the security parameter. The implementation uses asynchronous Speeding Dumbo and partial synchronous Hotstuff, scales to thousands of consensus nodes, and reports 320 ktx/sec throughput with 2.0 sec latency, together with up to a 12* improvement in throughput and a 50% reduction in latency relative to past solutions (Liu et al., 2024).

These two KRONOS systems occupy different layers of the stack—kernel fault tolerance versus distributed consensus—but they share a similar design ambition: to make failure modes explicit and analyzable. In the RTOS setting the target is kernel-visible state corruption; in the blockchain setting it is cross-shard atomicity under malicious clients and Byzantine nodes.

6. Other technical uses and cross-domain patterns

In X-ray astrophysics, Kronos is a charge-exchange atomic-data package and database rather than a fitting frontend. The relevant study states that the CX data sets are incorporated into the modeling packages SPEX and Kronos, and describes Kronos as the repository through which recommended charge-exchange cross sections and X-ray line ratios are stored and exported for spectral modeling. The paper extends its coverage to collisions between bare and H-like ions of C, N, O, Ne, Na, Mg, Al, and Si and the cometary neutrals H, H$T_\mathrm{eff} = 5892 \pm 52\ \mathrm{K}$3O, CO, CO$T_\mathrm{eff} = 5892 \pm 52\ \mathrm{K}$4, OH, and O, then uses those data in SPEX to model the XMM-Newton RGS spectrum of Comet C/2000 WM1 and constrain the H to H$T_\mathrm{eff} = 5892 \pm 52\ \mathrm{K}$5O ratio in the cometary atmosphere (Mullen et al., 2017).

Across these usages, KRONOS does not denote a stable disciplinary concept. It is instead a high-level label repeatedly attached to benchmark objects, data infrastructures, or architectures that mediate between heterogeneous inputs and a final scientific or engineering decision. This suggests a family resemblance rather than a unified definition: stellar abundances are mediated by differential chemical analysis, financial prices by hierarchical tokenization, proteomic state by graph or marker-aware embeddings, cross-shard state by a jointly managed buffer, and CX spectra by certified line-ratio tables. The encyclopedic significance of KRONOS therefore lies in its polysemy: it is a name reused for technically ambitious systems that expose latent structure in otherwise difficult observational, computational, or transactional settings.