Tempus: Temporal Structures Across Domains

• Tempus is a cross-domain motif that highlights research where temporal ordering and timing precision are central, from AI temporal reasoning to nanosecond X-ray event detection.
• It encompasses varied methodologies including temporal-unary computation in edge accelerators, iterative graph execution in GEMM frameworks, and rigorous ETL processes for unified academic systems.
• Studies under the Tempus label provide actionable insights on enhancing temporal precision, optimizing throughput, reducing power consumption, and abstracting time from relevant dynamic changes.

to=arxiv_search.search 大发官网 买天天中彩票"^^^^^^^^24: 24^^^^^^^^"{ OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^" 24 OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^" OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^^^^^", ^^^^^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^" 24^^^^^^^^^^^^24query\24: 24^^^^, ^^^^^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^" 24 OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^" ^^^^^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^" 24 OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^"

to=arxiv_search.search 玩北京赛车 彩神争霸可以json {"^^^^^^^^24query24 "^^^^^^^^24query\24: 24^^^^^^^^" 24^^^^^^^^^^^^24query\24: 24^^^^, "^^^^^^^^24query\24query\24 24^^^^^^^^" OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^^^^^", "^^^^^^^^24query\24max_results\24 24^^^^^^^^" to=arxiv_search.search 植物百科通 聚利ൻjson {"^^^^^^^^24query24 "^^^^^^^^24query\24: 24^^^^^^^^" 24^^^^^^^^^^^^24query\24: 24^^^^, "^^^^^^^^24query\24query\24 24^^^^^^^^" OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^^^^^", "^^^^^^^^24query\24max_results\24 24^^^^^^^^" to=arxiv_search.search รับเงินบาท 天天中彩票官方 json {"^^^^^^^^24query24 OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^"Remember This Event That Year? Assessing Temporal Information and Reasoning in LLMs^^^^^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^^^^^"", "^^^^^^^^24query\24: 24^^^^^^^^" 24^^^^^^^^^^^^24submittedDate\24 "^^^^^^^^24query\24query\24 24^^^^^^^^"relevance", "^^^^^^^^24query\24max_results\24 24^^^^^^^^" In the cited literature, Tempus is not a single unified construct but a recurring label for distinct research objects organized around time, temporality, or temporal efficiency. The name appears in work on numerical temporal knowledge and reasoning in LLMs, a Timepix^^^^24sort_by\24^^^^-based event-driven X-ray detector, low-precision edge deep-learning accelerators, a resource-invariant GEMM streaming framework for Versal AI Edge, the Tempus project “iKnow” for academic administration, AI-augmented histopathologic review within Tempus Labs, and theoretical analyses of temporal centrality and Machian time [(&&&^^^^24query4^^^^&&&); (&&&^^^^24: 24^^^^&&&); (&&&^^^^24max_results4^^^^&&&); (&&&^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^&&&); (&&&^^^^24submittedDate\24^^^^&&&); (&&&^^^^24descending\24^^^^&&&); (&&&^^^^24sort_by\24^^^^&&&); (&&&^^^^24sort_order\24^^^^&&&)].

^^^^24query\24^^^^. Uses of the name in the research literature

The common denominator across these usages is an explicit concern with temporal structure^^^^^^^^24: 24^^^^^^^^**time-indexed factual recall*, **timestamped event detection, **temporal-unary computation, **temporal scaling, or **time as an abstraction from change*. At the same time, the cited works are methodologically independent and arise from different research communities.

Domain	Tempus referent	Core function
LLM evaluation	TempUN and temporal reasoning work	Numerical temporal knowledge and reasoning
Photon science	TEMPUS detector	Photon counting and event-driven time-stamping
Edge DLA hardware	Tempus Core	Temporal-unary-binary convolution in NVDLA-compatible form
Edge SoC GEMM	Tempus framework	Resource-invariant temporal GEMM streaming
Academic software	Tempus project “iKnow”	Student-administration modernization and ETL migration
Digital pathology	Tempus Labs SmartPath	DNA-yield and macrodissection decision support
Network science and physics	Tempus Fugit; Machian time	Temporal betweenness; time abstracted from change

This distribution suggests that Tempus functions less as a canonical technical term than as a cross-domain naming motif for systems or theories in which temporal order, timing resolution, or temporal abstraction is central.

^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^. Temporal knowledge and reasoning in LLMs

One important strand associated with a “Tempus” or temporality ^^^^^^^^24query24^^^^^^^^ is the study of how LLMs retain, retrieve, and reason over time-indexed numerical facts. “Remember This Event That Year? Assessing Temporal Information and Reasoning in LLMs” distinguishes Temporal knowledge—answering specific time-indexed facts correctly—from Temporal reasoning—inferring patterns, comparisons, extrema, aggregates, and trends from multiple time points (&&&^^^^24query\24^^^^&&&). The work introduces TempUN, curated from Our World in Data (OWD) and aligned with major UN global issue categories, spanning ^^^^24query\24: 24^^^^,^^^^24: 24: 24: 24^^^^ BCE to ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24query\24: 24: 24^^^^ CE. Its instances have the form

PRESERVED_PLACEHOLDER_^^^^24: 24^^^^

with PRESERVED_PLACEHOLDER_^^^^24query\24^^^^ as country name, PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ as issue subcategory, and PRESERVED_PLACEHOLDER_^^^^24max_results\24^^^^ as a list of year-value pairs PRESERVED_PLACEHOLDER_^^^^24sort_by\24^^^^. Samples are formed as

PRESERVED_PLACEHOLDER_^^^^24submittedDate\24^^^^

The reported scale is ^^^^24sort_by\24sort_order\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^,^^^^24query24all:Tempus24sort_by\24^^^^ instances, ^^^^24all:Tempus24^^^^,^^^^24sort_by\24all:Tempus24descending\24 24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ samples, ^^^^24query\24: 24sort_order\24^^^^ subcategories, and ^^^^24query24^^^^ major categories. The filtered experimental subset TempUNs contains ^^^^24query\24^^^^,^^^^24all:Tempus24: 24descending\24^^^^ instances and ^^^^24query\24: 24sort_by\24^^^^,^^^^24query\24max_results\24: 24^^^^ samples, selected so each category has at least ^^^^24descending\24sort_order\24^^^^ continuous years of data between ^^^^24query\24all:Tempus24sort_by\24descending\24^^^^ and ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ and exhibits meaningful temporal dynamics. The benchmark operationalizes six MCQ categories^^^^^^^^24: 24^^^^^^^^DB-MCQs, CP-MCQs, WB-MCQs, RB-MCQs, MM-MCQs, and TB-MCQs, covering year spans from one to ten years. Evaluation is reported through three labels^^^^^^^^24: 24^^^^^^^^Correct, Incorrect, and Not Available / Information Not Available. Distractors are generated by

PRESERVED_PLACEHOLDER_^^^^24sort_order\24^^^^

The main experiments evaluate six base models^^^^^^^^24: 24^^^^^^^^**phi-^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^**, mistral-instruct, llama-^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^-chat, gpt-^^^^24max_results\24^^^^.^^^^24submittedDate\24, gpt-^^^^24sort_by\24^^^^, and gemini-pro. The paper notes that a larger count can be obtained only by including fine-tuned variants under Yearwise Fine-tuning, Continual Learning, and Random Fine-tuning. The empirical findings separate two failure modes^^^^^^^^24: 24^^^^^^^^**knowledge gaps*, where a model indicates that information is unavailable, and **incorrect responses, where it answers wrongly. In zero-shot evaluation, closed-source models produce “not available” outputs about *^^^^24query24^^^^.^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^%** of the time on average, while open-source models do so about ^^^^24max_results\24^^^^.^^^^24sort_by\24sort_order\24 of the time. Open-source models therefore guess more often, whereas closed-source models are more uncertainty-aware. Performance also degrades for older years and distant history. Fine-tuning reduces incorrect generations for open-source models, but also increases “Information not available” responses, and the rate of correct generations does not improve substantially.

In this usage, temporality is not merely metadata. It is the central axis along which model competence is probed^^^^^^^^24: 24^^^^^^^^year-specific retrieval, cross-year comparison, windowed sequence reconstruction, range aggregation, extrema identification, and trend reasoning.

^^^^24max_results\24^^^^. TEMPUS as a Timepix^^^^24sort_by\24^^^^-based X-ray detector

In photon science, TEMPUS denotes a DESY-developed X-ray detector system built around the Timepix^^^^24sort_by\24^^^^ ASIC, intended as a next-generation replacement for the LAMBDA/Medipix^^^^24max_results\24^^^^-based detector family (&&&^^^^24: 24^^^^&&&). The initial implementation is a single-chip prototype system designed to accelerate deployment to beamline users while still exploiting Timepix^^^^24sort_by\24^^^^’s larger active area. Compared with Medipix^^^^24max_results\24^^^^, Timepix^^^^24sort_by\24^^^^ is about ^^^^24max_results\24^^^^.^^^^24submittedDate\24 larger in area, retains a ^^^^24submittedDate\24submittedDate\24^^^^ µm pixel pitch, uses a ^^^^24sort_by\24sort_by\24query24^^^^ × ^^^^24submittedDate\24query\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ pixel matrix, and supports TSV (through-silicon via) technology with ^^^^24sort_by\24^^^^-side buttability. It also provides ^^^^24query\24sort_order\24^^^^ high-speed GWT links, each capable of up to ^^^^24query\24: 24^^^^.^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24sort_by\24^^^^ Gb/s in principle; the TEMPUS performance discussion uses ^^^^24submittedDate\24^^^^.^^^^24query\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ Gb/s per link, for a total potential throughput of ^^^^24query24: 24^^^^ Gb/s.

The detector has two operating modes. In photon counting mode, each above-threshold pulse is counted into ^^^^24query24^^^^-bit or ^^^^24query\24sort_order\24^^^^-bit counters under continuous read-write (CRW) operation, with frame rates up to ^^^^24sort_by\24: 24^^^^ kfps. In event-driven time-stamping mode, each hit is transmitted individually with ToA (Time-of-Arrival), ToT (Time-over-Threshold), and pixel address. The ToA uses ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24: 24^^^^ ps binning, and ToT provides coarse energy information with about ^^^^24query\24^^^^ keV resolution. The target regime is moderate to low flux X-ray measurements where timing matters, particularly nuclear resonance scattering (NRS) and X-ray photon correlation spectroscopy (XPCS) on sub-microsecond timescales.

The prototype includes a single-chip carrier board designed at DESY, a Timepix^^^^24sort_by\24^^^^ ASIC mounted near one edge of the board, a Xilinx Zynq UltraScale+ MPSoC evaluation board (HTG-Z^^^^24all:Tempus24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^) for control and readout, a housing with fans and thermal vias, and a DAQ server receiving data over ^^^^24query\24: 24: 24^^^^ GbE. The carrier board uses Megtron^^^^24sort_order\24^^^^ and routes high-speed traces for ^^^^24submittedDate\24^^^^.^^^^24query\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ Gb/s operation. Data from the FPGA is sent to the DAQ PC over Firefly optical fibers using UDP.

Experimental characterization was performed at PETRA III, beamline P^^^^24: 24query\24^^^^, and ESRF beamline ID^^^^24query\24sort_by\24^^^^. At PETRA III, with a ^^^^24max_results\24: 24: 24^^^^ µm-thick p-on-n silicon sensor, photons near ^^^^24query\24sort_by\24^^^^.^^^^24sort_by\24 keV for PRESERVED_PLACEHOLDER_^^^^24descending\24^^^^Fe studies, and a timing mode with ^^^^24sort_by\24: 24^^^^ electron bunches per revolution, the bunch spacing was about ^^^^24query\24all:Tempus24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ ns. Because of leakage-current issues, the bias voltage was limited to about ^^^^24query\24: 24: 24^^^^ V. The timing performance was estimated at about ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24^^^^ ns, and the measured FWHM time resolutions were ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^.^^^^24max_results\24^^^^ ns for the high-energy photons and ^^^^24all:Tempus24^^^^.^^^^24descending\24 ns for the low-energy photons. At ESRF, using a similar ^^^^24max_results\24: 24: 24^^^^ µm-thick p-on-n sensor but with bias up to ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24: 24^^^^ V, one high-speed data link at ^^^^24query\24^^^^.^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24query24^^^^ Gb/s supported a maximum event rate of about PRESERVED_PLACEHOLDER_^^^^24query24^^^^ hits/s, and the measured FWHM time resolutions improved to ^^^^24query\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^.^^^^24query\24^^^^ ns for the high-energy photons and ^^^^24query24^^^^.^^^^24submittedDate\24 ns for the low-energy photons.

A central correction step is the time-walk effect. TEMPUS uses the correlation between ToT and ToA^^^^^^^^24: 24^^^^^^^^a function is fitted to the ToT–ToA correlation, a ToA correction is derived from ToT, and corrected timestamps yield a narrower timing distribution. The PETRA III ToT spectrum showed two groups, around ^^^^24max_results\24: 24: 24^^^^ ns ToT and ^^^^24query\24query\24: 24: 24^^^^ ns ToT, attributed mainly to Fe KPRESERVED_PLACEHOLDER_^^^^24all:Tempus24^^^^ fluorescence at ^^^^24sort_order\24^^^^.^^^^24sort_by\24 keV and ^^^^24query\24sort_by\24^^^^.^^^^24sort_by\24 keV scattered photons, with a cut at about ^^^^24sort_order\24submittedDate\24: 24^^^^ ns ToT used for energy separation.

The first results establish that event-based X-ray time stamping works in practice with Timepix^^^^24sort_by\24^^^^, that the detector can resolve storage-ring bunch structure in the nanosecond regime, and that the current timing performance is limited mainly by the sensor, not the ASIC. Planned improvements include electron-collecting silicon sensors, possibly LGADs, higher-PRESERVED_PLACEHOLDER_^^^^24query\24: 24^^^^ materials such as GaAs or CdTe, use of all ^^^^24query\24sort_order\24^^^^ links at ^^^^24submittedDate\24^^^^.^^^^24query\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ Gb/s, and eventual multi-chip modules.

^^^^24sort_by\24^^^^. Tempus in edge AI hardware

In edge inference hardware, Tempus names two distinct architectures^^^^^^^^24: 24^^^^^^^^**Tempus Core*, a temporal-unary convolution engine for NVDLA-like accelerators, and **Tempus, a temporally scalable GEMM framework for **AMD Versal AI Edge* (&&&^^^^24max_results\24^^^^&&&, &&&^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^&&&). Both reject brute-force spatial growth as the dominant scaling strategy, but they do so in different ways.

Tempus Core is a temporal-unary-binary (tub) convolution core intended as a drop-in replacement for NVDLA’s convolution core (CC). It replaces the original CC with a modified CSC, a PCU (PE Cell Unit) replacing the CMAC, and the same style of CACC interface. The PCU is organized as a PRESERVED_PLACEHOLDER_^^^^24query\24query\24^^^^ PE array, and each multiplier is a tub multiplier. The design relies on ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^s-unary encoding, in which each unary bit/cycle is interpreted as a value of ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^. The paper contrasts worst-case latency for tuGEMM,

PRESERVED_PLACEHOLDER_^^^^24query\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^

with tubGEMM,

PRESERVED_PLACEHOLDER_^^^^24query\24max_results\24^^^^

and adapts the same temporal-unary-binary philosophy to convolution. Dataflow compliance is preserved through the relation

PRESERVED_PLACEHOLDER_^^^^24query\24sort_by\24^^^^

Evaluation uses ^^^^24sort_by\24submittedDate\24^^^^nm CMOS, Synopsys Design Compiler, Cadence Innovus, the NanGate^^^^24sort_by\24submittedDate\24^^^^ standard cell library, and a fixed ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24submittedDate\24: 24^^^^ MHz clock. At the full-core level, the PCU shows ^^^^24submittedDate\24all:Tempus24^^^^.^^^^24max_results\24 area reduction and ^^^^24query\24submittedDate\24^^^^.^^^^24max_results\24 power reduction relative to NVDLA’s CMAC. For a PRESERVED_PLACEHOLDER_^^^^24query\24submittedDate\24^^^^ PE array, INT^^^^24query24^^^^ results report ^^^^24descending\24submittedDate\24^^^^% area reduction and ^^^^24sort_order\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^% power savings, with ^^^^24submittedDate\24^^^^x iso-area throughput improvement for INT^^^^24query24^^^^ and ^^^^24sort_by\24^^^^x iso-area throughput improvement for INT^^^^24sort_by\24^^^^. Post-place-and-route analysis for a PRESERVED_PLACEHOLDER_^^^^24query\24sort_order\24^^^^ array in ^^^^24sort_by\24submittedDate\24^^^^nm CMOS gives ^^^^24: 24^^^^.^^^^24: 24query\24sort_order\24query24^^^^ mmPRESERVED_PLACEHOLDER_^^^^24query\24descending\24^^^^ area and ^^^^24sort_order\24^^^^.^^^^24query\24query\24sort_by\24sort_order\24 mW power for Tempus Core, versus ^^^^24: 24^^^^.^^^^24: 24max_results\24sort_order\24query\24^^^^ mmPRESERVED_PLACEHOLDER_^^^^24query\24query24^^^^ and ^^^^24query\24: 24^^^^.^^^^24descending\24: 24query\24max_results\24^^^^ mW for the CMAC Core. The paper also profiles MobileNetV^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ and ResNeXt^^^^24query\24: 24query\24^^^^, obtaining average INT^^^^24query24^^^^ latencies of ^^^^24max_results\24max_results\24^^^^ cycles and ^^^^24max_results\24query\24^^^^ cycles, respectively, under ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^s-unary encoding.

The ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24sort_order\24^^^^ Tempus framework addresses a different problem^^^^^^^^24: 24^^^^^^^^GEMM acceleration for LLM inference on the AMD Versal AI Edge VE^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24: 24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ SoC. Its defining principle is resource-invariant temporal scaling. Rather than increasing hardware resources with matrix size, it uses a fixed compute block of ^^^^24query\24sort_order\24^^^^ AIE-ML cores and scales through iterative graph execution, algorithmic data tiling and replication in programmable logic, cascade streaming, and a deadlock-free DATAFLOW protocol. The host computes

PRESERVED_PLACEHOLDER_^^^^24query\24all:Tempus24^^^^

and replication factors are defined as

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24^^^^

The cascade interface is ^^^^24submittedDate\24query\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^-bit wide on AIE-ML and supports partial-sum reduction at Initiation Interval PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24query\24^^^^.

On the XCVE^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24: 24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^-^^^^24query\24^^^^LSESFVA using AMD Vitis ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24sort_by\24^^^^.^^^^24query\24^^^^ and the AMD XPE tool, the framework achieves ^^^^24sort_order\24: 24descending\24^^^^ GOPS at ^^^^24query\24: 24^^^^.^^^^24sort_order\24descending\24descending\24^^^^ W total on-chip power for PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ INT^^^^24query\24sort_order\24^^^^ GEMM, with ^^^^24max_results\24^^^^.^^^^24submittedDate\24max_results\24descending\24 ms core computation latency, ^^^^24: 24^^^^.^^^^24: 24: 24^^^^% URAM, ^^^^24: 24^^^^.^^^^24: 24: 24^^^^% DSP, ^^^^24sort_order\24^^^^.^^^^24query\24sort_order\24 LUT, ^^^^24sort_order\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^.^^^^24submittedDate\24query24^^^^% BRAM, and ^^^^24descending\24^^^^.^^^^24sort_order\24submittedDate\24 CLB registers. Relative to ARIES, it reports ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24query\24query\24^^^^.^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^× higher prominence factor, ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^.^^^^24: 24^^^^× core frugality, ^^^^24descending\24^^^^.^^^^24query\24 power frugality, and a ^^^^24sort_order\24^^^^.^^^^24max_results\24 reduction in I/O demand. The paper’s central claim is that on a resource-limited edge SoC, temporal reuse can be more sustainable than large spatial arrays.

Taken together, these two hardware meanings of Tempus show a consistent architectural theme^^^^^^^^24: 24^^^^^^^^keep the surrounding deployment ecosystem intact, constrain resource growth, and push scalability into temporal scheduling, streaming, or unary time-domain encoding.

^^^^24submittedDate\24^^^^. Tempus in institutional software and clinical laboratory workflows

A separate use of the name occurs in academic information systems through the Tempus project “iKnow” and in molecular diagnostics through Tempus Labs [(&&&^^^^24submittedDate\24^^^^&&&); (&&&^^^^24descending\24^^^^&&&)]. In both settings, Tempus denotes organizational infrastructure rather than a generic theory of time.

In the iKnow case, the problem is migration from EURM’s legacy Student Administration Application (SAA) to the new iKnow database. The paper frames the work explicitly as an ETL (Extract, Transform, Load) process. The source side uses three separate MSSQL databases, one for each study cycle; the target is one unified MSSQL database. The migration therefore requires consolidation, rekeying, and preservation of relationships. The authors identify five main problems^^^^^^^^24: 24^^^^^^^^**foreign key constraints and loading order*, **target tables with no source equivalents, **preserving existing IDs, **distinguishing records by study cycle, and **same IDs, different entities across source databases. The prescribed *“populate by priority”** rule loads tables with no foreign keys first and dependent tables afterward. Free-text fields such as Nationality, Community, and Countries are normalized via SELECT DISTINCT. Where source IDs should be preserved, the paper uses SQL Server’s SET IDENTITY_INSERT: PRESERVED_PLACEHOLDER_^^^^24max_results\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ To consolidate the three-database architecture, the target introduces StudyCycles, and key translation is handled through mapping tables with fields NewKey, OldKey, and DBID. The ProgrammesCourses example shows how joins against ProgrammesKeys and CoursesKeys reconstruct target-side foreign keys. The case study’s broader significance is that successful Tempus-related software adoption depends not only on the new application itself, but also on careful ETL-style migration of legacy data into the new unified academic database.

Within Tempus Labs, the name denotes an operational context for SmartPath, an AI-augmented pathologist-in-the-loop system for optimizing DNA yield and tumor purity from FFPE slides. SmartPath uses a scanned ^^^^24sort_by\24: 24^^^^x H&E whole-slide image, a multi-field-of-view convolutional network with a ResNet-^^^^24query\24query24^^^^ backbone for tumor and lymphocyte region identification, and a U-Net-based model for nuclei detection. It extracts ^^^^24max_results\24^^^^,^^^^24sort_by\24sort_order\24query\24 features per slide across cell counts, tumor shape, cell nucleus shape, and cell nucleus texture. DNA yield per slide is predicted by a regularized linear regression model trained on ^^^^24query\24^^^^,^^^^24sort_order\24: 24submittedDate\24^^^^ slides, selected using ^^^^24max_results\24max_results\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ CRC slides, with final model choice log transform + L^^^^24query\24^^^^ regularization strength ^^^^24: 24^^^^.^^^^24: 24query\24^^^^ and validation correlation R = ^^^^24: 24^^^^.^^^^24query24query\24query24^^^^; the abstract summarizes predicted-vs-true correlation as R = ^^^^24: 24^^^^.^^^^24query24submittedDate\24^^^^. The number of slides to scrape is computed by

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24^^^^

then rounded down to the nearest integer, with UI operating points at ^^^^24query\24: 24: 24^^^^ ng, ^^^^24sort_by\24: 24: 24^^^^ ng, and ^^^^24query\24: 24: 24: 24^^^^ ng.

The internal validation trial enrolled ^^^^24submittedDate\24: 24query\24^^^^ clinical colorectal cancer slides, with a main analysis set of ^^^^24sort_by\24descending\24sort_order\24^^^^ samples^^^^^^^^24: 24^^^^^^^^**^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24max_results\24max_results\24^^^^ Trad** and ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24sort_by\24max_results\24^^^^ SmartPath. The primary result is an increase in first extractions landing in the ^^^^24query\24: 24: 24^^^^–^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24: 24: 24^^^^ ng target range^^^^^^^^24: 24^^^^^^^^**Trad 24^^^^^^^^^^^^24: 24^^^^.^^^^24submittedDate\24sort_order\24^^^^ ± ^^^^24: 24^^^^.^^^^24: 24sort_order\24sort_by\24^^^^**, SmartPath^^^^^^^^24: 24^^^^^^^^^^^^24: 24^^^^.^^^^24descending\24: 24^^^^ ± ^^^^24: 24^^^^.^^^^24: 24submittedDate\24query24^^^^, P = ^^^^24: 24^^^^.^^^^24: 24: 24submittedDate\24^^^^, described as a ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24submittedDate\24^^^^% relative increase. The improvement came mainly from fewer overshoots^^^^^^^^24: 24^^^^^^^^**Trad 24^^^^^^^^^^^^24: 24^^^^.^^^^24max_results\24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^ ± ^^^^24: 24^^^^.^^^^24: 24sort_order\24: 24^^^^**, SmartPath^^^^^^^^24: 24^^^^^^^^^^^^24: 24^^^^.^^^^24query\24query24^^^^ ± ^^^^24: 24^^^^.^^^^24: 24sort_by\24all:Tempus24^^^^, P = ^^^^24: 24^^^^.^^^^24: 24: 24query\24^^^^. Overall T-seq was not significantly improved, but for small, low-quality samples the result was Trad^^^^^^^^24: 24^^^^^^^^^^^^24sort_order\24 24^^^^ ± ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^.^^^^24descending\24descending\24^^^^ days, SmartPath^^^^^^^^24: 24^^^^^^^^^^^^24sort_by\24 ± ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24^^^^.^^^^24: 24sort_order\24^^^^ days, P = ^^^^24: 24^^^^.^^^^24: 24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24submittedDate\24^^^^. Covariate analysis found major imbalances for pathologist, extraction day-of-week, and extraction tech, and identified tissue area and extraction quality as dominant predictors for several outcomes. In this setting, Tempus denotes a laboratory environment in which AI is used not to replace specialist review but to make tissue-input decisions more quantitative.

^^^^24sort_order\24^^^^. Temporal structure as a theoretical and methodological problem

Two further uses of the Tempus motif are conceptual rather than infrastructural^^^^^^^^24: 24^^^^^^^^temporal centrality in social networks and Machian time in classical and quantum gravity [(&&&^^^^24sort_by\24^^^^&&&); (&&&^^^^24sort_order\24^^^^&&&)]. Here the focus shifts from engineering systems to the formal status of time in analysis and theory.

“Tempus Fugit^^^^^^^^24: 24^^^^^^^^The Impact of Time in Knowledge Mobilization Networks” argues that static social-network analysis is insufficient when edges and nodes have birth dates and persist thereafter. The proposed representation is a time-varying graph (TVG),

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24sort_by\24^^^^

and the temporal analogue of a path is a journey

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24submittedDate\24^^^^

with

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24sort_order\24^^^^

Because the Knowledge-Net setting has effectively zero latency and persistent edges, the paper concentrates on foremost journeys, i.e. earliest-arrival routes. Temporal betweenness is then defined through foremost increasing journeys and contrasted with classical static betweenness,

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24descending\24^^^^

The empirical network spans ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24: 24submittedDate\24^^^^ to ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24query\24query\24^^^^, growing from ^^^^24query\24: 24^^^^ vertices and ^^^^24query\24sort_by\24^^^^ edges in ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24: 24submittedDate\24^^^^ to ^^^^24max_results\24sort_order\24sort_order\24^^^^ vertices and ^^^^24descending\24submittedDate\24: 24^^^^ edges in ^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24: 24query\24query\24^^^^. The analysis introduces the categories rapids, brooks, invisible rapids, and invisible brooks to distinguish nodes whose temporal brokerage role diverges from their static centrality. The central methodological claim is that static and temporal centralities are complementary, not interchangeable.

“Machian Time Is To Be Abstracted From What Change?” addresses a more foundational question^^^^^^^^24: 24^^^^^^^^if time is abstracted from change, which change should count (&&&^^^^24sort_order\24^^^^&&&). The paper places Rovelli’s “any change,” Barbour’s “all change,” and Anderson’s sufficient totality of locally relevant change (STLRC) in explicit opposition. STLRC is presented as a generalization of astronomers’ ephemeris time, also called GLET^^^^^^^^24: 24^^^^^^^^**Generalized Local Ephemeris Time**. In the relational-mechanics formulation, the starting point is a parametrization-irrelevant action

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24query24^^^^

leading to the emergent Jacobi–Barbour–Bertotti (JBB) time

PRESERVED_PLACEHOLDER_^^^^24Tempus OR TEMPUS arXiv (Correa et al., 2024, Beniwal et al., 2024, Grailoo et al., 1 May 2026, Vellaisamy et al., 2024, Rad et al., 2015, Vučković et al., 2012, Anderson, 2012, Osinski et al., 2022)\24all:Tempus24^^^^

With configurational relationalism, the expression becomes

PRESERVED_PLACEHOLDER_^^^^24max_results\24: 24^^^^

At the quantum level, the timeless equation

PRESERVED_PLACEHOLDER_^^^^24max_results\24query\24^^^^

generates the frozen-formalism problem, and the paper argues that a semiclassical emergent time should again be interpreted as an STLRC-type construction. The result is a middle position^^^^^^^^24: 24^^^^^^^^time is abstracted neither from an arbitrary single change nor from literally all change, but from enough locally relevant change to achieve the required predictive accuracy.

Across both works, temporality is treated as structure rather than annotation^^^^^^^^24: 24^^^^^^^^in one case as the order of relation formation governing mobilization flow, in the other as the emergent product of dynamically relevant change.

^^^^24descending\24^^^^. Comparative significance

Across these domains, Tempus consistently marks work in which temporal organization is operationally decisive. In LLM evaluation, it names the challenge of answering when, comparing across years, and summarizing trends over windows or ranges. In detector instrumentation, it denotes nanosecond-regime event timing with ToA, ToT, and high-speed readout. In edge hardware, it refers to architectures that trade spatial growth for temporal-unary execution or iterative graph execution. In institutional software and pathology, it is attached to workflows where order, staging, and prediction over sequential operations determine system quality. In network science and physics, it names analyses that refuse to collapse dynamic processes into static representations.

A recurring misconception would be to treat these as instances of a single Tempus platform. The cited literature does not support that interpretation. Instead, it supports a narrower and more precise conclusion^^^^^^^^24: 24^^^^^^^^the label is reused for otherwise distinct contributions whose central technical problem is temporal structure—how to represent it, exploit it, measure it, or abstract it.