---
title: 'Tempus: Temporal Structures Across Domains'
url: https://www.emergentmind.com/topics/tempus
type: topic
---

# Tempus: Temporal Structures Across Domains

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{"query":"\"Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models\"", "max_results": 5, "sort_by": "relevance", "sort_order": "descending"}
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 large language models**, a **Timepix4-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** [2402.11997; 2404.07120; 2412.19002; 2605.00536; 1205.0104; 2203.13948; 1505.02810; 1209.1266].

## 1. Uses of the name in the research literature

The common denominator across these usages is an explicit concern with temporal structure: **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.

## 2. Temporal knowledge and reasoning in language models

One important strand associated with a “Tempus” or temporality query 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 Large Language Models” distinguishes **Temporal knowledge**—answering specific time-indexed facts correctly—from **Temporal reasoning**—inferring patterns, comparisons, extrema, aggregates, and trends from multiple time points [2402.11997]. The work introduces **TempUN**, curated from **Our World in Data (OWD)** and aligned with major UN global issue categories, spanning **10,000 BCE to 2100 CE**. Its instances have the form
\[
\langle C, I, L \rangle
\]
with \(C\) as country name, \(I\) as issue subcategory, and \(L\) as a list of year-value pairs \(\langle Y_t, V_t \rangle\). Samples are formed as
\[
\langle C, I, Y_t \rangle \rightarrow V_t.
\]

The reported scale is **462,894 instances**, **9,497,502 samples**, **106 subcategories**, and **8 major categories**. The filtered experimental subset **TempUNs** contains **1,907 instances** and **104,130 samples**, selected so each category has at least **76 continuous years of data between 1947 and 2022** and exhibits meaningful temporal dynamics. The benchmark operationalizes six MCQ categories: **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: **Correct**, **Incorrect**, and **Not Available** / **Information Not Available**. Distractors are generated by
\[
v_t + U_{0,1} * 10^{\log_{10} v_t + 1}.
\]

The main experiments evaluate six base models: **phi-2**, **mistral-instruct**, **llama-2-chat**, **gpt-3.5-turbo**, **gpt-4**, 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: **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 **8.22%** of the time on average, while open-source models do so about **3.46%** 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: year-specific retrieval, cross-year comparison, windowed sequence reconstruction, range aggregation, extrema identification, and trend reasoning.

## 3. TEMPUS as a Timepix4-based X-ray detector

In photon science, **TEMPUS** denotes a **DESY-developed X-ray detector system** built around the **Timepix4 ASIC**, intended as a next-generation replacement for the **LAMBDA/Medipix3-based detector family** [2404.07120]. The initial implementation is a **single-chip prototype system** designed to accelerate deployment to beamline users while still exploiting Timepix4’s larger active area. Compared with Medipix3, Timepix4 is about **3.5–4× larger in area**, retains a **55 µm pixel pitch**, uses a **448 × 512 pixel matrix**, and supports **TSV (through-silicon via)** technology with **4-side buttability**. It also provides **16 high-speed GWT links**, each capable of up to **10.24 Gb/s** in principle; the TEMPUS performance discussion uses **5.12 Gb/s per link**, for a total potential throughput of **80 Gb/s**.

The detector has two operating modes. In **photon counting mode**, each above-threshold pulse is counted into **8-bit or 16-bit counters** under **continuous read-write (CRW)** operation, with **frame rates up to 40 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 **200 ps binning**, and ToT provides coarse energy information with about **1 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 **Timepix4 ASIC** mounted near one edge of the board, a **Xilinx Zynq UltraScale+ MPSoC evaluation board** (**HTG-Z922**) for control and readout, a housing with fans and thermal vias, and a **DAQ server** receiving data over **100 GbE**. The carrier board uses **Megtron6** and routes high-speed traces for **5.12 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 P01**, and **ESRF beamline ID14**. At PETRA III, with a **300 µm-thick p-on-n silicon sensor**, photons near **14.4 keV** for **\(^{57}\)Fe** studies, and a timing mode with **40 electron bunches per revolution**, the bunch spacing was about **192 ns**. Because of leakage-current issues, the bias voltage was limited to about **100 V**. The timing performance was estimated at about **20 ns**, and the measured FWHM time resolutions were **23.3 ns** for the high-energy photons and **9.7 ns** for the low-energy photons. At ESRF, using a similar **300 µm-thick p-on-n sensor** but with bias up to **200 V**, one high-speed data link at **1.28 Gb/s** supported a maximum event rate of about **\(2\times10^7\) hits/s**, and the measured FWHM time resolutions improved to **12.1 ns** for the high-energy photons and **8.5 ns** for the low-energy photons.

A central correction step is the **time-walk effect**. TEMPUS uses the correlation between **ToT and ToA**: 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 **300 ns ToT** and **1100 ns ToT**, attributed mainly to **Fe K\(\alpha\)** fluorescence at **6.4 keV** and **14.4 keV** scattered photons, with a cut at about **650 ns ToT** used for energy separation.

The first results establish that event-based X-ray time stamping works in practice with Timepix4, 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-\(Z\) materials such as **GaAs** or **CdTe**, use of all **16 links** at **5.12 Gb/s**, and eventual multi-chip modules.

## 4. Tempus in edge AI hardware

In edge inference hardware, **Tempus** names two distinct architectures: **Tempus Core**, a temporal-unary convolution engine for NVDLA-like accelerators, and **Tempus**, a temporally scalable GEMM framework for **AMD Versal AI Edge** [2412.19002; 2605.00536]. 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 **\(k \times n\)** PE array, and each multiplier is a **tub multiplier**. The design relies on **2s-unary encoding**, in which each unary bit/cycle is interpreted as a value of **2**. The paper contrasts worst-case latency for tuGEMM,
\[
N \cdot (2^{w-1})^2,
\]
with tubGEMM,
\[
N \cdot (2^{w-2}),
\]
and adapts the same temporal-unary-binary philosophy to convolution. Dataflow compliance is preserved through the relation
\[
W \times F^T = \text{accum}(W \odot F).
\]

Evaluation uses **45nm CMOS**, **Synopsys Design Compiler**, **Cadence Innovus**, the **NanGate45** standard cell library, and a fixed **250 MHz** clock. At the full-core level, the **PCU** shows **59.3% area reduction** and **15.3% power reduction** relative to NVDLA’s **CMAC**. For a **\(16 \times 16\)** PE array, INT8 results report **75% area reduction** and **62% power savings**, with **5x iso-area throughput improvement for INT8** and **4x iso-area throughput improvement for INT4**. Post-place-and-route analysis for a **\(16 \times 4\)** array in **45nm CMOS** gives **0.0168 mm\(^2\)** area and **6.1146 mW** power for Tempus Core, versus **0.0361 mm\(^2\)** and **10.7013 mW** for the CMAC Core. The paper also profiles **MobileNetV2** and **ResNeXt101**, obtaining average INT8 latencies of **33 cycles** and **31 cycles**, respectively, under 2s-unary encoding.

The 2026 **Tempus** framework addresses a different problem: GEMM acceleration for LLM inference on the **AMD Versal AI Edge VE2302 SoC**. Its defining principle is **resource-invariant temporal scaling**. Rather than increasing hardware resources with matrix size, it uses a **fixed compute block of 16 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
\[
\mathrm{GRAPH\_ITER\_CNT} = \frac{\mathrm{GEMM\_SIZE\_A} \times \mathrm{GEMM\_SIZE\_B}}{\mathrm{DIM\_A} \times \mathrm{DIM\_B} \times \mathrm{SPLIT}},
\]
and replication factors are defined as
\[
\mathrm{REPLICATION\_FACTOR\_A/B} = \frac{\mathrm{GEMM\_SIZE\_B/A}}{\mathrm{DIM\_B/A} \times \mathrm{SPLIT}}.
\]
The **cascade interface** is **512-bit wide** on AIE-ML and supports partial-sum reduction at **Initiation Interval \(II=1\)**.

On the **XCVE2302-1LSESFVA784-E** using **AMD Vitis 2024.1** and the **AMD XPE tool**, the framework achieves **607 GOPS** at **10.677 W** total on-chip power for **\(1024^3\) INT16 GEMM**, with **3.537 ms** core computation latency, **0.00% URAM**, **0.00% DSP**, **6.16% LUT**, **62.58% BRAM**, and **7.65% CLB registers**. Relative to **ARIES**, it reports **211.2× higher prominence factor**, **22.0× core frugality**, **7.1× power frugality**, and a **6.3× 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: keep the surrounding deployment ecosystem intact, constrain resource growth, and push scalability into temporal scheduling, streaming, or unary time-domain encoding.

## 5. 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** [1205.0104; 2203.13948]. 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: **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`:
```sql
SET IDENTITY_INSERT iKnow.Faculty ON
INSERT INTO iKnow.Faculty
SELECT * FROM SAA.Faculty
SET IDENTITY_INSERT iKnow.Faculty OFF
```
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 **40x** H&E whole-slide image, a **multi-field-of-view convolutional network** with a **ResNet-18 backbone** for tumor and lymphocyte region identification, and a **U-Net-based model** for nuclei detection. It extracts **3,461 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 **1,605 slides**, selected using **332 CRC slides**, with final model choice **log transform + L1 regularization strength 0.01** and validation correlation **R = 0.818**; the abstract summarizes predicted-vs-true correlation as **R = 0.85**. The number of slides to scrape is computed by
\[
\text{number of slides to scrape} = \frac{\text{target yield}}{\text{predicted DNA yield per slide}},
\]
then **rounded down to the nearest integer**, with UI operating points at **100 ng**, **400 ng**, and **1000 ng**.

The internal validation trial enrolled **501 clinical colorectal cancer slides**, with a main analysis set of **476 samples**: **233 Trad** and **243 SmartPath**. The primary result is an increase in first extractions landing in the **100–2000 ng** target range: **Trad: 0.56 ± 0.064**, **SmartPath: 0.70 ± 0.058**, **P = 0.005**, described as a **25% relative increase**. The improvement came mainly from fewer overshoots: **Trad: 0.32 ± 0.060**, **SmartPath: 0.18 ± 0.049**, **P = 0.001**. Overall **T-seq** was not significantly improved, but for **small, low-quality samples** the result was **Trad: 6.90 ± 2.77 days**, **SmartPath: 4.97 ± 2.06 days**, **P = 0.025**. 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.

## 6. Temporal structure as a theoretical and methodological problem

Two further uses of the Tempus motif are conceptual rather than infrastructural: temporal centrality in social networks and Machian time in classical and quantum gravity [1505.02810; 1209.1266]. Here the focus shifts from engineering systems to the formal status of time in analysis and theory.

“Tempus Fugit: 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)**,
\[
\mathcal{G}=(V,E,\mathcal{T},\rho,\zeta),
\]
and the temporal analogue of a path is a **journey**
\[
\mathcal{J}=\{(e_1,t_1),(e_2,t_2),\dots,(e_k,t_k)\},
\]
with
\[
t_{i+1}\ge t_i+\zeta(e_i,t_i).
\]
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,
\[
B(v)=\sum_{u\neq w\neq v\in V}\frac{|P(u,w,v)|}{|P(u,w)|}.
\]
The empirical network spans **2005 to 2011**, growing from **10 vertices and 14 edges in 2005** to **366 vertices and 750 edges in 2011**. 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: if time is abstracted from change, **which** change should count [1209.1266]. 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**: **Generalized Local Ephemeris Time**. In the relational-mechanics formulation, the starting point is a parametrization-irrelevant action
\[
S = \sqrt{2}\int \sqrt{E - V}\, ds, \qquad ds := \sqrt{m_{ij}\,dq^i dq^j},
\]
leading to the emergent **Jacobi–Barbour–Bertotti (JBB) time**
\[
t_{\mathrm{JBB}} = t_{\mathrm{JBB}(0)} + \int \frac{ds}{\sqrt{2(E - V)}}.
\]
With configurational relationalism, the expression becomes
\[
t_{\mathrm{JBB}} = t_{\mathrm{JBB}(0)} + \underset{G}{\mathrm{extremum}} \int \frac{|d_g Q|_M}{\sqrt{2W}}.
\]
At the quantum level, the timeless equation
\[
\hat{H}\Psi = 0
\]
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: 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: in one case as the order of relation formation governing mobilization flow, in the other as the emergent product of dynamically relevant change.

## 7. 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: 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.

Source: https://www.emergentmind.com/topics/tempus