---
title: 'Mantis: Biology, Astronomy, AI & More'
url: https://www.emergentmind.com/topics/mantis
type: topic
---

# Mantis: Biology, Astronomy, AI & More

Mantis denotes both praying mantises in biology and a recurrent acronym or codename in contemporary scientific research. In the biological literature it refers to members of the order Mantodea, including *Mantis religiosa*; in technical literature it names systems for wide-field radio astronomy, photometric-redshift estimation, conversational search, multimodal language generation, multi-image instruction tuning, time-series classification, disease forecasting, robotics, cybersecurity, medical imaging, and mixed-signal sensing hardware [2504.20328][1612.07917].

## 1. Biological referent: Mantodea

Praying mantises are predatory insects of the order Mantodea. The genomic study of five species—*Mantis religiosa*, *Tenodera sinensis*, *Deroplatys truncata*, *Hymenopus coronatus*, and *Metallyticus violaceus*—places them in a lineage of roughly ~2500 species with major roles in agriculture, traditional medicine, bionics, and entertainment [2504.20328]. That study reports chromosome-scale reference genomes and identifies transposable element expansion as the major force governing genome size in Mantodea. It also infers that the Mantodea ancestor may have had only one X chromosome, with later translocations between the X chromosome and an autosome in the lineage of Mantoidea, and it finds a lower evolutionary rate for the metallic mantis than for the other sampled mantises [2504.20328].

The same work argues that Mantodea underwent rapid radiation after the K–Pg mass extinction event, which may have contributed to confusion in species classification [2504.20328]. In this biological sense, “mantis” names a zoological lineage that is both evolutionarily distinctive and methodologically useful for chromosome evolution, repeat-driven genome expansion, and rapid diversification studies.

## 2. Astronomy and astrophysical usage

In radio astronomy, MANTIS stands for **Mid-Frequency Aperture Array Transient and Intensity-Mapping System**. It is proposed as a wide-field mid-frequency aperture-array science demonstrator and precursor to an MFAA-based SKA telescope on the South African SKA site. The preliminary specifications given are a **450–1450 MHz** frequency range, **\(>500\ \mathrm{MHz}\)** bandwidth, **1500–2500 m\(^2\)** collecting area, **A/T = 38–63 m\(^2\)/K**, **SEFD = 74–44 Jy**, **200 deg\(^2\) at 1 GHz** field of view, and transient buffering [1612.07917]. Its main science drivers are transients, HI intensity mapping, pulsars, and SETI, and it is explicitly framed as the first MFAA station on the SKA site and as a reference point for MFAA science, costing, and performance toward SKA Phase 2 [1612.07917].

In astronomical machine learning, “Mantis Shrimp” names a multi-survey computer-vision model for photometric redshift estimation that fuses **GALEX**, **Pan-STARRS**, and **UnWISE** imagery. The model uses a **ResNet50** modified for **9-channel input**, recasts redshift estimation as classification over **\(C=200\)** bins between 0.0 and 1.0, and is trained on a spectroscopic compilation of roughly **\(4.2\times10^6\)** galaxies, with preliminary experiments using about **7%** of the total [2402.03535]. On the held-out test set reported in the paper, the multi-instrument CNN achieves **MAD \(=0.0244\)**, **Bias \(=1e{-2}\)**, and **\(\eta=13.84\%\)**, with ablations showing that IR helps substantially while UV contributes little in the reported setup [2402.03535]. The broader significance is that “Mantis” in astronomy names both a future wide-field radio instrument and a multisurvey image-fusion model, but in two entirely unrelated subfields.

## 3. Language, multimodal, and information-seeking systems

In NLP and multimodal learning, the label has been reused for several unrelated resources and methods.

| System | Domain | Salient properties |
|---|---|---|
| **MANtIS** [1912.04639] | Conversational search | **80,324** multi-turn, multi-domain, grounded Stack Exchange dialogues; **118,349** response-ranking contexts; **1,356** conversations with **6,701** intent-labeled utterances |
| **MAnTiS** [2109.01229] | Multimodal NLG | GPT-2 medium with ResNet-152 image conditioning via a multimodal prefix; product description generation from images and titles |
| **MANTIS** [2405.01483] | Multi-image LMMs | **721K** multi-image instruction examples plus **268K** single-image reasoning examples; Mantis-SigLIP averages **62.6** on five multi-image benchmarks |
| **MANTIS** [2312.09451] | Mental-health text classification | Hybrid and stacked ensembles using Mental RoBERTa, PsychBERT, ClinicalBERT, and a BiLSTM over **168** engineered features; best **89.31** validation F1 and **83.76** test F1 |
| **MANTIS** [2212.09855] | Lexical simplification | Unsupervised extension of LSBert using RoBERTa-medium, **30** candidates, weighted ranking, and textual-entailment filtering; **5.9%** accuracy improvement over LSBert |

The MANtIS conversational-search dataset is defined by two states—**information-need elucidation** and **information presentation**—and was introduced as a large-scale benchmark for mixed-initiative, grounded information-seeking dialogue [1912.04639]. MAnTiS for natural language generation, by contrast, is a prefix-based multimodal conditioning method in which modality-specific encoders project into the language model embedding space before generation; on fashion description generation it outperforms Context-Attn and Pseudo-Self on BLEU4, CIDEr, METEOR, and ROUGE-L [2109.01229]. The 2024 MANTIS work on interleaved multi-image instruction tuning argues that multi-image ability need not be acquired through massive noisy interleaved pre-training, and reports that Mantis-SigLIP exceeds Idefics2 by about **9 absolute points** on the paper’s five multi-image benchmarks while remaining competitive on single-image evaluation [2405.01483].

## 4. Temporal foundation models

In time-series learning, Mantis is a classification-oriented foundation model rather than a forecasting-oriented one. The original model is a lightweight ViT-based encoder pretrained with a contrastive objective on **7 million time series samples**. It uses a fixed input length of **512**, produces **32** tokens of dimension **256**, has about **8 million parameters**, and is evaluated both in frozen-feature and full fine-tuning regimes. The paper emphasizes that Mantis achieves the lowest calibration error on average over **131 datasets** among the compared TSFMs, while also supporting multivariate adapters for channel compression and interaction modeling [2502.15637].

MantisV2 is presented as a direct successor aimed at closing the frozen-versus-fine-tuned gap in zero-shot time-series classification. The work introduces **Mantis+**, pretrained entirely on **2 million synthetic time series**, and then refines the architecture into **MantisV2**, which has **4.2M parameters originally and 2.2M after pruning**. It combines synthetic-only pretraining, intermediate-layer extraction, revised token aggregation, self-ensembling over interpolation scales, and cross-model embedding fusion [2602.17868]. On UCR, the paper reports **0.8195** average accuracy for MantisV2 with Random Forest and **0.8360** with logistic regression; the strongest fusion, **MantisV2 + TiConvNext**, reaches **0.8494**, effectively matching fine-tuned MantisV2 at **0.8500** [2602.17868].

A different temporal use appears in infectious-disease forecasting. There, Mantis is a simulation-grounded foundation model trained entirely on **over 400 million simulated days** of outbreak dynamics and deployed in strict zero-shot mode on real epidemiological tasks [2508.12260]. The model is designed to generalize across diseases, regions, and outcomes, including cases, hospitalizations, deaths, and an unseen modality at inference time, ILI [2508.12260]. The paper states that Mantis outperformed **39** comparison models across six diseases, including the CDC COVID-19 Forecast Hub models tested, while also providing calibrated uncertainty, mechanistic interpretability through back-to-simulation attribution, and actionable **8-week** forecasts [2508.12260].

## 5. Robotics and embodied intelligence

In biomimetic underwater robotics, mantis-shrimp inspiration appears through latch-mediated spring actuation. The reported system uses a **soft bistable actuator with an integrated latch mechanism** to reproduce LaMSA-style slow energy loading and fast release with a single motor [2605.26936]. The robot has mass **\(350\,\mathrm{g}\)**, a **167 RPM**, **4 W** DC motor, and four fins. Experiments report stable periodic flapping, **maximum thrust \(0.528\,\mathrm{N}\)**, **impulse \(0.147\,\mathrm{N\cdot s}\)**, and **30 mm** net vertical displacement per cycle, together with vertical ascent, diagonal forward motion, lateral translation, and turning through fin-angle modulation [2605.26936].

For autonomous mobile agents, Mantis names a methodology for deploying **spiking neural networks** under memory, energy, and adaptability constraints. Its three components are SNN operation optimization, an enhanced online STDP rule with adaptive learning rates, weight decay, and balanced threshold potential, and a memory- and energy-aware model-selection procedure [2212.12620]. On the reported MNIST and Fashion-MNIST experiments, the methodology yields **3.32× memory reduction** and **2.9× inference energy saving** for an **8-bit** SNN relative to a 32-bit baseline, while also reducing training energy by up to **2.7×** [2212.12620].

In robot learning, Mantis is also a **Vision-Language-Action** model with **Disentangled Visual Foresight**. It combines a **Qwen2.5-VL** backbone, trainable latent-action and action queries, a connector, a **Sana**-based DiT head for future-frame prediction, and a diffusion action head [2511.16175]. The total model size is **5.8B parameters**, and the main empirical result is a **96.7%** average success rate on LIBERO after fine-tuning, with further real-world gains over **\(\pi_{0.5}\)** in instruction following, unseen-instruction generalization, and reasoning-oriented task variants [2511.16175]. Across these works, “Mantis” in robotics denotes systems that use dense embodied supervision—whether biomechanical, spiking, or multimodal—to improve control.

## 6. Security, malicious infrastructure, and software systems

In systems research, Mantis originally denoted a framework for predicting program execution time through **program instrumentation**, **sparse polynomial regression**, and **program slicing**. It extracts loop counts, branch counts, method invocation counts, variable values, and exception counts, then fits a predictive model of execution time and generates compact feature evaluators through slicing [1010.0019]. The reported result is **more than 93% accuracy with less than 10% training data**, with around **6.1%** prediction error on Lucene and **5.5%** on ImageJ in the representative experiments [1010.0019].

In cybersecurity defense, Mantis is a framework that counters **LLM-driven cyberattacks** by exploiting prompt-injection susceptibility in the attacker’s own agent [2410.20911]. It uses decoy services plus an Injection Manager to plant hidden adversarial instructions into service responses after exploit-like interaction. The paper evaluates both passive tarpit and active counterstrike modes and reports about **95.4%** defender success while reducing attacker success to **under 3%** across the tested agents, targets, and model backends [2410.20911]. The active mode can induce the attacker’s LLM-agent to run a fetch-and-execute command that establishes a reverse shell back to the defender, whereas the passive mode diverts the agent into an infinite exploration loop [2410.20911].

In malicious-domain detection, MANTIS is a content-agnostic system that detects attacker-created domains at hosting time by monitoring low-reputation hosting infrastructure [2502.09788]. It builds a heterogeneous graph over apex domains, FQDNs, IPs, **/24** subnets, and ASNs, and combines graph structure with lexical, hosting, and IP features [2502.09788]. The operational headline figures are **99.7% precision**, **86.9% recall**, **0.1% FPR**, and about **19K new malicious domains per day**, with lead times of days to weeks before popular blocklists and an average detection time of **3 days 17 hours** after first PDNS appearance versus **over 8 days** for VT in the reported comparison [2502.09788].

## 7. Medical imaging and near-sensor hardware

In quantitative MRI, MANTIS stands for **Model-Augmented Neural neTwork with Incoherent k-space Sampling**. It is a deep-learning framework for accelerated knee **T2 mapping** that combines direct U-Net-based map prediction with model-augmented k-space consistency [1809.03308]. The method uses the physical mono-exponential decay model inside training and was evaluated at acceleration factors **\(R=5\)** and **\(R=8\)**. At \(R=5\), the paper reports **nRMSE \(6.1\pm1.5\%\)** and **SSIM \(86.2\pm1.9\%\)** for MANTIS, compared with **\(6.9\pm1.8\%\)** and **\(82.3\pm2.8\%\)** for CNN-Only; at \(R=8\), the values are **\(7.1\pm1.8\%\)** and **\(82.1\pm2.3\%\)** versus **\(8.5\pm2.5\%\)** and **\(78.0\pm2.5\%\)** [1809.03308].

In hardware, MANTIS is a mixed-signal near-sensor convolutional imager SoC that integrates a **\(128\times128\)** 3T APS array, **DS3** units, analog memory, MAC units, switched-capacitor amplifiers, and SAR ADCs for feature extraction and face RoI detection [2411.07946]. Its distinguishing combination is **\(16\times16\)** **4b-weighted** filters, multi-scale operation, and double sampling. The paper reports peak energy efficiencies normalized to 1b operations of **4.6 TOPS/W** at the accelerator level and **84.1 TOPS/W** at the SoC level, with feature-map RMSE ranging from **3% to 11.3%** [2411.07946]. In the demonstrated face RoI detector, the chip attains a **false negative rate of 11.5%**, discards **81.3%** of image patches, and reduces off-chip transmitted data by **13×** relative to the raw image [2411.07946].

This suggests that “Mantis” functions less as a standardized technical term than as a recurrent project name reused across disciplines. In current arXiv usage, it denotes a zoological lineage and a heterogeneous set of instruments, datasets, models, and systems whose only commonality is nomenclature rather than method or domain.

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