AESOP-SynSM: Dual Domain Applications

• AESOP-SynSM Engine is a dual-use designation representing two distinct systems, one for simulating social media dynamics and one for adaptive optics calibration.
• In the AuraSight application, the system separates scenario orchestration from simulation execution, enabling controlled experiments with agent-based models and LLM-driven content generation.
• The adaptive optics instantiation leverages GPU acceleration to fuse geometric transformations and numerical derivatives, yielding efficient interaction matrices and tomographic reconstructors.

Searching arXiv for the cited sources and closely related records to ground the article. arxiv_search({"query":"id:(Ng et al., 10 Sep 2025) OR id:(Agapito et al., 5 Jun 2026)", "max_results": 5}) arxiv_search returned successfully. arxiv_search({"query":"SynIM adaptive optics synthetic interaction matrices tomography 2026", "max_results": 10}) AESOP-SynSM Engine is a designation used in the supplied arXiv literature for two technically distinct systems. In "AuraSight: Generating Realistic Social Media Data," it denotes a two-component pipeline in which AESOP (AI-Enabled Scenario Orchestration and Planning) encodes scenario structure and SynSM (Synthetic Social Media) simulates agent activity, interactions, and LLM-based content generation, emitting Twitter API V1 JSON traces (Ng et al., 10 Sep 2025). In the "AESOP-SynSM Engine: A Design & Implementation Guide" associated with SynIM, the same designation denotes a GPU-accelerated synthetic calibration backend that extends SynIM’s core algorithms to generate interaction matrices and tomographic reconstructors for SCAO, GLAO, MCAO and LTAO (Agapito et al., 5 Jun 2026). The term therefore has context-dependent meaning rather than a single stable referent.

1. Dual usage and nomenclature

The two documented usages differ in domain, inputs, outputs, and mathematical machinery.

Usage context	Core function	Principal outputs
AuraSight social-media simulation	Scenario orchestration plus synthetic social-media generation	Social-media-API-format JSONs (Twitter API V1)
SynIM adaptive-optics backend	GPU-accelerated synthetic calibration for AO	Interaction matrices and tomographic reconstructors

In the AuraSight usage, AESOP is a PySide6 GUI in which a non-technical user “draws” actors, groups, events, topics, and narratives, and SynSM reads the resulting JSON specification to execute three subroutines at each discrete timestep: Agent Selection, Interaction Network Construction, and Content Creation via an LLM (Ng et al., 10 Sep 2025). In the SynIM usage, AESOP-SynSM is instead a calibration engine built around composite affine transformations, numerical slope extraction, tomographic MMSE reconstruction, and CuPy GPU patterns (Agapito et al., 5 Jun 2026).

A common misconception would be to treat AESOP-SynSM as a single unified framework. The available literature instead documents two unrelated architectures that share a label. This suggests that any technical discussion of AESOP-SynSM must be anchored to its application domain.

2. AuraSight instantiation: scenario orchestration and synthetic social media

Within AuraSight, AESOP-SynSM is a hybrid approach of agent-based and generative artificial intelligence techniques for generating a quasi-realistic set X dataset for a fictional multi-day pop culture episode (Ng et al., 10 Sep 2025). The pipeline is explicitly divided into AESOP and SynSM.

AESOP produces a set of JSON files encoding actors, agent types and classes, group memberships, and an event timeline with topics and narrative descriptions associated with time windows and narrative-ratios. The designer specifies agents and types, including Humans (individuals, orgs), Bots with 15 classes such as SocialInfluence, Chaos, and Amplifier, as well as Dredgers and Randos. The designer also specifies groups such as OliverFans, EthalianSingers, EthalianNationalists, Viviblog, ACASystem, and DredgersOnlyBots, together with per-group roles: Full member, Leader, and Source.

SynSM operationalizes these specifications as a discrete-time simulation. At each timestep $t$, it reads the AESOP JSON and executes agent selection, interaction network construction, and content creation. The outputs are social-media-API-format JSONs in Twitter API V1 format, ready for downstream analytics or visualization (Ng et al., 10 Sep 2025).

The significance of this architecture lies in its separation of scenario design from simulation execution. AESOP handles scenario declaration, while SynSM handles temporal realization. This supports controlled experimentation on emergent network behavior formed from known sets of agents, rather than post hoc reconstruction from observational data.

3. Formal model, agent dynamics, and validation in AuraSight

SynSM is formalized as an agent-based Markov process with agent set $\mathcal{A}$, group set $\mathcal{G}$, discrete timesteps $\mathcal{T}=\{1,\dots,T\}$, and a global state $s_t$ containing active narratives, agent activities, and past messages (Ng et al., 10 Sep 2025). Each agent is characterized by

$$\mathrm{Agent}_i = \bigl(\tau_i,\;\lambda_i,\;\mathcal{M}_i,\;\mathbf{u}_i\bigr),$$

where $\tau_i$ is the active-hours schedule, $\lambda_i$ is the posting-rate range $[m_i,M_i]$, $\mathcal{M}_i$ is the agent class, and $\mathcal{A}$0 contains identity markers such as age, nationality, bot-flag, and leader/source roles.

Activation is governed by a bimodal function centered at the midpoint of the active-hours schedule, with $\mathcal{A}$1 controlling tapering and $\mathcal{A}$2 providing a small baseline. Once activated, the number of posts follows a Poisson distribution with parameter $\mathcal{A}$3. Interaction construction builds a temporary directed interaction graph $\mathcal{A}$4 over activated agents. For agents in the same AESOP-defined group, interaction probability is a mixture of a preferential-attachment term, a follow-the-leader term, and a random term, with weights $\mathcal{A}$5, $\mathcal{A}$6, and $\mathcal{A}$7. Content generation then prompts an LLM such as GPT-4.1-mini with a system prompt, agent persona, relevant few-shot examples, BEND-maneuver targets, and class-specific constraints. A retweet simply replicates an existing message.

Group structure is a hard constraint on interaction: no cross-group edges unless an agent belongs to both groups. Narrative ratio weights $\mathcal{A}$8 determine the relative likelihood of active narratives, and tone tags plus class-specific rules modulate style. A Dredger, for example, must include at least two dredge-words plus links. The report states that narrative ratios ensure diversity and avoid “all tweets about the same topic.”

Illustrative outputs include a Human original tweet by Oliver’s fan and a dredger edge-case post. Network structure imported into ORA shows a typical hub-and-spoke pattern around Oliver as a leader, together with bot swarms bridging between OliverFans and EthalianFans when agents belong to both groups (Ng et al., 10 Sep 2025). This is consistent with the interaction model’s leader bias and membership-based edge constraints.

The report presents no quantitative results, but it defines fidelity, diversity, and stability as the guiding criteria. Fidelity is to be measured by comparing degree distribution, clustering coefficient, and temporal posting patterns against real Twitter datasets, for example via Kolmogorov–Smirnov tests on degree distributions. Diversity is to be tracked through entropy of narrative selection per group and variance of linguistic features such as average sentence length and sentiment-score distribution. Stability is to be assessed by rerunning the same scenario with a fixed random seed and comparing network summary statistics and text-embedding distributions. Because outputs are in Twitter API V1 JSON format, the report notes direct compatibility with ORA and Gephi, which assists validation against organizational or network-science baselines.

A second common misconception would be to read “realistic” as equivalent to established empirical fidelity. The report explicitly states that no quantitative results are presented. The realism claim is therefore architectural and illustrative rather than benchmarked in the technical report.

4. SynIM instantiation: synthetic calibration backend for adaptive optics

In the adaptive-optics literature, AESOP-SynSM is described as a GPU-accelerated synthetic calibration backend that extends SynIM’s core algorithms to generate interaction matrices and tomographic reconstructors for SCAO, GLAO, MCAO and LTAO (Agapito et al., 5 Jun 2026). The design assumes familiarity with SynIM’s geometry engine, numerical slope extraction, tomographic MMSE reconstruction, and CuPy GPU patterns.

The geometric core is a composite affine transformation that merges all DM-to-WFS geometric mis-registrations—shifts, rotations, magnifications, anamorphism, and LGS cone scaling—into a single $\mathcal{A}$9 homogeneous matrix. Denoting scaling by $\mathcal{G}$0, rotation by $\mathcal{G}$1, translation by $\mathcal{G}$2, and anamorphic stretch by $\mathcal{G}$3, the composite transform is

$\mathcal{G}$4

A DM-grid point then maps to the WFS high-res grid through this single transform. The document states that all steps are fused in a single bilinear-interpolation call to avoid repeated resampling.

After the geometric warp, Shack–Hartmann slopes are computed via central finite differences on the subaperture grid. With $\mathcal{G}$5 the warped phase on the high-res pixel grid and $\mathcal{G}$6 the subaperture width in pixels, the method defines $\mathcal{G}$7- and $\mathcal{G}$8-slopes either through explicit finite-difference sums or equivalently through discrete convolution with kernels

$\mathcal{G}$9

followed by averaging over each subaperture. Sub-pixel interpolation is handled by pre-upscaling so that each subaperture edge aligns on integer pixel boundaries, applying the finite-difference kernels, and then binning by $\mathcal{T}=\{1,\dots,T\}$0 to obtain one slope per subaperture. Edge artifacts from bilinear warp are repaired by thresholding the binary pupil mask at $\mathcal{T}=\{1,\dots,T\}$1, dilating to find boundary pixels, and extrapolating values via $\mathcal{T}=\{1,\dots,T\}$2 or $\mathcal{T}=\{1,\dots,T\}$3 if only one reference exists.

This formulation places AESOP-SynSM, in the AO sense, within model-based synthetic calibration rather than social or textual simulation. Its primary aim is calibration fidelity under high dimensionality, not agent behavior synthesis.

5. GPU execution, software architecture, and benchmark behavior

The AO implementation leverages CuPy for all heavy array work, with explicit memory layouts for influence functions, warped phase arrays, and slope tensors (Agapito et al., 5 Jun 2026). The affine warp, bilinear interpolation, and slope convolution are fused into a single custom CUDA RawKernel. When multiple WFSs share the same influence-function cube, the system keeps the cube on GPU and loops only over the small per-WFS affine matrices, thereby avoiding repeated PCIe transfers. Atmospheric covariance and noise covariance matrices are computed through tiled outer-product accumulation with tree-based reduction in shared memory and a final global reduction.

The software architecture is partitioned into modules: geom/ contains CompositeAffine and EdgeRepair; slope/ contains NumericalDerivativeEngine and TelescopingSumEngine; im/ contains InteractionMatrixBuilder; cov/ contains AtmosphericCovariance, NoiseCovariance, and TomographicProjection; recon/ contains MMSEBuilder; and sprint_interface/ contains SPRINTUpdater. The documented pseudocode exposes two core routines: build_interaction_matrices(cfg) and build_tomographic_reconstructor(cfg, IM), with realtime_update(telemetry) estimating parameters through SPRINTUpdater, rebuilding interaction matrices and reconstructors, and loading them into the RTC.

The benchmark data compare the geometrically aligned numerical derivative with the G-tilt telescoping-sum estimator for the ELT M4 ground DM with 68×68 subaps and 4519 modes. The telescoping-sum method has max RMS error $\mathcal{T}=\{1,\dots,T\}$4, mean RMS error $\mathcal{T}=\{1,\dots,T\}$5, and standard deviation $\mathcal{T}=\{1,\dots,T\}$6, whereas the numerical derivative has max RMS error $\mathcal{T}=\{1,\dots,T\}$7, mean RMS error $\mathcal{T}=\{1,\dots,T\}$8, and standard deviation $\mathcal{T}=\{1,\dots,T\}$9. Timing data for a single LGS IM in float32 report 13.1 s on CPU (NumPy/SciPy) and 1.0 s on GPU (CuPy), corresponding to a 13× speed-up. For 72 ELT IMs in batch, the reported timings are approximately 480 s on CPU and 265.6 s on GPU, corresponding to 1.8× with batch reuse.

The implementation recommendations are specific: always enforce sub-pixel alignment by upscaling to integer subaperture size before derivative evaluation, and, for tomography, introduce $s_t$0 spatial binning in TomographicProjection to reduce dense matrix-multiplication dimensions by $s_t$1 with negligible fidelity loss. Closed-loop integration guidelines further specify SPRINT coupling for slowly varying misregistrations, real-time updates to AtmosphericCovariance using evolving $s_t$2 profiles, and modeling opto-mechanical aberrations by adding a “static DM” mode whose influence function is the known aberration. The documented pipeline is: acquire slope telemetry; let SPRINT estimate $s_t$3; build synthetic IM and MMSE reconstructor on the GPU in $s_t$4; and stream new gain matrices to the RTC without science interruption.

These claims are domain-specific. They do not generalize to the AuraSight system, despite the shared name.

6. Limitations, future work, and interpretive issues

The AuraSight report identifies four current limitations: no opinion-dynamics, static group membership, manual persona generation, and content realism that still depends on prompt quality (Ng et al., 10 Sep 2025). The absence of opinion-dynamics means that groups do not internally shift stance and narratives are static sequences. Static group membership means that cross-group links cannot organically form over time without manual AESOP setup. Manual persona generation limits consistency in identities across simulations. Prompt-dependent realism leaves room for improved statistical similarity to real tweets.

The proposed future work follows directly from these limitations. The report proposes integrating opinion-dynamics models such as DeGroot and Bounded Confidence so that agents update their stance as $s_t$5; automating persona synthesis via LLMs; exploring advanced prompting architectures or fine-tuning to tighten the match to real-world text distributions, for example by minimizing KL divergence between synthetic and real tweet n-gram distributions; and extending the system to multi-platform generation such as Telegram with cross-platform user linking.

The AO guide frames its forward trajectory less as limitations than as operational integration: SPRINT-coupled updates, turbulence-profile adaptation, and inclusion of static aberration modes are presented as closed-loop guidelines rather than an explicit deficiency list (Agapito et al., 5 Jun 2026). A plausible implication is that the adaptive-optics instantiation is positioned nearer deployment and operational strategy, whereas the social-media instantiation is positioned nearer exploratory simulation and methodological refinement.

Taken together, the literature presents AESOP-SynSM not as a single research program but as a reused designation attached to two distinct technical artifacts. In one case it is a modular hybrid of agent-based modeling, network-science scaffolded interactions, and LLM-driven content creation for synthetic social-media traces; in the other it is a GPU-accelerated synthetic calibration backend that fuses geometric registration, numerical differentiation, covariance construction, and MMSE reconstruction for next-generation adaptive optics.