Diffusion-Enhanced Transformer Neural Operator

• The paper introduces DETNO, a novel architecture that fuses transformer neural operators with diffusion-based denoising to enhance long-horizon traffic forecasting.
• It refines noisy traffic state queries through iterative DDIM updates, enabling accurate super-resolution predictions and mitigating spectral bias.
• Empirical results show DETNO outperforms baselines in stability and error metrics while preserving sharp congestion fronts and high-frequency details.

Searching arXiv for DETNO and closely related diffusion–neural-operator papers to ground the article in current literature. Diffusion-Enhanced Transformer Neural Operator (DETNO) denotes a unified architecture for long-term traffic forecasting that combines a transformer neural operator backbone with a diffusion-based refinement mechanism (Ahmad et al., 26 Aug 2025). In the reported formulation, the model is designed for predicting future traffic states over extended horizons from sparse observations while preserving high-frequency traffic phenomena such as shock waves, congestion fronts, and sharp density transitions, which conventional neural operators tend to smooth out (Ahmad et al., 26 Aug 2025). The architecture is query-based, supports arbitrary spatiotemporal query resolution, and is trained as a conditional denoising model in which noisy traffic-state query tokens are iteratively refined through DDIM denoising rather than being predicted in a single deterministic pass (Ahmad et al., 26 Aug 2025). Within the broader literature, DETNO belongs to a family of methods that combine operator learning with diffusion models, but it is more specific than diffusion-enhanced neural-operator pipelines that use non-transformer backbones or diffusion only as an output head (Oommen et al., 2024, Park et al., 4 Feb 2026).

1. Definition and problem setting

DETNO is introduced for long-term traffic forecasting under difficult, highly nonlinear traffic dynamics, with the explicit aim of preserving physically important high-frequency structures over extended rollout horizons (Ahmad et al., 26 Aug 2025). The traffic state over a spatiotemporal domain $X \times T$ is written as

$\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$

where $\rho$ denotes traffic density and $v$ velocity (Ahmad et al., 26 Aug 2025). The model receives sparse sensor observations and boundary-condition information and infers the full traffic field at query locations in space and time, after which forecasting is performed autoregressively over multiple windows (Ahmad et al., 26 Aug 2025).

The physical background is the Lighthill-Whitham-Richards (LWR) traffic model,

$$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$

which naturally produces steep gradients and discontinuity-like phenomena (Ahmad et al., 26 Aug 2025). The paper’s motivation is that neural operators are attractive because they learn mappings between function spaces and can generalize across conditions better than many purely data-driven traffic models, yet they suffer from spectral bias and therefore tend to produce overly smooth density and velocity fields (Ahmad et al., 26 Aug 2025). In this setting, over-smoothing is not merely a one-step accuracy defect; it degrades multi-step rollout because missing fine-scale structure corrupts the next input when predictions are fed back autoregressively (Ahmad et al., 26 Aug 2025).

The operator-learning formulation is written as

$\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$

where $\mathcal{A}$ is the input function space containing sensor measurements, coordinates, and boundary condition data, and $\mathcal{H}$ is the solution function space over the spatiotemporal domain (Ahmad et al., 26 Aug 2025). This places DETNO squarely within neural operator methodology, but with diffusion incorporated into the prediction mechanism rather than appended as an external post-processor.

2. Transformer neural operator backbone

DETNO is presented as an end-to-end architecture whose backbone is a transformer neural operator built around heterogeneous cross-attention, followed by self-attention, with Mixture-of-Experts (MoE) feed-forward blocks (Ahmad et al., 26 Aug 2025). The data are organized into a sensor set $$\{(\mathbf{x}^i,\mathbf{u}^i)\}_{i=1}^{N_{\text{sensor}}}$$ and a query set $$\{(\mathbf{q}^j,\mathbf{y}^j)\}_{j=1}^{N_{\text{pred}}}$$, where $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$0 are sensor space-time coordinates $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$1, $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$2 are sensor traffic states $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$3, $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$4 are query tokens $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$5, and $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$6 are ground-truth query states (Ahmad et al., 26 Aug 2025).

A notable architectural choice is that the query token contains both coordinates and a current estimate of the traffic state (Ahmad et al., 26 Aug 2025). During training, those state entries are noise-corrupted versions of the target; during inference, they are initialized from noise and iteratively refined (Ahmad et al., 26 Aug 2025). This makes the transformer operator itself part of a denoising process rather than a conventional coordinate-to-state decoder.

The model contains three encoders. The query encoder

$\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$7

maps query tokens into latent query representations $\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$8 (Ahmad et al., 26 Aug 2025). The branch encoder

$\boldsymbol{u}(x,t) = [\rho(x,t), v(x,t)]^\top,$9

acts on concatenated sensor coordinates and sensor measurements $\rho$0, producing operator-stream keys and values $\rho$1 (Ahmad et al., 26 Aug 2025). The diffusion encoder takes the denoising timestep $\rho$2, applies sine/cosine Fourier features, and processes the result by an MLP, yielding a latent $\rho$3 that is broadcast into diffusion-stream keys and values $\rho$4 (Ahmad et al., 26 Aug 2025).

The central mechanism is heterogeneous cross-attention with two separate context streams: $\rho$5 The operator stream conveys the observed traffic function encoded from sensors and boundary data, while the diffusion stream conveys the current denoising stage (Ahmad et al., 26 Aug 2025). The two context vectors are fused by summation and projection with a residual, after which a self-attention layer enforces spatiotemporal coherence among query points (Ahmad et al., 26 Aug 2025). The paper states that the model uses linear cross-attention and linear self-attention, following GNOT-style scalability, but does not provide a lower-level attention equation beyond $\rho$6 (Ahmad et al., 26 Aug 2025).

Each transformer block also contains an MoE module whose gating network is conditioned on each query’s spatiotemporal coordinates, thereby providing soft domain decomposition across different traffic regimes (Ahmad et al., 26 Aug 2025). This suggests specialization across regions such as smooth free-flow segments and sharp congestion fronts, although that interpretation is structural rather than presented as a formal theorem.

3. Diffusion enhancement and denoising formulation

The defining enhancement in DETNO is the reformulation of prediction as a few-step conditional denoising problem (Ahmad et al., 26 Aug 2025). In the forward corruption process, for noise level $\rho$7, Gaussian noise $\rho$8 is sampled and the target traffic state is corrupted as

$\rho$9

with the noisy query tokens formed as

$v$0

(Ahmad et al., 26 Aug 2025). The diffusion timestep used as input is distinct from physical forecast time: $v$1 and $v$2 is Fourier-embedded to form the diffusion-stream context (Ahmad et al., 26 Aug 2025).

DETNO uses $v$3-parameterization. Conditioned on $v$4, the model $v$5 predicts a diffusion velocity field, and the supervision target is

$v$6

(Ahmad et al., 26 Aug 2025). The training loss is

$v$7

The paper does not specify an additional supervised state reconstruction term, consistency term, physics loss, or regularizer, so the stated objective is the diffusion MSE alone (Ahmad et al., 26 Aug 2025).

At inference, the traffic-state components of the query tokens are initialized with pure noise, and DETNO iteratively denoises over $v$8 using DDIM updates (Ahmad et al., 26 Aug 2025). The exact DDIM reverse equation is not written explicitly in the paper; only the use of DDIM for deterministic and faster sampling with fewer evaluations is stated (Ahmad et al., 26 Aug 2025). This matters conceptually: in DETNO, “diffusion-enhanced” does not mean an external corrector acting after a deterministic operator prediction, but rather an operator architecture wrapped inside a denoising process.

Within the adjacent literature, this distinguishes DETNO from at least two other diffusion–operator couplings. "Generative Neural Operators through Diffusion Last Layer" attaches a latent conditional diffusion or flow-matching head to an otherwise deterministic neural operator backbone and is explicitly not transformer-specific (Park et al., 4 Feb 2026). "Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling" uses a two-stage pipeline in which a neural operator provides a coarse forecast and a conditional score-based diffusion model refines high-frequency content in physical space; it is sequential and not instantiated with a transformer operator backbone (Oommen et al., 2024). DETNO instead embeds denoising directly into the query-based transformer operator.

4. Training, forecasting protocol, and implementation

The case study uses a synthetic chaotic traffic dataset generated by solving the LWR PDE with a Godunov scheme (Ahmad et al., 26 Aug 2025). The simulated highway has length 5 km and total simulation time 25 min, and the forecasted variables are density $v$9 and velocity $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$0 (Ahmad et al., 26 Aug 2025). Initial conditions are multi-step piecewise constant density profiles with random step parameters and base inflow density $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$1, while downstream traffic-light control provides alternating red/green phases each lasting 1–2 minutes (Ahmad et al., 26 Aug 2025). The stated purpose is to induce discontinuities, congestion formation, shock propagation, and sharp density transitions (Ahmad et al., 26 Aug 2025).

The dataset contains 1300 traffic simulations in total, with 1000 used for training and 300 for testing; a validation split is not specified (Ahmad et al., 26 Aug 2025). Inputs comprise sparse sensor measurements from fixed locations, their coordinates $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$2, traffic measurements $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$3, and boundary-condition data from road endpoints (Ahmad et al., 26 Aug 2025). Targets are full traffic-state fields

$$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$4

evaluated at query locations (Ahmad et al., 26 Aug 2025).

Training is single-step, with a history window $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$5 minute and prediction horizon $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$6 minute (Ahmad et al., 26 Aug 2025). Long-term forecasting is performed only at inference via autoregressive rollout: the first prediction window uses real sensor data, subsequent windows sample the previous prediction at fixed sensor coordinates, those sampled values become pseudo-sensor inputs, and boundary-condition data are combined with the pseudo-sensors before repeating (Ahmad et al., 26 Aug 2025). The reported horizon is up to 8 rollout steps (Ahmad et al., 26 Aug 2025).

The reported implementation details are specific. The sensor encoding block is a 2-layer MLP with hidden dimension 64 and GELU; the query token encoding block is a 2-layer MLP with hidden dimension 64 and GELU; the time embedding block uses Fourier embedding with 64 frequencies and maximum period 10000, followed by an MLP $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$7 with GELU (Ahmad et al., 26 Aug 2025). The cross-attention block is repeated 3 layers, each attention block uses 4 heads, the MoE contains 3 experts, each expert is a 2-layer MLP $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$8, the gating network is $$\frac{\partial \rho(x, t)}{\partial t} + \frac{\partial}{\partial x} \left[\rho(x, t) \cdot v(\rho(x, t))\right] = 0, \quad (x, t) \in X \times T,$$9, and the output projection is a 2-layer MLP with hidden dimension 64 and GELU (Ahmad et al., 26 Aug 2025).

The diffusion wrapper uses a DDIM scheduler with $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$0 refinement steps and trained beta values computed as

$\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$1

for $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$2 in reverse order from $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$3 to $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$4, with

$\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$5

(Ahmad et al., 26 Aug 2025). The optimizer, learning rate, batch size, number of epochs, learning-rate schedule, hardware, and training time are not specified in the paper and therefore remain unspecified (Ahmad et al., 26 Aug 2025).

5. Empirical results and ablation findings

DETNO is compared against ON-Traffic, a DeepONet-based traffic operator baseline, and GNOT, a transformer-based neural operator without diffusion refinement (Ahmad et al., 26 Aug 2025). Table 1 reports both single-step and rollout-step-8 metrics using MSE and MAE. At step 1, ONTraffic achieves MSE 0.009 and MAE 0.038, GNOT achieves MSE 0.003 and MAE 0.018, and DETNO achieves MSE 0.002 and MAE 0.022 (Ahmad et al., 26 Aug 2025). Thus DETNO has the best single-step MSE, whereas GNOT has slightly better single-step MAE (Ahmad et al., 26 Aug 2025).

At step 8, ONTraffic has MSE 0.279 and MAE 0.272, GNOT has MSE 0.019 and MAE 0.038, and DETNO has MSE 0.008 and MAE 0.030 (Ahmad et al., 26 Aug 2025). The model sizes are 1.40M for ONTraffic, 1.13M for GNOT, and 1.16M for DETNO (Ahmad et al., 26 Aug 2025). The main reported conclusion is therefore that DETNO’s improvement is not attributable to a dramatically larger parameter count (Ahmad et al., 26 Aug 2025).

The paper emphasizes rollout stability through relative error growth from step 1 to step 8. ONTraffic’s MSE grows by 30.53× and MAE by 7.10×, GNOT’s MSE grows by 6.32× and MAE by 2.11×, and DETNO’s MSE grows by 4.39× and MAE by 1.34× (Ahmad et al., 26 Aug 2025). The text also states that, relative to GNOT at step 8, DETNO achieves 96.0% improvement in MSE and 26.3% improvement in MAE, although the MSE percentage is noted in the source material as inconsistent with the raw values $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$6 (Ahmad et al., 26 Aug 2025). The raw table values themselves show substantial step-8 improvement.

Qualitative evidence is organized around density fields, error maps, spatial profiles, and frequency spectra (Ahmad et al., 26 Aug 2025). DETNO is reported to yield smaller error along sharp transition regions such as congestion fronts, while GNOT’s error grows over rollout time and spreads spatially (Ahmad et al., 26 Aug 2025). In spatial density profiles at $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$7 minutes, DETNO tracks sharp nonlinear segments better, whereas GNOT smooths transitions and shows local bias near discontinuities (Ahmad et al., 26 Aug 2025). In averaged frequency spectra over 300 test rollouts, DETNO follows ground-truth energy much more closely, especially in the high-wavenumber regime, while ONTraffic and GNOT exhibit earlier spectral roll-off (Ahmad et al., 26 Aug 2025). This spectral evidence is used to support the mechanism claim that preserving high-frequency content early reduces compounding rollout errors (Ahmad et al., 26 Aug 2025).

The ablation studies isolate several design choices. Hidden dimension 64 yields MSE 0.0080, outperforming 32 with 0.0142 and 128 with 0.0215 (Ahmad et al., 26 Aug 2025). For number of experts, 3 experts gives MSE 0.0050, compared with 2 experts at 0.0066, 4 experts at 0.0080, and 5 experts at 0.0060 (Ahmad et al., 26 Aug 2025). For minimum noise, $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$8 gives MSE 0.0050, outperforming $\mathcal{G} : \mathcal{A} \rightarrow \mathcal{H},$9 at 0.0103, $\mathcal{A}$0 at 0.0100, and $\mathcal{A}$1 at 0.0099 (Ahmad et al., 26 Aug 2025). For refinement steps, 10 steps gives MSE 0.0050, compared with 5 steps at 0.0054 and 1 step at 0.0131 (Ahmad et al., 26 Aug 2025). For cross-attention design, the concatenated single-stream variant gives MSE 0.0053, whereas two-stream cross-attention gives 0.0050 (Ahmad et al., 26 Aug 2025). The selected configuration is therefore hidden dimension 64, 3 experts, minimum noise $\mathcal{A}$2, 10 refinement steps, and two-stream cross-attention (Ahmad et al., 26 Aug 2025).

6. Relation to adjacent diffusion–operator research

DETNO should be situated within a broader but heterogeneous literature on coupling diffusion models and neural operators. One important neighboring line is conditional diffusion refinement of operator outputs. "Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling" trains a neural operator first and then conditions a score-based diffusion model on its output, with the diffusion stage restoring high-frequency turbulent structure in physical space (Oommen et al., 2024). That work establishes a coarse-to-fine design in which the operator captures low-wavenumber evolution and the diffusion model repairs spectral deficiencies, but it does not use a transformer neural operator in its experiments and does not integrate diffusion inside a transformer operator architecture (Oommen et al., 2024).

A second line is diffusion as a probabilistic output head rather than a modification of operator internals. "Generative Neural Operators through Diffusion Last Layer" introduces the diffusion last layer (DLL), in which a deterministic neural operator backbone produces an input-dependent low-rank basis and a conditional diffusion or flow-matching model is run only over the coefficients in that basis (Park et al., 4 Feb 2026). The paper explicitly states compatibility with neural-field or transformer-based operator backbones, but all experiments use FNO, and the contribution is a modular uncertainty-aware output head rather than a transformer-specific operator (Park et al., 4 Feb 2026). Relative to DETNO, DLL is narrower: it enhances only the output head and leaves the operator evolution law deterministic (Park et al., 4 Feb 2026).

Other related directions differ more substantially. "Wavelet Diffusion Neural Operator" performs diffusion-based generative modeling of entire PDE trajectories in the wavelet domain and emphasizes multiresolution transfer and abrupt-change handling, but it uses U-Net denoisers rather than a transformer neural operator backbone (Hu et al., 2024). "NeurOp-Diff" combines a neural-operator prior with a U-Net diffusion model for continuous remote sensing image super-resolution, yet again without a transformer operator (Xu et al., 15 Jan 2025). "Towards Signed Distance Function based Metamaterial Design" combines a Neural Operator Transformer (NOT) for forward prediction with a conditional diffusion model for inverse design, making it unusually close in spirit to a diffusion-enhanced transformer neural operator framework, but the diffusion model and NOT are coupled procedurally rather than jointly trained as a single forecasting model (Liu et al., 1 Apr 2025). "CViT: Continuous Vision Transformer for Operator Learning" provides a transformer neural operator with continuous query decoding and strong arbitrary-resolution behavior, but it does not employ generative diffusion modeling in the denoising-diffusion sense (Wang et al., 2024).

This comparative landscape suggests that DETNO occupies a specific design point: it is neither merely a probabilistic last layer nor a sequential operator-plus-refiner cascade. A plausible implication is that DETNO’s distinctiveness lies in combining query-based transformer operator learning and diffusion denoising in one architecture targeted at long-horizon traffic rollouts, with spectral preservation as the primary operational objective.

7. Scope, significance, and limitations

DETNO’s reported practical advantage is long-horizon rollout stability under sparse observations and PDE-like traffic dynamics (Ahmad et al., 26 Aug 2025). The model is designed to preserve shock waves, congestion boundaries, and steep density gradients that are central to traffic evolution but often lost under spectral bias in standard neural operators (Ahmad et al., 26 Aug 2025). Because the architecture is query-based, the paper also states that it enables super-resolution queries at arbitrary space-time coordinates rather than being tied to a fixed output discretization (Ahmad et al., 26 Aug 2025). This links DETNO to broader operator-learning goals of resolution flexibility and function-space prediction.

At the same time, the paper’s scope is restricted. The evaluation is conducted in a controlled synthetic LWR–Godunov setting rather than on real traffic sensor networks (Ahmad et al., 26 Aug 2025). The traffic physics are therefore simplified to first-order LWR dynamics, and generalization to real-world traffic data is not demonstrated (Ahmad et al., 26 Aug 2025). Inference also incurs additional cost because DDIM denoising requires iterative refinement steps; the paper argues that this cost is modest because only 10 refinement steps are used, but it remains greater than a single forward-pass operator such as GNOT (Ahmad et al., 26 Aug 2025). Reproducibility is limited by the absence of optimizer, learning rate, epoch count, hardware, throughput, and training-time details (Ahmad et al., 26 Aug 2025). The work further offers an intuitive and empirical argument for spectral preservation and rollout stability, but no formal theorem establishing those properties (Ahmad et al., 26 Aug 2025).

A further conceptual limitation is that DETNO is demonstrated only for traffic forecasting. The paper explicitly places the method in the broader context of scientific machine learning and operator learning, so transfer to other PDE-governed tasks with sharp features and rollout instability is presented as plausible in principle, but such transfer is not experimentally shown (Ahmad et al., 26 Aug 2025). This suggests that DETNO is best regarded, at present, as a domain-specific instance of a more general research program: embedding conditional denoising dynamics inside transformer neural operators to counteract over-smoothing and compounding rollout error in long-horizon operator prediction.