NeurIPS 2026 Papers

Amorphous (disordered) materials require simulation cells with at least hundreds to thousands of atoms to accurately capture their diverse local atomic environments, compared to crystalline materials that can be described by unit cells containing a few to hundreds of atoms. To advance the design of amorphous materials with desired properties and facilitate the exploration of their vast design space, generative inverse design has emerged as a promising approach. It aims to directly output materials with properties closely aligned with the desired ones using probabilistic generative models conditioned on desired properties, which can be more resource efficient than the traditional trial-and-error approach. However, due to the inherent stochasticity of probabilistic generative models, when element assignments are unconstrained, a large portion of generated materials may be charge unbalanced, and no existing methods can effectively mitigate this limitation. In this work, we propose AMGenC, a new generative inverse design method for amorphous materials that can guarantee the generation of charge balanced samples, with minimal additional computational overhead and without sacrificing inverse design accuracy. AMGenC achieves this through an element noise that gives the generation process a starting point centered around charge balance, and the combination of a per-step soft projection and a final discrete projection for steering the elements toward exact charge balance throughout the generation. We perform extensive experiments on two amorphous materials datasets. Experimental results provide evidence that AMGenC achieves its design goal.

We argue that in multiple areas of AI for drug discovery (AIDD), deep learning models are not learning the meaningful biological or chemical features they were hypothesised to capture, but instead learn non-generalisable features, for example, from dataset biases. To address this, we propose the systematic use of misaligned baselines. We define misaligned baselines as models that rely on signals purposely misaligned with the intended learning objective. Rather than modelling the underlying biology or chemistry directly, these baselines draw on reductionist sources of information, heavily perturbed input features, or heuristic models. Competitive performance of such baselines reveals when models are not learning the biologically meaningful representations they were designed to learn. By examining case studies across multiple branches of AIDD, we demonstrate that misaligned baselines have consistently exposed such failure modes and crucially informed the evaluation and improvement of these models. We argue that adopting them as standard practice will help to ensure that progress in AIDD reflects genuine advances rather than artefacts of data-generating processes or evaluation design.

There are now thousands of measurement instruments used to evaluate language models, and the resulting scores are routinely used to support consequential claims about capabilities and risks. Yet, the evidence linking benchmark scores to constructs (i.e., abstract concepts such as reasoning ability or safety) is often implicit, incomplete, or absent. We introduce AI Construct Lexis, a versioned ontology and interactive artifact that extracts and organizes relationships among constructs, behaviors, and measurement instruments as claimed in the literature for LLM evaluation. Drawing on over 8,337 published papers from selected top AI venues, we use language models to extract constructs, definitions, behavioral indicators, and measurement instruments, yielding 977 distinct constructs, 1,694 measurement instruments, 830 behavioral indicators, and 5,900 relationships. The Lexis captures relationships claimed in the published literature, not necessarily validated relationships; valid score interpretation still requires empirical evidence that the instruments capture the constructs they are used to support. By exposing the field’s implicit measurement assumptions, the Lexis enables their inspection, testing, falsification, and refinement into evidence-based relationships. On expert-annotated samples, the extraction pipeline achieves high agreement on extracted concepts while revealing substantial ambiguity in the boundaries of constructs and behaviors. By making these relationships explicit, AI Construct Lexis provides infrastructure for a more rigorous and cumulative science of AI evaluation.

Atomic electron tomography (AET) aims to recover the three-dimensional arrangement, chemical identity, and temporal evolution of individual atoms. Yet most reconstruction methods are still evaluated primarily by voxel-level image similarity, such as PSNR, SSIM, or Fourier shell correlation. In this work, we show that this evaluation practice can select scientifically incorrect reconstructions: a volume that looks accurate can contain atoms that are shifted, merged, missed, hallucinated, chemically mislabeled, or assigned inconsistent identities through time. We introduce AET-Bench, a dataset and benchmark for atom-first evaluation of atomic electron tomography. AET-Bench contains 755 samples across a six-tier realism ladder, spanning clean analytical simulations, binary alloys, 4D atomic dynamics, multislice scattering, real experimental tilt series, and Brownian Pt nanocrystal trajectories. It defines four complementary tasks and evaluates 14 core method families with 21 logged method entries under a unified protocol. AET-Bench reveals a central failure mode hidden by voxel-only evaluation. On the AET-Sim-Mono tier, high-SSIM neural and Gaussian reconstructions can recover substantially fewer atoms than classical or sparsity-regularized methods: for example, 4D-GS achieves strong voxel similarity but only 43.9\% atom recall, while FISTA-TV reaches 95.1\% atom recall despite much lower SSIM. This inversion persists across atom-matching thresholds, showing that it is not an artifact of a single cutoff. On more realistic multislice and real tiers, atom recovery further degrades, exposing a physics gap between visually plausible volumes and reference atomic models. For 4D reconstruction, we find that smooth temporal density fields do not guarantee persistent atom identities. AET-Bench establishes atom-level fidelity as a necessary evaluation axis for AET. Our results suggest that future AET methods should not be judged only by how reconstructions look, but by whether they recover the atoms, species, and trajectories that scientists actually use.

Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system rather than merely answer a prompt. A useful evaluation must let agents choose experiments, interpret finite evidence, and make causal decisions, while keeping the environment coherent across repeated observations and interventions and preserving objective ground truth. We introduce ACED-BENCH (Active Causal Evidence and Decision Benchmark), a neuro-symbolic benchmark built around this requirement. The key idea is to separate the readable scientific surface from the formal causal world: LLMs generate natural-language scenarios and task text, while hidden probabilistic graphical models define the causal graph, data-generating process, intervention semantics, validation checks, and oracle answers. This yields scientific worlds that are natural for agents to investigate but exact enough for scalable, programmatic evaluation. The main leaderboard ACED-CORE contains 180 tasks: 60 set-valued causal ancestry/descendancy questions and 120 constrained causal-decision tasks. We also report ACED-QUAL, a 150-question yes/no qualification check using the same hidden-PGM interface. We evaluate seven contemporary LLMs and two earlier-generation models across three active agent designs. ACED-QUAL checks whether agents can use the interface to draw conclusions from evidence, while ACED-CORE tests whether they can decide what evidence to collect and when to commit to an answer. Evidence-ledger analysis localizes failures within the investigation: agents test the wrong intervention, lose relevant evidence, or continue after sufficient evidence has appeared. These results suggest that reliable AI co-scientists need explicit support for experiment design, evidence retention, answer commitment, and stopping.

Performance predictors are pivotal in accelerating evaluation in NAS. Cross-dataset predictors are trained on (dataset, architecture, performance) triplets to efficiently estimate architectural performance for unseen datasets. However, existing cross-dataset predictors rely on a naive concatenation of dataset and architecture features. This fusion ignores the architecture-aware nature, where the same dataset manifests as distinct representations under different architectures, thereby limiting generalization. To tackle this issue, we propose ACDP, an Architecture-aware Cross-Dataset Performance Predictor that dynamically adjusts dataset feature extraction based on specific architecture. Initially, ACDP develops an elastic mechanism to extract raw dataset traits, providing a configurable lever for the trade-off between efficiency and accuracy. Furthermore, ACDP leverages a hypernetwork to determine dataset feature extractor parameters conditioned on architectural features, transforming raw dataset traits into tailored representations. ACDP excels in both specialization and generalization on six unseen datasets. Notably, in NAS-Bench-201, ACDP achieves 0.758/0.741 Kendall's Tau on CIFAR-10/100, surpassing single-dataset SOTAs trained with over 100 target samples. In MobileNetV3, ACDP discovers architectures outperforming cross-dataset SOTAs, notably gaining +2.04% accuracy on Aircraft. Code: https://anonymous.4open.science/r/ACDP/

Current embodied VLM evaluation relies on static, expert-defined, manually annotated benchmarks that exhibit severe redundancy and coverage imbalance. This labor‑intensive paradigm drains computational and annotation resources, inflates costs, and distorts model rankings, ultimately stifling iterative development. To address this, we propose Agentic Automatic Evaluation (A2Eval), the first agentic framework that automates benchmark curation and evaluation through two collaborative agents. The Data Agent autonomously induces capability dimensions and assembles a balanced, compact evaluation suite, while the Eval Agent synthesizes and validates executable evaluation pipelines, enabling fully autonomous, high-fidelity assessment. Evaluated across 10 benchmarks and 32 models, A2Eval compresses evaluation suites by 85\%, reduces overall computational costs by 77\%, and delivers up to a 4.6$\times$ speedup while preserving evaluation quality. Crucially, A2Eval corrects systematic ranking biases, improves human alignment to Spearman's $\rho=0.85$, and maintains high ranking fidelity (Spearman's $\rho=0.87$), establishing a new standard for high-fidelity, low-cost embodied assessment. Our code and data will be public upon acceptance.

We theoretically and empirically establish a previously unexplored connection between Long-Tailed Recognition (LTR) and Continual Learning (CL). Specifically, we show that training on a long-tailed dataset drives model parameters into an $\mathcal{O}(1/\sqrt{\mathrm{IF}})$ neighborhood of the dominant-class solution, under both uniform and exponentially decaying cardinality distributions. Building on this result, we prove that the CL objective upper-bounds the balanced LTR loss, revealing that LTR can be reformulated as a sequential learning problem. Motivated by this connection, we introduce Continual Learning for Long-Tailed Recognition (CLTR), a principled framework that leverages standard off-the-shelf CL methods to sequentially learn Head and Tail classes while mitigating catastrophic forgetting. Extensive experiments on CIFAR100-LT, CIFAR10-LT, ImageNet-LT, and Caltech256 validate our theoretical predictions and demonstrate strong performance across diverse LTR benchmarks. In the foundation-model regime, CLTR with DualPrompt on a CLIP backbone outperforms standard adaptation baselines and is competitive with specialized methods using external semantic supervision. Our work bridges LTR and CL both theoretically and empirically, providing a principled approach for addressing long-tailed learning using standard CL strategies.

We present a systematic benchmark of out-of-distribution (OOD) detection CSFs through a representation-centric lens. Our study spans CNN and ViT backbones, multiple training paradigms, four image-classification source datasets (CIFAR-10, CIFAR-100, SuperCIFAR-100, and TinyImageNet), and OOD datasets grouped into near, mid, and far regimes using CLIP-derived semantic distances. To compare CSFs across these settings, we employ a multiple-comparison-controlled rank pipeline that identifies top cliques of statistically indistinguishable winners under threshold-free ranking metrics (AURC and AUGRC). The main empirical finding is that the competitive detector family depends more on the learned representation than on score design alone. For both CNNs and ViTs, simple probabilistic scores dominate misclassification detection. On CNNs, margin-based scores are strongest in near-OOD regimes, while geometry-aware scores such as NNGuide, fDBD, and CTM become more competitive as shift severity increases. On fine-tuned ViTs, the top cliques are led mainly by reconstruction- and residual-based scores. To interpret these ranking shifts, we analyze the last-layer representation using Neural Collapse (NC) metrics. The resulting picture is consistent across architectures: prototype- and boundary-aware scores become stronger when the representation is more collapsed and better aligned with classifier weights, whereas weaker-collapse regimes favor gradient- and manifold-based scores. Building on these insights, we propose two contributions: a simple PCA-based projection-filtering procedure that improves detector performance, and an approach that uses NC measurements computed from a trained classifier to predict its competitive out-of-distribution detector shortlist, without requiring any additional OOD data.

Deep Q-Networks (DQNs) approximate value iteration by freezing a target network, forming Bellman-optimality targets, and running an inner regression loop that minimizes a smooth \emph{surrogate loss} (e.g., MSE/Huber loss) on data from a replay buffer. Yet, control is governed by the Bellman optimality operator's $\gamma$-contraction in $\ell_\infty$, whereas common objectives (MSE/Huber loss) measure progress in an averaged $\ell_2/\ell_1$ geometry; thus, improving the surrogate need not reduce the worst-case Bellman error that drives performance. To bridge this gap we connect the progress in the inner loop for each surrogate to the Bellman residual under the $\ell_\infty$ norm. Our analysis provides explicit thresholds denoted by $\alpha$ under which sufficient progress guarantees a $\ell_\infty$ contraction up to a standard approximation floor. This yields new convergence guarantees for DQN with fixed targets trained using MSE or Huber-type losses, and motivates a novel soft-$\ell_\infty$ surrogate that smoothly approximates the sup-norm and better matches the $\ell_\infty$ geometry resulting in the least stringent inner loop accuracy requirements. Across Atari benchmarks, soft-$\ell_\infty$ consistently reduces the worst-case Bellman residual and matches or improves returns relative to standard objectives, providing a practical, geometry-aligned recipe for stable DQN training.

How much of zero-shot benchmark accuracy is captured by word-level alignment between a pre-training corpus and the benchmark target? We study this question through word-level unigram cross-entropy (UCE), a tokenizer-agnostic, model-free measure of lexical alignment between a reference corpus and a target benchmark. Likelihood training allocates probability mass according to the pre-training corpus, and likelihood-based zero-shot evaluation partly reads out the lexical alignment between this mass and the benchmark target. Cleanly tracing how the lexical imprint of pre-training data scales with corpus--benchmark alignment requires controlled from-scratch pre-training that varies corpus identity while holding the training recipe fixed. Across this controlled sweep, spanning $11$ zero-shot benchmarks, $4$ pre-training corpora, and $3$ model scales, we find that corpus--benchmark lexical alignment, UCE, consistently tracks zero-shot accuracy on every benchmark, with corpora more aligned with the benchmark (lower UCE) achieving higher accuracy. The same signal extends from measurement to control at finer scopes. At the document level, strengthening the imprint by selecting training documents to maximize lexical alignment with the benchmark yields a data-selection rule that improves accuracy over same-size random sampling and an importance-resampling baseline across sources and scales. At the prompt level, matching the imprint per prompt by routing each prompt to the drafter whose domain corpus is most lexically aligned with it gives a training-free routing rule for speculative decoding and improves throughput over a generalist-drafter baseline. Taken together, pre-training corpora leave a measurable lexical imprint on zero-shot behavior, and UCE provides a model-free probe of lexical alignment between reference and target data.

AI security has become a critical issue as Deep Neural Networks (DNNs) are inherently vulnerable to adversarial attacks. While previous studies have demonstrated that adversarial training is one of the most effective defensive strategies, improvements in robustness often show significant class-wise disparities. This has established fair adversarial training, which aims to enhance the robustness of the worst-performing classes, as a crucial research direction. Although existing class-wise approaches can improve performance by adjusting individual class weights, they face severs scalability limitation on large-scale datasets due to the sparse volume of data available per class. To mitigate this issue without relying on explicit class-wise information, we propose a novel tri-regime optimization framework that fundamentally decouples the distinct sources of adversarial error at the sample level. Specifically, our method systematically suppresses unlearnable outliers via reliability gating, prioritizes genuine boundary threats through dynamic reweighting, and down-weights already safe examples to prevent redundant optimization. By isolating and prioritizing the effective adversarial frontier, our method strictly prevents robust overfitting and achieves superior worst-class robustness while maintaining high clean accuracy.

Quantum reinforcement learning (QRL) has emerged as a promising approach for controlling quantum systems and accelerating learning tasks with quantum resources. However, most existing QRL formulations still retain a classical reinforcement learning interface: states and actions are often encoded in computational bases and rewards are extracted through measurement. This is less natural for fully quantum environments in which the relevant state information may live in an unknown basis and intermediate measurements can disturb the trajectory. This motivates a fully QRL framework in which the agent, environment, rewards, and return accumulation are all represented quantum mechanically. In this paper, we take a step toward this goal and introduce a new QRL framework in which state, action, reward, and reward-accumulation registers evolve without intermediate measurement. In this framework, both the policy and the environment are modeled by *conditional quantum channels*: the policy updates the action register conditioned on the state, while the environment updates the state and reward registers conditioned on the action, without measuring or overwriting the corresponding control register. We then propose a quantum policy-gradient algorithm for parameterized conditional quantum channels with a trainable control basis. We also derive finite- and infinite-horizon gradient estimators using a new parameter-shift rule. The algorithm samples a single shifted policy block from a geometric distribution, yielding an unbiased gradient estimator under bounded rewards. Finally, we establish convergence of the resulting stochastic gradient-ascent procedure to stationary points under standard smoothness and step-size assumptions.

Accurately attributing causes of death is vital for global health, yet fewer than 5% of deaths in resource-constrained regions are medically certified. To assign causes to these unlabeled deaths at scale, practitioners traditionally rely on verbal autopsy, using supervised statistical models to classify based on structured survey data. However, modern mortality surveillance increasingly collects rich, unstructured multimodal data, such as free-text caregiver narratives and postmortem diagnostics, which traditional supervised statistical models struggle to seamlessly integrate. In this paper, we present a comprehensive, multimodal benchmark for cause-of-death classification using data from the Child Health and Mortality Prevention Surveillance (CHAMPS) network, a unique surveillance platform spanning nine countries across South Asia and Sub-Saharan Africa. Using this dataset, we introduce an evaluation framework designed to rigorously assess diagnostic reasoning, moving beyond traditional metrics that fail to capture complex clinical realities. We demonstrate the utility of this benchmark by evaluating zero-shot large language models against supervised baselines across various data modalities. Our results reveal distinct differences in how these modeling approaches synthesize unstructured medical evidence. This benchmark provide a rigorously defined resource for assessing clinical reasoning in next-generation mortality surveillance.

Block-wise post-training quantization (PTQ) compresses LLMs by minimizing per-block reconstruction error on a small calibration set. We show that the \emph{composition} of this set (200 samples) is a targeted lever at extreme bit widths, acting only on iterative methods. In a controlled study spanning six data-dependent PTQ methods plus a data-free baseline, six models (1.7B--8B) across three families plus 70B verification, three bit widths, and a 10-task evaluation with four-seed replication, we find a sharp \textbf{near-cliff method-class divide}. In each method's pre-cliff regime (where the model retains predictive function), iterative methods (SignRound~V2, OmniQuant, AQLM) gain 0.74--1.93pp on the 10-task average from switching Pile to a fragility-guided dataset (FragCal-DC), while single-pass analytical methods (GPTQ, AWQ) show near-zero response ($|\Delta|{\le}0.30$pp). Both classes are evaluated on functional Pile baselines (0.42--0.61, well above chance); above the cliff, every method becomes data-composition-insensitive. A reconstruction-loss paradox (the method that most reduces its proxy loss gains nothing downstream, while the method that barely moves the proxy gains the most) sharpens the mechanism; fixed-reference held-out evaluation confirms the proxy--downstream disconnect. A wrong-answer contamination ablation retains 87--91\% of the improvement and a data-dependent single-pass control (RTN-opt) shows zero response, isolating the iterative optimization loop. A weight-space probe shows SignRound-V2 preserves higher per-group scale heterogeneity than GPTQ on 9 of 9 (model, width) cells; a scale-flatten knockout shows this heterogeneity is necessary for iterative fidelity. We release FragCal-DC, the discriminative-task instance used here, as a drop-in JSON for the evaluated setting.

Low-rank adaptation (LoRA) is the de facto method for parameter-efficient fine-tuning of large neural networks, achieving strong task adaptation with minimal trainable parameters and low optimization cost. Beyond this efficiency, practitioners have observed two striking properties: *robustness to catastrophic forgetting* and the ability to *merge independently trained adapters* into a single model that performs competitively across multiple tasks. These properties are central to continual and multi-task learning, yet remain poorly understood. In this work, we provide a theoretical explanation through a unified *margin-based perspective*. We analyze LoRA in multiclass linear classification under a near-orthogonal task regime with $l_2$-regularization, and characterize the optimal LoRA adapter across regularization regimes. Our analysis shows that, in an intermediate regime of regularization parameters, the optimal adapter aligns with the *max-margin solution* on the fine-tuning data. Building on this characterization, we derive two key consequences. First, we obtain closed-form expressions for the margins on pre-training and fine-tuning data, revealing a precise margin trade-off: the regularization parameter controls the balance between retention and adaptation. Second, we analyze adapter merging, proving that merged models achieve positive margin on each task and deriving optimal mixing coefficients that balance margins across tasks and maximize the margin over their union. These results lead to a simple, training-free merging rule, which we term *margin-based LoRA merging* (MaLoRA). Experiments on modern architectures and real datasets validate our theoretical predictions, showing that MaLoRA matches or outperforms several adapter-merging baselines across a range of vision and language classification tasks.

Class-incremental learning (class-IL) faces a structural obstacle: discriminative sequential training optimises only the diagonal blocks of a pairwise loss matrix, leaving the inter-task blocks---task confusion (TC)---unminimised. A recent Infeasibility Theorem makes this precise, proving that no discriminative class-IL learner can attain the joint-training optimum even when catastrophic forgetting (CF) is perfectly addressed. Yet the state of the art consists almost entirely of discriminative methods built on pretrained foundation models that approach joint-training performance. We explain this regime by showing that pretraining \emph{exponentially attenuates} the obstacle. For any pretrained backbone $\phi$, the TC gap of any CF-optimal prototype-based discriminative learner---a class that captures the test-time classifier of SLDA, RanPAC, FeCAM, and the prototype branches of EASE and InfLoRA---is bounded by $\exp(-\gamma_{\min}(\phi)/8)$, where $\gamma_{\min}(\phi)$ is the worst-case Mahalanobis margin between task centroids in feature space: a single, computable scalar that determines how much TC remains. Combined with a finite-sample convergence analysis under heavy-tailed gradient noise and a representation drift bound governing backbone fine-tuning, this yields a diagnostic decomposition of the class-IL excess risk into $E_{\mathrm{CF}} + E_{\mathrm{TC}}$, with disjoint direct dependencies on optimisation budget and backbone quality. Across 12 (backbone, dataset) configurations spanning ViT-IN21K, DINOv2, and CLIP plus four random-backbone anchor points, the pooled empirical slope of $\log(\mathrm{TC\ gap})$ versus $\widehat\gamma_{\min}$ on softmax cross-entropy heads is $-0.119$ (bootstrap 95\% CI $[-0.134, -0.105]$), consistent with the predicted $-1/8$ and excluding the conservative fall-back $-1/16$. The slope fit extends the rate beyond the formal LDA/QDA scope. $E_{\mathrm{CF}}$ scales as $K^{-(\alpha-1)/\alpha}$ as predicted, with the rate steepening by more than $3\times$ under gradient clipping; representation drift scales linearly in the backbone learning rate ($r = +0.998$). A theory-derived training procedure---whose schedule is determined by $\widehat\gamma_{\min}$, the tail index $\widehat\alpha$, and warm-up estimates of smoothness and curvature---is competitive with the strongest pretrained-CIL baselines across the four standard benchmarks.

Graphic-design layouts are structured, multi-layered artifacts: a single composition specifies the position, type, z-order, and styling of dozens of components, and a single template typically begets a family of sibling variants that share its structural theme but differ in content, palette, or imagery -- properties that flat-canvas layout benchmarks discard. We introduce \textsc{GraphicDesignBench} (GDB), a diagnostic benchmark of $16$ tasks along two axes: \emph{spatial composition} ($8$ understanding + $4$ generation) and \emph{template variants} ($3$ understanding + $2$ generation), grounded in a new dataset of $1{,}148$ real-world templates with full component hierarchy, per-element styling, and sibling groupings. Across four frontier VLMs (GPT-5.4, Gemini-3.1-Pro / Flash-Lite, Claude-Opus-4.6) and two image generators (GPT-Image-1.5, Gemini-3.1-Flash-Image), GDB exposes concrete and dissociated gaps: component detection sits an order of magnitude below natural-image baselines, layer-order reasoning decouples from other spatial skills, partial completion collapses from single- to multi-element settings, and a simple font-Jaccard baseline matches or exceeds frontier VLMs on sibling matching and clustering. We release the dataset, tasks, metrics, and per-model outputs to track where current models are, and are not, ready to act as design collaborators.

We present 3D consistency tokens, a compact set of 3D anchors that ground cross-view correspondence in explicit geometry. While feedforward Gaussian models amortize per-scene optimization into a single forward pass, their rendering quality still falls short of optimization-based pipelines. A primary cause lies in the relatively inaccurate correspondences they recover by comparing each image token to others across views based on appearance and semantic cues, which can be ambiguous in repetitive or semantically uniform regions. The proposed consistency tokens alleviate such ambiguity by routing image tokens that look alike but originate from different parts of the scene to distinct anchors, so that cross-view matches are established through shared 3D locations rather than appearance alone. We construct the consistency tokens from point clouds produced by either Structure-from-Motion or modern geometry foundation models, both of which can be noisy or incomplete. To address this, we adopt a bidirectional cross-attention architecture in which the two token sets co-refine one another, with image tokens gaining geometric grounding from the consistency tokens and the consistency tokens being corrected by the appearance cues of the images. On DL3DV-10K and in cross-dataset evaluations on Mip-NeRF 360 and Tanks \& Temples, our approach surpasses prior feedforward models and attains quality competitive with optimization-based pipelines, while preserving single-pass efficiency.