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NeurIPS 2026

Simplifying Transformer-Based U-Net Neural Physics Simulators

Pietro Sittoni, Francesco Tudisco

U-Net--style architectures are widely adopted for modeling physical systems, as their multiscale structure enables efficient processing of high-resolution data while reflecting the hierarchical structure of many physical phenomena. The most successful U-Net-based neural physics simulators often combine convolutions, Fourier layers, or specialized transformer mechanisms such as windowed or axial attention; however, these structured components can limit adaptability across different spatial and spatiotemporal dimensions. In this work, we revisit transformer-based U-Nets with the goal of simplifying attention-based neural physics simulators without sacrificing multiscale efficiency or predictive accuracy. We introduce \texttt{UFlex}, a simple attention-based U-Net whose blocks are obtained through only minimal modifications to standard self-attention, allowing the same architecture to be applied across one-, two-, three-dimensional, and spatiotemporal regular-grid problems without major design changes. Evaluated on seven challenging benchmarks (four 2D and three 3D), \texttt{UFlex} scales to resolutions of up to $512 \times 512$ in 2D and $256 \times 128 \times 256$ in 3D, while reducing training memory and accelerating training compared to state-of-the-art transformer baselines, all while achieving state-of-the-art predictive accuracy.

NeurIPS 2026

Simplified Reversible Residual Networks

Erland Brandser Olsson, Zhirong Yang

The de facto standard way of training neural networks requires storing activations at each layer to memory, in order to compute gradients in backpropagation. As progress has often been made by stacking larger and more layers, memory consumption has now become a major bottleneck for making frontier models available to everyone. In this work, we take advantage of the Residual Network (ResNet) layer formulation that is now used in many state-of-the-art neural network architectures to recompute the activations at each layer on-the-fly during backpropagation instead of storing them in memory. We propose a modification to Reversible Residual Network (RevNet) that overcomes its limitation of requiring to replace the ResNet layer network function with two new network functions, by setting either one to an identity function. This modification is simple yet has a significant advantage because now there is no need to change the original ResNet architecture. Our method can work as a drop-in replacement for layers with residual connections such as in ordinary ResNets or Transformers with practically no loss in performance while offering significant reductions in memory consumption. With this simple modification, we are able to maintain the same computational cost and ease of use as activation checkpointing, which requires no architectural changes, while leveraging a reversible procedure such that we only need to store the activations from the last layer. The method is called Simplified RevNet, and compared to previous work in reversible architectures, we here propose a simpler and more streamlined approach that comes in two variants based on which of $F$ or $G$ in RevNet becomes an identity function. Empirically, we demonstrate performance at practically the same level as the non-reversible counterparts on ImageNet image classification with ResNets and on OpenWebText language modeling with Transformers.

NeurIPS 2026

SimpleEvol: Efficient Intelligence Conversion via Less Human Prior in Automated Heuristic Design

Jianghan Zhu, Cong Zhang, Rongjie Zhu, Chi Zhang, Zhiguang Cao

Large language models (LLMs) have emerged as powerful tools for automated heuristic design (AHD), enabling iterative generation and refinement of heuristics. However, the dominant paradigm embeds LLMs as narrow, fixed components, such as crossover or mutation, within heavily hand‑engineered evolutionary frameworks. We argue this misapprehends LLMs. It treats them as specialized tools rather than general reasoners, constrains them to low‑level operations, and underutilizes their autonomy. Moreover, the extensive human priors in these frameworks violate the bitter lesson principle that general methods scaling with computation surpass hand‑crafted solutions. This raises a key question: *which AHD framework designs best convert stronger LLM capabilities into better heuristics?* To address this, we propose metrics for AHD framework handcraftedness (AHI) and intelligence conversion efficiency (ICE). Evaluating ten LLMs across three challenging optimization problems, we obtain a notable finding that frameworks with fewer human priors consistently yield higher ICE. Based on this finding, we propose SimpleEvol, which removes nearly all structural constraints and allows the LLM to operate autonomously. SimpleEvol consistently achieves the highest ICE, often by a large margin. Our results challenge the trend toward complex AHD pipelines and point to a simpler and more model‑centric alternative, suggesting that reducing human priors is a more effective strategy to scale up with model intelligence.

NeurIPS 2026

Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization

Seongjae Kang, Dong Bok Lee, Hyungjoon Jang, Sung Ju Hwang

Semi-supervised learning (SSL) has emerged as a practical solution for addressing data scarcity challenges by leveraging unlabeled data. Recently, vision-language models (VLMs), pre-trained on massive image-text pairs, have demonstrated remarkable zero-/few-shot performance that often surpasses SSL approaches due to their exceptional generalization capabilities. This gap motivates us to question: how can we effectively harness the powerful generalization capabilities of VLMs into task-specific models? Knowledge distillation (KD) offers a natural framework for transferring VLM capabilities, but we identify that it suffers from gradient conflicts between supervised and distillation losses. To address this challenge, we propose Dual-Head Optimization (DHO), which introduces dual prediction heads for each distinct signal. We observe that DHO resolves gradient conflicts, enabling improved feature learning compared to single-head KD baselines, with practical benefits of minimal computational overhead and test-time hyperparameter tuning without retraining. Extensive experiments across 15 datasets show that DHO consistently outperforms KD baselines, often outperforming teacher models with smaller student models. DHO also achieves new state-of-the-art performance on both in-distribution ImageNet semi-supervised learning and out-of-distribution generalization across ImageNet variants. We will publicly release our code and model checkpoints to facilitate future research.

NeurIPS 2026

Simple Test-Time Refinement for Plot-to-Code Generation via Visual-Code Diagnostics

Peiming Guo, Jinpeng Huang, Yangbin Sun, GaoXiong Cao, Ensheng Shi, Yuchi Ma, Yu Zhang, Meishan Zhang, Baotian Hu, Min Zhang

Plot-to-code generation has been greatly advanced by recent multimodal large language models (MLLMs). However, existing studies mainly focus on one-pass generation or iterative sampling guided by verifier-based filtering, which does not align with the typical human programming workflow of generate first, then iteratively error-location and fix. This naturally motivates a test-time refinement paradigm for plot-to-code generation.In this work, we present the first study on plot-to-code generation via iterative test-time refinement. We propose a Visual-Code Diagnostics framework that enables refinement through visual feedback from source code. Specifically, we first categorize generation errors into four coarse-grained types, and then train lightweight discriminators to recognize and localize these errors based on the rendered visual outputs and corresponding code. Unlike traditional compiler- or debugger-based verification, our approach performs iterative refinement guided by one diagnostics system, enabling more effective semantic and visual correction during test time. Extensive experiments demonstrate that our method consistently improves generation quality across diverse generators and significantly outperforms strong test-time scaling baselines.

NeurIPS 2026

Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization

Haris Moazam Sheikh

Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to explicitly account for trade-offs among non-preferential objectives. In this work, we show that such complexity might be unnecessary. We propose a framework that extends standard single-objective acquisition functions directly to the multi-objective setting through a hypervolume-based transformation. We further extend hedge strategies for acquisition functions, which are typically used only in single-objective optimization, to the multi-objective regime. Our approach requires minimal modification to existing Bayesian optimization pipelines and avoids the need for bespoke multi-objective formulations. We demonstrate how a broad class of commonly used single-objective acquisition functions and hedge strategies can be adapted in a principled manner to handle multiple objectives, while preserving their intuitive interpretation and computational efficiency. Empirically, we evaluate the proposed methods across a range of synthetic and real-world multi-objective benchmarks. Despite their simplicity, our extensions consistently match or outperform more complex state-of-the-art MOBO methods in terms of optimization performance and sample efficiency. These results suggest that effective multi-objective Bayesian optimization can be achieved by reusing and carefully extending well-established single-objective acquisition strategies, offering a simpler and more flexible alternative to existing approaches.

NeurIPS 2026

Simple Baselines are Competitive with Code Evolution

Yonatan Gideoni, Sebastian Risi, Yarin Gal

Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing code. While code evolution pipelines have shown impressive performance across domains, many are highly complex and are typically not compared to simpler alternatives. To prevent bad comparisons from stagnating the field, we propose two simple baselines and compare them to popular pipelines across three domains: finding better mathematical bounds, designing agentic scaffolds, and machine learning competitions. Surprisingly, the baselines match or outperform existing methods across all domains, prompting a closer look at which factors drive performance in different settings. For mathematical bounds, we find that the search space matters far more than the exact search algorithm, with both simple and complex methods performing similarly given the same search space. Thus, expanding the search space is what matters for improving performance, and does so across all pipelines. Moreover, different domain knowledge embedded in prompts can significantly affect the search's efficiency, potentially leading to overestimating a pipeline's sample efficiency. For automated agentic scaffold design, we show that typical high-variance evaluations lead to all automated search methods picking subpar scaffolds relative to a simple majority vote. To mitigate this, we propose improved evaluation procedures that reduce stochasticity while keeping the search economically feasible. Overall, these results indicate that further improving code evolution's performance depends more on the setup surrounding the search pipeline than on the pipeline itself. We hope these insights will enable developing new code evolution methods and help spur meaningful progress in the field.

NeurIPS 2026

Similarity-Constrained Reweighting for Complex Query Answering on Knowledge Graphs

Daniel Daza, Alberto Bernardi, Luca Costabello, Christophe Gueret, Michael Cochez, M.C. Schut

Machine learning models for answering logical queries on knowledge graphs estimate the likelihood of answers that are not reachable via direct traversal. Recent work has proposed extending such queries with \emph{soft entity constraints}, which require the \emph{target variable} to be similar or dissimilar to specified sets of entities. We study a more general formulation where similarity constraints are specified on an arbitrary variable in a query, and introduce SCORE: a computationally efficient and interpretable method for incorporating similarity constraints on an arbitrary variable of a query. SCORE is based on a lightweight and interpretable score adjustment function that only requires tuning two hyperparameters on a validation set. Our experiments show that SCORE effectively incorporates preferences while maintaining strong query answering performance. It significantly improves ranking quality when constraints apply to the target variable and performs effectively in the more general case involving constraints on intermediate variables.

NeurIPS 2026

SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning

Haoqiang Kang, Xiaokang Ye, Yuhan Liu, Siddhant Hitesh Mantri, Lingjun Mao, James Fleming, Drishti Regmi, Lianhui Qin

LLM/VLM-based digital agents have advanced rapidly thanks to scalable sandboxes for coding, web navigation, and computer use, which provide rich interactive training grounds. In contrast, embodied agents still lack abundant, diverse, and automatically generated 3D environments for interactive learning. Existing embodied simulators rely on manually crafted scenes or procedural templates, while recent LLM-based 3D generation systems mainly produce static scenes rather than deployable environments with verifiable tasks and standard learning interfaces. We introduce SimWorld Studio, an open-source platform built on Unreal Engine 5 for generating evolving embodied learning environments. At its core is SimCoder, a tool/skill-augmented coding agent that writes and executes engine-level code to construct physically grounded 3D worlds from language/image instructions. SimCoder self-evolves by using verifier feedback (e.g., compilation errors, physics checks, VLM critiques) to revise environments and autonomously add reusable tools and skills to its library. Generated worlds are exported as Gym-style environments for embodied agent learning. SimWorld Studio further enables co-evolution between environment generation and embodied learning: agent performance feedback guides SimCoder to generate adaptive curricula near the learner’s capability frontier, so that environments become increasingly challenging as the embodied agent improves. Three case studies on embodied navigation show that self-evolution improves generation reliability, generated environments substantially improve embodied agent performance that generalizes to unseen benchmarks, and co-evolution yields an 18-point success-rate gain over fixed-environment learning and a 40-point gain over an untrained agent.

NeurIPS 2026

SimVLA: Attributing Gains in VLA Models Through Controlled Ablation

Yuankai Luo, Woping Chen, Tong Liang, Zhenguo Li

Vision-Language-Action (VLA) models often bundle architectural changes with different pretraining data, backbone scales, and optimization recipes, obscuring what actually drives progress. We introduce SimVLA, a deliberately minimal VLA—a standard vision-language backbone with a lightweight continuous-action head—as a controlled testbed for this attribution problem. Across LIBERO, CALVIN, WidowX, and Google Robot, one-knob-at-a-time ablations show that training dynamics are the largest measured driver, producing performance swings of $\Delta$$\approx$54-89\%, while task configuration and architecture have smaller effects under matched settings. Despite using a small backbone and no additional large-scale robot-data VLA pretraining before benchmark-specific training, SimVLA reaches 98.6\% average success on LIBERO, remains competitive across the other benchmarks, and transfers to held-out real-robot scenes. These results suggest that simple, carefully controlled VLA baselines remain underexplored and provide a calibration point for future architectural claims within the VLM-encoder plus lightweight-head family.

NeurIPS 2026

SimSD: Simple Speculative Decoding in Diffusion Language Models

Junxia Cui, Haotian Ye, Runchu Tian, Hongcan Guo, Jinya Jiang, Haoru Li, Chaojie Ren, Yiming Huang, Kaijie Zhu, Zhongkai Yu, Kun Zhou, Jingbo Shang

Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decoding. However, their masked language modeling formulation remains incompatible with standard token-level speculative decoding, one of the most effective acceleration techniques for AR models. In AR decoding, the causal mask preserves temporally valid token-level contexts, enabling a target model to verify multiple drafted tokens in a single forward pass. In contrast, dLLMs rely on mask tokens and bidirectional attention, causing the effective context to change across denoising steps and preventing direct token-level speculative verification. To bridge this gap, we propose a simple but effective speculative decoding algorithm for diffusion language models, named SimSD, which mainly adopts a plug-and-play masking strategy that equips dLLMs with temporally valid token-level contexts for speculative decoding. Our method explicitly introduces reference tokens from draft-model predictions and designs an attention mask that regulates their interaction with current-step tokens, allowing dLLMs to compute valid logits for drafted tokens in a single forward pass. This restores the key verification ability provided by causal masking in AR models while preserving the parallel decoding advantages of dLLMs. The proposed method is training-free and can be flexibly integrated with other acceleration techniques such as KV cache and blockwise decoding. Experiments on SDAR-family dLLMs across four benchmarks show that our method achieves up to \(7.46\times\) higher decoding throughput while maintaining and even improving average generation quality. Our code will be publicly released.

NeurIPS 2026

SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization

Yan Sun, Guoxia Wang, Jinle Zeng, Jiabin Yang, Shuai Li, Li Shen, Dacheng Tao, Dianhai Yu, Haifeng Wang

Pretraining large language models (LLMs) with next-token prediction has led to remarkable advances, yet the context-dependent nature of token embeddings in such models results in high intra-class variance and inter-class similarity, thus hindering the efficiency of representation learning. While similarity-based regularization has demonstrated benefit in supervised fine-tuning and classification tasks, its application and efficacy in large-scale LLM pretraining remains underexplored. In this work, we propose the SimReg, an embedding similarity regularization loss that explicitly encourages token representations with the same ground-truth label within each sequence to be more similar, while enforcing separation from different-label tokens via a contrastive loss. Our analysis reveals that this mechanism introduces gains by enlarging multi-classification margins, thereby enabling more efficient classification. Extensive experiments across dense and Mixture-of-Experts (MoE) architectures demonstrate that SimReg consistently accelerates training convergence by over 30% and improves average zero-shot downstream performance by over 1% across standard benchmarks. Further ablation studies and analyses offer practical insights into hyperparameter tuning and loss effectiveness.

NeurIPS 2026

SiliconBench: Speed, Memory, and Fidelity for LLM Inference on Apple Silicon

Ranran Haoran Zhang, Aysa Fan, David M Correia, Alex Cheema, Rui Zhang

Apple Silicon is a major consumer platform for local LLM inference, with at least ten serving engines competing for users. Speed-only leaderboards on this platform mislead: the fastest stack often claims memory the rest of the machine needs, or silently produces wrong output. We introduce \ourbench, a benchmark that evaluates ten Apple Silicon inference stacks through three lenses: throughput and latency at concurrency 1, 8, and 16; peak memory under contention with the operating system; and fidelity as weighted F1 on a classification task against an NVIDIA reference run on identical weights. The benchmark covers three model releases and two workload types (short chat and multi-turn agent prompts averaging ${\sim}$4K input tokens). A maintainer agent re-runs the full benchmark weekly; in its first five runs it caught a single upstream library bump that broke three of ten stacks through three distinct failure modes and diagnosed a chat-template error that corrupted one stack's fidelity scores. No audited stack performs well across all three lenses, and the stack rankings shift when memory and fidelity are read alongside speed. The candidate set, stacks without disqualifying gaps under any lens, reduces from ten to four. A separate multi-node comparison shows that tensor parallelism over RDMA scales decode $1.3\times$ on two nodes, while pipeline parallelism over TCP regresses $16$--$19\%$. In every case the gaps are in the engines, not the hardware: Apple Silicon provides sufficient memory, compute, and interconnect; the engines have not yet exploited it. We release the harness, per-run results, and weekly journals.

NeurIPS 2026

SiliciclasticReservoirs: A Million-Reservoir Dataset and Flow-Matching Foundation Model for 3D Siliciclastic Reservoir Generation

Ilgar Baghishov, Elnara Rustamzade, Graeme Henkelman, John Foster, Michael J Pyrcz

3D subsurface geological models underpin hydrocarbon exploration, geothermal energy, underground gas storage, and geological carbon storage. A decade of deep generative facies modeling has yet to yield a model that generalizes across geological settings, scales to realistic reservoir extents, and conditions naturally on well data, bottlenecked by the absence of a large-scale, multi-environment, property-rich dataset. We release SiliciclasticReservoirs, the largest and most diverse public 3D dataset of siliciclastic (sandstone-bearing) subsurface reservoirs to date: $10^6$ voxelized reservoirs at $64{\times}64{\times}32$ resolution spanning eight depositional settings (deep-water turbidite lobes, deltas, and six fluvial channel architectures from the Alluvsim family), each annotated with binary and six-class facies labels, per-voxel porosity and permeability, and a natural-language caption describing the reservoir's geological setting and petrophysical attributes. To our knowledge, it is the first siliciclastic dataset to pair 3D geological models with petrophysical properties at this scale, the first to span this breadth of settings in a unified release, and the first to ship text captions, supporting future multimodal and language-conditioned training for text-to-reservoir generation and retrieval. Building on SiliciclasticReservoirs, we train ResFlow, a flow-matching foundation model that, from a single set of weights, generates any environment under continuous parametric control, conditions on arbitrary well configurations via classifier-free guidance, and assembles reservoirs at arbitrary spatial extent through overlapping-tile MultiDiffusion, with no retraining. SiliciclasticReservoirs (CC-BY 4.0), ResFlow (MIT), and the data-generation engine ResMill (MIT) are publicly released.

NeurIPS 2026

Signed-Permutation Coordinate Transport for RMSNorm Transformers

John Sweeney

Modern LLM workflows increasingly move coordinate-indexed objects across checkpoints: steering vectors, sparse autoencoders, top-$k$ neuron sets, attribution lists, and merge alignments. This is only well posed after fixing the model's residual-stream gauge. We show that the native discrete gauge is architecture-dependent: LayerNorm residual charts have permutation gauge $S_d$, up to a global sign flip, while RMSNorm residual charts with generic per-channel gain have signed-permutation gauge $B_d = S_d \ltimes \{\pm 1\}^d$. Thus permutation-only alignment is symmetry-incomplete for RMSNorm models. We introduce sign-marginalized Hungarian matching and prove a sharp population failure mode: with decorrelated source coordinates, raw signed-correlation matching has a structural permutation-accuracy ceiling equal to the fraction of positive signs in the true gauge up to $O(d \cdot 2^{-d})$, whereas sign-marginalized matching removes this obstruction. We then make coordinate-preserving transport, rather than function-level merging, the primary object: composing saved-checkpoint local $B_d$ gauges along same-base fine-tuning trajectories recovers 91.1% of cross-run coordinates at 1500 steps versus 60.3% for endpoint matching, and the gain is not explained by merely routing through the base. The recovered gauge transfers tools that permutation-only alignment breaks: TinyLlama SAE reconstruction has NMSE $0.004$ under $B_d$ recovery versus $1.08$ under $S_d$; Qwen sentiment steering preserves 95.8% of its effect versus 17.2%; refusal steering reverses sign under $S_d$. Coordinate-preserving merge tests show the same mechanism. The same covariance governs stateful training: signed transport of AdamW state preserves the resumed trajectory, while permutation-only state transport starts from a functionally identical checkpoint but follows a different trajectory. Finally, we give gauge-sweep audits for index-level interpretability claims: coordinate names are reproducible only relative to an explicit gauge.

NeurIPS 2026

Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks

Jinhao Li, Yuhao Sun, Zhiyuan Ma, Hao He, Xinche Zhang, Xing Chen, Li Jin, Sen Song

Recurrent spiking neural networks (RSNNs) are a promising substrate for energy-efficient control policies, but training them for high-dimensional, long-horizon reinforcement learning remains challenging. Population-based, gradient-free optimization circumvents backpropagation through non-differentiable spike dynamics by estimating gradients. However, with finite populations, high variance of these estimates can induce harmful and overly aggressive update steps. Inspired by trust-region methods in reinforcement learning that constrain policy updates in distribution space, we propose **Signal-Adaptive Trust Regions (SATR)**, a distributional update rule that constrains relative change by bounding KL divergence normalized by an estimated signal energy. SATR automatically expands the trust region under strong signals and contracts it when updates are noise-dominated. We instantiate SATR for Bernoulli connectivity distributions, which have shown strong empirical performance for RSNN optimization. Across a suite of high-dimensional continuous-control benchmarks, SATR improves stability under limited populations and reaches competitive returns against strong baselines including PPO-LSTM. In addition, to make SATR practical at scale, we introduce a bitset implementation for binary spiking and binary weights, substantially reducing wall-clock training time and enabling fast RSNN policy search.

NeurIPS 2026

Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise

Dingzhi Yu, Hongyi Tao, Yuanyu Wan, Lijun Zhang

While adaptive gradient methods are the workhorse of modern machine learning, sign-based optimization algorithms such as Lion and Muon have recently demonstrated superior empirical performance over AdamW in training large language models (LLM). However, a theoretical understanding of why sign-based updates outperform variance-adapted methods remains elusive. In this paper, we aim to bridge the gap between theory and practice through the lens of heavy-tailed gradient noise, a phenomenon frequently observed in language modeling tasks. Theoretically, we introduce a novel generalized heavy-tailed noise condition that captures the behavior of LLMs more accurately than standard finite variance assumptions. Under this noise model, we establish sharp convergence rates of SignSGD and Lion for generalized smooth function classes, matching or surpassing previous best-known bounds. Furthermore, we extend our analysis to Muon and Muonlight, providing what is, to our knowledge, the first rigorous analysis of matrix optimization under heavy-tailed stochasticity. These results offer a strong theoretical justification for the empirical superiority of sign-based optimizers, showcasing that they are naturally suited to handle the noisy gradients associated with heavy tails. Empirically, LLM pretraining experiments validate our theoretical insights and confirm that our proposed noise models are well-aligned with practice.

NeurIPS 2026

SierpinskiCam: Camera-Controlled Video Retaking with Sierpinski Triangle Pattern Cues

Suttisak Wizadwongsa, Hyelin Nam, Supasorn Suwajanakorn, Jeong Joon Park

Generating novel renderings of a scene along user-defined camera trajectories from a single monocular video, dubbed video retaking, is a compelling but difficult problem in content creation and visual effects. Existing geometry-guided approaches reconstruct a 4D representation from the source video and render it along the target trajectory to condition video diffusion models. However, this guidance degrades as the target camera departs from the source trajectory, leaving newly revealed regions sparse or entirely missing. We propose SierpinskiCam, which addresses this limitation by augmenting geometry-based guidance with Sierpinski dome texture cues that contains rich trackable features even under large viewpoint changes. We further introduce a reference video conditioning mechanism that appends source-video tokens to the target-token sequence and separates the two streams with negative RoPE indices, enabling appearance grounding without architectural modification or per-video adaptation. Extensive experiments show that SierpinskiCam achieves significant gains in camera controllability, geometric consistency, and video quality across diverse and challenging retaking scenarios.

NeurIPS 2026

Shuffle and Joint Differential Privacy for Generalized Linear Contextual Bandits

Sahasrajit Sarmasarkar

We present the first algorithms for generalized linear contextual bandits under shuffle differential privacy and joint differential privacy. While prior work on private contextual bandits has been restricted to linear reward models---which admit closed-form estimators---generalized linear models (GLMs) pose fundamental new challenges: no closed-form estimator exists, requiring private convex optimization; privacy must be tracked across multiple evolving design matrices; and optimization error must be explicitly incorporated into regret analysis. We address these challenges under two privacy models and context settings. For stochastic contexts, we design a shuffle-DP algorithm with regret $\tilde{O}\!\big(d^{3/2}\sqrt{T \log T} + d^{5/4} \sqrt{T/\varepsilon} (\log T)^{3/4}\big)$ in the dominant term, matching the non-private rate $\tilde{O}(d \sqrt{T \log T})$ in the leading-in-$T$ term up to a multiplicative factor of $\sqrt d$ and a privacy correction term of $\tilde{O}(d^{5/4}\sqrt{T \log T/\varepsilon})$. For adversarial contexts, we provide a joint-DP algorithm with regret $\tilde{O}\!\big(d\sqrt{T}\log T + d^{3/4}\sqrt{T/\varepsilon}\,(\log T)\,(d+\log T)^{1/4}\big)$ matching the non-private rate $\tilde{O}(d\sqrt{T}\log T)$ in the leading term. Unlike prior work on locally private GLM bandits, our methods require no spectral assumptions on the context distribution beyond $\ell_2$ boundedness.

NeurIPS 2026

Show Me What You Don’t Know: Efficient Sampling from Invariant Sets for Model Validation

Armand Rousselot, Joran Wendebourg, Ullrich Kothe

The performance of machine learning models depends on discovering features in the data that are relevant to solving the given task while ignoring irrelevant variation. To check and visualize whether this is the case, we propose ‘invariance auditing’, a diagnostic tool to analyze feature extractors. Invariance auditing generates diverse inputs which all yield the same features as a given query input, and are therefore treated as identical by the model. Using external knowledge about the task at hand, this enables assessing if the learned invariances do or do not conform to the task's required semantics. Unlike existing work where a dedicated generative model is trained for each feature extractor, our algorithm is training-free and exploits a pretrained diffusion or flow-matching model to sample invariant inputs. Our fiber loss -- which penalizes feature mismatch -- guides the denoising process so the output shares the same representation as the query input. This replaces days of training with a single guided generation procedure at the same quality. Experiments on popular datasets and model types demonstrate that our auditing method reveals invariances spanning very desirable and concerning behavior. For instance, it detects cases where Qwen-2B places patients with situs inversus (heart on the right side) in the same fiber as typical anatomy.

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