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NeurIPS 2026

Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?

Trisha Mittal, Akshay Mehra, Joshua Kimball

Dataset distillation (DD) has emerged as a prominent approach in data centric machine learning, aiming to synthesize compact training sets for efficient training by compressing the information in large datasets into a small number of synthetic samples. However, DD methods are often evaluated under inconsistent evaluation protocols, ranging from standard ERM to single/multi‑teacher supervision, making it difficult to isolate the effectiveness of distilled data from evaluation. Moreover, many prior methods claim that DD outperforms data pruning approaches such as coreset selection (CS), based on the assumption that restricting condensed datasets to subsets of real samples fundamentally limits their expressiveness. In this work, we critically evaluate DD methods through large-scale experiments using standardized datasets and evaluation protocols to assess their intrinsic effectiveness. We benchmark seven state-of-the-art DD methods on ImageNet-1K, ImageNet-100, and ImageNette, using three widely adopted training protocols against three CS strategies. Our results show that while some DD methods fail to outperform even simple random subsets, the SOTA DD approaches are comparable to or worse than coresets on large‑scale datasets and incur a substantially higher cost for construction. Beyond accuracy, we also evaluate the representativeness, diversity, and quality of condensed sets, and find that coresets consistently achieve better coverage of the original data distribution. These findings highlight the limited practical advantages of current DD methods and show that coresets remain competitive and are often a more computationally efficient alternative for data-centric learning.

NeurIPS 2026

Rethinking Contrastive Targets in Radiology Language-Image Pretraining

Yunhe Gao, Ashwin Kumar, Jiaming Liu, Chong Wang, Maya Varma, Fuying Wang, Stefania L Moroianu, Louisa Fay, Sergios Gatidis, Akshay Chaudhari, Curtis Langlotz

CLIP-style contrastive image--text pretraining is widely used in radiology data, where each image is paired with one report and every other report in the batch is treated as a negative. Under this recipe, two failures appear, neither systematically addressed by existing contrastive learning theory: zero-shot evaluation peaks then degrades while training loss keeps decreasing, and training fails to learn at all at large batches. We hypothesize the cause is structural rather than radiology-specific: many unpaired reports share clinical findings with a given image, so CLIP's paired-identity target is systematically biased, not merely noisy. To test this, we construct CLEVR-Radiology, a synthetic testbed that preserves only this overlapping-fact structure and reproduces both failures. With evidence that the structure is sufficient to reproduce the failures, we develop a target-misspecification theory that derives both failures from a single deterministic gradient bias amplified by batch size, and use it to motivate a minimal compatibility-based correction, CorLIP: a text is treated as positive whenever its clinical assertions agree with the image, with no other change to the training pipeline. Beyond benchmark improvements, CorLIP removes the post-peak degradation, makes large-batch training succeed where it previously collapsed, substantially amplifies the gains from more data, and is the only configuration that remains stable up to B=2048. The correction also transfers from chest X-ray to 3D CT modalities.

NeurIPS 2026

Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder

Zidan Wang, Yaqian Li, Xiaokai Zhang, Kun He, Kaiwen Long, Hanpeng Liu

CLIP serves as a foundational vision-language model and the de facto vision encoder for downstream VLMs such as LLaVA. Post-training offers a resource-efficient route to refine CLIP, but recent work argues that the standard contrastive loss is unsuitable for post-training due to catastrophic forgetting under small batches, motivating designs that abandon the contrastive objective in favor of distillation. We revisit this premise and find that the reported forgetting is not caused by insufficient negatives, but by an inappropriate magnitude of the contrastive temperature $\tau$: with $\tau$ set sufficiently small, contrastive loss becomes the strongest single-loss post-training objective. Building on this finding, we propose \textbf{ComCLIP}, a complementary post-training framework that freezes CLIP's text encoder---preserving compatibility with downstream VLMs---and trains the vision encoder under three complementary losses operating in the shared contrastive space: a properly-tempered contrastive loss for image-text alignment, an MSE anchoring loss against the original CLIP for preserving the pretrained feature manifold, and a relational distillation loss from DINOv2 for fine-grained visual knowledge injection. With only one epoch on small data, ComCLIP consistently outperforms prior post-training methods on ViT-B/16 and ViT-L/14 across zero-shot classification, retrieval, linear probing, and MMVP, achieving up to $\mathbf{+7.41}$ points on MMVP. Furthermore, ComCLIP serves as a drop-in vision encoder for LLaVA-7B, improving average performance across $8$ standard VLM benchmarks without any re-alignment of the LLM or projector.

NeurIPS 2026

Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching

Chenyu Zhang, Yuhang Cao, Yingxi Lu, Daru Du, Jing Shao, Jiajun Liu, Ruoqu Chen, Liu Cao, Yicheng Liu, Hang Zhao, Mengdi Xu

Autoregressive Vision-Language-Action (VLA) models offer a scalable path to robot learning, yet existing action tokenizers treat tokenization as a compression problem, producing representations that are semantically misaligned with the autoregressive backbone. We propose CATok, a causal action tokenizer that reframes tokenization as a causally structured generative process. CATok introduces a conditional annealing mechanism that extracts action tokens by progressively annealing a flow-matching process: each token is conditioned on all preceding tokens and encodes the residual reconstruction signal at a specific noise level, establishing a coarse-to-fine causal token space whose generative semantics are structurally aligned with autoregressive modeling. A token-conditioned flow-matching decoder built on Multimodal Diffusion Transformer (MMDiT) reconstructs continuous action chunks from these discrete tokens with the precision of hybrid diffusion-head architectures. This discrete bottleneck enforces knowledge insulation by design, cleanly separating high-level semantic reasoning from low-level motor execution without requiring explicit attention masking. Extensive evaluations across three simulation benchmarks demonstrate that CATok consistently surpasses existing tokenization methods in both reconstruction fidelity-compression tradeoff and inference efficiency, while improving VLA task success rate, establishing a high-performance, scalable foundation for purely autoregressive VLA systems. Our project page is available at https://causalactiontokenizer.github.io.

NeurIPS 2026

Rethinking CT Synthesis through Semantics-Structure Alignment

Riyu Qiu, Qichao Zhou, Jiacheng Wang, Liansheng Wang

Mask-guided CT synthesis bridges structural annotations with imaging appearance, facilitating data augmentation and clinical tasks such as radiotherapy planning. Despite plausible visual realism, current methods seek optimal solutions by treating all tissues uniformly, failing to capture intrinsic CT properties such as tissue-specific distributions and strict anatomy, leading to semantic misalignment and structural hallucinations. To address this, we decompose CT synthesis into semantics-structure alignment through the proposed Semantics and Structure Loss. Specifically, the Semantic Intensity Distribution (SID) Loss partitions the broad Hounsfield Unit (HU) range into semantic subspaces to achieve tissue-specific intensity alignment. Building on SID-based semantic alignment, the Structural Anatomy (SA) Loss further improves structural fidelity by regularizing gradient manifolds within clinically-informed spatial domains. Extensive experiments across GAN, Diffusion, Flow, and foundation models demonstrate the broad compatibility and consistent gains of our approach. A user study further confirms the clinical realism and mask fidelity, while downstream segmentation demonstrates the practical utility. Code will be released after acceptance.

NeurIPS 2026

Rethinking Bayesian Optimization for Co-Optimizing LLM Training Configurations

Zhiliang Chen, Alfred Leong, Shao Yong Ong, Apivich Hemachandra, Gregory Kang Ruey Lau, Chuan Sheng Foo, Zhengyuan Liu, Nancy F. Chen, Bryan Kian Hsiang Low

Fine-tuning an LLM to maximize performance on a downstream task requires finding the optimal data and model configuration. This is a black-box optimization problem that Bayesian optimization (BO) addresses by sequentially evaluating training configurations and using observed performance feedback to adaptively guide the search towards better configurations. However, directly applying BO to the joint data-model space suffers from two fundamental challenges: the prohibitive cost of full LLM training at each iteration, and the large number of BO iterations required for effective exploration due to the high problem dimensionality. This paper introduces JoBS, a BO-based approach that co-optimizes data and model configurations without requiring a full training run at every iteration. JoBS allocates an initial portion of the optimization budget to learn a scaling-law-inspired performance predictor that estimates fully trained LLM performance from only a small number of training steps. The remaining budget is then used to run BO with this predictor, eliminating the need for full training runs and enabling JoBS to explore significantly more data and model configurations within the same budget. We analyze JoBS' average regret and derive the optimal budget allocation between predictor learning and BO iterations. Empirically, JoBS outperforms independent data and model optimization methods and existing multi-fidelity BO baselines across a diverse set of downstream tasks. Our code is available at https://github.com/a35453779/JoBS.

NeurIPS 2026

Rethinking Attention in Depth for Operator Learning

Jaehyeon Lee, Kiwon Um, JungHyun Han, Min-Koo Kang

Transformers have rapidly emerged as powerful surrogate models for learning solution operators for partial differential equations (PDEs). Standard attention tends to exhibit depth-wise representational bottlenecks across layers, with the role of depth remaining underexplored in modeling complex PDEs. In this paper, we rethink the role of depth by promoting it to an explicit computational dimension of attention kernel construction, rather than a passive architectural parameter. With this strategy, each attention block's native representation is enriched via a gated mixture of inter-layer differences, enabling information to be coupled and propagated across layers. The depth-wise hierarchy admits a theoretically motivated multi-kernel interpretation with weakly correlated kernels and improved generalization bounds. Beyond dense feature aggregation, our approach yields a more expressive form of depth-wise attention, mitigating spectral collapse, preserving interaction-scale diversity, and improving intra- and inter-layer route utilization. Extensive empirical evaluations demonstrate its broad compatibility across state-of-the-art solvers, presenting consistent performance gains on eight PDE benchmarks.

NeurIPS 2026

Rethinking "RL Generalizes, SFT Memorizes": The Role of SFT Data

Yunlong Hou, Fengzhuo Zhang, Yuan Cheng, Jiachun Pan, Xingyao Li, Zhuoran Yang

Large language models trained with Reinforcement Learning (RL) with verifiable rewards exhibit strong reasoning ability and broad generalization, whereas models trained with Supervised Fine-Tuning (SFT) are often viewed as more prone to memorization and limited transfer. This paper rethinks this distinction through the lens of SFT training data. First, we study the role of data source and show that it is critical: a carefully mixed SFT dataset substantially outperforms data generated solely by a larger model. Second, we study the role of data scale and show that matching the number of correct rollouts between SFT and RL greatly improves SFT generalization, while matching the total rollout budget enables SFT to generalize as well as RL. Combining these two factors further enables SFT to generalize even better than RL. Third, by using LLM annotations to characterize the solution methods in training rollouts, we show that larger datasets cover more tail methods and that these tail methods provide generalizable reasoning signals. Finally, we support these empirical findings theoretically by analyzing the training dynamics of shallow transformers under both RL and SFT.

NeurIPS 2026

Rethink Action Chunking in VLA Through Human Motor Control

Wenxi Chen, Yuejiang Liu, Zijian He, Shaoshuai Mou, Yan Gu

Action chunking is widely used in recent vision–language–action (VLA) systems to mitigate inference latency and improve temporal consistency. Yet, open-loop execution can reduce reactivity and precision, and cross-chunk coherence remains difficult to guarantee. We revisit action chunking through its neurobiological origins and organize current shortcomings around three axes: VLA architecture, representations of sequential actions, and mechanisms for sampling and executing chunks. A case study further illustrates the coherence–reactivity trade-off across representative sampling and execution choices. Building on these observations, we outline brain-inspired directions to improve the reactivity, coherence, and safety of chunking-based VLAs in dynamic and complex tasks beyond quasi-static tabletop manipulation. This position paper aims to broaden how the community thinks about action chunking and to motivate redesigns better aligned with future physical intelligence.

NeurIPS 2026

Retain-Neutral Surrogates for Min-Max Unlearning

Junhao Cai, Dohun kim, Dowon Kim, Sung Il Choi, Chengjun Jin, Juhyun Park, Changhee Joo

Machine unlearning seeks to remove the influence of designated training data while preserving performance on the remaining data. Approximate unlearning can be viewed as a local editing problem; in min-max unlearning, the key local object is the surrogate point at which the retain objective is evaluated. When forget and retain gradients are strongly aligned, an unconstrained forget-maximizing perturbation can move to a surrogate point that increases retain loss. We propose Retain-Orthogonal Surrogate Unlearning (ROSU), which constrains the inner surrogate construction by maximizing first-order forget gain subject to zero first-order retain change under a fixed perturbation budget. This yields a closed-form retain-orthogonal perturbation, a lightweight transported outer update, and amplification along the retain-neutral direction. Our analysis establishes (i) a curvature-controlled second-order bound on retain damage, (ii) a positive-alignment regime in which ROSU strictly reduces surrogate retain loss relative to standard min-max perturbations, and (iii) near-equivalence when the two gradients are nearly orthogonal. Across vision and language benchmarks (CIFAR-10/100, Tiny-ImageNet, TOFU, WMDP), the empirical pattern follows this geometry: ROSU gives its clearest gains in high-coupling regimes while remaining competitive elsewhere.

NeurIPS 2026

Rest-Tuning: Data-Efficient Adaptation of EEG Foundation Models to Individuals via Resting-State Signals

Yinuo Zhang, Xinyu Fu, Qing Wang, Yinte Zhang, Junjie Yu, Kexin Lou, Quanying Liu

EEG foundation models (FMs) offer strong population-level priors but degrade on individual subjects due to anatomy-driven distribution shifts. We propose Rest-Tuning, a task-agnostic calibration framework that personalizes a population FM using just 1–3 minutes of unlabeled resting-state EEG. Rest-Tuning employs masked teacher-student alignment to produce a subject-calibrated backbone, reused as initialization across multiple tasks from the same subject. Across two multi-subject datasets and four EEG FMs, Rest-Tuning outperforms task-only adaptation, with up to 13.3% absolute gain in downstream task accuracy and over 40% average reduction in labeled task data. Calibration saturates quickly and requires under 3 minutes of compute per subject. Representation analyses show reduced subject-to-population distance and smoother LoRA adapter interpolation paths, indicating improved compatibility between rest- and task-adapted solutions. These results establish resting-state EEG as a practical, fast, and reusable calibration interface, enabling population EEG FMs to function as reliable, subject-specific BCIs and serve as reusable initialization for downstream task adaptation.

NeurIPS 2026

Response Time Enhances Alignment with Heterogeneous Preferences

Federico Echenique, Alireza Fallah, Baihe Huang, Michael Jordan

Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this standard approach assumes that all labelers share the same underlying preferences, ignoring the fact that real-world labelers are highly heterogeneous and usually anonymous. Consequently, relying solely on binary choice data fundamentally distorts the learned policy, making the true population-average preference unidentifiable. To overcome this critical limitation, we demonstrate that augmenting preference datasets with a simple, secondary signal—the user's response time—can restore the identifiability of the population's average preference. By modeling each decision as a Drift-Diffusion Model (DDM), we introduce a novel, consistent estimator of heterogeneous preferences that successfully corrects the distortions of standard choice-only labels. We prove that our estimator asymptotically converges to the true average preference even in extreme cases where each anonymous labeler contributes only a single choice. Empirically, across both synthetic and real-world datasets, our method consistently outperforms standard baselines that otherwise fail and plateau at a bias floor. Because response times are essentially free to record and require zero user tracking or identification, our results bring promises and open up new opportunities for future data-collection pipelines to improve the social benefit without requiring user-level identifiers or repeated elicitations.

NeurIPS 2026

Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data

Qiyang Zhang, Xinhao Li, Lei Shi, Zheng Lin, Jinfeng Wen, Ao Zhou, Shangguang Wang

Onboard satellite models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated. However, updating large-scale model parameters in orbit presents significant challenges due to the limited uplink bandwidth of Low Earth Orbit (LEO) satellite systems, particularly for hyperspectral satellite imagery, where high-dimensional spectral–spatial inputs lead to increased model size and update costs. Existing full fine-tuning methods are thus expensive to retrain and difficult to deploy under strict communication constraints. To address this challenge, we propose NE-LoRA, a parameter-efficient adaptation framework for bandwidth-constrained onboard hyperspectral model updates. NE-LoRA combines a primary low-rank branch with a nonlinear auxiliary branch to capture both global update trends and complex spectral–spatial variations. Additionally, we introduce a differentiated training strategy for multi-matrix adapters, motivated by the asymmetric initialization and gradient dynamics of different adapter matrices. Experiments on four hyperspectral datasets and three representative backbone models demonstrate that NE-LoRA consistently outperforms LoRA-based baselines and remains competitive with, and in several cases superior to, full fine-tuning. Across the evaluated settings, NE-LoRA updates only a small fraction of the total parameters on average while preserving low deployment overhead, offering a favorable accuracy–communication trade-off for onboard hyperspectral adaptation.

NeurIPS 2026

Resolving Time-Frequency Ridge Crossings via Frequency-Rate Lifting

Pingping Pan, You Li, Yunjian Zhang, Mu-Jiang-Shan Wang

Ridge crossings, where two components share the same instantaneous frequency (IF) at a given time, are a fundamental bottleneck in multi-component time-frequency (TF) mode decomposition. We trace this difficulty to \emph{insufficient representational dimensionality} and resolve it by lifting signals into the 3-D time-frequency-frequency-rate (TFFR) space $(t,f,\dot{f})$, where crossing ridges separate because they typically differ in the instantaneous frequency rate (IFR). We formalize this via a separability proposition and confirm it empirically through Monte Carlo simulation (crossing rate: 78.6\% in 2-D $\to$ 0.9\% in 3-D). Based on this principle, we propose \textbf{TFFR-Net}, a query-based masked transformer that jointly predicts per-ridge masks and dense IFR maps. An IFR feedback loop injects the estimated frequency rate back into each query embedding, enabling the decoder to perform instance discrimination in TFFR space rather than the TF plane. On a synthetic benchmark with up to six chirp components under broadband noise (0--25\,dB SNR), TFFR-Net consistently reduces IF MAE by at least 5.8\% and Dice loss by at least 5.4\% relative to competing methods. Evaluation on real-world signals (bat echolocation, power-system oscillation, earthquake vibration) demonstrates effective generalization beyond synthetic training data. The source code is available at \url{https://anonymous.4open.science/r/TFFR-92D2/}.

NeurIPS 2026

Resolving AdaBoost Cycling with LLMs: A Computer-Assisted Counterexample

Erik Wang

We give a computer-assisted counterexample to the open question posed by Rudin, Schapire, and Daubechies in COLT 2012, of whether exhaustive AdaBoost always converges to a finite cycle. The construction is based on a block-product gadget whose two factors share an exact period-2 orbit for their 5-step branch maps, but whose linearized return maps have dominant eigenvalues with an irrational logarithmic ratio. This irrationality forces the burst-winner sequence to have an irrational asymptotic frequency, precluding eventual periodicity. All assertions are certified by exact rational arithmetic in two independent computer algebra systems. We also document the collaborative workflow that led to the construction, in which one model produced the key technical arguments; another model summarized and critiqued different versions of that work, proposing new directions to pursue; and the authors orchestrated the process by selecting directions to prioritize, resolving ambiguities, heavily refining each argument, and verifying the final solution. We present this as one of the first in-depth case studies of LLM-assisted mathematical research in which a long-standing open problem from theoretical machine learning is resolved, and detail the advantages and challenges of such research.

NeurIPS 2026

Resolution-Aware Structural Density Peak Clustering

Jie Yang, Hsiang-Ting Chen, Yan Ma, Xinyan Liang, Avinash Singh, Liang Du, Cheng-You Lu, Chenglong Zhang, Bingbing Jiang, Weiping Ding, Wei Chen

Density Peak Clustering (DPC) focuses on identifying centers and propagates labels at the sample level, which makes density estimation cutoff-sensitive and assignment chain-vulnerable, yielding suboptimal performance under heterogeneous densities and complex cluster structures. This paper argues that density peaks inference should instead be performed on resolution-controlled structural units: density becomes a stable count statistic, relative distance inherits the separation of the enclosing hierarchy, and assignment runs over a compact graph of structural units rather than long sample-to-sample chains. This view is realized in Resolution-Aware Structural Density Peak Clustering (RSDP), which builds a constrained hierarchy, selects an admissible level via a single resolution parameter, and applies a one-pass DPC rule on the selected structural units. Under a faithful-realization condition, it is proved that RSDP's empirical $\gamma$-ranking recovers the dominant population units and that its one-pass assignment recovers the sampled ground-truth partition. On synthetic and real-world datasets, RSDP achieves the highest ACC and NMI against state-of-the-art DPC variants, exceeding the strongest by 19.91% and 9.09% in average ACC and NMI, respectively.

NeurIPS 2026

Resilient Semi-Supervised Inference with Heterogeneous Unlabeled Data

Mengyuan Wang, Chengde Qian, Haojie Ren, Changliang Zou

Model-assisted semi-supervised learning offers a powerful paradigm for enhancing the efficiency of statistical estimation and inference by leveraging black-box predictions on unlabeled data. However, the validity of these methods typically relies on the strict assumption that labeled and unlabeled populations share identical distributional characteristics. Consequently, state-of-the-art approaches, such as prediction-powered inference, become fragile when this assumption is violated. To address the fragility, we propose a robust framework for semi-supervised learning that remains reliable under distributional heterogeneity. By embedding a robust statistical calibrator into the prediction-rectification mechanism, the approach effectively reduces the bias arising from shifted or corrupted unlabeled samples. To maximize data utility, we introduce an adaptive cross-validation procedure to select the optimal calibrator, ensuring a reliable trade-off between statistical efficiency and robustness. Theoretical analysis confirms the consistency of the proposed estimator under mild conditions, while empirical results demonstrate its significant superiority over conventional baselines in heterogeneous environments.

NeurIPS 2026

Resilient Latent Readouts for Long-Context Question Answering

Jingyi Liao, Wenhao Sun, YITING LI, Zhao Jin, Khin Mi Mi Aung, Xun Xu, Zhuoyi Lin, Rong-Cheng Tu, Dacheng Tao

Long-context question answering often asks a model to answer from extended documents, multi-turn dialogues, or persistent histories. Directly prefilling the full history preserves all observed tokens, but it is expensive and can expose generation to substantial irrelevant or spuriously salient context, which may hinder evidence localization and answer synthesis. This motivates compact memory interfaces that provide a short, query-conditioned readout of the history. Existing systems predominantly instantiate this readout as text, where the reader sees only what was selected and any evidence left out is unrecoverable, leaving the interface fragile to selection errors. We argue that memory readout has two coupled axes that should be optimized separately: evidential coverage, whether all right support is selected, and inferential resilience, how much answerability remains under imperfect selection. To jointly address these two axes, we propose LIRA, a latent-memory reader that exposes full-prefix contextualized states rather than re-encoded text. It amortizes one full-prefix pass into a reusable latent store; for each query, it localizes candidate positions with calibrated endogenous attention, repairs incomplete support with coverage-aware planning, and packs selected states into a position-consistent readout for autoregressive generation. Across three open-weight backbones on LoCoMo and Loong, LIRA is the strongest non-oracle compact-memory method and surpasses Full Context on 8 of 9 LoCoMo metrics. When localization misses all supporting evidence, LIRA improves F1 by up to 13.3 points over a text-native control with identical positions, suggesting that full-prefix states preserve answer-relevant influence discarded by text-native re-encoding.

NeurIPS 2026

Resilient Byzantine Agreement with Predictions

Julien Dallot, Darya Melnyk, Tijana Milentijević, Stefan Schmid, Patrik Welters

The Byzantine Agreement (BA) problem is a fundamental task in distributed computing where nodes in a network need to agree on a common output in the presence of arbitrary (worst-case) node failures. In real-world applications, nodes can be monitored over time to provide ML predictions of faulty behavior in a network. In this work, we answer the question of whether predictions can improve the fault tolerance of a distributed system without sacrificing correctness when the predictions are wrong. We consider a prediction-augmented study of Byzantine agreement in which each node receives, in addition to its input bit, a predicted set of honest nodes. We focus on algorithmic resilience --- the maximum number of faulty nodes an algorithm can tolerate --- and present algorithms and impossibility results whose resilience depends on the accuracy of the predictor. As our first main result, we bring a complete characterization of the consistency--robustness trade-offs in both the non-authenticated and authenticated settings: for $n$ nodes and a parameter $\alpha \in [0, 1]$, we present algorithms that tolerate up to $\alpha \cdot n$ faulty nodes when the predictor is correct (consistency), and up to $\frac{1-\alpha}{2} \cdot n - 1$ faulty nodes when the predictor is arbitrarily wrong (robustness). In the authenticated setting, the robustness bound improves to $(1-\alpha) \cdot n - 1$. We prove matching impossibility results, showing that these tradeoffs are optimal and independent of the particular prediction system. Our second main result characterizes smoothness: the rate at which resilience degrades as the predictor becomes less accurate. We show that resilience linearly decreases in the number of wrong predictions as long as that number stays within a constant fraction of $n$. Concretely, in the non-authenticated setting, each additional wrong prediction loses one unit of resilience, whereas in the authenticated setting, the decline is halved, since two wrong predictions are needed to lose one unit of resilience.

NeurIPS 2026

Resilience Matters for Embodied Agents System: New Metrics, Systematic Evaluation, and Optimization

Yapeng Liu, Yuanzhao Zhai, Xudong Gong, Feng Dawei, Bo Ding, Lin Wang, Huaimin Wang

As Embodied Agents System (EAS) move into physical domains such as autonomous navigation and household assistance, reliable execution becomes critical for physical task completion and collaboration trust. Recent EAS reliability evaluation works typically focus on outcome-centric metrics as success rate or safety-only scores to assess an agent's performance, which collapse diverse execution trajectories into coarse outcomes. Therefore they ignore a critical property of EAS -- which we define as the **Resilience** -- that reflects how EASs recover, stabilize, and extend under perturbations and across iterative updates. The lack of resilience is particularly critical in open-world environments due to continuous unexpected disruptions, thus directly affecting the quality of EAS deployment. To address this problem, we gain insight from the resilience-engineering concepts to EAS groundings and propose a novel resilience evaluation framework that can be flexibly applied to any EAS. Specifically, we define the first comprehensive resilience metrics suite for EASs system that exposes *Rebound*, *Stability*, and *Graceful Extensibility* across embodied tasks execution, providing a practical grounding for EAS resilience analysis. We further implement the resilience evaluation layer that transforms execution evidence into assessments for comparison, diagnosis and optimization. Across 400 household tasks with 10 representative EAS baselines, our evaluation reveals process-level distinction hidden by outcome metrics, including recovery cost differences among successful episodes ($\Delta C_{\mathrm{rec}}=25.2$), increased instability under semantic perturbations, and task-family-specific degradation under stress. Metrics-guided optimizations reduce recovery cost by 42.94%, increase stability by 19.87%, and improve graceful extensibility completion by 10.08%, showing the diagnostic effect of resilience evaluation. Beyond these targeted gains, our results reveal trade-offs among resilience characteristics, suggesting that a resilient EAS construction should be evaluated and configured according to deployment-specific requirements. Our code and data are available at .

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