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NeurIPS 2026

Residual-Autoregressive Context for 3D Gaussian Splatting Compression

Wenqing Wang, Huimin Zeng, Yun Fu

3D Gaussian Splatting (3DGS) has emerged as a promising method that enables fast and high-quality novel-view rendering. However, its large parameter size remains a bottleneck for storage and transmission. We propose *RacerGS*, a compact 3DGS compression framework that achieves scene reconstruction at significantly reduced storage sizes with preserved fidelity. To obtain compact and spatially coherent features, we design a hash projection model that projects the anchor hash encodings into compact context features. Additionally, we introduce a FiLM-modulated autoregressive GRU context model that utilizes causal dependencies among anchor feature groups and uses recurrent hidden states to refine entropy-model parameters, improving bitrate efficiency. Furthermore, we adaptively entropy-code quantized residuals of anchor attributes around their context-predicted means, reducing symbol entropy by leveraging spatial consistency. Overall, *RacerGS* achieves more than **$\mathbf{150}\boldsymbol{\times}$** compression over vanilla 3DGS and **$\mathbf{28}\boldsymbol{\times}$** over Scaffold-GS, while providing comparable rendering quality. Extensive experiments across five datasets demonstrate our superiority over prior 3DGS compression methods in both storage reduction and rate-distortion performance.

NeurIPS 2026

Residual Paving: Diagnosing the Routing Bottleneck in Selective Refusal Editing

Bryce Hinkley, Peyman Najafirad

We study selective refusal editing as a three-way control problem: induce non-refusal on designated edit prompts while preserving benign behavior and harmful refusals outside the edit set. We introduce Residual Paving, a routed residual editing method for frozen instruction-tuned transformers that separates route selectivity, whether to intervene, from residual-edit capacity, what edit to apply. An early-layer router predicts a scalar gate and expert mixture; when active, prompt-conditioned bottleneck residual experts apply later-layer residual updates while leaving the backbone unchanged. This decomposition supports an oracle-routing diagnostic where only the learned scalar gate is replaced with the held-out edit/keep label, leaving the residual editor and frozen backbone fixed. On the primary Gemma-3-4B-IT held-out split, learned Residual Paving reduces edit refusal from 88.6% to 4.0%, with 95.5% benign distribution preservation and 87.3% harmful distribution preservation. Same-protocol one-direction steering controls are much weaker on edit success, leaving edit refusal at 86.8% for Edit-target ActAdd and 78.9% for DIM-style refusal steering. The remaining failure is off-target harmful-keep degradation: harmful refusal remains below the frozen-base rate, 65.3% vs. 81.6%. Across six backbones, oracle routing improves the keep-side diagnostic score on every reported row, with median gain +12.9 pp, supporting the interpretation that learned route selectivity is the main observed bottleneck. Trajectory diagnostics on two backbones further suggest directed movement toward edit-target continuations rather than generic refusal suppression.

NeurIPS 2026

Residual Expertise Is Not Decision Value

Nidhish Shah, Shaurjya Mandal, Asfandyar Azhar

Audits of human-AI complementarity target the wrong estimand. They ask whether the human knows more than the model; deployment asks whether that knowledge would change the action. Residual predictive expertise is not residual decision value. Deployment rewards actions, and a posterior movement that does not cross a reward-induced action boundary changes nothing; a signal can be informative under log-loss and leave the deployed action unchanged. We formalize the missing quantity as **boundary regret**: the regret of holding the model's action after observing the human. In any finite-action Bayesian decision problem, the value of consulting the human equals expected boundary regret, formally verified in Lean 4. The identity turns when humans help into a geometric question: which decision facets does the human signal move belief across? Complementarity is therefore reward-relative. A given human signal can be valuable under one reward matrix and worthless under another, the human's knowledge unchanged; the boundary moved, not the expertise. The gap is widest precisely where asymmetric costs dominate: high-stakes clinical and operational decisions. Audits built on residual predictive expertise overstate the value of human review and route scarce review toward cases where the human is informative but the action will not move. Boundary regret is the quantity to measure.

NeurIPS 2026

Residual Calibration via Local Feature-Space Refinement

Xihao Wang, Chengyao Yu, Ruixing Ming

Deep neural networks often struggle to provide reliable confidence estimates, making post-hoc calibration essential for reliable decision-making. Existing post-hoc methods largely rely on score-based transformations, which improve marginal calibration but often fail to capture spatially varying miscalibration in local regions of the learned feature space. To address this gap, we propose *Local Adaptive Probability Estimation* (LAPE), a post-hoc residual calibration framework. Specifically, LAPE treats the global calibrator output as a stable anchor and refines the individual prediction by leveraging non-parametric local evidence from neighboring calibration samples. As a plug-and-play refinement module, LAPE effectively utilizes the information about feature-space geometry and can reuse the calibration data that are used to construct the global calibrator. Furthermore, we extend LAPE to the setting with multi-view inputs through *Cascade Feature Augmentation Fusion* (CFAF). Extensive experiments demonstrate that the proposed methods can improve the calibration quality of various existing baselines and remain efficient and robust across different settings.

NeurIPS 2026

Reset Dependence Is Embodied: Training Protocol Changes Which Morphologies Appear Optimal

Kevin Qiu, Zhao Yang, Marek Cygan

In a 100-body robot co-design benchmark, the height threshold that resets a falling robot changes which bodies appear optimal. We train PPO controllers for DERL-evolved UNIMAL morphologies under Standard (τ = 0.20), Strict (τ = 0.50), and Reset-free (τ = 0) protocols, then evaluate every policy under a common protocol. Strict and Reset-free training share only 5 of their top-10 bodies and have low rank agreement (Spearman ρ = 0.21). The effect is family-specific, not a uniform penalty: averaging per-body retention ratios, Reset-free training reduces Floor bodies' per-step reward by 45% relative to Standard training but reduces variable-terrain bodies by 16%. Under Strict, the losses are 22% for Floor and 34% for variable-terrain bodies, while multi-task bodies are most stable across protocols. Blocked ANOVA and morphology-level permutation tests support the protocol-by-family interaction. A SAC validation panel planned before analysis finds the same Strict-versus-Reset-free family ordering under a different optimizer. Selecting bodies by their minimum reward across the three training protocols recovers ≥91% of every single-protocol oracle's top-k average reward (k ∈ {5, 10, 20}; all bootstrap lower bounds ≥84.3%). Co-design benchmarks should state the reset rule, audit ranking sensitivity, and report protocol-robust selections when choosing bodies.

NeurIPS 2026

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity

Ammar Mahran, Artavazd Maranjyan, Peter Richtarik

Asynchronous stochastic gradient descent (ASGD) is a standard way to exploit heterogeneous compute resources in distributed learning: instead of forcing fast workers to wait for slow ones, the server updates the model whenever a gradient arrives. Vanilla ASGD applies each arriving gradient with the same weight. When local data distributions are heterogeneous, this becomes problematic: faster workers contribute more updates, and we show theoretically that the method is biased toward a frequency-weighted average of the local objectives rather than the desired global objective. Existing remedies typically move away from the simple ASGD template by introducing gathering phases, buffering, or extra memory. We show that this is unnecessary. Keeping the standard ASGD mechanism, we recover the correct objective by rescaling worker-specific stepsizes in proportion to their computation times, so that each worker contributes the same aggregate learning rate over a cycle. In the non-convex setting, under smoothness and bounded heterogeneity assumptions, we prove that the resulting method, Rescaled ASGD, converges to stationary points of the correct global objective in the fixed-computation model. Its time complexity matches the known lower bound in the leading term, while the effects of staleness and data heterogeneity appear only in lower-order terms. Experiments confirm that the method converges to the correct objective and is competitive with state-of-the-art baselines.

NeurIPS 2026

ResKV: Residual-based Channel-wise Unstructured Pruning for KV Cache Compression

Yue Chen, Jinze Li, Dajiang Liu

In modern LLM applications, the demand for extended context lengths continues to surge, making KV cache a major performance bottleneck in long-context inference. Channel-wise pruning has emerged as a prevalent KV cache compression paradigm; however, existing solutions overlook inherent similarities among KV vectors, resulting in limited pruning ratios and noticeable performance degradation. To tackle this issue, we present ResKV, a novel residual-driven channel-wise pruning framework that fully exploits KV vector similarity for effective compression. ResKV first clusters highly similar KV vectors via cosine similarity metrics, then calculates residual vectors by subtracting each cluster’s centroid from the original features. Subsequent channel-wise unstructured pruning and per-token quantization are exclusively applied to these residual components. During inference, preserved residuals are combined with their corresponding centroids to accurately recover dense KV representations. Benefiting from the low magnitude and sparse, near-zero properties of centroid residuals, both pruning and quantization incur only negligible approximation errors. Experimental results show that ResKV reduces peak memory consumption by 45.6% and improves inference throughput by 4.27× against dense FlashAttention inference, while sustaining competitive model performance. Under identical sparsity constraints, ResKV outperforms state-of-the-art channel-wise KV pruning methods by achieving up to 2.26 points higher accuracy and 2.63× faster end-to-end throughput.

NeurIPS 2026

ResFusion: Medical Image Fusion Driven by Implicit-Forward Diffusion and Time-aware Joint Optimization

Jiahua Xu, Dawei Zhou, Zhengkun Yu, Lei Hu, Lecong Xiong, Zaiyi Liu, Nannan Wang, Xinbo Gao

Standard training of diffusion models typically relies on a forward diffusion process anchored by ground-truth target images. However, in image fusion tasks, the scarcity of real fused images makes it difficult to formulate a forward process, thereby precluding the standard training. Fortunately, our visual analysis reveals that image fusion can be effectively modeled without standard training or reliance on the real images. Nevertheless, the misalignment of input image pairs remains a significant bottleneck for fusion quality. Recognizing that one-off registration is ill-suited to the progressive generation of diffusion models, we propose a unified registration-fusion framework driven by a time-aware joint optimization mechanism. Specifically, we design a time-aware registration network to progressively optimize registration to guide fusion, while the fusion results provide feedback constraints to the registration network at each time step. This mechanism facilitates joint optimization of registration and fusion, significantly improving the quality of fused results. Experimental results show that the proposed method performs excellently on multiple datasets, validating its effectiveness and superiority.

NeurIPS 2026

Repurposing Video Diffusion Transformers for Cross-View Temporal Object Correspondence

Siyoon Jin, Dahyun Chung, Honggyu An, Sangbeom Lim, Wonjin Nam, Jaewoo Jung, WonJun Moon, Seungryong Kim

Cross-view object correspondence identifies the same object across views. However, existing methods treat this as a frame-level matching problem, necessitating a query mask at every frame. Furthermore, without modeling temporal context, they suffer from severe ambiguity under appearance changes or occlusions. Therefore, we introduce cross-view temporal object correspondence, which requires preserving object identity across both views and time from a query mask. To address this, we propose Track, a streamlined framework which repurposes a pretrained video DiT with semantic and spatiotemporal priors for RGB-to-mask latent transport, jointly modeling cross-view matching and temporal tracking. However, naive video DiTs do not guarantee accurate cross-view grounding and often attend to semantically similar yet incorrect objects across views. Thus, we introduce focal cross-view attention alignment, hard negative conditioning, and local context crop strategies to ensure reliable grounding. Finally, beyond these architectural enhancements, we also tackle the lack of dense annotation by introducing a pseudo-label curation pipeline that converts sparse Ego-Exo4D annotations into dense supervision to enable effective video-level training. Our highly competitive performances validate the effectiveness of Track as a streamlined framework, ensuring robust temporal consistency and identity preservation. Code and weights will be released.

NeurIPS 2026

Representing Part-Whole Hierarchy with Nested Neuronal Coherence

Hao ZHENG, Hui Lin, Rong Zhao

Human vision flexibly extracts part-whole hierarchies from visual scenes, but representing such structures remains a key challenge for neural networks. Inspired by the neural syntax hypothesis in neuroscience, we propose a framework for representing hierarchical part-whole relationships through nested neuronal coherence, characterized by its continuous and distributed nature. Nested neuronal coherence refers to dynamical states where distributed activations are temporally correlated to form cell assembly sequences across timescales, argued to represent neural syntax. Building on this framework, we develop a cortical-inspired hybrid model, Composer, which dynamically achieves emergent nestedness when given images. To evaluate the emergent hierarchy, we create four synthetic datasets and three quantitative metrics, demonstrating the model’s ability to parse scenes of varying complexity. Overall, our work advances a systematic paradigm spanning representation, implementation, and evaluation toward building human-like vision in neural networks.

NeurIPS 2026

Representation Rigidity in Face Embeddings: Orthogonal Identifiability and Backfill-Free Compatibility

Yongle Zhao, Zhichao Chen, Yin Xie, Jun Wang, Ziyong Feng

Upgrading a deployed face-recognition model usually makes new query embeddings incompatible with the legacy gallery, forcing costly backfilling of stored embeddings. We show that this incompatibility is often much simpler than it appears: independently trained angular-margin face models exhibit representation rigidity, where their embedding spaces are well approximated by a single global orthogonal transformation. We formalize this phenomenon as Procrustes rigidity, derive a finite-calibration recoverability bound, and estimate the orthogonal adapter from a small paired calibration set via closed-form Procrustes alignment. The resulting deployment protocol is backfill-free: transport upgraded queries into the legacy coordinate system, then reuse the existing gallery without re-embedding stored identities. Across 45 models spanning three architectures, two losses, three data scales, and five seeds, we find strong rigidity within architecture families, non-monotonic scaling with data size, and robust cross-model retrieval. On the 1.6M-image IFRT benchmark, Procrustes-aligned queries recover at least $99.8\%$ of mono-model Rank-1 accuracy for same-family upgrades, while lightweight residual correction substantially narrows the cross-architecture gap.

NeurIPS 2026

Representation Preconditioning for Efficient Diffusion Model Training

Deyuan Liu, Peng Sun, Xufeng Li, Tao Lin

Diffusion transformers learn useful internal representations during training, and learning them from scratch can add a burden to efficient training. Alignment mitigates this burden by adding an auxiliary loss that regularizes diffusion hidden states with features from pretrained visual encoders. In this work, we revisit the role of this alignment signal. Rather than using pretrained representations only as a regularizer during full diffusion training, we use them as a preconditioning signal before diffusion training. The key observation is that early layer alignment from clean latents to pretrained representations can be much cheaper than optimizing the full flow matching objective over the entire backbone, yet provides a better initialization for subsequent diffusion training. We instantiate this idea as Embedded Representation Warmup (ERW). ERW first aligns early layers from clean latents to pretrained representations, and then trains the full model with the standard diffusion objective and decaying alignment. This initialization allows subsequent diffusion training with alignment to converge more efficiently than starting alignment from scratch. Counting warmup in the total budget, ERW improves convergence in SiT based diffusion training. On ImageNet $256\times256$, ERW reaches FID=1.41 in 350 epochs with SiT-XL/2; it also reaches FID=2.04 on ImageNet $512\times512$ and improves FID on MS-COCO text-to-image generation.

NeurIPS 2026

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Johan Obando-Ceron, Lu Li, Scott Fujimoto, Pierre-Luc Bacon, Aaron Courville, Pablo Samuel Castro

Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on planning and complex training pipelines, making it unclear which components are essential for scalability. We revisit this question and argue that the primary driver of scalable multitask RL is not model-based control, but _representation learning_. In particular, we show that combining predictive, model-based representations with high-capacity value function approximation is sufficient to achieve strong performance, without planning. We evaluate a simple model-free algorithm, MR.Q, that integrates auxiliary predictive objectives into a scalable actor-critic architecture. This approach outperforms a recent world-model-based method and a range of deep RL baselines across a diverse suite of multitask continuous control tasks, while significantly reducing computational overhead and improving wall-clock efficiency. We observe consistent improvements with increased model capacity and show through ablations that predictive representation learning is critical for performance.

NeurIPS 2026

Representation Fréchet Loss for Visual Generation

Jiawei Yang, Zhengyang Geng, Xuan Ju, Yonglong Tian, Yue Wang

We show that Fréchet Distance (FD), long considered impractical as a training objective, can in fact be effectively optimized in the representation space. Our idea is simple: decouple the population size for FD estimation (e.g., 50k) from the batch size for gradient computation (e.g., 1024). We term this approach FD-Loss. Optimizing FD-Loss reveals several surprising findings. First, post-training a base generator with FD-Loss in different representation spaces consistently improves visual quality. Under the Inception feature space, a one-step generator achieves 0.72 FID on ImageNet $256\times256$. Second, the same FD-Loss repurposes multi-step generators into strong one-step generators without teacher distillation, adversarial training or per-sample targets. Third, FID can misrank visual quality: modern representations can yield better samples despite worse Inception FID. This motivates FDr$^{k}$, a multi-representation metric. We hope this work will encourage further exploration of distributional distances in diverse representation spaces as both training objectives and evaluation metrics for generative models. Code and checkpoints will be open-sourced.

NeurIPS 2026

Representation Forcing for Bottleneck-Free Unified Multimodal Models

Yuqing Wang, Zhijie Lin, Ceyuan Yang, Yang Zhao, Fei xiao, Hao He, Qi Zhao, Zihan Ding, Fu-Yun Wang, Shuai Wang, Youliang Zhang, Haoqi Fan, Xihui Liu

Unified multimodal models (UMMs) aim to handle perception and generation in a single model. Yet existing UMMs still rely on a frozen, separately pretrained VAE for image generation, imposing a structural bottleneck. Naively removing it introduces a quality gap, as the model must learn both high-level structure and low-level details from raw pixels. In this paper, we propose Representation Forcing (RF), a technique that closes this gap by making representation prediction a native capability of the decoder. Concretely, RF forces the decoder to autoregressively predict visual representations as intermediate tokens before pixels; these tokens then stay in context to guide pixel diffusion within the same backbone. By turning representations from perception outputs into generation targets, RF eliminates the need for any external generative latent space. We find that RF benefits both understanding and generation. On image generation, our pixel-space model with RF matches state-of-the-art VAE-based unified models. On image understanding, pixel-space RF generally outperforms its VAE-based variant. Together, these results offer an effective step toward end-to-end, bottleneck-free UMMs.

NeurIPS 2026

Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation

Daniel P. Jeong, Charles Q Li, Hossein Hosseiny, Nitya M Bhalla, Fatma U Morency, Pradeep Ravikumar, Zachary Lipton, Michael Oberst

Radiologists follow heterogeneous reporting practices. Two radiologists examining the same image and identifying the same clinical findings might nevertheless compose superficially distinct reports, varying in terminology, shorthand, formatting, and level of detail. These variations in reporting norms represent an under-appreciated obstacle in efforts to evaluate AI-based radiology report generation (RRG) models, where machine-generated reports are typically assessed based on their concordance with human-generated references. In this paper, we quantify the sensitivity of established evaluation metrics to variations in reporting practices, revealing impacts significant enough to alter the rankings of models. We introduce a radiologist-informed taxonomy of variations in radiology reporting practice and a method (ReRef) that rewrites reference reports along the axes of our taxonomy while preserving clinical interpretation. For instance, when comparing the performance of nine RRG models on MIMIC-CXR using the GREEN metric, condensing the discussion of normal findings in the reference reports causes Libra to drop from first place to third while CheXOne rises from third to first. Our results suggest that current metrics may fail to decouple clinical interpretation from conformity to reporting practices and that choosing the "right" references that accurately reflect the desired reporting practices can be important in practice. To support future research, we release MIMIC-CXR-Ext-ReRef, a radiologist-validated dataset of 120 (original, alternative) reference report pairs derived from MIMIC-CXR.

NeurIPS 2026

RepoZero: Can LLMs Generate a Code Repository from Scratch?

Zhaoxi Zhang, Yiming Xu, Jiahui Liang, Weikang Li, Xiaoshuai Chen, Liwei Qian, Xin Pei, Jizhou Huang, Rui Sun, Yunfang Wu

Large Language Models (LLMs) have recently shown remarkable progress in code generation, yet their ability to construct complete software repositories from scratch remains poorly understood. A fundamental bottleneck is the lack of verifiable and scalable evaluation: existing benchmarks either focus on patch-based editing or rely on human or LLM-based judgments, which introduce bias and limit reproducibility. In this work, we present RepoZero, the first benchmark that enables fully automated, execution-based verification of repository-level generation from scratch. Our key idea is to reformulate generation as repository reproduction: given only API specifications, an agent must re-implement an entire repository such that its behavior matches the original implementation. This design allows for strict black-box validation via output equivalence, while naturally supporting large-scale construction by reusing existing open-source repositories. To further mitigate data leakage and shortcut solutions, we introduce cross-language constraints and a sandboxed evaluation protocol. Building on this benchmark, we propose an Agentic Code-Test Evolution (ACE) framework that performs iterative test generation and error-driven refinement, enabling effective test-time scaling for repository-level synthesis. Extensive experiments across multiple state-of-the-art LLMs and agent frameworks reveal that even the strongest models achieve only limited pass rates (30\% - 55\%), exposing a substantial gap between current capabilities and real-world software development requirements. Our results establish RepoZero as a challenging, scalable, and reliable testbed for end-to-end code generation, and highlight self-verification via test generation as a critical direction for advancing LLM-based coding agents.

NeurIPS 2026

RepoScope: Bridging Physical Structure and Logical Flow for Intent-Driven Repository Documentation

Zezhou Yang, Chaozheng Wang, Hailiang Huang, Peihua Zhang, TingPeng, yuetang deng, Chenxiong Qian

Repository-level documentation generation is vital for human comprehension and autonomous agents, yet existing approaches fail to align high-level architectural intent with granular implementation details, resulting in documentation that lacks both global coherence and local fidelity. To address this, we propose RepoScope, a repository documentation generation framework that achieves alignment via two synergistic innovations. Specifically, RepoScope employs Physico-Logical Partitioning by jointly modeling directory structures and call graphs, effectively bridging the gap between physical layouts and logical dependencies while creating coherent documentation units. Within this partitioned space, we introduce SAIL (Seeded Architectural Intent Lift), a mechanism that projects architectural intent top-down to steer granular analysis, which is then revised and lifted bottom-up via code evidence to ensure global-local consistency. Our experimental results on CodeWikiBench demonstrate state-of-the-art performance in aligning with human-authored documentation. Furthermore, the validation on real-world industrial systems shows that RepoScope yields superior correctness and completeness in empowering autonomous agents for CLI extraction compared to existing baselines.

NeurIPS 2026

RepoMirage: Probing Repository Context Reasoning in Code Agents with Perturbations

Hanyu Li, Yichi Zhang, Speed Zhu, Hang Su, Jun Zhu, Yinpeng Dong

Code agents are currently having skillful performance on repository-level software engineering benchmarks, but it remains unclear whether success on end-to-end tasks such as issue resolution truly reflects $\textit{repository context reasoning}$, the ability to identify the task-relevant information across multiple files and reason over the relations among them. To investigate this question, we introduce RepoMirage, a two-stage evaluation suite built on SWE-Bench Verified that adopts perturbation as a diagnostic tool to increase the demand for context reasoning by transforming how the repository is exposed. First, RepoMirage-Perturb applies three types of semantics-preserving repository-level perturbations, revealing a clear performance drop when correct solving requires broader context access. RepoMirage-Extend further turns perturbation-targeted structural bottlenecks into explicit tasks beyond issue resolution, where the average performance declines from 66.8\% in the original setting to 25.3\%, indicating a significant deficiency in repository context reasoning. Further trajectory analysis reveals an exploration drift, where agents access broader repository context but fail to turn it into effective structure information. Motivated by this observation, we propose RepoAnchor, a structure-first prototype workflow that separates repository exploration from downstream problem solving, and show that explicit structural scaffolding yields notable gains. These results uncover an previously overlooked gap in repository context reasoning for code agents and suggest that stronger structure-aware methods are potential to improve them.

NeurIPS 2026

Replicas-as-Variables: A Planner for Throughput-Optimal Routing in LLM Deployment

Shaojiang Wang, Geer Yang, Wenbo Wang, Bin WU, Pengcheng Wang

Deploying Large Language Models on heterogeneous GPU clusters is often bottlenecked by rigid parallelism strategies that fail to fully exploit hardware asymmetry. To address this, we introduce ReVar, a holistic framework that optimizes replica placement and activation routing by treating replica counts as primary decision variables for all model components, including both dense stages and MoE experts. By modeling inference as a coupled resource-allocation and flow-routing problem, ReVar replaces static pipelines with fluid execution graphs that utilize uneven replication and multi-path routing. Supported by rigorous theoretical foundations, including proving the problem's NP-hardness, providing a MILP formulation for throughput upper bounds, and deriving closed-form solutions for constrained regimes, ReVar delivers significant practical gains. In evaluations, it achieved the highest throughput in 14 out of 15 settings, demonstrated up to a 3.1 times improvement in steady-state throughput in mixed-GPU environments, and was successfully validated on a real physical heterogeneous cluster.

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