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NeurIPS 2026

SolvMix: Learning Formulation-State Landscapes for Liquid Electrolyte Conductivity Prediction

Kexin Zhang, Minzhang Li, Guotao Qiu, Tianqi Zhao, Yuna Zhang, Yibing Shan, Ye Mei, Xushan Zhao, Jingyi Yu

Electrolyte conductivity is not a property of isolated molecules, but a response over a formulation landscape defined by solvents, salts, composition, salt mole ratio, and temperature. The same molecular component can play different effective roles as its amount and operating conditions change, while the underlying formulation state is observed only through macroscopic conductivity measurements. This makes conductivity prediction a distinctive mixture learning problem: a model must learn how molecular identities become formulation-specific states, rather than merely how molecules are represented. We introduce SolvMix, a framework for learning formulation-state landscapes for electrolyte conductivity prediction. SolvMix constructs component tokens for solvent and salt molecules, calibrates them into amount-aware component states before mixture interaction, and predicts conductivity through a temperature-formatted readout. We evaluate SolvMix on three curated electrolyte conductivity benchmarks, CALiSol-23, DiffMix, and Bamboo-mixer, covering diverse solvents, salts, compositions, salt mole ratios, and temperatures. Across standard splits on all three benchmarks and binned out-of-distribution (OOD) splits on Bamboo-mixer, SolvMix outperforms strong molecular-representation, mixture-aggregation, and geometric-interaction baselines under the reported protocols. Ablations show that the best-performing formulation-state construction combines amount-and-type-aware component tokens, multiplicative amount calibration, token-level interaction followed by mean pooling, and late-concat temperature formatting. OOD results further show clear gains under formulation-level shifts, where static component descriptors and local interaction patterns are least aligned with the required generalization. These results suggest that electrolyte conductivity prediction benefits from treating formulation-state construction as a first-class modeling problem, beyond molecular representation learning or explicit local interaction modeling.

NeurIPS 2026

Solaris: Building a Multiplayer Video World Model in Minecraft

Oscar Michel, Georgy Savva, Daohan Lu, Suppakit Waiwitlikhit, Timothy Meehan, Dhairya P Mishra, Egor Gikalo, Srivats Poddar, Jack Lu, Saining Xie

Existing action-conditioned video generation models (video world models) are limited to single-agent perspectives, failing to capture the multi-agent interactions of real-world environments. We introduce Solaris, a multiplayer video world model that simulates consistent multi-view observations. We develop a multiplayer data system designed for robust, continuous, and automated data collection on video games such as Minecraft. Unlike prior platforms built for single-player settings, our system supports coordinated multi-agent interaction and synchronized videos + actions capture. Using this system, we collect 12.64 million multiplayer frames and propose an evaluation framework for multiplayer movement, grounding, building, and view consistency. We train Solaris using a staged pipeline that progressively transitions from single-player to multiplayer modeling, combining bidirectional, causal, and Self Forcing training. In the final stage, we introduce Checkpointed Self Forcing, a memory-efficient Self Forcing variant that enables a longer-horizon teacher. Results show our architecture and training design outperform existing baselines. We also demonstrate that scaling beyond two players is possible by training a 3-player proof-of-concept model and simulating up to 16 players on our hardware with efficient attention. Through open-sourcing our system and models, we hope to lay the groundwork for a new generation of multi-agent world models.

NeurIPS 2026

Soft Token Alignment for Cross-Lingual Reasoning

Jiayi He, Jungsoo Park, Wei &quot, Coco&quot, Xu, Alan Ritter

Multilingual large language models often produce inconsistent reasoning and answers across semantically equivalent prompts in different languages. Prior work suggests that intermediate representations can be relatively language-agnostic, but generation becomes increasingly language-specific as models commit to discrete output tokens. This is problematic because language-specific lexical choices can cause semantically equivalent reasoning paths to diverge across languages. This motivates searching for a cross-lingual alignment signal that is less tied to any single vocabulary item or script. We propose **SOLAR**, an auxiliary objective for supervised fine-tuning that aligns _soft-token_ representations across languages, using English as a pivot. Soft tokens are probability-weighted mixtures over the vocabulary embeddings, yielding continuous representations that can aggregate information from semantically related tokens across languages. We then align each non-English soft-token summary to its English counterpart in the shared embedding space. Across four multilingual reasoning benchmarks, SOLAR improves accuracy by up to +17.7 points over the base model and +3.8 over standard supervised fine-tuning, with the largest gains on low-resource languages. SOLAR also strengthens final-layer cross-lingual similarity and substantially reduces language-cluster separability, suggesting that aligning soft-token representations helps preserve shared semantic structure during multilingual reasoning.

NeurIPS 2026

Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities

Marco Bagatella, Thomas Rupf, Georg Martius, Andreas Krause

Recent advancements in zero-shot reinforcement learning (RL) have facilitated the extraction of diverse behaviors from unlabeled, offline data sources. In particular, forward-backward algorithms (FB) can retrieve a family of policies that approximately solves any standard RL problem (with additive rewards, linear in the occupancy measure), given sufficient capacity. While retaining zero-shot properties, we tackle the greater problem class of RL with general utilities, in which the objective is an arbitrary differentiable function of the occupancy measure. This setting is strictly more expressive, capturing tasks such as distribution matching or pure exploration, which may not be reduced to additive rewards. We show that this additional complexity can be captured by a novel, maximum entropy (soft) variant of the forward-backward algorithm, which recovers a family of stochastic policies from offline data. When coupled with zero-order search over compact policy embeddings, this algorithm can sidestep iterative optimization schemes, and optimizes general utilities directly at test-time. Across both didactic and high-dimensional experiments, we demonstrate that our method retains favorable properties of FB algorithms, while also extending their range to more general RL problems.

NeurIPS 2026

SocialDirector: Training-Free Social Interaction Control for Multi-Person Video Generation

Liangyang Ouyang, Ruicong Liu, Caixin Kang, Yifei Huang, Yoichi Sato

Video generation has advanced rapidly, producing photorealistic videos from text or image prompts. Meanwhile, film production and social robotics increasingly demand multi-person videos with rich social interactions, including conversations, gestures, and coordinated actions. However, existing models offer no explicit control over interactions, such as who performs which action, when it occurs, and toward whom it is directed. This often results in wrong person performing unintended actions (actor-action mismatch), disordered social dynamics, and wrong action targets. To address these challenges, we present SocialDirector, a training-free interaction controller that enhances the generation model by modulating cross-attention maps. SocialDirector contains two modules: Social Actor Masking and Directional Reweighting. Social Actor Masking constrains each person's visual tokens to attend only to their own textual descriptions via a spatiotemporal mask, avoiding actor-action mismatch and disordered social dynamics. Directional Reweighting amplifies attention to directional words (e.g., "leftward", "right"), leading each action towards its intended target. To evaluate generated social interactions, we annotate existing datasets with interaction descriptions and build a fully automated evaluation pipeline powered by open-source VLMs. Experiments on different video generation models show that SocialDirector significantly improves interaction fidelity and approaches the upper bound set by real videos.

NeurIPS 2026

Social Interaction Breaks Replicate Independence in Controlled-Clone LLM Agents

Yandan Zheng, Haoran Luo, Anh Tuan Luu

Multi-agent LLM evaluations and large-scale social simulations often treat multiple copies of the same model as comparable replicates after they interact. We test this assumption with SocCogEval, a controlled-clone assay for Social Cognitive Transfer (SCT), and find that it fails. Agents share the same base model, system prompt, and decoding parameters. When briefs are assigned, the brief is the only initial difference. On Qwen3-122B-FP8, the matched interaction contrast holds asymmetric briefs fixed and toggles 30 rounds of interaction. It yields stable off-topic BFI-10 response-profile drift on two topically disjoint scenarios. The effect is Delta =+0.92 on Greenfield (95% CI [+0.61,+1.22], g =+1.07) and Delta =+ 0.91 on Riverton ([+0.60,+1.24], g =+ 0.96, about 0.18 Likert points per BFI factor. Brief-only controls do not explain the drift. A verbatim-history ablation with SRM extraction disabled remains positive, so typed memory extraction is not the source of the signal. Five additional model variants provide positive but uneven breadth. Five of ten non-primary scenario cells are positive at alpha = 0.05, and no variant shows a significant reversal. The result limits iid-clone assumptions in multi-agent evaluation: interacting clones are not independent replicates unless paired no-interaction controls show otherwise.

NeurIPS 2026

SoccerNarrate: Event-Grounded Streaming Soccer Commentary with Macro-Window Preference Alignment

Zihan Jia, Zhilin Dai, Zhengming Zhang, Min Yang, Zhenpeng Huang, Jiaqi Li, Caixia Sun, Xi Chen, Liang Li, Junlan Feng, Gangshan Wu, Limin Wang

Existing soccer commentary models are often designed for pre-segmented clips or localized events. When deployed on untrimmed full-match videos with sliding windows, they can produce delayed, repeated, or poorly synchronized commentary. Recent streaming video-language models make low-latency full-match narration feasible, but they often favor general real-time descriptions rather than professional, event-grounded soccer commentary. They may describe nearby actions while failing to mention key events such as goals, cards, substitutions, and offsides correctly and on time. In this paper, we introduce **SoccerNarrate**, a data, model, and evaluation framework that bridges low-latency streaming narration with event-grounded professional soccer commentary. First, we construct **SoccerNarrate-Data**, a large-scale time-anchored dataset with word-level ASR alignment, quality filtering, and entity calibration. Second, we perform **macro-window preference alignment(MWPA)**, which aligns the model with complete event-level commentary semantics by comparing causal multi-step rollouts from the same streaming prefix while keeping second-level inference unchanged. We further use event-mismatched human commentaries as counterfactual negatives to emphasize event semantics over commentary style. Third, we introduce **SoccerNarrate-Eval**, an event-centric benchmark based on temporally constrained event entailment. Full-match experiments show that SoccerNarrate improves event coverage and event-aligned precision over strong offline and streaming baselines. Data and models will be released.

NeurIPS 2026

SoDeArena: A Socially-Situated Reasoning Benchmark for Large Language Models

Senhao Yang, Ziling Yuan, Xiaoran Yang, Yaqing Wang, Runpeng Xie, Chen Huang, Lexiang Wang, Shuang Xu, Yiqin Yang, bo xu

In recent years, Large Language Models (LLMs) have demonstrated remarkable performance in tasks such as mathematical reasoning and code generation. However, as LLMs are increasingly deployed as autonomous agents, their reasoning capabilities in dynamic, human-like social interactions remain largely unexplored. To bridge this gap, we propose the benchmark SoDeArena—Social Deduction Arena, designed to evaluate the socially-situated reasoning capabilities of LLMs through a diverse set of interactive games. Through large-scale multi-agent simulations and human interaction experiments, we have identified three key insights for improving current LLMs: (1) the overall proficiency of models in social deduction is fundamentally bottlenecked by model scale and reasoning capabilities; (2) models lack proactive intent management, defaulting instead to strategic passivity and output conformity during multi-agent interactions; and (3) LLMs exhibit dual behavioral biases, manifesting as a rigid safety alignment in adversarial roles and a pathological reliance on shallow positional patterns over robust reasoning processes. These findings point to potential directions for the future development of AI systems with robust socially-situated reasoning capabilities.

NeurIPS 2026

SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining

Yifan Zhang, Zunhai Su, Shuhao Hu, YangRui, Wei Wu, Yulei Qian, Yuchen Xie, Xunliang Cai

As the context length of large language models (LLMs) grows, the key-value (KV) cache memory overhead becomes a critical bottleneck, limiting long-context inference efficiency. While FP8 attention has shown substantial promise in innovations like FlashAttention-3, its integration into the decoding phase of the DeepSeek Multi-head Latent Attention (MLA) architecture presents notable challenges. These challenges include numerical heterogeneity arising from the decoupling of positional embeddings, misalignment of quantization scales in FP8 PV GEMM, and the need for optimized system-level support. In this paper, we introduce SnapMLA, an FP8 MLA decoding framework optimized to improve long-context efficiency through the following hardware-aware algorithm-kernel co-optimization techniques: (i) RoPE-Aware Per-Token KV Quantization: Motivated by our analysis of the heterogeneous quantization sensitivity inherent to the MLA KV cache, this approach preserves the RoPE part in high precision. Furthermore, per-token granularity is employed to align with the autoregressive decoding process and maintain quantization accuracy. (ii) Pre-posed Scale Domain Alignment, where we skillfully project RoPE components into the quantization domain to mitigate pipeline stalls caused by mixed-precision accumulation; (ii) Quantized PV Computation Pipeline Reconstruction: Addresses the misalignment of quantization scales in FP8 PV computation caused by the shared KV structure of the MLA. (iii) End-to-End Dataflow Optimization: Establishes an efficient data read-and-write workflow using specialized kernels, ensuring streamlined data flow and improved performance. Extensive experiments on state-of-the-art MLA LLMs show that SnapMLA achieves up to a 1.91× improvement in throughput on long-output decoding workloads while maintaining near-parity benchmark quality compared with the BF16 baseline on the evaluated reasoning and code-generation benchmarks. The code corresponding to this work is included in the supplementary material.

NeurIPS 2026

Smoothed Score Queries and the Complexity of Sampling

Jingbo Liu

We study the query and communication complexity of sampling from high-dimensional Gaussian distributions using gradient information. In the standard oracle model, exact gradients expose only matrix-vector products with the precision matrix, leading to polynomial approximation barriers and a characteristic $\sqrt{\kappa}$ dependence on the condition number. We show that this barrier disappears when the sampler is allowed to query \emph{smoothed gradients}, namely scores of Gaussian-convolved distributions. For a Gaussian target with precision $\Lambda$, a smoothed-score query at noise level $\tau$ gives access to the resolvent $(\Lambda+\tau^{-1}I)^{-1}$. Combining geometrically spaced noise levels with sinc-quadrature rational approximation, we obtain a sampler with $$ q = O\left( \left(\log\kappa+\log(\sqrt d/\varepsilon)\right) \log(\sqrt d/\varepsilon) \right) $$ smoothed-gradient queries for total variation error $\varepsilon$, improving the condition-number dependence from $\sqrt{\kappa}$ to logarithmic. We also study finite-bit gradient oracles. Using coordinatewise quantization of the transformed smoothed-gradient answers and a final dithering step, we obtain a sampling scheme whose total communicated gradient information is polylogarithmic in $\kappa$; in particular, for fixed dimension and accuracy, the bit complexity is $O(\log^2\kappa)$. To complement these upper bounds, we introduce a channel-synthesis, or reverse-Shannon, converse technique for sampling lower bounds. This converts total-variation simulation guarantees into communication requirements and yields an $\Omega(\log\kappa)$ lower bound on the required gradient information. Together, these results identify smoothed gradients as a strictly more powerful oracle for sampling and give nearly matching upper and lower bounds for its finite-bit complexity.

NeurIPS 2026

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

Guangcheng Zhu, Shenzhi Yang, Haobo Wang, Xing Zheng, Yingfan MA, Xuening Feng, Zhongqi Chen, Bowen Song, Weiqiang Wang, Gang Chen

Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset. To this end, data-efficient RLVR methods have been widely studied from two perspectives: (i) data selection methods identify a small subset of "golden" samples that yield near-full-data performance, but they rely on a pre-existing pool of labeled data. (ii) unsupervised RLVR methods train the model using its own internal supervision signals on large-scale unlabeled data, yet they exhibit suboptimal performance. Accordingly, we investigate the "*pick in the dark*" setup for RLVR, which aims to select, without prior supervision, unlabeled samples that are most beneficial for training and worthy of annotation. Through systematic analysis, we demonstrate that smart picks hinge on a well-calibrated uncertainty estimator to enable strategic partitioning of data for adaptive training regimes. Building on this insight, we propose **PivotTrace**, a three-way data triage framework that leverages attention dynamics to trace *metacognitive pivots* during reasoning. By precisely quantifying uncertainty through pivot density, PivotTrace achieves automated data routing to synergistically maximize both annotation and training efficiency. Empirically, PivotTrace surpasses the fully supervised LRM with only **29.3%** annotated samples and **2.75**$\times$ faster convergence.

NeurIPS 2026

Small Model Portfolios for Many Deployment Profiles: Submodular Coverage under Bundled Constraints

Ji Cheng

Deploying deep learning models across heterogeneous hardware requires each model to jointly satisfy a bundle of coupled constraints on accuracy, latency, memory, and energy. Maintaining a specialist for every deployment scenario is operationally prohibitive, so practitioners need a small portfolio of $K$ models that collectively covers many deployment profiles. Existing hardware-aware NAS and Pareto-based approaches target marginal objectives and do not directly address this downstream decision. We formalize it as Deployment Portfolio Selection over a fixed pre-measured archive, where a profile is covered only when a single retained model simultaneously satisfies its entire constraint bundle. The induced objective is weighted maximum coverage, which is NP-hard but monotone submodular, so greedy selection inherits a $(1-1/e)$ guarantee, and sample-then-optimize admits a finite-sample bound. Across an 18-family sweep on HW-NAS-Bench, direct bundled-coverage optimization is most valuable in the small-portfolio, low-coverability regime: gains concentrate on the hardest profiles, are most robust at $K = 3$, and persist under three non-uniform demand models. Complementary analyses of remeasurement noise, runtime, and compact feasibility storage clarify when bundled coverage is preferable to scalarized or Pareto-based alternatives.

NeurIPS 2026

Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation

Atsushi Nitanda, Dake Bu, Yueming Lyu, Tanya Veeravalli

We study Slowly Annealed Langevin Dynamics (SALD), a sampler for tracking a path of moving target distributions and approximating the terminal target through time slowdown. We establish non-asymptotic convergence guarantees via a KL differential inequality, showing that slowdown improves tracking through contraction of intermediate targets and the complexity of the path. Motivated by training-free guided generation with pretrained score-based generative models, we further introduce Velocity-Aware SALD (VA-SALD), which explicitly incorporates the underlying marginal distributions of the pretrained model and uses slowdown to correct the additional deviation induced by guidance. This yields a principled framework for training-free guided generation for diffusion-based and related generative model families, together with convergence guarantees that clarify the roles of intermediate functional inequalities and guidance bias.

NeurIPS 2026

SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks

Gabriel Orlanski, Devjeet R Roy, Alexander Yun, Changho Shin, Alex Gu, Albert Ge, Dyah Adila, Nicholas Roberts, Frederic Sala, Aws Albarghouthi

Software development is iterative, yet agentic coding benchmarks hide design issues through their single-shot setup. Recent iterative benchmarks attempt to remedy this but heavily constrain an agent's design decision space, making it impossible to faithfully measure how their decisions shape future extensions. We introduce SlopCodeBench, a benchmark comprising 36 problems and 196 checkpoints in which agents repeatedly extend their own solutions. Unlike prior iterative benchmarks, our evolving specifications demand architectural decisions but leave internal structure to the agent. We measure two forms of degradation: structural erosion (concentrated complexity) and verbosity (redundant code). Evaluating 15 coding agents across open and closed models, we find that no agent fully solves any problem end-to-end, and the best agent passes 14.8% of checkpoints. Quality degrades across checkpoints, with structural erosion rising in 77% of trajectories and verbosity in 75.5%. Compared to 473 open-source Python repositories, agent code is 2.3x more verbose and 2.0x more eroded, and the human repositories degrade less often and by smaller margins across their git histories. Explicit quality guidance reduces initial verbosity and erosion by up to a third, without affecting degradation rates. SlopCodeBench identifies a failure mode that single-shot benchmarks cannot detect: agents that pass checkpoints while producing code that erodes and bloats with each extension.

NeurIPS 2026

Slide P2V-Bench: A Cross-Domain Benchmark for Slide-Centric Scientific Paper-to-Presentation Video Generation

Ligang Huang, Zhou Liu, Wentao Zhang

Scientific communication increasingly relies on slide-based narrated videos rather than static PDFs. However, transforming a paper into a presentation video is not a conventional text-to-video task. It requires preserving technical rigor, rendering dense multimodal assets faithfully, and synchronizing narration, subtitles, and cursor motion over long horizons. Current research faces two critical bottlenecks: (1) Benchmark Gap: existing evaluations are restricted to narrow domains or structured LaTex inputs with limited long-range supervision; (2) Evaluation Gap: current protocols often overlook the joint measurement of content fidelity, channel alignment, and actual knowledge transfer. To bridge these gaps, we introduce SLIDE P2V-BENCH, a cross-domain bench mark comprising 108 high-quality paper-video pairs spanning four domains and various durations, coupled with an auditable multi-dimensional evaluation suite. Furthermore, we propose PAPER2MEDIA, a slide-grounded agentic pipeline that produces editable presentations by synchronizing decoupled media channels via a shared semantic clock. Extensive experiments demonstrate that PAPER2MEDIA significantly outperforms existing automated baselines and, notably, achieves performance comparable to human-authored presentations in terms of narrative coherence, visual quality, and knowledge conveyance efficiency.

NeurIPS 2026

Sliced Wasserstein Meets Quantum Optics: Provable Wavefunctions Tomography with Scarce Noisy Measurements

Takahiro Kajisa

Quantum state tomography of pure non-Gaussian states is fundamentally limited by scarce and noisy data due to measurement-induced collapse and experimental drift. We introduce a novel reconstruction framework that parameterizes the wavefunction directly via an overparameterized neural network, trained using rank-1 factored gradient descent. We rigorously prove that this formulation's loss landscape is devoid of spurious local minima and strict saddles, guaranteeing all critical points are global minima. By utilizing the max-sliced Wasserstein-1 distance, our loss perfectly maps to the structure of homodyne measurements, structurally preventing gradient vanishing in low-shot regimes. On noisy two-mode cat states, our method achieves continuous fidelity improvements, successfully bypassing the noise-floor plateaus that limit standard iterative maximum-likelihood estimation.

NeurIPS 2026

Sliced Inner Product Gromov–Wasserstein Distances

Xiaoyun Gong, Gabriel Rioux, Ziv Goldfeld

The Gromov--Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical and computational scaling remains an issue for high-dimensional problems. Slicing techniques offer an appealing route to scalability, but, unlike Wasserstein distances, GW problems do not generally admit closed-form solutions in one-dimension. We resolve this problem for the GW problem with inner product cost (IGW), propose a sliced IGW distance that enjoys a natural rotational invariance property, and comprehensively study its structural and computational properties. Numerical experiments validating our theory are presented, followed by applications to heterogeneous clustering of text data and language model representation comparison.

NeurIPS 2026

SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation

Yuanhe Tian, Yan Song

CT report generation (CTRG) requires models to summarize three-dimensional anatomical context and pathological findings from hundreds of axial slices. Existing methods typically learn a direct image-to-text mapping, providing limited mechanisms for modeling how CT evidence evolves across slices or how reports respond to controlled changes in latent lesion-related factors. We propose SliceWorld, a CT-specific world-state framework that treats an axial CT scan as an ordered sequence along the z-axis. SliceWorld encodes prefix CT evidence into factor-aware latent states containing anatomy, lesion, and uncertainty components, and projects these states into world tokens used for multi-step future-slice feature prediction, lesion-factor intervention, and LLM-based report generation. The model is first pretrained on CT slice sequences with predictive, factor-aware, and counterfactual objectives, and is then fine-tuned on paired CT-report data. Experiments on M3D-Cap and CT-RATE show that SliceWorld improves natural language generation metrics and clinically oriented automatic evaluation. Further analyses demonstrate multi-horizon future-slice prediction, measurable factor alignment, reduced-slice robustness, and selective lesion-sensitive report modulation.

NeurIPS 2026

Skipping Domain Shifts: Domain Memory Retention Enhanced Hyperspectral Single-Source Domain Generalization

Taiqin Chen, Hao Sha, Xiaochen Feng, Chunyu Chen, Xiangyu Chen, Ke Chen, Yongbing Zhang

Hyperspectral single-source domain generalization aims to train a robust model capable of overcoming domain shifts using only one available source domain for cross-scene hyperspectral image (HSI) analysis. Existing methods typically adopt data augmentation to broaden decision boundaries or utilize style transfer techniques for test-time alignment. However, these studies either fail to completely bridge the domain gap or inadvertently disrupt the discriminative spectral information, leading to suboptimal generalization and degraded classification performance on the target domain. To overcome these limitations, we propose a novel method, termed the domain memory retention framework, which optimizes domain memory during training to explicitly encode the semantic structure of the source domain. During inference, the target features are projected to the source distribution by leveraging this memory, thereby skipping domain shifts and improving generalization performance across unseen scenes. Specifically, the memory is formulated as a codebook, consisting of multiple semantic codewords. We further develop a semantic richness constraint and a perturbation robustness constraint to enhance representation diversity while empowering domain invariance for the memory. Extensive experiments conducted on three remote sensing datasets demonstrate that the proposed method outperforms state-of-the-art methods.

NeurIPS 2026

SkipSR: Faster Super-Resolution with Token Skipping

Rohan Choudhury, Shanchuan Lin, Jianyi Wang, Hao Chen, Qi Zhao, Feng Cheng, Lu Jiang, Kris Kitani, László Jeni

Diffusion-based super-resolution (SR) is a key component in video generation and video restoration, but is slow and expensive, limiting scalability to higher resolutions and longer videos. Our key insight is that many regions in video are inherently low-detail and gain little from refinement, yet current methods process all pixels uniformly. To take advantage of this, we propose SkipSR, a simple framework for accelerating video SR by identifying low-detail regions directly from low-resolution input, then skipping computation on them entirely, only super-resolving the areas that require refinement. This simple yet effective strategy preserves perceptual quality in both standard and one-step diffusion SR models while significantly reducing computation. In standard SR benchmarks, our method achieves up to 60$\%$ faster end-to-end latency than prior models on 720p videos with no perceptible loss in quality.

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