NeurIPS 2026 Papers

Disentangling writing style from semantic content is a fundamental challenge in literary text modeling. Style-content entanglement causes models to rely on semantic content for authorship attribution (AA) tasks and memorize author-specific content for style imitation (SI) tasks. We propose EDITORS (Editing LoRA Subspaces), a diagnose-then-deflate framework that adapts pretrained large language models and prioritizes stylistic features, making it effective for both AA and SI tasks. EDITORS first trains a diagnostic LoRA adapter using style-neutral statements derived from the training corpus to identify a content-orientedsubspace. It then trains the final style adapter under activation-space deflation that projects input activations away from the identified content-oriented directions. We demonstrate that EDITORS is empirically effective: On three AA benchmarks spanning literature, social media, and news, EDITORS achieves state-of-the-art accuracy, outperforming the strongest existing baseline by up to 29%. On SI, EDITORS generates stylistically faithful and diverse passages, outperforming baselines in achieving style similarity while adhering to content specifications.

Euclidean combinatorial optimization problems (ECOPs), such as the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP), possess inherent symmetries under the two-dimensional Euclidean group E(2), including rotations, reflections, and translations. Existing learning-based methods, including recent diffusion-based methods, rely on data augmentation or regularization to approximate E(2)-equivariance. This paper presents EDISCO, the first discrete diffusion model for ECOPs with exact E(2)-invariant generative distributions over node-index solutions. EDISCO introduces an E(2)-equivariant edge-score network coupled with a categorical continuous-time Markov chain over discrete edge variables, and exact posterior sampling provides efficient multi-step inference. This design gives EDISCO a local geometric inductive bias: edge neighborhoods with the same relative geometry and combinatorial context are represented consistently regardless of absolute position or orientation, making learning more efficient and inference more robust than non-equivariant methods. EDISCO outperforms previous learning-based state-of-the-art solvers on synthetic TSP from 100 to 10000 nodes and CVRP from 50 to 2000 customers, while using only 33--50\% of the training instances. Trained only on uniform synthetic data, EDISCO also outperforms competing learning-based baselines under spatial distribution shift and CVRP constraint-tightness shift. Code is available at https://anonymous.4open.science/r/EDISCO-7F54.

Deep learning has become the dominant paradigm for vision-based spacecraft pose estimation, where models are predominantly trained on synthetic data due to the prohibitive cost of acquiring real in-orbit imagery. When deployed, these models suffer significant performance degradation due to the sim-to-real domain gap, which existing benchmarks address by pairing synthetic training data with Hardware-in-the-Loop (HIL) real imagery. Yet a critical factor remains consistently overlooked: in real orbital scenarios, illumination varies with the spacecraft's position along its orbit, inducing severe appearance shifts that existing benchmarks neither control nor study. We introduce ECLIPSE, the first benchmark designed to isolate illumination as an independent experimental variable. ECLIPSE pairs a large-scale photorealistic synthetic training set with a fully labeled HIL evaluation set of 14 approach trajectories, each replayed identically under 4 controlled lighting conditions, enabling rigorous evaluation of illumination-robust and domain-adaptation methods. Through this controlled design, our experiments confirm that lighting is the dominant driver of the sim-to-real performance gap in spacecraft pose estimation.

CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned stream---stdout, errors, file contents---records the consequences of its actions. We argue that this stream is a supervision signal, but standard agent RL largely discards it: GRPO-style training updates action tokens while ignoring environment responses, even though rollouts already contain them. We introduce ECHO (Environment Cross-entropy Hybrid Objective), an auxiliary loss that trains the policy to predict observation tokens alongside the policy gradient. ECHO reuses the same forward pass, requires no additional rollouts, and turns terminal feedback into a dense signal that remains informative even on failed trajectories. This objective shapes the policy toward a world model of terminal dynamics, plausibly improving its priors over which actions are likely to succeed. On TerminalBench 2, ECHO nearly doubles pass@1: Qwen3-8B improves from 2.70% to 5.17%, and Qwen3-14B from 5.17% to 10.79%. From base Qwen3-8B, ECHO+GRPO matches expert-SFT-then-GRPO on held-out terminal tasks without the expert demonstrations required for SFT, and closes roughly half this gap on TerminalBench 2. Without verifier rewards, ECHO can lead to self-improvement through exploration alone, suggesting environment observations are not merely context for actions but supervision for the policy itself.

Reinforcement learning (RL) is a critical stage in post-training large language models (LLMs), involving repeated interaction between rollout generation, reward evaluation, and centralized learning. Distributing rollout execution offers opportunities to leverage more cost-efficient inference resources, but introduces challenges in wide-area coordination and policy dissemination. We present ECHO-2, a distributed RL framework for post-training with remote inference workers and non-negligible dissemination latency. ECHO-2 combines centralized learning with distributed rollouts and treats bounded policy staleness as a user-controlled parameter, enabling rollout generation, dissemination, and training to overlap. We introduce an overlap-based capacity model that relates training time, dissemination latency, and rollout throughput, yielding a practical provisioning rule for sustaining learner utilization. To mitigate dissemination bottlenecks and lower cost, ECHO-2 employs peer-assisted pipelined broadcast and cost-aware activation of heterogeneous workers. Experiments on GRPO post-training of LLMs ranging from 4B to 32B parameters under real wide-area bandwidth regimes show that ECHO-2 significantly improves cost efficiency while preserving RL reward comparable to strong baselines.

While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether they perform actual step-by-step reasoning or just rely on superficial visual cues. To investigate this, we introduce **ECG-Reasoning-Benchmark**, a novel multi-turn evaluation framework comprising over 6,400 samples to systematically assess step-by-step reasoning across 17 core ECG diagnoses. Our comprehensive evaluation of state-of-the-art models reveals a systematic shortfall in executing multi-step logical deduction. Although models possess the medical knowledge to retrieve clinical criteria for a diagnosis, they exhibit consistently low success rates in maintaining a complete reasoning chain, primarily failing to ground the corresponding ECG findings to the actual visual evidence in the ECG signal. These results demonstrate that current MLLMs bypass actual visual interpretation, highlighting a limitation in their visual grounding capabilities and motivating robust, reasoning-centric medical AI. The code and data are available at https://anonymous.4open.science/r/ecg-reasoning-benchmark-anonymized-BB3C.

The availability of open 12-lead electrocardiogram (ECG) datasets has significantly increased the number of studies focusing on automated ECG analysis. However, existing datasets often have limitations due to unverified annotations, overly processed data, or the absence of features such as fiducial points of the ECG. In this paper, we present a comprehensive collection of ECGs, with each record meticulously annotated by a team of seven cardiologists. Over 2,500 ECG records were annotated, with most records reviewed by at least four physicians. 1,527 ECG records of the dataset contain detailed annotations of PQRST segments, verified by two cardiologists. The dataset constitutes the largest collection of manually delineated ECG records to date, offering a valuable resource for researchers and practitioners in cardiology. This dataset, the first of its kind available for open use with multi-expert annotations, is suited for a variety of research tasks. It can be used for ECG abnormality classification, for analyzing patterns and annotation complexities across different heart abnormalities, and for tasks related to ECG delineation.

Deploying large language models on edge devices requires mitigating two major bottlenecks: the computational cost of the model and the memory overhead of the KV cache. While combining model quantization and KV-cache eviction seems like a natural solution, we reveal that this naive pipeline suffers from a critical failure mode. The issue stems from a hidden conflict: modern eviction methods rely on attention scores to identify important tokens, yet quantization inherently adds noise to these exact scores. Consequently, quantization does not merely degrade the numerical precision of the KV cache—it actively alters the eviction decisions, often causing the model to discard crucial context. To overcome this non-orthogonal degradation, we propose EAT, a training framework that integrates discrete eviction behavior into quantization-aware training. EAT computes eviction from quantized attention, applies the resulting mask in the forward pass, and uses a masked straight-through estimator in the backward pass. The method requires no architectural changes and adds no inference-time parameters. Systematic evaluations show that EAT mitigates the failure mode of naive quantization-plus-eviction pipelines. Under W4A16 + KV-INT8 with 50% KV eviction, eviction-unaware composition reduces the five-task average from 38.37 to 29.04, whereas EAT recovers it to 39.44 under the same cache budget. Real-world deployment on the MediaTek Dimensity 9500 demonstrates that, under a 50% KV-eviction budget, EAT improves decoding throughput by 64.1% and reduces total memory traffic by 20.4% compared to quantized full-KV inference. Mechanistic analyses suggest that the improvement is associated with reduced attention sink behavior rather than with sharper attention.

Vision–Language–Action (VLA) models offer a unified framework for robotic manipulation by integrating visual perception, language understanding, and control generation. However, existing VLA systems still struggle to generalize across diverse tasks, scenes, and camera viewpoints, and often produce coarse or unstable actions. We argue that these limitations are closely tied to the structural properties of actions in VLA settings, including the inherent multi-peaked nature of action distributions, the token-based symbolic reasoning of pretrained VLM/VLA backbones, and the effective finite resolution imposed by real-world robotic control. Motivated by these properties, we introduce E0, a tweedie discrete diffusion framework that formulates action generation as iterative denoising over quantized action tokens. By operating in a discrete action space with a principled diffusion process, E0 naturally aligns with token-based reasoning, supports fine-grained yet executable action control, and avoids the distributional mismatch of masking-based discrete diffusion. Experiments on LIBERO, VLABench, ManiSkill, and a real-world Franka arm demonstrate that E0 achieves state-of-the-art performance across diverse environments, outperforming strong baselines by 8.3\% on average.

With the rise of large language models (LLMs), generative engines have become powerful alternatives to traditional search, reshaping retrieval tasks. In e-commerce, for instance, conversational shopping agents now guide consumers to relevant products. This shift has created the need for generative engine optimization (GEO)---improving content visibility and relevance for generative engines. Despite its growing importance, current GEO practices are largely ad hoc, and their impacts remain poorly understood, especially in the e-commerce setting. We address this gap by introducing E-GEO, the first dataset built specifically for e-commerce GEO. E-GEO contains 13,747 realistic, multi-sentence consumer product queries, each paired with 10 relevant Amazon listings, capturing rich intent, constraints, preferences, and shopping contexts that existing datasets miss. Using this dataset, we conduct the first large-scale empirical study of e-commerce GEO across five frontier generative engines, seven popular LLM rewriters with a simple prompt, and fifteen hand-crafted rewriting heuristics. We further formulate GEO as a tractable optimization problem and develop a lightweight prompt meta-optimization algorithm that significantly improves over heuristic baselines. Notably, the optimized prompts reveal a stable, domain-agnostic pattern, suggesting the existence of a "universally effective" GEO strategy. Finally, we red-team the GEO system through both heuristic and optimization-based attacks and show that, under a simple in-prompt defense, gains from GEO reflect genuine content improvement rather than manipulation, anchoring GEO as a substantive, well-defined optimization problem.

Tracking plasma profiles, such as electron temperature and rotation, remains a challenging task in tokamak nuclear fusion. While RL-based approaches offer a promising path for multi-input-multi-output control systems, existing frameworks overlook two key obstacles: partial observability of plasma dynamics and the diagnostic mismatch between offline training and online execution. We propose a novel offline RL framework to address these challenges. Specifically, we train the control policy to condition on the latent context that is extracted from a trained plasma dynamics model. This dynamics-informed latent context encodes useful information about operating regimes, enabling the policy to adapt as the plasma evolves. To mitigate the gap between offline and real-time observation, we learn a converter that maps plasma diagnostics reconstructed offline to their real-time-observable counterparts, thereby exposing policy training to the real-time plasma diagnostics available in live experiments. In offline evaluations, the proposed method substantially improves tracking performance and robustness across a diverse set of discharges. We report real-time deployment results on DIII-D Tokamak Fusion Facility, showing feasibility under challenging actuator conditions.

Autoregressive Diffusion Transformers have emerged as a powerful paradigm for long video generation. However, they are often constrained by prohibitive computational overhead and substantial memory footprints. This inefficiency arises from dense computation mechanisms that allocate uniform processing resources across all spatiotemporal regions, failing to leverage the inherent temporal redundancy of video where motion is typically confined to sparse areas. In this paper, we propose **Dynamics-Aware Sparse Attention (DASA)**, a unified framework that shifts from dense computation to sparse, motion-driven generation to eliminate both storage and computational redundancies. Inspired by video codec principles, our approach explicitly decouples video content into dynamic and static regions. To mitigate memory bottlenecks, we selectively compress the historical KV cache by retaining only dynamic features within the generated chunk. Furthermore, to reduce computational overhead, we employ a region-level computation allocation strategy based on localized dynamics. Additionally, a dynamics-guided distillation method is introduced to enhance performance via a lightweight distillation training process. Experiments on VBench demonstrate that our approach accelerates autoregressive video generation by 2.16$\times$, while preserving high visual fidelity.

Large language models are remarkably capable, yet how computation propagates through their layers remains poorly understood. A growing line of work treats depth as discrete time and the residual stream as a dynamical system, where each layer's nonlinear update has a local linear description. However, previous analyses have relied on scalar summaries or approximate linearizations, leaving the full spectral geometry of trained LLMs unknown. We perform full Jacobian eigendecomposition across three production--scale LLMs and show that training installs a monotonic spectral gradient through depth---from non-normal, rotation-dominated early layers to near--symmetric late layers---together with a cumulative low-rank bottleneck that funnels perturbations into a small fraction of the residual stream's effective dimensions. Our experiments reveal that this gradient and the dimensional collapse are learned rather than architectural, and is largely dissolved when structured non-normality is removed. We further show that the topological positioning of graph communities predicts whether the Jacobian amplifies or suppresses them, with the sign of the coupling determined by the local operator type, a relationship absent at initialization. These results map a learned spectral geometry in LLMs that links perturbation propagation and compression to the network's functional topology.

Generative AI systems are increasingly deployed at scale to support human users in creative tasks across a variety of domains. However, as more users adopt the same AI agent (e.g., ChatGPT), the resulting artifacts may become increasingly homogeneous, leading to a collapse in collective diversity. Prior work has documented this risk, but lacks a systematic framework for understanding how collective diversity changes as AI adoption scales across co-creation settings. In this work, we conduct a large-scale crowdsourcing study to understand and model the dynamics of collective diversity, quality, and effort in human–AI co-creation for three creative tasks. We find that current AI agents used in co-creation settings can improve artifact quality and reduce effort, but that these gains come at the cost of declining collective diversity as adoption increases. We further show that more structured and fine-grained human-AI interaction, along with more diverse agent designs, can mitigate this collapse. Finally, we derive predictive models that capture how collective diversity scales across co-creation settings, providing valuable insights for the design of AI agents that aim to preserve collective diversity.

Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models, primarily due to their ability to enable parallel decoding. Despite this advantage, most existing DLMs rely on a fixed generation length specified prior to decoding, which restricts their flexibility in real-world applications. While a few recent works attempt to support flexible-length generation, they typically suffer from notable limitations: some require costly retraining to accommodate variable-length outputs, while others depend solely on local confidence signals during decoding. Such local criteria fail to capture the evolving structure of the sequence, often resulting in suboptimal generation quality. In this paper, we propose a training-free, Bayesian structured decoding framework that formulates flexible-length generation as a dynamic structural inference problem. Our approach learns the posterior inference over the dynamic block length, block formations and growth, and block decoding order within a unified Bayesian inference framework to jointly reason about how much to grow, where to grow, and how to organize content structurally. At each window expansion step, the method integrates local uncertainty with structural signals to (i) dynamically expand the sequence via adaptive length growth, (ii) infer block boundaries through Chinese Restaurant Process (CRP)-style partitioning, and (iii) allocate different number of decoding steps for different blocks and determine block decoding order via context-aware scheduling. This yields a unified mechanism that supports dynamic structured generation, including both flexible block expansion and block organization, while maintaining coherence. Extensive experiments across multiple benchmarks demonstrate that our approach significantly improves generation quality and flexibility over existing fixed-length and flexible-length baselines. These results highlight the advantage of Bayesian structured decoding for diffusion language model, providing a principled and efficient solution for structured text generation.

Class-Incremental Learning (CIL) requires models to continuously acquire new classes without forgetting previously learned ones. A dominant paradigm involves freezing a pre-trained model and training lightweight, task-specific adapters. However, maintaining task-specific parameters hinders knowledge transfer and incurs high retrieval costs, while naive parameter fusion often leads to destructive interference and catastrophic forgetting. To address these challenges, we propose Dynamical Adapter Fusion (DAF) to construct a single robust global adapter. Grounded in the PAC-Bayes theorem, we derive a fusion mechanism that explicitly integrates three components: the optimized task-specific adapter parameters, the previous global adapter parameters, and the initialization parameters. We utilize the Taylor expansion of the loss function to derive the optimal fusion coefficients, dynamically achieving the best balance between stability and plasticity. Furthermore, we propose a Robust Initialization strategy to effectively capture global knowledge patterns. Experiments on multiple CIL benchmarks demonstrate that DAF achieves state-of-the-art (SOTA) performance.

Manipulating dynamic objects remains an open challenge for Vision-Language-Action (VLA) models. Although recent VLAs generalize well in static manipulation, dynamic scenes introduce a latency-induced perception–execution mismatch: object states continue to evolve during inference, making actions predicted from past observations stale at execution time. We present DynamicVLA, a latency-aware VLA model for dynamic object manipulation. It combines a compact 0.4B architecture and convolutional vision encoder for efficient multimodal inference with a continuous inference schedule that overlaps reasoning and execution for non-blocking control. Latent-aware Action Streaming then discards latency-invalid action prefixes and executes only the temporally valid suffix of each predicted chunk, preserving action-time alignment under dynamic object motion. To fill the missing foundation of dynamic manipulation data, we introduce the Dynamic Object Manipulation (DOM) benchmark, built with an automated collection pipeline that gathers 200K synthetic episodes across 2.8K scenes and 206 objects, and enables fast collection of 2K real-world episodes without teleoperation. Extensive evaluations in simulation and on real robots show that DynamicVLA improves dynamic manipulation success under changing object motion, perception-heavy instructions, and unseen motion patterns.

The $k$-center problem is a fundamental clustering variant with applications in learning systems and data summarization. In several real-world scenarios, the dataset to be clustered is not static, but evolves over time, as new data points arrive and old ones become stale. To account for dynamicity, the $k$-center problem has been mainly studied under the sliding window setting, where only the $N$ most recent points are considered non-stale, or the fully dynamic setting, where arbitrary sequences of point arrivals and deletions without prior notice may occur. In this paper, we introduce the dynamic setting with lifetimes, which bridges the two aforementioned classical settings by still allowing arbitrary arrivals and deletions, but making the deletion time of each point known upon its arrival. Under this new setting, we devise a deterministic $(2+\varepsilon)$-approximation algorithm with $\widetilde{O}(k/\varepsilon)$ amortized update time and memory usage linear in the number of currently active points. Moreover, we develop a deterministic $(6+\varepsilon)$-approximation algorithm that, under ‘‘tame” update sequences, has $\widetilde{O}(k/\varepsilon)$ worst-case update time and heavily sublinear working memory.

Diffusion models have achieved impressive performance in video generation, but their iterative denoising process remains computationally expensive due to the large number of tokens processed at each timestep. Recently, progressive resolution sampling has emerged as a promising acceleration approach by reducing latent resolution in early stages. However, scaling this idea to video generation remains challenging, as the additional temporal dimension introduces diverse spatio-temporal demands across different videos, and compressing only a single dimension often leads to limited acceleration or degraded quality. Therefore, we propose \textbf{DVG}, a \textbf{D}ynamic \textbf{V}ideo \textbf{G}eneration framework that jointly allocates computation across time and space, automatically selecting content-aware acceleration strategies without manual tuning or retraining. \textbf{DVG} achieves near-lossless acceleration across models and tasks, reaching up to \textbf{7$\times$} speedup on HunyuanVideo and HunyuanVideo-1.5, and \textbf{18$\times$} when combined with distillation, demonstrating its potential as a key component in today’s large-scale efficient video generation systems. \emph{Our code is in supplementary material and will be released on Github.}