NeurIPS 2026 论文列表

We study strongly adaptive regret for Online Convex Optimization (OCO) with time-varying movement costs, a setting that evaluates a learner's performance over arbitrary sub-intervals. This framework captures complex sequential decision-making problems where both the environment and the cost of switching decisions fluctuate over time. We propose a novel algorithm which, for any interval $[a,b] \subseteq [T]$, achieves a data-dependent regret bound of $ \widetilde{\mathcal{O}}\left( \sqrt{(D^2+DP_{[a,b]})\sum_{t \in [a,b]}(\\|g_t\\|^2+\lambda_t\\|g_t\\|)} \right) $, where $D$ is the diameter of the decision domain, $P_{[a,b]}$ is the path length of the comparator sequence over $[a,b]$, $g_t$ is the gradient of the loss, and $\lambda_t$ is the movement-cost coefficient at round $t$. Key to our approach is a novel reduction to sleeping experts with movement costs, a previously unexplored variant that is of independent interest. As a result of this development, we further establish the first strongly-adaptive guarantees under delayed feedback. Our approach also naturally extends to OCO with time-varying memory, where we obtain strongly-adaptive guarantees which improve the best-known bounds.

Knowledge distillation generally assumes a strong-to-weak relationship where stronger teachers yield better students. In this work, we examine this assumption about distillation in large language model (LLM) pretraining. By varying architecture sizes and training token budgets, we create strong-to-weak, same-level, and weak-to-strong teacher-student relationships. We study distillation's effectiveness under these relationships with different mixes between language and distillation loss. Three findings emerge: (1) with proper loss mixing, weak-to-strong and same-level distillation improves over standard pretraining, where even small and undertrained teachers benefit large students; (2) making the teacher stronger can lead to saturated or even reversed gains; (3) distillation improves generalization (out-of-domain, downstream) more readily than in-domain fitting. Our results provide practical guidance for choosing the teacher model with the proper loss for more effective LLM pretraining distillation.

Stroke-based rendering is often optimized as a pixel-level reconstruction task. This over-reliance on global error reduction not only degrades strokes into fragmented curves due to a lack of structured planning, but also allows locally defective primitives to be concealed by overlapping layers. To address these issues, we introduce a model-guided framework for stroke-audited rendering. Our framework uses Quadratic Bezier Splatting to analytically model continuous curved strokes with efficient differentiable alpha compositing. Built on this renderer, a feedforward multi-scale stroke decoder provides a structural initialization with learned stroke-scale allocation, while test-time stroke optimization supplies the reconstruction capacity needed for high-fidelity refinement. We further propose a single-stroke audit mechanism that explicitly evaluates local stroke quality, preventing defective primitives from surviving optimization through layer-wise compensation. Experiments demonstrate our approach achieves highly coherent layouts, improving PSNR by up to 20\% and yielding a 58.8$\times$ renderer speedup with a 16\% more compact representation compared to representative SBR baselines.

Neural network verifiers aim to provide formal guarantees on model behavior, but existing verification benchmarks are fundamentally limited by their lack of ground-truth labels. As a result, verifier evaluation relies on indirect heuristics, which prevents exact scoring and systematic studies of verifier failure modes. We address this gap by introducing a reusable framework for generating verification instances whose ground-truth robustness labels are known a priori through analytic construction. Our framework led to the discovery of multiple numeric tolerance concerns and implementation bugs in popular verifiers, highlighting the need for ground-truth labels. Additionally, to systematically study verifier failure modes, we introduce the verification Difficulty Profile, a collection of estimable quantities capturing distinct sources of instance hardness. Using our framework and these profiles, we evaluate five state-of-the-art verifiers and show that different instances stress distinct aspects of the verification pipeline. We show that these results can aid the future development of verifiers as they provide actionable targets for improving numerical reliability, relaxation quality, and search behavior. Our code is publicly available: https://github.com/NeurIPSAnon12345/VeriStressGT.git.

As LLM agents advance rapidly in autonomy, the risk of hidden objectives that bypass safety constraints becomes increasingly plausible. Chain-of-thought (CoT) monitoring offers a key control mechanism by reading the agent's reasoning and flagging misalignment before it materializes into action, yet its reliability depends on the realism of the red-team it is stress-tested against. Existing red-teaming efforts apply uniform attack templates that treat the side task as an exogenous insertion, yielding abrupt anomalies any reasonable monitor can flag while leaving the subtler, contextually grounded evasions of a capable agent largely unprobed. We therefore ask: How robust is CoT monitoring against agents that strategically conceal misalignment? To answer this, we introduce TraceMRT (Trace-level Monitor Red-Teaming), an automated red-teaming framework that reframes the side task as a coherent sub-goal of the primary task and searches trace-shaping strategies whose induced trajectories complete the misaligned objective while reading as natural execution. Through black-box attacks on state-of-the-art monitors (e.g., GPT-5.2, and Claude-4.5), we reveal substantial vulnerabilities even in top models. To better counter these vulnerabilities, we further improve monitoring scaffolding with Bayesian Trajectory Monitoring, integrating global context and local evidence to better detect covert misalignment. Empirically, the method significantly consolidates the monitor with 0.91 AUC and 0.74 TPR@FPR=0.05.

Tabular prediction has long been dominated by gradient-boosted decision trees and specialized deep tabular models, while large language models (LLMs) remain difficult to make competitive despite their cross-task adaptability and transparent reasoning traces. Existing LLM-for-tabular methods mainly adapt tables into textual inputs, but often fail to capture key structural properties of tabular data. In this work, we propose Permutation Relative Policy Optimization (PRPO), a reinforcement learning post-training method that strengthens LLMs for tabular prediction by injecting column-permutation invariance as a structural prior. By constructing label-preserving column permutations and estimating advantages both within and across them, PRPO converts sparse outcome rewards into denser and more stable optimization signals. Extensive experiments on 139 OpenML datasets show that our 8B model reaches a genuinely competitive regime against strong specialized tabular baselines. It achieves strong fully supervised performance, dominates cross-dataset zero-shot settings, and performs on par with 32-shot strong baselines. Moreover, it substantially outperforms much larger general-purpose and reasoning LLMs, including up to a 53.17% improvement over DeepSeek-R1 (685B). These results show that structural-prior RL post-training is an effective route for making LLMs competitive in tabular prediction.

Deep neural networks have achieved significant success on in-distribution (ID) tasks, but out-of-distribution (OOD) samples during inference can lead to unreliable predictions. To address this issue, distance-based methods exploit the geometric structure of feature space for OOD detection. However, methods based on static information geometry fail to address geometry distorted by ill-distributed samples. Although prior work reduces the misclassification of OOD samples as ID by adjusting the residual space with real-time features, it fails to address the misclassification of ID samples as OOD. In addition, existing distance-based methods ignore class-specific characteristics when computing deviation feature and rely on the nearest prototype for uncertainty estimation, leading to suboptimal OOD detection performance. To address these issues, we propose Adaptive Mahalanobis Gap (AMG). Specifically, we first strengthen the principal space along real-time feature directions to reduce the misclassification of ID samples as OOD. Subsequently, we incorporate class variance and prototype norms to mitigate bias of deviation feature from class-wise distributional differences. Finally, we introduce the Mahalanobis gap scoring for uncertainty scoring to overcome bias from single-prototype reliance. Experiments on CIFAR-10/100 and ImageNet-1k show that AMG consistently outperforms state-of-the-art methods in FPR@95 and AUROC. Our code is available at https://anonymous.4open.science/r/AMG_AR.

Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video (and multimodal) large language models (LLMs) as task guidance assistants. A crucial capability of the real-world success of a prospective task guidance assistant is its ability to intervene proactively as soon as a mistake is apparent in order to guide the user. To evaluate this crucial capability, we introduce Ego-MC-Bench (Mistake Corrections), a benchmark for evaluating *reactive, step-by-step* task guidance in realistic cooking scenarios. Extensive experiments show that Ego-MC-Bench is highly challenging for state‑of‑the‑art video LLMs. We argue that a key reason is the limited availability of training data for fine-tuning models on this task. Although there exists a wide range of cooking video datasets, existing datasets lack examples of mistakes along with appropriately timed interventions. To help address this data limitation, we also introduce Ego-CoMist, a counterfactual synthetic dataset created by transforming non‑interactive cooking videos into supervised training examples showing proactive interventions. We show that fine-tuning on Ego-CoMist yields performance gains especially for smaller and more efficient video LLMs that are well suited for delivering assistance on edge devices.

Inferring the evolution of high-dimensional and multi-modal (e.g., spatio-temporal) physical fields from irregular sparse measurements in real time is a fundamental challenge in science and engineering. Existing approaches, including diffusion-based generative models and functional tensor methods, typically operate in offline settings, depend on full temporal observations, or incur substantial inference cost. We propose StreamPhy, an end-to-end framework that enables efficient and accurate streaming inference of full-field physical dynamics from incoming irregular sparse measurements. The framework integrates a data-adaptive observation encoder that is robust to arbitrary observation patterns, a structured state-space model that supports memory-efficient online updates across irregular time intervals, and an expressive Functional Tensor Feature-wise Linear Modulation (FT-FiLM) decoder for continuous-field generation. We prove that FT-FiLM is more expressive than the functional Tucker model, admitting a richer function class for handling complex dynamics. Experiments on three representative physical systems under challenging sampling patterns show that StreamPhy consistently outperforms state-of-the-art baselines, with at least 48\% improvement in accuracy and up to 20--100x faster inference than diffusion-based methods.

Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as $\pi_{0.5}$ operate under a single-frame paradigm, limiting their ability to retain past observations and develop precise spatial perception. In this paper, we propose StreamPI, a streaming multimodal temporal modeling framework that equips single-frame VLA with temporal reasoning capability without introducing any additional parameters. One core design is instruction-anchored temporal modeling. It treats each (visual observation, language instruction) pair as an atomic temporal unit: bidirectional attention within each pair enables cross-modal fusion, while causal attention across pairs preserves autoregressive streaming inference. This ensures the language instruction serves as a persistent semantic anchor throughout task execution. To bridge the gap between synchronous training and asynchronous real-robot deployment, we introduce a random-interval streaming training strategy: a proper inter-frame interval (e.g., every 3 frames) enables faster and smoother action execution. Beyond this, randomizing the interval further improves robustness to frame-timing perturbations, supporting asynchronous deployment in practice. Furthermore, by leveraging the length extrapolation capability of the LLM backbone, StreamPI seamlessly inherits pretrained single-frame weights and supports flexible single-frame and multi-frame inference. Experiments on real-robot tasks spanning memory-dependent and precise perception scenarios, as well as the simulation benchmark LIBERO, demonstrate that StreamPI outperforms $\pi_{0.5}$ across diverse tasks.

While streaming omni-video understanding demands continuous perception and proactive, real-time interaction, this crucial area remains largely under-explored. Current omni-modal methods are inherently designed for offline settings, limiting their applicability in streaming scenarios due to two fundamental flaws. First, they lack robust mechanisms to manage continuously growing audio-visual context over long horizons and cannot autonomously initiate responses at opportune moments. Second, existing benchmarks are predominantly confined to offline, single-turn question answering, failing to capture continuous, multi-turn streaming interactions. To bridge these gaps, we propose StreamOV, a novel Streaming Omni-Video understanding framework for efficient online audio-visual reasoning with bounded memory and proactive response triggering. Specifically, StreamOV introduces a multimodal evidence-guided long-short term memory that condenses historical audio-visual context into compact informative evidence under a fixed budget. It further employs a hidden-state-driven trigger to decide when to respond, avoiding explicit silence-token generation and external routers. We also curate SOVBench, the first comprehensive benchmark for online, multi-turn omni-modal evaluation. Extensive experiments show that StreamOV achieves state-of-the-art performance across diverse streaming and omni-video benchmarks, demonstrating its effectiveness for both online and offline video understanding.

Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intention for realistic applications such as Augmented Reality (AR) glasses. While prior streaming benchmarks evaluate temporal reasoning, none measure whether Multimodal Large Language Models (MLLMs) can interpret or leverage human gaze signals within a streaming setting. To fill this gap, we introduce StreamGaze, the first benchmark designed to evaluate how effectively MLLMs utilize gaze for temporal and proactive reasoning in streaming videos. StreamGaze introduces gaze-guided past, present, and proactive tasks that comprehensively assess streaming video understanding. These tasks evaluate whether models can use real-time gaze signals to follow shifting attention and infer user intentions based only on past and currently observed frames. To build StreamGaze, we develop a gaze–video Question Answering (QA) generation pipeline that aligns egocentric videos with raw gaze trajectories through fixation extraction, region-specific visual prompting, and scanpath construction. This pipeline produces spatio-temporally grounded QA pairs that reflect human perceptual dynamics. Across all StreamGaze tasks, we observe substantial performance gaps between state-of-the-art MLLMs and human performance, highlighting key limitations in gaze-based temporal reasoning, intention modeling, and proactive prediction. We further provide detailed analyses of gaze prompting strategies, reasoning behaviors, and task-specific failure modes, offering insights into current limitations and directions for future research. All data and code are publicly available to support research in gaze-guided streaming video understanding.

In practice, the reasoning strategy often matters more than the model. Rather than calling a fixed large model once per input, a well-designed strategy breaks the problem into steps and uses the right model for each, achieving better accuracy at lower cost. Existing methods have tried to automate strategy design by searching over a fixed set of strategy templates, but the templates themselves limit what reasoning patterns can be discovered. We propose Strategist, a meta-agent that designs a high-performing inference strategy for a given task. Each strategy is a structured executable topology that defines how agents plan, interact, verify, and route across models. Given a task and a small development set, Strategist proposes candidate strategies, runs them on real samples, and revises them against measured accuracy and cost. Every strategy and its components are reusable, forming an evolving library that grows with each new task and compounds what future tasks can build from. On 30 benchmarks, Strategist matches or beats the strongest baselines with an average of 8.5 absolute percentage points higher accuracy at 48% lower inference cost.

When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The typical solution is to redesign the predictor itself to explicitly account for strategic interactions. In practice, however, decision makers are often constrained to adjusting coarser levers within existing prediction pipelines. For example, healthcare organizations often select which features to exclude based on perceived manipulability, while using standard regularization procedures to shrink the coefficients of retained features. In this work, we initiate a formal study of strategic classification through *feature selection* and its interaction with *ridge regularization*. Our main finding is that excluding features based on manipulability alone is generally suboptimal. We provide a fine-grained characterization of the performance of a feature subset under optimal regularization, yielding new insights for policy design. Motivated by this characterization, we develop a practical algorithm for jointly choosing the feature set and the level of ridge regularization. Through a real-world case study on a healthcare payments benchmark, we illustrate how our algorithm can guide the design of coarse policy levers in practice. Our results provide a principled, practical framework for mitigating the effects of strategic behavior in algorithmic decision-making systems.

Public benchmarks play a significant role in steering LLM development, as market incentives drive model developers to optimize for leaderboard performance. However, fundamental information limitations mean that evaluators are only able to cover a subset of socially relevant capabilities with their evaluation tasks. On the other hand, model developers often have additional private benchmarks that are unavailable to evaluators. The resulting information asymmetry opens the door to strategic task specialization that can lead to suboptimal social welfare. We propose randomized evaluation mechanisms (effectively private benchmarks) as a way to incentivize model developers to train to cover a broader set of socially relevant tasks, including those unknown to the evaluator. We formalize this as a dynamic game with information asymmetry between an evaluator and a model developer. We prove that randomized evaluation aligns incentives to the developer’s prior belief over possible evaluated tasks, and is socially optimal when that belief matches society, but information leakage degrades this over rounds. However, if the evaluator can continually update their known task set, alignment can be asymptotically recovered. We illustrate a variance–leakage–correction tradeoff in semisynthetic experiment with a latent factor structure over MMLU-Pro.

Machine learning (ML) predictions are increasingly being used to guide decision-making, giving rise to the problem of decision-focused learning (DFL) where predictors are optimized for downstream decision quality rather than accuracy alone. However, existing work assumes a single decision-maker optimizing in isolation. This paper formalizes *strategic* decision-focused learning, where an ML system predicts an exogenous state that some agents observe before playing a game. For example, a park ranger may predict wildlife locations to allocate anti-poaching patrols against strategic poachers. While the exogenous state is unaffected by agent actions, predictions influence agents' strategies and the resulting equilibrium. We find that strategic considerations fundamentally change the learning problem. In particular, we show the prediction accuracy–equilibrium payoff landscape can be non-monotonic---i.e., better predictions can degrade performance. We propose algorithmic approaches to address these challenges and validate them across benchmarks in wildlife conservation and infrastructure protection. Our theory and experiments highlight the importance of accounting for strategic interactions when designing predictors.

The standard rule in unconstrained causal policy learning is simple: treat individuals with nonnegative CATE. We show that this rule can fail when treatment eligibility is strategically manipulable. Agents may move or report covariates after a policy is announced, so treatment is assigned using manipulated covariates while welfare remains determined by baseline CATEs. This creates a mismatch between who receives treatment and who benefits from treatment. We formalize this setting as strategic causal policy learning and show that strategic welfare equals the CATE-weighted mass of baseline types that can reach the treatment region. This access-burden representation leads to three distinct strategic thresholds: a welfare threshold, a safe threshold, and a fair threshold. For linear CATE models, we derive closed-form threshold corrections. Homogeneous costs admit a single buffer correction that recovers the ideal positive-CATE allocation, while heterogeneous costs make welfare maximization, no-exploit safety, and subgroup fairness select different policies. Experiments illustrate these tradeoffs and show when group-specific corrections can restore the ideal allocation.

Visual context is a powerful cue for object recognition: scenes inform what, where, and at what scale objects are likely to appear. Yet over-reliance on context becomes a failure mode when models predict objects based on their typical neighbours rather than the objects' own visual features, a phenomenon we refer to as typicality bias. While this issue has been extensively studied in the pre-Transformer era, it has often been assumed that architectural and scale improvements would naturally solve it. Despite recent attempts in isolated settings, a systematic diagnostic across the modern vision stack remains absent. In this work, we provide such a diagnostic framework. Specifically, we measure typicality bias via normalized pointwise mutual information (NPMI) over object pairs, and introduce STRANGE-Bench (Synthetic Typicality-RANked Grounded Evaluation Benchmark): a photorealistic, layout-controlled benchmark spanning 11 typicality tiers from typical to never-co-occurring object pairs. Across \five model families (specialist detectors, open-vocabulary detectors, self-supervised, vision-language, and multimodal large language models) and three recognition tasks}(supervised detection, open-vocabulary detection, and classification), we find the bias to be highly prevalent: gaps of 5--11\% persist when objects appear outside their typical company, and they do not consistently shrink with scale. This persistence motivates a complementary question: how to mitigate it? Data augmentation is a natural lever, since it directly shapes the training distribution that produces the bias; yet existing augmentations are designed for visual diversity, not for breaking co-occurrence shortcuts as they sample from the very distribution that produces typicality bias, preserving rather than perturbing it. We therefore propose Anchor-Atypicality Sampling (AAS), a simple context-aware variant of paste-based augmentations that uses the NPMI signal to deliberately tilt the training-time co-occurrence distribution toward atypical partners. AAS reduces the typicality gap by up to 3.3 pp on natural-image COCO splits and 17.4 pp on synthetic STRANGE-Bench across three representative model families (specialist detection, open-vocabulary detection, self-supervised classification), without sacrificing overall performance.

Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which weakens both exploration and credit assignment over extended trajectories. In this work, we present Strategic Trajectory Abstraction (StraTA), a simple framework that introduces an explicit trajectory-level strategy into agentic reinforcement learning (RL). StraTA samples a compact strategy from the initial task state, conditions subsequent actions on that strategy, and trains strategy generation and action execution jointly with a hierarchical GRPO-style rollout design, further enhanced by diverse strategy rollout and critical self-judgment. Experiments on ALFWorld, WebShop, and SciWorld show that StraTA consistently improves both sample efficiency and final performance over strong baselines. StraTA reaches success rates of 93.1% on ALFWorld and 84.2% on WebShop, surpassing the strongest baselines by 2.3% and 11.4% respectively. On SciWorld, StraTA attains a 63.5% overall score, outperforming frontier closed-source models by 6.1% and prior RL baselines by 6.5%.