NeurIPS 2026 论文列表

Self-supervised monocular depth estimation is appealing for its potential to scale with unlabeled data, yet existing methods often degrade when trained on diverse scenes. This study finds that, under self-supervision, models tend to encode dataset-specific structural biases rather than transferable scene geometry, leading to cross-domain optimization conflicts. To address this issue, this study proposes the MaSS framework, aiming to make self-supervised depth learning scalable to diverse data. MaSS introduces a dedicated structural representation pathway explicitly decoupled from contextual features, which is guided by a novel scene-structure discrimination objective to learn generalized, layout-aware structural representations for depth decoding. Trained and evaluated on four diverse datasets, both individually and jointly, MaSS substantially outperforms prior self-supervised methods and is the first to consistently benefit from mixed-domain training. Zero-shot evaluation on eight additional datasets further demonstrates its remarkable generalizability, particularly on those entirely out-of-distribution scenes. The source code will be publicly available upon publication.

We introduce **AutoSpec**, a neural network framework for discovering iterative spectral algorithms for large-scale numerical linear algebra and numerical optimization. Our self-supervised models adapt to input operators using coarse spectral information (e.g., eigenvalue estimates and residual norms), and predict recurrence coefficients for computing or applying a matrix polynomial tailored to a downstream task. The effectiveness of **AutoSpec** relies on three ingredients: an architecture whose inference pass implements short, executable numerical linear algebra recurrences; efficient training on small synthetic problems with transfer to large-scale real-world operators; and task-defined objectives that enforce the desired approximation or preconditioning behavior across the range of spectral profiles represented in the training set. We apply **AutoSpec** to discovering algorithms for representative tasks on spd matrices: accelerating matrix function approximation; accelerating sparse linear solvers; and spectral filtering/preconditioning for eigenvalue computations. On real-world matrices, the learned procedures deliver orders-of-magnitude improvements in accuracy and/or reductions in iteration count, relative to basic baselines. We find clear connections to classical theory: the induced polynomials may exhibit equioscillation behavior characteristic of Chebyshev polynomial approximation.

It is widely agreed that when AI models assist decision-makers in high-stakes domains, they should communicate the confidence of their predictions. However, empirical evidence suggests that decision-makers often struggle to determine when to trust a prediction based solely on this communicated confidence. In this context, recent theoretical and empirical work suggests a positive correlation between the utility of AI-assisted decision-making and the degree of alignment between the AI confidence and the decision-makers' confidence in their own predictions. Crucially, these findings do not yet elucidate the extent to which this alignment influences the complexity of learning to make optimal decisions through repeated interactions. In this paper, we address this question in the canonical case of binary predictions and binary decisions. We first show that this problem is equivalent to a two-armed online contextual learning problem with full feedback, and establish a lower bound of $\Omega ( \sqrt{|\mathcal{H}| \cdot |\mathcal{B}| \cdot T} )$ on the expected regret any learner can attain, where $\mathcal{H}$ and $\mathcal{B}$ denote the sets of possible human and AI confidence values. We then demonstrate that, by leveraging the alignment between AI and human confidence, a learner can attain an expected regret of $\mathcal{O}(\sqrt{|\mathcal{H}| \cdot T\log T})$ and, in the regime where $\mathcal{B}$ is countable and $\sqrt{|\mathcal{H}|} = \mathcal{O}(\log T)$, a non-trivial generalization of the Dvoretzky-Kiefer-Wolfowitz inequality improves the regret bound to $\mathcal{O}(\sqrt{T\log T})$. Taken together, these results reveal that alignment can reduce the complexity of learning to make decisions with AI assistance. Experiments on real data from two different human subject studies where participants solve simple decision-making tasks assisted by AI models with different degrees of alignment complement our theoretical results.

Benders decomposition (BD) is a widely used solution approach for solving two-stage stochastic programs arising in real-world decision-making under uncertainty. However, it often suffers from slow convergence as the master problem grows with an increasing number of cuts. In this paper, we propose Reinforcement Learning for BD (RLBD), a framework that adaptively selects cuts using a neural network-based stochastic policy. The policy is trained using a policy gradient method via the REINFORCE algorithm. We evaluate the proposed approach on a two-stage stochastic electric vehicle charging station location problem and compare it with vanilla BD and LearnBD, a supervised learning approach that classifies cuts using a support vector machine. Numerical results demonstrate that RLBD achieves substantial improvements in computational efficiency and exhibits strong generalization to problems with similar structures but varying data inputs and decision variable dimensions.

Extremal combinatorics seeks rare mathematical structures that satisfy hard constraints while maximizing a global objective. Most existing neural systems for mathematical discovery train a model to generate complete configurations from scratch. We argue that this framing mismatches the dynamics of effective search: high-quality solutions are more effectively found by preserving a feasible partial core and conditionally regenerating the remaining uncertain objects. We formulate extremal structure discovery as object-level active-core completion: the model is trained to conditionally complete masked objects given a feasible partial core, capturing inter-object relations rather than fitting a distribution over complete configurations. We instantiate this with a full-attention Transformer over object-aligned slots, and propose Inpainting PatternBoost, a variant of PatternBoost. In each iteration, elite constructions from a top pool are partially destroyed by random or structured remasking; the model learns to reconstruct the missing objects, and their completions are reinserted into the pool. We evaluate on the no-three-in-line problem and maximum sum-of-radii circle packing in the unit square, under a single shared abstraction. On no-three-in-line, our framework recovers the optimal $2n$-point configuration for $n$ up to $18$, and reaches $2n-1$ points for $n=19, 20, 25$, outperforming prior neural baselines. On circle packing, where FlowBoost combines neural generation with gradient-based local search, our approach matches or exceeds FlowBoost when paired with the same local search; even without local search, it reaches an average of 88.1% of FlowBoost's reported score across instances.

Large language model (LLM)-based coding agents achieve strong results on controlled benchmarks yet routinely produce pull requests that real maintainers reject. The cause is not functional incorrectness but a lack of organicity: generated code ignores project conventions, duplicates internal APIs, and violates implicit architectural constraints. The latest repository snapshot is insufficient-it reveals the final codebase state, not the change patterns that shaped it. We make three contributions. (1) Paradigm: we propose next-commit prediction as a paradigm for learning agentic coding skill from a repository's own history-each historical commit is a self-supervised target whose oracle diff supplies dense supervision. (2) Evaluation: we establish Organicity Evaluation as a measurable objective for coding agents and contribute the Learning to Commit benchmark-5 curated open-source GitHub repositories under strict per-repository temporal splits-scoring patches along file localisation, internal API reuse, patch bloat, and code-style consistency. (3) Method: we instantiate the paradigm with the Learning to Commit framework, in which the agent performs supervised contrastive reflection: it blindly attempts each historical commit, contrasts its prediction against the oracle, and incrementally distils a reusable skill document that conditions subsequent generations. On the Learning to Commit benchmark and on SWE-bench Pro reframed under our temporal-split protocol, our framework consistently improves organicity on held-out future tasks and further lifts test-pass rates on SWE-bench Pro, narrowing the gap between benchmark success and real-world mergeability.

We study the problem of learning to bid when the bidder’s *value is dynamic*, i.e., when the current value depends on past outcomes. Specifically, we consider a bidder participating in repeated second-price auctions whose value depends on the time elapsed since their last successful bid, with auctions arriving in continuous time and only aggregated feedback revealed at the end of the horizon. Such a bidder must **(1)** balance the immediate benefit of winning the current auction against its impact on future values and **(2)** learn unknown environmental parameters. We derive regret bounds for a class of learning methods that combine plug-in estimators with a differential-equation characterization of the optimal policy, and show that a specific confidence bound algorithm learns the optimal policy with a near optimal regret of $\tilde{\mathcal{O}}(\log N)$ for piecewise linear primitives, and $\tilde{\mathcal{O}}(N^{1/3})$ for general, smooth primitives, achieving these regrets without explicit randomization. These theoretical results are supported by numerical experiments.

Edge-cloud collaboration between large language models (LLMs) and small language models (SLMs) offers a promising way to leverage the problem-solving capability of LLMs while keeping private data on device. Existing methods typically have the LLM generate guidance from an initial user query, which the on-device SLM combines with local private data to produce the final response. However, the LLM's general guidance often does not align with the user's personalized needs, limiting the utilization of the LLM's capabilities. It has been observed that the on-device SLM exhibits metacognitive capabilities, which allow it to recognize its limitations and adjust its reasoning process. Motivated by this, we transform the on-device SLM into a metacognitive inquirer agent that can assess its internal state and actively decide *when*, *what*, and *how* to query the LLM for personalized guidance. Specifically, we propose Active Inquiry via Metacognitive Actions (AIMA). AIMA learns an on-device policy that jointly optimizes the selection of high-level metacognitive actions and the action-conditioned query refinement. We further introduce a privacy-constrained rewriting mechanism that detects and eliminates sensitive information leakage in the refined query. Extensive experiments and theoretical analysis demonstrate that our method outperforms existing methods.

Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen categories. However, such supervision often biases retrieval models toward the semantics of seen categories rather than the underlying appearance characteristics that generalize across categories, thereby limiting retrieval performance on unseen categories. To tackle this, we propose GAPan, a Generative Appearance Prior alignment network that reformulates the learning objective from category prediction toward appearance modeling. Technically, GAPan treats retrieval features with an invertible density model based on normalizing flows. In the forward direction, the flow maps all instance features into a latent density space, where each seen category is modeled by a class-conditional Gaussian prior and optimized via exact likelihood estimation. This formulation preserves richer appearance details by leveraging the invertible property of the flows. In the reverse direction, samples from the high-density regions of these learned priors are mapped back to the feature space to produce appearance-aware anchors that reflect intra-category variation. These anchors supervise a prior-driven alignment objective that aligns retrieval embeddings with category-specific appearance distributions, thereby improving generalization to unseen categories. Evaluations demonstrate that our GAPan achieves state-of-the-art performance on both widely-used fine- and coarse-grained benchmarks.

All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded by the designer's intuition. We introduce the first transferable learned attack, enabled by the observation that fine-tuning any model on any corpus yields unlimited labeled data, since membership is known by construction. This removes the shadow model bottleneck and brings membership inference into the deep learning era: learning what matters rather than designing it, with generalization through training diversity and scale. We discover that fine-tuning language models produces an invariant signature of memorization detectable across architectural families and data domains. We train a membership inference classifier exclusively on transformer-based models. It transfers zero-shot to Mamba (state-space), RWKV-4 (linear attention), and RecurrentGemma (gated recurrence), achieving 0.963, 0.972, and 0.936 AUC respectively. Each evaluation combines an architecture and dataset never seen during training, yet all three exceed performance on held-out transformers (0.908 AUC). These four families share no computational mechanisms, their only commonality is gradient descent on cross-entropy loss. Even simple likelihood-based methods exhibit strong transfer, confirming the signature exists independently of the detection method. Our method, Learned Transfer MIA (LT-MIA), captures this signal most effectively by reframing membership inference as sequence classification over per-token distributional statistics. On transformers, LT-MIA achieves 2.8$\times$ higher true positive rate at 0.1\% false positive rate than the strongest baseline. The method also transfers to code (0.865 AUC) despite training only on natural language texts. Our results imply that leakage from memorization is intrinsic to cross-entropy training; architectural innovation within this paradigm did not escape our attack. Code and the trained classifier are provided.

Understanding how complex molecular systems transition between metastable states is a central challenge in computational chemistry and biophysics. A key tool for characterizing these transitions is the committor function, defined as the probability that a trajectory initiated from a given configuration reaches one state before another, which encodes detailed mechanistic information about transition pathways. First, we curate a public benchmark of empirically determined committor values for alanine dipeptide. Second, we develop a "committor-in-the-loop" framework that combines a self consistent loss function with weighted ensemble (WE) simulation: WE adaptive bins are biased by the learned committor, which is in turn trained on WE samples. Across a two-channel 2D system and alanine dipeptide settings, our WE-based framework consistently outperforms greedy baselines, demonstrating the importance of principled exploration. Together, the benchmark and framework provide both a standard for evaluating committor learning methods and a new method for studying rare transitions in high-dimensional molecular systems.

In-context reinforcement learning (ICRL) leverages the in-context learning capabilities of transformer models (TMs) to efficiently generalize to unseen sequential decision-making tasks without parameter updates. However, existing ICRL methods rely on explicit reward signals during pretraining, which limits their applicability when rewards are ambiguous, hard to specify, or costly to obtain. To overcome this limitation, we propose a new learning paradigm, \emph{In-Context Preference-based Reinforcement Learning} (ICPRL), in which both pretraining and deployment rely solely on preference feedback, eliminating the need for reward supervision. We study two variants that differ in the granularity of feedback: \emph{Immediate Preference-based RL} (I-PRL) with per-step preferences that generalize dueling bandits, and \emph{Trajectory Preference-based RL} (T-PRL) with trajectory-level comparisons. We first show that supervised pretraining, a standard approach in ICRL, remains effective under preference-only context datasets, demonstrating the feasibility of in-context solving unseen tasks using only preference signals. To further improve data efficiency, we introduce alternative preference-native frameworks for I-PRL and T-PRL that directly optimize TM policies from preference data without requiring reward signals nor optimal action labels. Experiments on dueling bandits, navigation, and continuous control tasks demonstrate that ICPRL enables strong in-context generalization to unseen tasks, achieving performance comparable to ICRL methods trained with full reward supervision.

Markov Games are the standard formal model for multi-agent reinforcement learning, capturing agents that act in a shared state and optimize rewards over time. However, in real-world settings, agents’ decisions are often influenced by unobserved factors, such as cognitive biases, behavioral tendencies, or intuitive signals, that also affect rewards and future states. Ignoring these unobserved confounders can make standard learning methods converge to suboptimal policies. In such settings, optimal play may require policies that condition on counterfactual signals, requiring reasoning at the counterfactual layer of the Pearl Causal Hierarchy. In this paper, we introduce Causal Markov Games (CMGs), a framework for modeling sequential multi-agent decision making in the presence of unobserved confounding. We show that CMGs strictly generalize Markov Games, with arbitrarily large gaps between classical interventional equilibria and causal counterparts. We then develop two learning algorithms under different observability assumptions. The first, CNash-VI-FO, is a model-based learner with finite-sample guarantees when agents’ natural actions or intuitions are revealed post hoc. The second, CNash-VI-NO, is an explore-then-exploit procedure with asymptotic guarantees for the setting in which opponents’ natural actions are never observed. To address scalability, we further provide a drop-in counterfactual augmentation of deep MARL. Empirically, on a confounded windy variant of the Multi-Particle Environment and the Iterated Causal Prisoner’s Dilemma, counterfactual agents strictly dominate their non-causal counterparts.

Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials where mistakes repeat rather than accumulate into experience. Drawing upon human reflective practitioners, we introduce Reflective Test-Time Planning, which integrates two modes of reflection: \textit{reflection-in-action}, where the agent uses test-time scaling to generate and score multiple candidate actions using internal reflections before execution; and \textit{reflection-on-action}, which uses test-time training to update both its internal reflection model and its action policy based on external reflections after execution. We also include retrospective reflection, allowing the agent to re-evaluate earlier decisions and perform model updates with hindsight for proper long-horizon credit assignment. Experiments on our newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark show significant gains over baseline models, with zero-shot generalization to photorealistic HM3D environments and real-robot experiments on a Franka Panda arm. Ablations confirm that reflection-in-action and reflection-on-action are mutually dependent, and that retrospective reflection achieves better credit assignment than step-wise external feedback at lower computational overhead. Qualitative analyses further highlight behavioral correction through reflection.

We study the sequential decision-making problem where at each time the learner selects an assortment of bounded size and receives a ranking of the selected items. Ranking feedback arises naturally when human evaluators or automated LLM judges compare multiple options simultaneously. It is often easier and more reliable than assigning absolute scores, which are typically poorly calibrated across evaluators, while yielding substantially more information than a single pairwise comparison. We focus on rankings that are constructed according to the random utility model with linear utilities, which includes the popular Plackett-Luce model as a special case. We show that improved regret minimisation is possible under full rank breaking with a refined analysis based on 1-factorisations and Baranyai's theorem that fully exploits independence in the pairwise comparisons. This gives a tighter confidence interval around the true utility parameter which is crucial in our analysis for regret that improves with maximum assortment size. Furthermore, it matches the regret lower bound for our model in case the learner is permitted to play multi-sets of the maximum bounded size. Our work gives a principled approach to learning from ranking feedback that shows consistent improvement with the maximum assortment size.

Reinforcement learning from verifiable rewards (RLVR) suffers from sparse outcome signals, creating severe exploration bottlenecks on complex reasoning tasks. Recent on-policy self-distillation methods attempt to address this by utilizing language feedback to generate dense, token-level supervision. However, these approaches rely on a fixed, passive teacher to interpret the feedback. As the student policy improves, the teacher's zero-shot assessment capabilities plateau, ultimately halting further learning. To overcome this, we propose Variational Policy Distillation (VPD), a framework that formalizes learning from language feedback as a Variational Expectation-Maximization (EM) problem. VPD co-evolves both policies: in the E-step, the teacher is actively refined on trajectory outcomes via an adaptive trust-region update, translating textual feedback into a dynamically improved target token distribution. In the M-step, the student internalizes this dense distributional guidance on its own on-policy rollouts. By continuously improving the teacher's ability to extract actionable signals from textual critique, VPD overcomes the limitations of passive distillation. Evaluated across diverse sources of diagnostic feedback on scientific reasoning and code generation tasks, VPD consistently outperforms both standard RLVR and existing self-distillation baselines. Finally, by stress-testing our framework on rigid mathematical reasoning and cold-start regimes, we illuminate the fundamental bounds of feedback-driven self-distillation compared to pure environment-driven RL.

Foundation models pretrained on similar data distributions tend to encode semantically aligned concepts; yet these representations remain entangled with model-specific inductive biases, which hinders both unified interpretability and cross-model feature reuse. Existing approaches address this by aligning concepts across model-specific representation spaces, but do not explicitly account for model-specific biases, resulting in limited cross-model reconstruction fidelity (low $R^2$) and poor feature transferability. We propose the *Gated Bottleneck Sparse Autoencoder* (GB-SAE), a framework that takes multiple—homogeneous or heterogeneous—foundation models as input and factorizes their representations into shared and private components. GB-SAE jointly learns a single model-agnostic semantic dictionary and, for each model, a private residual dictionary, via a learnable gating mechanism that encourages competition between the two. Conceptually, we cast unified multi-model interpretability as a representation factorization problem and solve it through bottlenecked sparse dictionary learning. Empirically, GB-SAE achieves unified and faithful interpretability across diverse architectures, high reusability of the learned shared dictionary, and strong generalization to downstream tasks—capabilities that alignment-based paradigms, by design, cannot fully support.

Semantic visual classification commonly aligns visual embeddings with semantic spaces induced by pretrained text encoders. However, such spaces are typically high-dimensional, weakly aligned with visual feature geometry, and insufficiently discriminative for downstream classification tasks. We propose TaSS, a framework for learning a task-adaptive low-dimensional semantic space that preserves semantic structure while explicitly optimizing class separability. TaSS is constructed through a parameterized geometry transformation of pretrained text embeddings that enlarges inter-class margins via distance reshaping and improves intra-class compactness through prototype-preserving semantic constraints. The resulting semantic space maintains meaningful semantic relationships while producing a discriminative geometry tailored for classification. Frozen visual encoder features are subsequently aligned to TaSS using a lightweight MLP, enabling improved discriminative learning without fine-tuning pretrained backbones. We further provide theoretical analysis showing that TaSS preserves semantic consistency while enforcing enhanced inter-class separation and controlled intra-class variance. Extensive experiments on skeleton-based human action recognition, video classification, and image classification benchmarks demonstrate consistent improvements over existing semantic alignment approaches, with gains exceeding 3 percentage points on average and up to 8.5 points on challenging datasets.

Visual textures, defined as spatially homogeneous image regions containing repeated elements (e.g. a field of grass, the bark of a tree), are ubiquitous in visual scenes and provide important cues for recognizing materials and objects. Existing texture models extract essential statistics from a single texture image, and can then generate high-quality samples that are visually similar to the original by matching these statistics. However, their statistics are either hand-designed or based on a network pretrained for another purpose (e.g., object recognition). Here, we develop the first principled method for unsupervised learning of a set of statistics that are used to constrain a maximum entropy probability model. We leverage methods developed for generative diffusion models to derive training and sampling procedures, and compare these to the traditional method of sampling via matching the statistics. Despite the compactness of our trained model (512 statistics), it generates texture images whose quality is as good or better than the current state-of-the-art model (~177k statistics). A more direct comparison of the two models, obtained by synthesizing images that are indistinguishable for one model but maximally different for the other, reveals their relative strengths and weaknesses. Finally, we show that, unlike previous statistical texture models, a straight trajectory in the representation space of our model generates homogeneous texture samples that interpolate smoothly between the features of the two end points.