NeurIPS 2026 论文列表

Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but evaluated against a cross-family panel of three frontier judges, reducing dependence on any single evaluator. Our framework separates two sources of divergence: verifier failure, where the training verifier credits rubric criteria that reference verifiers reject, and rubric-design limitations, where even strong rubric-based verifiers favor responses that rubric-free judges rate worse overall. Across medical and science domains, weak verifiers produce large proxy-reward gains that do not transfer to the reference verifiers; exploitation grows over training and concentrates in recurring failures such as partial satisfaction of compound criteria, treating implicit content as explicit, and imprecise topical matching. Stronger verifiers substantially reduce, but do not eliminate, verifier exploitation. We also introduce a self-internalization gap, a verifier-free diagnostic based on policy log-probabilities, which tracks reference-verifier quality, detecting when the policy trained using the weak verifier stops improving. Finally, in our setting, stronger verification does not prevent reward hacking when the rubric leaves important failure modes unspecified: rubric-based verifiers prefer the RL checkpoint, while rubric-free judges prefer the base model. These disagreements coincide with gains concentrated in completeness and presence-based criteria, alongside declines in factual correctness, conciseness, relevance, and overall quality. Together, these results suggest that stronger verification reduces reward hacking, but does not by itself ensure that rubric gains correspond to broader quality gains.

EEG foundation models (FMs) offer strong population-level priors but degrade on individual subjects due to anatomy-driven distribution shifts. We propose Rest-Tuning, a task-agnostic calibration framework that personalizes a population FM using just 1–3 minutes of unlabeled resting-state EEG. Rest-Tuning employs masked teacher-student alignment to produce a subject-calibrated backbone, reused as initialization across multiple tasks from the same subject. Across two multi-subject datasets and four EEG FMs, Rest-Tuning outperforms task-only adaptation, with up to 13.3% absolute gain in downstream task accuracy and over 40% average reduction in labeled task data. Calibration saturates quickly and requires under 3 minutes of compute per subject. Representation analyses show reduced subject-to-population distance and smoother LoRA adapter interpolation paths, indicating improved compatibility between rest- and task-adapted solutions. These results establish resting-state EEG as a practical, fast, and reusable calibration interface, enabling population EEG FMs to function as reliable, subject-specific BCIs and serve as reusable initialization for downstream task adaptation.

The Byzantine Agreement (BA) problem is a fundamental task in distributed computing where nodes in a network need to agree on a common output in the presence of arbitrary (worst-case) node failures. In real-world applications, nodes can be monitored over time to provide ML predictions of faulty behavior in a network. In this work, we answer the question of whether predictions can improve the fault tolerance of a distributed system without sacrificing correctness when the predictions are wrong. We consider a prediction-augmented study of Byzantine agreement in which each node receives, in addition to its input bit, a predicted set of honest nodes. We focus on algorithmic resilience --- the maximum number of faulty nodes an algorithm can tolerate --- and present algorithms and impossibility results whose resilience depends on the accuracy of the predictor. As our first main result, we bring a complete characterization of the consistency--robustness trade-offs in both the non-authenticated and authenticated settings: for $n$ nodes and a parameter $\alpha \in [0, 1]$, we present algorithms that tolerate up to $\alpha \cdot n$ faulty nodes when the predictor is correct (consistency), and up to $\frac{1-\alpha}{2} \cdot n - 1$ faulty nodes when the predictor is arbitrarily wrong (robustness). In the authenticated setting, the robustness bound improves to $(1-\alpha) \cdot n - 1$. We prove matching impossibility results, showing that these tradeoffs are optimal and independent of the particular prediction system. Our second main result characterizes smoothness: the rate at which resilience degrades as the predictor becomes less accurate. We show that resilience linearly decreases in the number of wrong predictions as long as that number stays within a constant fraction of $n$. Concretely, in the non-authenticated setting, each additional wrong prediction loses one unit of resilience, whereas in the authenticated setting, the decline is halved, since two wrong predictions are needed to lose one unit of resilience.

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

Facial video–based remote photoplethysmography (rPPG) estimates physiological signals from subtle spatiotemporal variations in facial videos, but remains vulnerable to head motion, illumination changes, and environmental perturbations. Existing methods mainly distinguish rPPG-related variations from motion and lighting artifacts at the signal or feature level, while largely neglecting the optical reflection properties of human skin. In this work, we formulate a skin reflection exponential model to describe the nonlinear relationship among skin reflectance, illumination, motion-induced variations, and blood-volume changes. Guided by this model, we propose a physically inspired rPPG estimation framework that explicitly incorporates skin reflectance priors into signal extraction. Given a facial video, facial keypoints are first detected and encoded to represent motion dynamics, while illumination-related features and candidate rPPG representations are jointly modeled under the proposed exponential formulation. By constraining rPPG estimation with skin-reflection physics, the framework improves separation of physiological components from motion and illumination disturbances. Experiments demonstrate improved robustness under challenging conditions.

Predictions on digital platforms must adapt over time, as individuals continuously update their beliefs through social interactions. At the same time, changing predictions can in turn influence the content people are exposed to and hence the very beliefs they seek to predict, rather than merely serving as passive observations. These emerging dynamics make it challenging to understand the long term effects of predictive systems on society. In this work, we blend models from network science with concepts from performative prediction to initiate the study of opinion dynamics in predictive loops. In our model, opinions and predictions co-evolve: a platform's predictions influence individual opinions, which then evolve through peer interactions and form the training data for future platform model updates. We demonstrate that this co-evolution induces a novel equilibrium that qualitatively differs from standard network equilibria. In particular, we show how standard predictive objectives can drive networks toward consensus even under conditions where classical opinion-dynamics models lead to disagreement. This emerges because predictive systems dynamically adapt to changing opinions, and learning objectives create spillover effects among individuals beyond the topology of the network. We further analyze systematic deviations from standard prediction and demonstrate amplified effects of targeted platform interventions on equilibrium outcomes, compared to classical network intervention analyses. We complement our results with simulations on real social network data and parametric learning settings. Together, our results illustrate performativity as an important, yet so far neglected, qualifying factor in social networks.

Rheumatoid arthritis (RA) assessment from hand radiographs requires multi-level analysis and modeling of anatomical structures and fine-grained local pathological changes. However, existing public resources do not support such multi-level analysis, often lacking full-hand coverage, fine-grained annotations, and consistent integration with clinical scoring systems. In particular, annotations that enable quantitative analysis of bone erosion (BE) remain scarce. RAM-H1200 contains 1,200 hand radiographs from a regional, multi-institutional Japanese cohort collected across six medical centers in the Sapporo region, with multi-level annotations including (i) whole-hand bone structure instance segmentation, (ii) pixel-level BE masks, (iii) Sharp/van der Heijde (SvdH)-defined joint regions of interest, and (iv) joint-level SvdH scores for both BE and joint space narrowing (JSN). It provides a common evaluation resource covering anatomical structure modeling, localized erosive pathology analysis, and clinically standardized RA severity assessment from the same radiographs. The proposed BE masks enable, for the first time, quantitative BE analysis beyond coarse categorical grading by providing explicit spatial supervision for lesion extent and morphology. To our knowledge, RAM-H1200 is the first public benchmark resource providing whole-hand bone structure instance segmentation, pixel-level BE delineation, and clinically grounded joint-level SvdH scoring for both BE and JSN. Results across benchmark tasks show that anatomical modeling is substantially more mature than quantitative BE analysis: whole-hand bone segmentation achieves strong performance, whereas BE segmentation remains a major open challenge. By providing multi-level annotations spanning anatomical structure modeling, quantitative lesion analysis, and clinically grounded SvdH scoring, RAM-H1200 provides a comprehensive benchmark resource for RA analysis on hand radiographs. Benchmark \& Code: https://github.com/YSongxiao/RAM-H1200 Dataset Repository: https://huggingface.co/datasets/TokyoTechMagicYang/RAM-H1200-v1

We present a method of quantum composite hypothesis testing with small error, which enables us to establish quantum lower bounds in nonparametric statistics and high-dimensional functional estimation. This is achieved by introducing trigonometric polynomials into the quantum polynomial method, thereby generalizing the quantum phase-estimation lower bound of Mande and de Wolf (ESA 2023). As applications, we settle the quantum complexities of several problems in property testing and functional estimation with small error: - For $\ell_2$-closeness testing, we show that the approach of Luo et al. (*IEEE Trans. Inf. Theory* 2024) is optimal. - For Tsallis entropy estimation, where $q=2$ corresponds to the Gini impurity, we show that the approaches of Buhrman et al. (*Phys. Rev. Lett.* 2001) and Ekert et al. (*Phys. Rev. Lett.* 2002) are optimal for integer $q \geq 2$, and that the approach of Chen et al. (ICALP 2026) is near-optimal for real $q \geq 1.5$. - For pure-state Uhlmann fidelity and trace distance estimation, we show that the approach of Wang (*IEEE Trans. Inf. Theory* 2024) is optimal.

Best arm identification, also known as pure exploration, is a fundamental problem in the multi-armed bandit (MAB) model. Recent quantum algorithms achieve $O(n/\Delta_{[2]})$ query complexity for identifying the best arm with high constant probability, but all of them require $\Omega(\log(1/\Delta_{[2]}))$ or $\Omega(\log n)$ adaptive rounds. Motivated by the practical cost of adaptivity on near-term quantum hardware, where each round incurs substantial overhead from state re-preparation and classical--quantum communication, we study quantum best arm identification with no or very limited adaptivity. On the lower bound side, we prove that any non-adaptive quantum algorithm must make $\Omega(n\log n)$ queries when $\Delta_{[2]}=O(1)$, establishing a separation between adaptive and non-adaptive algorithms. To complement this lower bound, we present an algorithm that identifies the best arm with high constant probability using $O(n/\Delta_{[2]})$ queries and only $\log^*(n)+1$ adaptive rounds. En route to the lower bound, we show that the instances and techniques used to prove adaptivity lower bounds for classical bandits, such as those of Agarwal et al. [COLT'17], do not extend to the quantum setting. As such, we employ the polynomial method to prove our adaptivity lower bounds. To the best of our knowledge, this represents the first application of the polynomial method in this context, which may be of independent interest.

Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property prediction, but their evaluation as machine learning targets has been complicated by the absence of a principled out-of-distribution evaluation protocol at the atomic level. In this work, we propose a held-out evaluation protocol that clusters atomic environments by SOAP descriptors and computes metrics accounting only for cluster labels unseen during training. Following this procedure, we use 5×5 cross-validation and Tukey's HSD to run a statistically rigorous comparison of E(3)-equivariant against non-equivariant, rotationally augmented models for predicting electron populations and multipoles of H, C, N, and O atoms. Building on our results, we introduce the Quantum Topological Neural Network (QT-Net), a rotationally augmented, non-equivariant graph neural network. We show that QT-Net can be used to infer properties of atoms in molecules from QM9 outside our training set, and that these inferred properties can yield improvement when used as input features for downstream molecular property prediction. To further validate the framework, molecular dipole moments computed from QT-Net's per-atom outputs recover the ground-truth values reported in QM9. We release all code and data, including a JAX implementation of QT-Net, to support the broader use of learned QTA properties as inductive biases for atomic-scale molecular machine learning.

Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these higher-order interactions trade off between speed and accuracy. To overcome this limitation, we introduce ProxySHAP. ProxySHAP reconciles the high sample efficiency of tree-based proxy models with a principled path to consistency via residual correction. On a theoretical level, we derive a polynomial-time generalization of interventional TreeSHAP to compute exact interaction indices for tree ensembles, successfully bypassing exponential tree-depth dependencies in prior methods. Furthermore, we formally analyze the residual adjustment strategy, characterizing the specific conditions under which Maximum Sample Reuse (MSR) corrects proxy bias without its variance scaling exponentially with interaction size. Extensive benchmarking demonstrates that ProxySHAP sets a new state-of-the-art standard for approximation quality, including in large-scale applications with thousands of features. By achieving the lowest error in both small- and large-budget regimes, ProxySHAP significantly outperforms the prior best estimators ProxySPEX and KernelSHAP-IQ, while also delivering superior performance on downstream explainability tasks.

Vector quantization via random projection followed by scalar quantization is a fundamental primitive in machine learning, with applications ranging from similarity search to federated learning and KV compression. While dense random rotations yield clean theoretical guarantees, they require $\Theta(d^2)$ time. The randomized Hadamard transform $HD$ reduces this cost to $O(d \log d)$, but its discrete structure complicates analysis and leads to weaker or purely empirical compression guarantees. In this work, we study a variant of this approach: \emph{dithered quantization} with a single randomized Hadamard transform. Specifically, the quantizer applies $HD$ to the input vector and subtracts a random scalar offset before quantizing, injecting additional randomness at negligible cost. This approach is used by popular quantizers such as RaBitQ, and is a slight modification of others, including TurboQuant and EDEN. We prove that this approach provides mean squared error bounds that asymptotically match those achievable with truly random rotation matrices. For example, we prove a dithered version of TurboQuant achieves mean squared error $\bigl(\pi\sqrt{3}/2 + o(1)\bigr) \cdot 4^{-b}$ at $b$ bits per coordinate, where the $o(1)$ term vanishes uniformly over all unit vectors and all dimensions as the number of quantization levels grows.

Deep learning has driven substantial progress in modeling fMRI responses, yet most existing approaches are optimized for a single objective---such as neural encoding, stimulus decoding, or post hoc interpretation---making it difficult to obtain a coherent account of neural representations. We introduce a unified framework that connects vision foundation models to brain activity while supporting neural analysis from multiple complementary perspectives. Our approach replaces black-box features with a sparse, interpretable representation composed of visual prototypes, and links these prototypes to voxel responses through a linear readout. This design makes the model transparent at both levels: prototype activations expose the visual concepts present in the stimulus, while the learned linear weights characterize each voxel's selectivity. Using this interface, we identify which visual concepts drive neural responses and where they appear in the image, reveal cortical organization through voxel tuning in prototype space, and perform controlled stimulus synthesis to probe cortical selectivity through targeted interventions. Together, these results establish prototype-based representations as a unified and interpretable foundation for studying visual representations in the human brain.

Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to characteristically sparse and unevenly sampled multi-wavelength time series (light curves), severely limiting the performance on downstream tasks. Interpolating light curves help in mitigating this problem, where Gaussian Processes (GPs) are the current standard. However, GPs struggle with cross-wavelength correlations, require *a priori* kernel specification, and cannot handle ensembles of diverse transient and variable classes. Furthermore, GPs and other existing models (*e.g.*, Bayesian neural networks), scale poorly and must be fit to each light curve individually, transferring no information between light curves. We introduce Attentive Neural Processes (ANPs) for light curve reconstruction, combining the probabilistic framework of GPs with the scalability of deep learning, and we demonstrate their use as a fast, data-driven, universal light curve interpolator. Unlike prior processes tailored to single object classes, our approach is morphologically agnostic. By meta-learning on a diverse population of simulated transients, ANPs shift the computation cost to the training phase, enabling amortized inference - particularly important at the eve of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), which will produce massive datasets for transient astrophysics. We evaluate our models on realistic LSST cadences for its 6-filters across 14 transient classes. The ANP outperforms all our benchmark models, including a suite of GP and neural-network based models, on all regression quality, feature recovery, and rigorous probabilistic calibration metrics. Our ANP performs probabilistic interpolation on all filters in $\sim 10^{-6} $ seconds -- over four orders of magnitude faster than the next-best neural benchmark and over five orders of magnitude faster than GPs -- making them suitable for on-line use on the LSST alert stream estimated to carry $\sim10^{7}$ events/night. The ANP avoids neural networks' overconfidence and GPs' underconfidence, delivering sharp, highly calibrated uncertainties. This work establishes the neural process family as a scalable, probabilistic foundation for real-time transient science in the LSST era.

In real-world AI for Science scenarios, retrieval systems are increasingly expected to support experimental workflows such as experimental design and method reproduction. These workflows require retrieving protocols that satisfy a set of interdependent constraints, such as specific materials and operating conditions. However, systematic evaluation of retrievers in such multi-constraint scenarios remains lacking. To address this gap, we introduce ProSearch, a scientific retrieval benchmark for multi-constraint experimental protocol retrieval. ProSearch has three key features: (1) realism, as queries are instantiated from real method paragraphs using expert-defined experimental fields, query templates, and filtering rules; (2) complexity, involving 60 distinct fields, such as experimental subjects, materials, and operating conditions, together with dependencies among these fields; and (3) diagnosticity, as each query contains 3 to 5 explicit hard constraints and covers challenging cases such as numeric constraints and negation constraints. We construct ProSearch through LLM-based extraction, data-driven constraint selection, controlled query generation, and evidence-based relevance validation. The final benchmark contains 1,551 high-quality queries across six experimental science domains. We use ProSearch to systematically evaluate 19 representative retrievers, spanning lexical matching, general dense retrieval, scientific retrieval, and reasoning-oriented retrieval. The results reveal that current retrievers generally struggle with multi-constraint protocol retrieval, with the best-performing model achieving an average nDCG@10 of 0.290. Further constraint-wise and error analyses reveal key failure modes of existing retrievers.

This paper presents a post-Bayesian approach to online filtering in nonlinear state-space models, capable of avoiding over-confident inferences in settings where either the dynamical model, the measurement model, or _both_, could be misspecified. This is addressed using _predictively oriented_ (PrO) posteriors, an emerging paradigm in which learning (i.e., posterior concentration) occurs if and only if the overall model is well-specified, without strict adherence to the Bayes' theorem. As the characterisation of PrO posteriors is challenging, our main technical contribution is a fast approximate linear-Gaussian update procedure, analogous to an (iterated) extended Kalman filter (EKF). The methodology, which we call PrO-EKF, has no tunable hyper-parameters and has a computational cost comparable to that of existing filtering methods. Performance is empirically assessed on a range of linear and non-linear applications, in which the state-space model is systematically misspecified.

In long-term multi-session neural recordings, the same latent population dynamics are often observed through changing subsets of neural channels due to electrode drift, signal instability, or session-dependent recording variability. This creates a partially observed system-identification problem: each session provides only an incomplete view of a shared dynamical system, and missing channels are not merely absent inputs but unobserved variables coupled to the same latent dynamics. Existing approaches often rely on observation-pattern-specific mapping, which treats missing channels passively and leaves the shared latent dynamics weakly constrained. We propose CHAMA (CHannel-Aware Masked Attention), a mask-conditioned predictive representation learning framework for identifying shared neural dynamics under heterogeneous channel availability. CHAMA introduces a single global observation interface conditioned on channel-availability masks, allowing observed and unobserved channels to jointly constrain latent dynamics. The key idea is bidirectional: missing channels impose structural constraints on the latent representation, while the learned latent dynamics enable principled recovery of missing activity. To improve identifiability under sparse observations, CHAMA aggregates causal temporal windows, effectively trading temporal context for missing spatial information. Across synthetic dynamical systems and real multi-session neural recordings, CHAMA improves missing-channel completion, forecasting, latent dynamical fidelity, and downstream behavioral decoding over conditional adapter, zero-padding, and masked-autoencoding baselines. These results show that dynamics-aware recovery preserves globally consistent and behaviorally relevant structure beyond point-wise reconstruction accuracy. The source code is available at: \url{https://anonymous.4open.science/r/CHAMA-D691}.

Long-horizon dialogue agents must keep user memories stable enough for personalization while adapting when the user genuinely changes. Existing memory systems often append every observation or overwrite old entries by heuristic rules, which makes transient noise hard to distinguish from real belief drift. We introduce PreCoMem, a memory consolidation framework inspired by predictive coding that represents memory as confidence-weighted beliefs about the user's state. Its core signal, Effective Surprisal, combines semantic deviation and contradiction evidence, then downweights this signal when retrieval is diffuse and unreliable. According to effective surprisal, a two-threshold gate maps each turn to one of three deterministic updates: MAINTAIN reinforces supported beliefs, PROFILE stores ambiguous cues as low-confidence hypotheses, and CORRECT softly decays reliably contradicted facts. The PROFILE stage acts as an explicit waiting room, preventing premature commitment to noisy evidence. Experiments on LoCoMo, LongMemEval, and PersonaMem-v2 show consistent state-of-the-art accuracy; our new DiMoBench confirms that PreCoMem adapts to non-stationary belief drift without over-reacting to noise. The code is available at: \url{https://anonymous.4open.science/r/PreCoMem-9000}.

This position paper argues that the research community must act to maintain research pipeline reliability despite increasing threats from GenAI. We start in the pre-GenAI era by highlighting some of the prominent critiques of the research pipeline, consisting of: (a) Authoring, (b) Reviewing, and (c) Evaluating researchers based on bibliometrics. Then, we discuss some of the effects GenAI has had on each research pipeline component based on the best available data, limited though it may be. Importantly, due to opaqueness of the submission pipeline, it is difficult to measure both the impact of GenAI and the efficacy of interventions, which leads us to call for greater transparency. Last, we conclude by offering three alternative views and ten paths forward that may mitigate some of the issues we highlight by keeping human researchers as the primary driver of science