NeurIPS 2026 论文列表

Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effective for classical tasks, these foundations collapse at the frontier of Vision-Language-guided Image Editing and Generation (VL-IEG), where transformations yield correspondences that are perceptually obvious yet physically discontinuous. To transcend spatio-temporal priors, we draw inspiration from human cognition: learning a generalized model of identity-preserving visual consistency rather than relying on physical constraints. To achieve this, we introduce FreeMatching, a unified framework that extracts identity-preserving knowledge from foundation models and diverse datasets. The model's capability is forged through a two-stage training paradigm: supervised pre-training on diverse annotated datasets, followed by weakly-supervised refinement on data without dense annotations. Experimentally, FreeMatching not only achieves performance competitive with SOTA methods on classical benchmarks but also establishes the first strong baseline for the challenging VL-IEG task. Furthermore, we demonstrate its utility as a quantitative metric for evaluating identity-preserving consistency in generative editing, aligning closely with human judgment.

It remains unclear whether quantum machine learning (QML) truly holds an advantage when tackling practical and meaningful tasks. In exploring this question, the importance of classical data encoding, often overlooked, becomes increasingly evident. Amplitude encoding, which can embed $2^n$ classical data into $n$ qubits, is widely used due to its apparent efficiency. However, its potential limitations for QML have yet to be fully explored. In this paper, we establish a theoretical result for the limitations caused by quantum encoding and point out the existence of a concentration phenomenon in amplitude encoding. Compared to prior work, our theoretical result operates under more general conditions and leads to a stronger conclusion. This concentration phenomenon causes the classification predictions to remain close to random guessing, regardless of the training process. Our findings shed new light on a long-standing puzzle in the field of QML: why some QML models perform well on simple datasets like MNIST but fail to generalize to more complex practical tasks. By highlighting the pivotal role of encoding design in QML, our work clearly indicates that future research needs to focus more on the design of classical data encoding to advance the effectiveness of QML.

Evaluating large language models (LLMs) today rests on *fixed benchmarks* that apply the same set of items to any model, producing ceiling and floor effects that mask capability gaps. We argue that the most informative evaluation signal lies at the *boundary*, where the per-prompt pass probability is near 0.5, and propose **Dynamic Boundary Evaluation (DBE)**, which actively locates each model's boundary and places it on a globally comparable difficulty scale. DBE delivers three artifacts: (i) a calibrated item bank covering safety, capability, and truthfulness, with per-item difficulty labels validated across 9 reference LLMs; (ii) **Skill-Guided Boundary Search (SGBS)**, a search algorithm that finds boundary items for a given target LLM using only API-level query access; and (iii) an evaluation protocol that places a new LLM on a unified ability scale and grows the evaluation set adaptively when the target falls outside the bank's coverage. We instantiate DBE on four categories spanning safety (harmful refusal, over-refusal), capability (constrained instruction following), and truthfulness (multi-turn sycophancy resistance). The resulting evaluation covers a broader model spectrum without saturation while remaining compatible with existing datasets.

AI progress is increasingly tracked through evaluations, such as AI benchmarks, yet small perturbations in evaluation pipelines can produce unstable scores and model rankings. We recast benchmark reliability as a measurement problem and formalize it as a signal-to-noise ratio derived from a crossed random-effects decomposition. This framework yields a direct mapping between variance components and expected rank stability, linking theoretical reliability to observable leaderboard concordance. Modern AI benchmarks operate in a high-dimensional regime with many items and relatively few evaluated models, where classical item-level reliability measures are ill-posed. We define a lower bound for benchmark item-level reliability classically, and we further introduce a tractable proxy, $\lambda^\bigstar_6$, that preserves the variance structure underlying reliability without modifying the estimand through sparsity or dimensionality reduction. Across diverse benchmarks, we show that selecting items via $\lambda^\bigstar_6$ produces smaller subsets that achieve higher ranking reliability than random subsampling at fixed evaluation budgets. Finally, we analyze reliability under positive distribution shift--as often observed in the current AI ecosystem--by partitioning models into lower- and higher-performing cohorts. We show that reliability is population-dependent and frequently declines as between-model variance shifts among stronger systems. Our results establish a principled framework for diagnosing and improving benchmark reliability and demonstrate that ranking stability is neither intrinsic nor static, but a measurable and optimizable property of the benchmark–population pairing.

Biosynthetic gene clusters (BGCs) are co-located genes that encode the biosynthesis of secondary metabolites, a major source of clinically used antibiotics and anticancer agents. Recent advances in deep learning, particularly self-supervised foundation models, have spurred growing interest in BGC sequence modeling, but evaluation infrastructure has lagged behind. Current practice often relies on coarse classification accuracy over small, experimentally curated datasets, making it difficult to discriminate model capabilities or assess downstream utility. We introduce BGC-Bench, an evaluation suite designed to resolve this limitation by systematically incorporating biochemical knowledge in the benchmark design. In terms of generalization, we test whether representations transfer to genetically and chemically novel samples; for functionality, we examine application-relevant tasks, including retrieval, prediction, and active learning. BGC-Bench reveals that no single model class dominates: pretrained foundation models perform strongly under out-of-domain generalization but remain sensitive to training conditions; compositional baselines are competitive and lead in cross-modal retrieval; and active-learning gains vary substantially across tasks, strategies, and model families. Beyond these BGC-specific findings, BGC-Bench highlights a broader principle: domain-informed benchmark design over existing datasets enables better evaluation in data-scarce scientific domains.

LLM benchmarks are often treated as coherent measurement units, yet they are heterogeneous collections of instances spanning diverse domains, skills, formats, and contexts. As a result, aggregate benchmark scores can conflate model capability with benchmark composition, obscuring what existing benchmarks actually cover. We introduce BenchHub, a composition-aware evaluation framework that represents benchmarks as distributions over instance-level attributes. BenchHub integrates 54 benchmarks comprising 839K samples across 10 languages. It enables researchers to inspect benchmark contents, uncover reusable coverage hidden behind benchmark names, transparently compare benchmarks, and construct controllable evaluation sets for application-aligned model selection. Using BenchHub, we show that similarly motivated benchmarks can differ substantially in internal composition, existing benchmarks often contain reusable coverage beyond their stated purposes, models exhibit fine-grained category-level performance variation hidden by aggregate scores, and model rankings can shift under different reweighting and resampling configurations. Our results motivate evaluation practices that make benchmark composition explicit, inspectable, and controllable.

Structured generation remains a critical bottleneck for Multimodal Large Language Models (MLLMs). Current post-training paradigms struggle to resolve this: Supervised Fine-Tuning (SFT) hits a rigid ceiling, while scalar-reward Reinforcement Learning (e.g., DPO, GRPO, SimPO) suffers from a policy-level signal gap. Compressing long routing trajectories into sparse signals induces a short-completion bias and traps RL near the SFT baseline, whereas vanilla self-distillation actively regresses. To systematically quantify this joint visual-structural-relational reasoning, we introduce OracleGraph, a 10,000-page 2.5D historical document benchmark featuring a 12-dimensional diagnostic VQA suite and a structured-hallucination taxonomy. To overcome these optimization pathologies, we propose PRISM, a Policy-Reward Integrated Self-distillation framework. PRISM leverages multimodal information asymmetry via a Hindsight Teacher guided by a Diagnostic Graph Report (DGR). By synergizing Softmax Reward-Weighted Aggregation (preserving Graph-F1 rankings) with a Length-Normalized Auxiliary Preference Loss, PRISM explicitly neutralizes catastrophic truncation, yielding a $+3.81$pp Graph Reward gain and a $+2.3$pp schema-parsing improvement over SDPO ($p < 0.001$). While PRISM's nominal $+1.02$pp mean gain over SFT is not statistically significant ($p = 0.12$), it drastically stabilizes optimization, achieving $2.4\times$ lower cross-seed variance. Crucially, on a cross-book out-of-distribution (OOD) probe, PRISM's stability advantage amplifies to a $14\times$ variance reduction over SFT, confirming robust structural stabilization. Code and datasets are available at https://anonymous.4open.science/r/PRISM2026.

Many safety and alignment failures of large language models (LLMs) occur due to out-of-distribution (OOD) situations: unusual prompt or response patterns that are unforeseen by model developers. We systematically study whether LLM monitoring pipelines can detect these **OOD alignment failures** by introducing a benchmark called Misalignment Out of Distribution Benchmark (**MOOD**). It is difficult to find failures that are truly OOD for off-the-shelf models trained on vast safety datasets. We sidestep this by including a restricted training set in MOOD that we use to train our own monitors, as well as seven test sets with diverse alignment failures that are outside the training distribution. Using MOOD, we find that guard models (safety classifiers) often fail to generalize OOD. To fix this, we propose combining guard models with OOD detectors. We test four types of OOD detectors and find that a combination of a guard model and Mahalanobis distance-based OOD detector can improve recall from 39% to 47%. We also establish positive scaling trends across model scales for monitors that combine a guard model and OOD detector; we find that incorporating OOD detection into monitoring achieves a higher recall gain than using a guard model with 20 times more parameters. Our work suggests that OOD detection should be a crucial component of LLM monitoring and provides a foundation for further work on this important problem.

Self-supervised learning (SSL) has received notable attention in the graph machine learning community, enabling the efficient use of unlabeled data. However, evaluation setups vary significantly across studies, and baseline tuning often receives limited attention, making it difficult to draw reliable conclusions about model performance. In this paper, we reevaluate four representative graph SSL methods within a unified setup across a diverse set of datasets. We focus on rigorously tuning both SSL and supervised methods by employing enhanced GNN architectures and comprehensive hyperparameter optimization. We observe that, contrary to prior literature, two popular generative methods, MaskGAE and GraphMAE, regularly fail to outperform well-tuned supervised baselines. In contrast, the contrastive methods BGRL and GRACE consistently perform better than both the generative methods and supervised baselines. We hypothesize that this discrepancy arises because BGRL and GRACE capture information from both graph structure and node features, whereas MaskGAE and GraphMAE focus on a single source of information. We support this hypothesis through an analysis on carefully designed synthetic data. Motivated by our observations, we advocate for designing SSL objectives that capture both feature and structural information. To verify the effectiveness of this approach, we propose a simple generative method, GrASP, which reconstructs both graph structure and node features. Despite its simplicity, GrASP outperforms all other evaluated approaches and can be considered as a strong baseline in future studies.

Large language models can achieve strong benchmark scores without proportional gains in broader capability, but diagnosing when this occurs remains difficult. We study this gap through benchmark shadows: support-concentrated data regimes that may concentrate learning around narrow evaluation-relevant patterns while limiting broader representational development. Under fixed architecture, tokenizer, optimizer family, and training budget, we compare a coverage-expanding baseline with two support-concentrated regimes: redundant repetition and frequency-concentrated rewriting. Spectral, rank-based, and layer-wise update diagnostics reveal distinct parameter-space signatures: repetition-concentrated training is largely recoverable after later diverse training, whereas frequency-concentrated support collapse leaves more persistent footprints. Correlational analyses of open-source multimodal model families reveal analogous structural patterns that co-occur with asymmetric benchmark profiles, while a prompt de-duplication case study shows that surface redundancy alone does not induce the same regime-level effects. Benchmark scores alone are therefore insufficient to characterize capability; parameter-space diagnostics provide complementary signals about training quality, coverage, and generalization-related regime effects.

Adaptive immune receptor repertoire sequencing data has emerged as a promising potential modality for disease diagnosis, relying on computational methods to analyze T-cell receptor (TCR) sequences from an individual’s blood sample. Published methods rely on different cohorts, data preprocessing pipelines, and evaluation metrics, making direct comparison across methods challenging. We have developed BenchRep-T, a unified benchmark that standardizes multiple publicly available TCR repertoire datasets and evaluates nine computational approaches, spanning statistical enrichment of shared sequences, feature-engineered ensembles, deep learning, and graph-based clustering. BenchRep-T evaluates methods on four tasks: disease classification across conditions, performance scaling under restricted sequence-sampling depth, recovery of known antigen-specific driver sequences, and evaluation of sensitivity to demographic confounding. Under controlled evaluation, simple baselines prove competitive, with tree-based models trained on V- and J-gene usage and short sequence motifs approaching the classification performance of more complex methods. Our findings underscore the complexity of modeling TCR repertoire data, and show that no single method dominates across all tasks. BenchRep-T provides a framework for rigorous and reproducible evaluation of TCR repertoire classification methods to accelerate the development of immune repertoire-based diagnostics.

We introduce a mathematical framework for studying contraction of the distributional Bellman operator under Maximum Mean Discrepancy (MMD). We show that contraction reduces to conditional positive definiteness of an associated kernel constructed from the base kernel, which we call the Bellman difference kernel. Focusing on radial kernels, we use Schoenberg's theorem to connect this condition to Bernstein functions, yielding contraction criteria, valid in arbitrary dimension, that can be checked analytically or numerically. Our framework unifies known results for the Gaussian RBF, power-distance, and multiquadric kernels; using numerical search, we further identify 12 new contraction-inducing kernels. We also propose *chaining*, a general construction that produces contraction-inducing kernels at any prescribed rate. To reconcile theory with practice, we introduce conditional positive definiteness on a set, which explains why the Gaussian RBF kernel can remain a valid choice on bounded supports despite its lack of global contraction. Finally, we identify a fundamental tradeoff between contraction rate and discriminative power, where kernels that contract faster tend to discriminate less between distributions.

AI evaluation pipelines often produce conflicting judgments rather than clean labels. In pairwise LLM evaluation, this conflict is especially visible: disagreement can arise from ambiguous items, underspecified rubrics, heterogeneous or unstable human raters, or an LLM judge whose verdict changes when the response order is swapped. We propose BayesJudge, an online Bayesian meta-evaluation layer for conflicting human-LLM judgment streams. For each comparison, BayesJudge estimates a panel-relative posterior verdict distribution over the two responses, with a tie or ambiguity state when such labels are available. At the same time, it estimates rater-specific human confusion matrices and LLM presentation-order bias. The method uses tie-open labels to keep ambiguity observable and paired order-swapped judge calls to separate response quality from presentation effects. We formulate the exact online posterior recursion and use a scalable Rao--Blackwellized assumed-density SMC approximation for streaming inference. Controlled synthetic experiments demonstrate recovery of prespecified evaluator parameters and illustrate two protocol-level identifiability mechanisms: tie-open labels expose ambiguity mass, and order-swapped paired judgments separate item preference from position bias. On real-world SummEval dataset, BayesJudge successfully detects systematic presentation-order effects in LLM judge outputs, infers distinct expert and crowdworker behavior signatures without rater metadata, and produces posterior uncertainty estimates that correlate with human disagreement. Our code is available at https://anonymous.4open.science/r/BayesJudge-0879.

Long-tailed recognition is commonly formulated as a problem of imbalanced supervision, where rare classes suffer from insufficient training examples. In this work, we study a complementary source of difficulty: under severe scarcity, tail classes may be forced into fine-grained discrimination before the model has acquired sufficiently reliable coarse semantic structure. We refer to this phenomenon as granularity mismatch under scarcity. To address it, we propose BasicLT, a two-stage framework that augments an underlying fine-grained recognition model with basic-level abstraction and commitment-guided selective differentiation. In Stage 1, BasicLT learns an explicit basic-level branch and aligns it with the family-level distribution induced by the fine-grained predictor, encouraging stable coarse semantic organization before additional fine-grained refinement is introduced. In Stage 2, a frozen Stage 1 teacher provides stable family-level commitment signals, and a family-conditioned residual refinement module is trained only on committed medium-shot and few-shot samples. At inference time, the residual correction is activated conservatively according to student-side commitment, so that fine-grained refinement acts as a conditional within-family correction rather than an unconditional auxiliary classifier. We evaluate BasicLT on CIFAR-100-LT, CIFAR-10-LT, ImageNet-LT, iNaturalist 2018, and Places-LT. Across these benchmarks, BasicLT achieves competitive performance against strong long-tailed baselines, with the most consistent gains appearing in medium-shot and few-shot regimes. Ablations and analyses further show that both basic-level abstraction and commitment-guided selective refinement are important, supporting the view that controlling the semantic granularity of discrimination can complement conventional rebalancing strategies for long-tailed learning. Code is available at Supplement.

Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-value, labor-intensive profession, we introduce BankerToolBench (BTB): an open-source benchmark of end-to-end analytical workflows routinely performed by junior investment bankers. To develop an ecologically valid benchmark grounded in representative work environments, we collaborated with 502 investment bankers from leading firms. BTB requires agents to execute senior banker requests by navigating data rooms, using industry tools (market data platform, SEC filings database), and generating multi-file deliverables—including Excel financial models, PowerPoint pitch decks, and PDF/Word reports. Completing a BTB task takes bankers up to 21 hours, underscoring the economic stakes of successfully delegating this work to AI. BTB enables automated evaluation of any LLM or agent, scoring deliverables against 100+ rubric criteria defined by veteran investment bankers to capture stakeholder utility. Testing 9 frontier models, we find that even the best-performing model (GPT-5.4) fails nearly half of the rubric criteria and bankers rate 0% of its outputs as client-ready. Our failure analysis reveals key obstacles (such as breakdowns in cross-artifact consistency) and improvement directions for agentic AI in high-stakes professional workflows.

Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles capture nonlinearities but require periodic retraining and large replay buffers. Linear models update efficiently per observation with $O(1)$ memory, but are fundamentally restricted to linear reward structures. We propose Residual Quantization (RQ) as a representation layer to bridge this gap. An offline-trained RQ codebook maps continuous contexts into discrete centroid assignments across $\ell$ levels, set dynamically through a shadow mechanism. This enables a spectrum of additive bandit algorithms that achieve nonlinear expressivity with strictly bounded memory. Across 13 datasets, RQ variants beat their non-RQ counterparts on 11 of 13 datasets, often by wide margins, while matching doubling-retrain XGBoost and neural baselines at up to $1000\times$ less memory.

Correlation metrics such as the Pearson Correlation Coefficient (PCC) and Concordance Correlation Coefficient (CCC) are standard regression metrics and learning objectives, but their non-decomposable nature poses a challenge for stochastic optimization. Since these metrics depend on dataset-level moments (mean, variance, covariance), naive mini-batch training with local statistics yields biased, high-variance gradients whose expectation does not align with the population gradient. To bridge this "batch-to-population'' (B2P) gap, we propose **B2P-Corr**, a novel framework that transforms correlation objectives into decomposable pseudo-losses driven by lightweight global "moment sketches''. We introduce two efficient variants: **B2P-EMA**, which utilizes exponential moving averages and stop-gradient operators to ensure asymptotic gradient tracking with $O(B)$ cost ($B$ is batch size); and **B2P-U**, which uses sampled cross-batch U-statistic estimators and a reservoir-based stabilization with an $O(B)$ implementation. Theoretically, we quantify the $O(B^{-1})$ bias of naive mini-batch optimization, prove a moving-target tracking result for B2P-EMA under a fast-sketch / slow-parameter regime, and provide an idealized control-variate analysis for B2P-U. Experiments on two synthetic and three real-world datasets demonstrate B2P-Corr achieves stable optimization and higher correlation than naive mini-batch optimization with negligible overhead.

Understanding the reasoning behind deep learning model predictions is crucial in cheminformatics and drug discovery, where molecular design determines their properties. However, current evaluation frameworks for Explainable AI (XAI) in this domain often rely on artificial datasets or simplified tasks, employing data-derived metrics that fail to capture the complexity of real-world scenarios and lack a direct link to explanation faithfulness. To address this, we introduce B-XAIC, a novel benchmark constructed from real-world molecular data and diverse tasks with known ground-truth rationales for assigned labels. Through a comprehensive evaluation using B-XAIC, we reveal limitations of existing XAI methods for Graph Neural Networks (GNNs) in the molecular domain. This benchmark provides a valuable resource for gaining deeper insights into the faithfulness of XAI, facilitating the development of more reliable and interpretable models.

We study the fundamental and timely problem of learning long sequences in autoregressive modeling and next-token prediction under model misspecification, measured by the joint Kullback--Leibler (KL) divergence. Our goal is to characterize how the sequence horizon \(H\) affects both approximation and estimation errors in this joint-distribution, sequence-level regime. By establishing matching upper and lower bounds, we provide, to our knowledge, the first complete characterization of long-horizon error behavior under the natural joint KL objective, with improved rates and optimality justification relative to existing work. On the approximation side, we show that joint KL admits a horizon-free approximation factor, in sharp contrast to Hellinger-based analyses that exhibit an \(\Omega(H)\) dependence for computationally efficient methods; this isolates the choice of divergence as the source of approximation amplification. On the estimation side, we prove a fundamental information-theoretic lower bound of order \(\Omega(H)\) that holds for both decomposable policy classes and fully shared policies, matching the \(\widetilde O(H)\) upper bounds achieved by computationally efficient algorithms. Our analysis clarifies the landscape of recent autoregressive learning results by aligning the log-loss training objective, the sequence-level evaluation metric, and the approximation metric through a sharp joint-KL oracle theory. We further show that these joint-KL guarantees imply policy learning regret bounds at rates matching prior imitation learning literature.