NeurIPS 2026 论文列表

Algorithmic ranking systems increasingly dictate high-stakes outcomes, yet most fairness interventions implicitly assume that protected attributes are categorical. When sensitive attributes are inherently continuous (e.g., age, income, or health scores), standard practices rely on arbitrary discretization, which discards crucial fine-grained information and destroys the natural ordinality of the data. In this paper, we study the problem of fair ranking with continuous protected attributes without relying on thresholding. We formalize fairness via the Kendall correlation between the ranking and the continuous attribute, and measure utility loss via the Kemeny distance from an initial score-based ranking. To solve this, we propose Fair Bubble Sort (FBS), a highly efficient adjacent-swap algorithm that minimizes utility loss subject to a strict continuous fairness constraint. We provide strong theoretical guarantees, proving that FBS is exactly optimal for the unweighted Kemeny distance. Extensive experiments on synthetic and real-world datasets show that FBS is scalable and consistently outperforms discretization-based baselines, achieving a superior fairness-accuracy Pareto front.

Coding agents are increasingly used as iterative development partners, but most benchmarks still evaluate one specification followed by one final assessment. This fails to capture whether agents can maintain executable artifacts as requirements evolve. We introduce EvoCode-Bench, a benchmark of 26 stateful coding tasks spanning 227 evaluated rounds. Each task preserves the agent's workspace across 5--15 rounds, expresses requirements through observable behavior, and uses cumulative executable tests to check both new requirements and regressions against still-active prior ones. We evaluate 13 coding agents with two complementary metrics: MT@4, a four-attempt fail-stop multi-round score, and SR, a single-round score from a reference-completed prior state. For most agents, SR exceeds MT@4 by 22--40 points, indicating that isolated instruction-following does not imply persistent reliability. The gap also changes model rankings: the highest-SR agent (78.9) ranks only third in persistent execution (44.0 MT@4). Even the strongest agents achieve only \~50\% success on multi-turn metrics, and aggregate pass rate drops below half of round-1 performance by round~5. Failure analysis reveals tier-dependent behavior: weaker agents fail early, while stronger agents survive long enough to expose specification-tracking and regression failures. These results show that single-round and persistent multi-turn coding evaluate different capability dimensions. We release the benchmark data and Harbor multi-turn infrastructure.

Real-world scenes often contain static, slow, and fast components within the same field of view. Frame cameras are inherently inefficient in this setting, as their global exposure and fixed sampling must follow the fastest motion and therefore oversample the rest of the scene. Event cameras provide asynchronous local sensing, but standard sensors use a single global contrast threshold, leading to a trade-off between sensitivity and redundancy: low thresholds preserve static information but over-trigger under fast motion, while high thresholds reduce data volume but lose slow or static structures. We propose a multi-threshold event imaging model that assigns motion-compatible thresholds to different scene components, enabling faithful multi-velocity-scale imaging with fewer events. We also introduce a practical acquisition pipeline that implements this model on existing event cameras. Experiments on synthetic and real scenes validate the effectiveness of our approach in preserving both static structures and fast dynamics while reducing data volume.

Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct missing spans within a broader storyline, commonly referred to as text infilling, while preserving consistency with both the local context and the global storyline. Despite using text infilling as a pre-training objective in many Large Language Models (LLMs), their actual performance on real-world narrative infilling remains underexplored. In this paper, we address this gap by introducing a multi-domain benchmark of ∼9.2K instances for narrative infilling, constructed by masking one to three sentences across four narrative types: encyclopedic text, commonsense stories, news articles, and visual narratives. Using this benchmark, we evaluate 20 instruction-tuned open-source LLMs ranging from 1.5B to 70B parameters across varying levels of instruction specificity and reasoning guidance. Outputs are assessed using standard automatic metrics and a qualitative framework covering five narrative dimensions. Results show that model scale does not reliably predict infilling quality: Gemma-2-2B achieves the highest qualitative score (4.02/5), outperforming models over ten times larger, including DeepSeek-Qwen-32B (3.77/5, ↓6.6%) and LLaMA-3.3-70B (3.71/5, ↓8.3%). We further find that explicit reasoning offers limited benefits as chain-of-thought reasoning yields only a marginal improvement (+0.6%). Additionally, short narratives and domain characteristics emerge as stronger predictors of task difficulty than infill position alone for narrative infilling in current LLMs.

How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of decentralized coordination in markets, we study this question through an agent economy in which agents compete via auctions for the right to act, exchange payments, and accumulate wealth from environmental rewards. These simple economic signals induce decentralized credit assignment, driving planning without global orchestration or explicit communication protocols. The population evolves through economic selection: effective agents accumulate wealth and are mutated via exploitation, while ineffective ones go bankrupt and are replaced via exploration. We show that, initialized with weak agents, the economy produces emergent multi-step reasoning strategies and outperforms stronger monolithic baselines across five agentic tasks, including mathematical reasoning, financial research, scientific research, accelerator design, and distributed-system optimization. We further provide theoretical insights into how economic dynamics shape agent behaviors, linking local incentives to long-term global performance. Our results suggest a new path to multi-agent intelligence: rather than engineering coordination, we can design decentralized incentive structures under which it automatically emerges.

Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared task specification layer that can turn heterogeneous recordings into reusable benchmark units. Existing standards organize files, metadata, and provenance, but they do not specify EEG tasks under a common language and rulebook, leaving critical task semantics scattered across papers, code, and manual interpretation. We investigate whether heterogeneous public EEG datasets can be standardized through a structured task specification language paired with a shared rulebook. Our methodology represents each benchmark entry as a task document synchronized with an executable task kernel, with the rulebook defining task fields, evidence requirements, document-kernel alignment, review states, and machine-checkable constraints. Using this methodology, we release a community-reviewed EEG benchmark corpus centered on 53 completed and reviewed entries with 245 task definitions spanning diverse paradigms, and we introduce NeuroDoc and NeuroAudit as the operational support layer for rulebook-guided drafting, upgrading, review, amendment, and release management. We further examine whether the resulting benchmark units can be instantiated in a shared downstream setting across four EEG foundation model backbones, providing execution-based evidence for reusable, auditable, and executable EEG benchmarking infrastructure. Project resources are available at https://anonymous.4open.science/r/anonymous_reviewe_neurodoc.

Driver attention prediction is crucial for interpretable driver monitoring and human-like autonomous driving. Existing methods typically formulate this task as a scene-to-heatmap regression problem, failing to reveal the underlying dynamic attention redistribution mechanism. To address this issue, we propose a scene-task-aware competitive allocation framework, which decomposes driver attention as a bottom-up visual saliency prior and a top-down scene-task utility representation. An adaptive product-of-experts allocation rule is designed to fuse these two heterogeneous branches in a competitive manner, where scene-task demand dynamically modulates the dependence on top-down task utility, and driving control urgency adjusts spatial attention concentration via a learnable attention budget temperature. To explicitly differentiate task-critical regions from visually prominent yet task-irrelevant distractors, we incorporate competition-aware contrastive learning to enhance feature discrimination in the latent space. Extensive experiments demonstrate that our method achieves strong and balanced performance, with a Kullback–Leibler divergence of 0.98 and a normalized scanpath saliency of 4.15 on the DR(eye)VE dataset, as well as a Pearson’s correlation coefficient of 0.67 and a histogram similarity of 0.54 on the BDD-A dataset. Ablation studies and diagnostic analyses further validate that our framework yields interpretable attention allocation behaviors, including urgency-induced spatial entropy contraction and scene-task-demand-guided dynamic attention reallocation.

Multi-task reinforcement learning (MTRL) aims to train a single agent to efficiently optimize performance across multiple tasks simultaneously. However, jointly optimizing all tasks often yields imbalanced learning: agents quickly solve easy tasks but learn slowly on harder ones. While prior work primarily attributes this imbalance to conflicting task gradients and proposes gradient manipulation or specialized architectures to address it, we instead focus on a distinct and underexplored challenge: \emph{imbalanced data allocation}. Standard MTRL allocates an equal number of environment interactions to each task, which over-allocates data to easy tasks that require relatively few interactions to solve and under-allocates data to hard tasks that require substantially more experience to solve. To address this challenge, we introduce Distributionally Robust Adaptive Task Sampling (DRATS), an algorithm that adaptively prioritizes sampling tasks furthest from being solved. We derive DRATS by formalizing MTRL as a feasibility problem from which we derive a minimax objective for minimizing the worst-case return gap, the difference between a desired target return and the agent's return on a task. In benchmarks like MetaWorld-MT10 and MT50, DRATS improves data efficiency and increases worst-task performance compared to existing task sampling algorithms.

Time series anomalies take diverse forms, and individual detectors encode different anomaly scoring principles that rarely cover all anomaly types. Recent unsupervised time-series anomaly detectors provide complementary scoring signals, yet deploying them as an ensemble preserves the inference and maintenance cost of every individual detector. We study whether heterogeneous teacher detectors can be distilled into a single lightweight detector in the unsupervised setting, where no anomaly labels are available to define a shared target. **AnoMix** addresses this by extracting weak pairwise supervision from the training-set anomaly scores of frozen teachers. It normalizes each teacher score sequence independently and trains the student with CoRank, a multi-teacher ranking-distillation objective that converts teacher score gaps into pairwise supervision through teacher consensus. Across seven benchmark datasets, AnoMix successfully integrates complementary teacher scoring principles into a single deployable model, improving over individual detectors and static score ensembles while using a compact student with under 80k parameters on the benchmark settings. A zero-shot variant further shows that the distilled ranking signal transfers to unseen datasets and can outperform large pretrained time-series foundation models for anomaly detection.

Multi-Task Learning (MTL) is a fundamental problem in machine learning that has been extensively studied over the past decade. Recently, a variety of optimization-based MTL approaches have been proposed to jointly learn multiple tasks by modifying the optimization trajectory. In this paper, we argue that the design of mainstream optimization-based MTL methods implicitly assumes compatibility with momentum-free optimizers, which may limit the understanding of their effectiveness in modern training regimes. In practice, the instantaneously derived gradients from MTL operations contribute only marginally to the final parameter updates when momentum is present, resulting in an amortized rather than instant de-conflicting effect. Moreover, this amortized behavior can be further degraded under high-curvature optimization dynamics. To address this issue and enable the effective integration of mainstream MTL methods with advanced optimizers (e.g., Adam and Muon), we propose \texttt{APT} (Applicability of advanced oPTimizers), a lightweight framework featuring an adaptive momentum mechanism that balances the trade-off between instant and amortized de-conflicting. Furthermore, we show that the Muon optimizer can be interpreted as an implicit MTL learner, and we introduce a lightweight direction preservation strategy to better align with its orthogonalization process. Extensive experiments across four standard MTL benchmarks demonstrate that \texttt{APT} consistently enhances existing MTL approaches, yielding substantial performance gains.

Many real world processes can be represented as compositions of functions along a Directed Acyclic Graph (DAG). In causal modelling these correspond to the underlying mechanisms, in engineering to the multiple fidelities present, and in gene-regulatory networks to the transcription factors. These functions are partially observed across the DAG, with noisy and heterogeneously sampled measurements, posing significant challenges for reconstruction, uncertainty propagation, and inference. To tackle these we place priors over functions and naturally arrive at Deep Gaussian Processes over DAGs. We theoretically study their prior-collapse behaviour, and the effect of graph topology and intermediate observations on the preservation of information. We obtain almost-sure lower bounds on the asymptotic frequency of depths at which distinction between inputs is preserved, identify broad kernel classes for which these hold, and prove an observation by \citep{dunlop2018} on the role of input connections. We offer a structured variational approximation that retains graph dependencies, satisfies compositional uncertainty, and captures the explaining-away behaviour of colliders. Finally, we empirically validate our theoretical results and our methodology, and model a latent-collider DAG, a protein signalling network, and a multi-fidelity heavy-ion collision task, attaining state-of-the-art performance while recovering the low-fidelity contributions and yielding explainability over the simulator hierarchy.

Existing multi-task active learning (MTL-AL) methods predominantly rely on the linear scalarization of heterogeneous objectives, such as uncertainty, diversity, and gradient conflict.We argue that this entangled formulation is fundamentally limited, as linear combination fails to capture Pareto-optimal trade-offs under conflicting objectives. To address this limitation, we propose a paradigm shift from combination to deconstruction and introduce a principled framework that rethinks MTL-AL along three orthogonal dimensions. First, we identify the Conflict Paradox: contrary to conventional wisdom, samples with high gradient conflict are not noise to be avoided but carry maximal information about unresolved inter-task trade-offs. We therefore decouple sample selection, which embraces conflict, from optimization, which resolves it via gradient surgery. Second, we introduce Orthogonal Decomposition, separating the acquisition objective into two distinct axes: Informativeness and Exploration. This two-stage process, consisting of subspace projection followed by manifold coverage, prevents ``diversity dilution'' by excluding uninformative outliers before enforcing diversity. Third, we propose Cross-Task Mutual Information (CTMI), a distribution-free proxy for inter-task dependency derived from a variational lower bound under the Maximum Entropy principle. Extensive experiments on COCO, Cityscapes, and NYUv2 — including comparisons against recent multi-task active learning baselines — demonstrate consistent improvements over state-of-the-art MTL-AL methods, highlighting that structural rethinking of acquisition yields larger gains than incremental heuristic design. Our code is publicly available at: \href{https://anonymous.4open.science/r/multitask_active_learning-1545}{\texttt{https://anonymous/CTMI}}

Offline multi-task reinforcement learning (Offline MTRL) heavily depends on the quality and distribution of pre-collected data. However, existing methods mainly focus on algorithmic optimization, with less emphasis on data-level improvements to enhance learning ability and generalization performance. This paper, from a data perspective, reveals three key bottlenecks that limit Offline MTRL performance:(i) ineffective utilization of prompts length under diverse task complexities, and (ii) semantic irrelevance of randomly sampled prompt segments, (iii) misleading supervision induced by fragmented and discontinuous trajectories. To address these challenges, we propose DaCe-DT, a robust offline MTRL framework designed to be insensitive to heterogeneous task complexities and data quality, featuring length-gated prompt masking (LGPM), retrieval-augmented prompt construction (RAPC), and value-adaptive return calibration (VARC). Together, these mechanisms enable DaCe-DT to deliver data-centric prompt adaptation and trajectory refinement, resulting in robust multi-task generalization and stable policy learning amid heterogeneous offline data and tasks. Experimental results on Meta-World show that DaCe-DT consistently outperforms state-of-the-art methods, achieving an average improvement of 11.73\% on optimal datasets and an improvement of 13.34\% on suboptimal datasets, demonstrating its effectiveness in learning stably from imperfect data and improving overall multi-task performance.

Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-view feed-forward geometry estimators. In this work, we demonstrate that by re-distilling these multi-view models-their internal knowledge of 3D geometry-into a single-view estimator, we can obtain enhanced 3D consistent foundational features. Our key idea is to construct a multi-view teacher by fusing pretrained 2D foundation features with multi-view geometric features, and refining the fused representation with a discriminative ranking objective. Through our discriminative distillation framework, we enforce the learned features to be both 3D consistent and locally distinctive, while keeping them aligned with the feature space of the original foundation model to preserve the semantic structure of the pretrained representation. Consistency and local discriminability are critical for 3D computer vision problems such as forming semantic and geometric correspondences across images. To demonstrate the effectiveness of our method, we perform comprehensive experiments spanning multiple angles: direct feature analysis, dense prediction transfer, and explicit 3D lifting and rendering. Across these evaluations, our method consistently produces stronger 3D-aware foundation features that improve multi-view consistency and local discriminability while preserving the semantic transferability of the original representation.

Dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance comparable to those trained on the original datasets. To further improve the performance of synthetic datasets, various training pipelines and optimization objectives have been proposed, greatly advancing the field of dataset distillation. Recent decoupled dataset distillation methods introduce soft labels and stronger data augmentation during the post-evaluation phase and scale dataset distillation up to larger datasets. However, this raises a question: Is accuracy still a reliable metric to fairly evaluate dataset distillation methods? Our empirical findings suggest that the performance improvements of these methods often stem from additional techniques rather than the inherent quality of the images themselves, with even randomly sampled images achieving superior results. Such misaligned evaluation settings severely hinder the development of DD. Therefore, we propose DD-Ranking, a unified evaluation framework, along with new general evaluation metrics to uncover the true performance improvements achieved by different methods. By refocusing on the actual information enhancement of distilled dataset, DD-Ranking provides a more comprehensive and fair evaluation standard for future research advancements.

We cast image segmentation refinement as a \textit{discrete} diffusion process. To solve this task we introduce Cyclic Discrete Diffusion (CDD), a novel forward–reverse formulation with a cyclic label topology that maps a discrete label set to the uniform distribution and back. Instead of injecting Gaussian noise as is done for continuous diffusion, CDD evolves segmentation masks through label jumps: at each step, every pixel either remains in its current class or jumps to the next label with a fixed rate. This jump-or-stay mechanism defines a finite-state Markov process with bounded forward and reverse steps, featuring analytically consistent and tractable reverse dynamics. CDD operates natively in a multi-class label space, enabling joint refinement of all classes in a single pass without requiring class-wise decomposition, as commonly needed in prior binary refinement approaches. The proposed formulation admits a finite and controllable number of refinement steps, for which we provide a theoretical characterization based on spectral gap analysis. We evaluate CDD across diverse segmentation tasks, including multi-class semantic, instance-level, and binary segmentation refinement. Our method consistently improves both region accuracy and boundary quality across multiple backbones and datasets.

Multi-task learning (MTL) aims to jointly optimize multiple related tasks to obtain shared representations within a single model. Recent research has focused on developing MTL algorithms to alter optimization dynamics through task re-weighting or gradient manipulation. However, in this paper, we first empirically identify that current MTL approaches are highly sensitive to the model initialization, complicating empirical evaluation and limiting practical reliability, which has been overlooked but appears to be critical. To better understand this phenomenon, we provide a local theoretical analysis that motivates the connection between early-stage MTL optimization behavior and curvature around initialization. Motivated by this analysis, we propose a Curvature-guided Parameter Initialization (CPI) approach tailored for MTL. Specifically, CPI performs a short warm-up phase to probe local curvature around the initial parameters and heuristically biases the initialization toward empirically favorable curvature profiles for subsequent multi-task optimization. The proposed approach is optimizer-agnostic, requires no modification to existing MTL algorithms, and can be seamlessly integrated as a plug-in prior to standard training. Extensive experiments on standard MTL benchmarks show that CPI improves the aggregate multi-task trade-off while remaining compatible with mainstream MTL methods.

Language agents are moving from single-user assistants to shared collaborators in workspaces, forums, and group chats. A shared agent observes interleaved messages from multiple users, maintains persistent context, and executes tool actions, creating a boundary problem absent from standard single-user agents: the agent must decide not only whether an instruction is safe, but which user or task it is allowed to govern. We identify cross-user poisoning (CUP), where an adversary injects a message into shared context that is later applied while the agent serves a benign user, causing unauthorized actions or responses outside the instruction's intended scope. We validate CUP on two deployed multi-user agents, Continua and ElizaOS, and introduce MURMUR, a framework for evaluating shared-context agents under concurrent multi-user interactions. Across Slack, Workspace, and Airline domains, CUP achieves high attack success, persists across later interactions, and remains substantially more effective than matched prompt-injection attacks. We evaluate boundary-scoping defenses and find that context summarization alone is insufficient, while task clustering and provenance prompting substantially reduce non-adaptive propagation. These results show that robust multi-user agents require explicit user/task scoping rather than generic input filtering or context compression.

The quadratic assignment problem (QAP) is widely considered one of the most difficult NP-hard combinatorial optimization problems, and one that remains challenging for neural constructive methods. We introduce CIMP, a Constructive policy trained by Imitation from Multi-expert teachers and built around an explicit Pairwise pre-conditioning block. CIMP reaches a 11.02% mean optimality gap on the 133 QAPLIB instances with published reference values, across 5 fixed same-config seeds, in a single constructive pass — without local search or per-instance refinement — with inference times between 0.1 and 3.1 seconds per instance. The model is trained on 400 synthetic instances with n ≤ 25. The most recent published neural reference reporting full QAPLIB results is SAWT [Tan and Mu, ICML 2024], a learn-to-improve (L2I) method that iteratively refines an assignment per instance after training on 5,120 synthetic instances, and reports a 26.8% mean gap. CIMP operates in a strictly more constrained learn-to-construct (L2C) regime and from a much smaller training budget, yet attains a substantially lower mean gap. The pre-block builds a state-dependent facility–location representation before scoring, a hybrid context-biased decoder mixes learned content similarity with QAP-specific algorithmic bias terms, and a sequence-distribution distillation objective is derived from a diversity-filtered pool of heuristic teachers. Family-wise and size-conditioned analyses, supported by a secondary normalized score, indicate that the gain is broadly consistent across QAPLIB families and across instance sizes beyond the training range.