NeurIPS 2026 论文列表

Single-cell multi-omics technologies jointly measure multiple modalities from the same cell, such as gene expression, chromatin accessibility, and surface protein abundance, providing a richer view of cellular state than any single modality alone. A central challenge is how to learn a multimodal cell representation that preserves the different types of information contained in these co-profiled measurements. Existing integration methods often emphasize either cross-modal correspondence, which benefits retrieval and imputation, or fused discriminative structure, which benefits classification, but these objectives can favor different aspects of the data. As a result, a representation optimized for one setting may fail to preserve information needed for another. We introduce scDecomp, a decomposed representation learning framework for co-profiled single-cell multi-omics data. Motivated by the concepts of redundancy, uniqueness, and synergy, scDecomp separates multimodal information into three role-specific components: a redundancy component ($R$) for information shared across modalities, a uniqueness component ($U$) for modality-specific information, and a synergy component ($S$) for interaction-dependent multi-omic signals. This design allows the learned representation to retain shared cell-state structure while also preserving modality-specific and cross-modal regulatory information. On single-cell multi-omics benchmarks, the $R$ component achieves a 5.0\% reduction in FOSCTTM compared with the strongest baseline, while $U+S$ improves cell type classification accuracy by 1.38\%. Branch-level analyses further show that the decomposed components support complementary aspects of representation quality, suggesting that structured decomposition provides a practical strategy for learning more informative multimodal cell representations.

We propose a fully decentralized $Q$-learning dynamic for infinite-horizon discounted Markov potential games in which agents observe the global state and their own realized payoffs, but do not know the reward functions, the transition probabilities, the potential function, or the policies and the actions of the other players. In the proposed dynamic each player maintains a local estimate of its continuation payoff for each state and action and updates its policy through a smoothed best response. Importantly, the smoothing parameter is adjusted by using an online estimate of the discounted state-visitation distribution, so that players explore more in rarely visited states while exploitation is encouraged in states that are frequently visited. We prove that the induced policy sequence converges almost surely to a set of approximate Nash equilibria of the Markov potential game, with an approximation guarantee that depends only on easily computable parameters. We illustrate the performance of the proposed learning dynamic via numerical experiments on a routing game and compare it with existing independent learning schemes based on persistent $\theta$-greedy exploration.

As a variant of the Multi-Agent Pathfinding (MAPF) problem, Multi-Goal Multi-Agent Pathfinding (MG-MAPF) requires planning conflict-free paths for a team of agents to visit sequences of preassigned goal vertices. Existing search-based methods suffer from poor scalability due to their reliance on centralized control, while decentralized learning-based approaches struggle with long-horizon planning under partial observability. Furthermore, existing paradigms rarely address joint goal ordering across agents, where uncoordinated goal sequences cause multiple agents to simultaneously converge on the same region, increasing congestion and conflicts. To address these challenges, we propose the Intent-aware Multi-Goal Multi-Agent Pathfinding (IMMP), a hierarchical framework for decentralized MG-MAPF that explicitly models spatial congestion at the high level and neighbor intent at the execution level. The high-level planner encodes a spatial prior via spatially-balanced goal ordering and path planning, reducing conflict pressure passed to the low-level policy. The low-level module resolves real-time conflicts via a reinforcement learning policy augmented with a neighbor intent prediction module, trained end-to-end through a prediction-guided collaborative proximal policy optimization. Extensive experiments demonstrate that IMMP achieves strong scalability and generalization, Extensive experiments demonstrate that IMMP achieves strong scalability and generalization, reducing SoC by an average of 19.77\% compared to the best-performing decentralized baseline, while maintaining robust performance. Our code is available at https://anonymous.4open.science/r/IMMP-CB2E/

In industrial machine vision, removing specular highlights is crucial for accurate surface analysis; however, existing open research remains largely confined to natural images, limiting its efficacy on highly reflective metallic surfaces. When used for zero-shot inference, these methods generate artifacts, making them unreliable for specialized industrial scenes. Since current state-of-the-art methods rely on pixel-level supervision, adapting them to novel industrial subjects is often impractical. Such adaptation requires highlight-free ground truths across hundreds of multi-view scenes, necessitating hardware-intensive cross-polarization, which is difficult to implement at scale in uncontrolled real-world settings. To address these challenges, we propose DeGlare, a flexible training framework for highlight removal in specialized industrial settings. Our approach leverages only a single-view scene by exploiting its multi-illumination observations together with a latent permutation strategy, enabling an encoder-decoder–style architecture to implicitly learn diffuse–specular decomposition. This eliminates the dependency on the burdensome manual annotation required by state-of-the-art supervised methods. We demonstrate the practical effectiveness of our approach on a real-world industrial robotic rig data set, where DeGlare achieves promising performance under limited data and annotation constraints.

The remarkable scalability of Transformers has expanded their application to 3D computer vision, where camera-aware positional encoding is crucial for providing spatial cues in multi-view geometry. Recent advancements have established the practice of using camera parameters---such as extrinsics or projection matrices---as relative positional encoding into the query, key, and value vectors of the attention mechanism. However, when scaling up the training recipe of novel view synthesis (NVS) models with the camera-based positional encoding, we observe a significant issue: model performance stagnates in the late stages of training. In this paper, we investigate the cause of the performance bottleneck when scaling up and demonstrate that storing rotation and translation given by the positional encoding in the same dimensions of the value vector causes indeterminacy in their independent identification, hindering training scalability. To address this, we propose Decoupled Pose Positional Encoding (DPPE), a novel camera-based positional encoding that explicitly decouples rotation and translation. Extensive evaluations on NVS tasks demonstrate that DPPE enables stable long-term training even in scaled-up training setup. Furthermore, it exhibits superior generalization performance in extrapolation settings, such as handling an increased number of viewpoints and zoom-in scenarios.

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.

Communication enhances cooperative multi-agent reinforcement learning (MARL) under partial observability. In real networks, however, each agent's available bandwidth varies over time due to changing link quality, interference, and congestion. Most existing MARL communication policies decide whether to transmit based on message importance or novelty, or are trained under fixed bandwidth conditions. As a result, they cannot adapt sending frequency to bandwidth variations, leading to packet loss when bandwidth decreases or missed information when bandwidth recovers. To address this, we propose Dual-Decoupled Adaptive Communication (DDACOM)---a framework that decouples message sending and message generation from the action policy. The message-sending module ranks each message by its deviation from teammates' information and the sender's history, and uses a dynamic token bucket to convert the probe-estimated safe rate into a real-time token budget, enabling the communication frequency to track the safe rate online. The message-generation module is trained independently with receiver-side attention feedback and counterfactual marginal value, ensuring each transmitted message meaningfully contributes to cooperation. On SMAC and MPE, DDACOM achieves leading cooperative performance among baseline methods. Under dynamic network conditions in ns-3 simulation, DDACOM sustains over \(95\%\) safe-rate utilization and packet loss below \(0.3\%\) while reducing communication frequency to less than half of full broadcast, confirming its adaptability to runtime network variation.

Training and evaluation in multi-channel imaging (MCI) remains challenging due to heterogeneous channel configurations arising from varying staining protocols, sensor types, and acquisition settings. This heterogeneity limits the applicability of fixed-channel encoders commonly used in general computer vision. Recent Multi-Channel Vision Transformers (MC-ViTs) address this by enabling flexible channel inputs, typically by jointly encoding patch tokens from all channels within a unified attention space. However, unrestricted token interactions across channels can lead to feature dilution, reducing the ability to preserve channel-specific semantics that are critical in MCI data. To address this, we propose Decoupled Vision Transformer (DC-ViT), which explicitly regulates information sharing using Decoupled Self-Attention (DSA), which decomposes token updates into two complementary pathways: spatial updates that model intra-channel structure, and channel-wise updates that adaptively integrate cross-channel information. This decoupling mitigates informational collapse while allowing selective inter-channel interaction. To further exploit these enhanced channel-specific representations, we introduce Decoupled Aggregation (DAG), which allows the model to learn task-specific channel importances. Extensive experiments across three MCI benchmarks demonstrate consistent improvements over existing MC-ViT approaches.

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.

Building hydrogen station networks, electric vehicle (EV) charging grids, or vaccine distribution systems requires coordinating two separately operated graphs - demand-side facilities and the supply-side networks they depend on - where each operator's payoff depends on the other's choices. We formalize this as the Co-Evolving Graph Game (CEGG), a setting that sits between facility location, interdependent-network analysis, and graph-based multi-agent reinforcement learning (MARL) but is not jointly addressed by any of them. We propose Cross-Graph Attention with Policy Mirror Descent (CGA-PMD), pairing cross-graph attention - which lets each agent read only the partner-graph nodes structurally coupled to its own - with a policy mirror descent update for stable training. Across three reward domains and a range of practical network scales, we find that the structure of the coupling selects which method wins: at small scale most methods are competitive, but as scale grows or coupling becomes irregular, only CGA-PMD remains productive while own-only, unmasked-attention, and Proximal Policy Optimization (PPO)-based variants all fail in distinct ways. Our theoretical analysis explains why: PMD's Kullback-Leibler (KL) regularization admits an iteration-uniform cumulative drift bound while PPO's clip update does not, predicting CGA-PMD's stability in the hard regime where PPO-based methods collapse.

Large language models are increasingly deployed as autonomous agents that plan and act over long-horizon interactions, learning from both final outcomes and rich environmental feedback. However, optimizing from such interaction experience remains challenging, as feedback is heterogeneous and rarely carries explicit reward information. Existing post-training approaches rely on outcome-driven methods, such as reinforcement learning with verifiable rewards (RLVR), which primarily exploit final success signals while leaving interaction experience and feedback underutilized. In sparse-reward, long-horizon tasks, this often results in **distribution sharpening**: the policy reinforces a narrow set of already-successful behaviors, without substantially improving the feedback-grounded agency needed for broader problem-solving capacity (e.g., Pass@$k$). We propose ***Counterfactual Distillation***, a framework that converts exploration-derived experience into trainable policy supervision. Specifically, we organize exploration as a tree-structured search process, where the agent reflects on past decision points and makes new experience-guided decisions to form alternative branches. These corrections are then distilled as counterfactual targets: we train the model to predict the revised action from the original history without explicitly providing the experience, thereby introducing out-of-distribution behavior patterns and expanding exploration capacity. Across diverse interactive coding and agentic tasks, our method outperforms outcome-driven baselines such as GRPO and experience-based methods such as Early Experience, with gains of up to **14%** on Pass@128. When interleaved with reinforcement learning updates, it further raises the performance ceiling, yielding over **10%** improvement in Pass@1. We provide an [anonymized implementation](https://anonymous.4open.science/r/Counterfactual-Distillation-0E71).

Small language models deployed under cost, latency, or privacy constraints cannot simply switch to larger models when reasoning fails. Multi-candidate generate then-rerank offers a natural test-time scaling route. However, more candidates do not always yield higher accuracy. We identify a previously overlooked bottleneck between generation and selection: a correct answer may appear in the candidate pool yet fail to be selected because it is incomplete, unparseable, or misaligned with the answer mode the fixed selector expects. We term this loss the hidden interface tax and formalize a coverage–conversion decomposition that distinguishes raw oracle coverage from selectable coverage, the subset of problems where a correct candidate can actually be consumed by the selector. We propose Compatibility Aware Candidate Construction (CACC), an interface layer between the candidate source and the fixed selector that repairs fragments, aligns answer modes, and removes selector-confusing noise without retraining either component. On numeric reasoning (GSM8K, competition_math), CACC raises both oracle coverage and final accuracy. On GPQA Diamond, CACC alone improves final accuracy from 5.6% to 19.2% (+13.6pp); combined with proposer-side strengthening it reaches 25.8% (+20.2pp). On MMLU-Pro, CACC exposes a coverage–conversion gap: answer-mode-compatible construction raises oracle coverage, but final gains require additional proposer-side distribution shaping.

Mode connectivity studies reveal that high-performing neural networks are often connected by low-loss paths in parameter space. We investigate the analogue of this phenomenon in cooperative multi-agent reinforcement learning (MARL), where a solution is not a single model but a team of decentralized policies. We introduce coordination connectivity, a diagnostic evaluating whether linear interpolation between the actor parameters of two trained policy teams preserves high team return. The resulting coordination barrier measures whether two successful teams are separated by a performance cliff along the joint-policy path. We evaluate this diagnostic on the StarCraft Multi-Agent Challenge (SMAC) across five controlled training histories, isolating the effects of shared initialization, continued training, parameter decoupling, and HAPPO-style separate-actor specialization. Across three SMAC maps and four seeds, policy teams derived from a shared MAPPO checkpoint exhibit near-zero interpolation barriers, even after non-shared specialization. In contrast, interpolation paths involving independently trained HAPPO teams exhibit substantially larger barriers. Mixed-team assembly, single-agent hot-swap, clone-all, role-swap, and weight-space probes further indicate that shared-initialized agents specialize into distinct roles while remaining within a low-barrier coordination component. These results suggest that shared initialization implicitly structures the joint-policy space of cooperative MARL, providing a mode-connectivity-inspired lens for analyzing coordination, specialization, and same-source cross-version team compatibility.

Discovering cooperative behavior in multi-agent reinforcement learning (MARL) is challenging due to the combinatorial complexity of joint state-action spaces, which hinders the emergence of coordinated behaviors from trial-and-error alone. Intrinsic rewards are often used to aid discovery, but naively combining them with team objectives can distort the learning signal, compromising task performance. In this paper, we propose EFXPLORER, a constrained exploration framework that maximizes exploration objectives subject to a constraint that preserves established task performance. We solve it via an epigraph reformulation that introduces adaptive exploration budgets. This approach separates intrinsic rewards from task objectives and regulates exploration through task feasibility. To further encourage diverse and temporally extended exploration, we incorporate a successor distance-based intrinsic reward that captures long-horizon dependencies. Empirically, our method outperforms strong baselines and induces novel cooperative strategies across SMAX, VMAS, and MPE benchmark suites.

We present a novel theoretical analysis of Federated SARSA (FedSARSA) with linear function approximation and local training. We establish convergence guarantees for FedSARSA in the presence of heterogeneity, both in local transitions and rewards, providing the first sample and communication complexity bounds in this setting. At the core of our analysis is a new, exact multi-step error expansion for single-agent SARSA, which is of independent interest. Our analysis precisely quantifies the impact of heterogeneity, demonstrating the convergence of FedSARSA with multiple local updates. Crucially, we show that FedSARSA achieves linear speed-up with respect to the number of agents, up to higher-order terms due to Markovian sampling. Numerical experiments support our theoretical findings.

Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal restrictions in open ended, multi-agent environments. Current methods for measuring the dynamics of open-ended multi-agent systems are limited to qualitative inspection. In this paper, we extend the process-theoretic notion of adaptive control charts to multi-agent systems to enable automated monitoring. Using simulation, we demonstrate that adaptive control charts are necessary for monitoring multi-agent systems that can learn from their environment. We further demonstrate, both empirically and theoretically, that adaptive control charts are susceptible to adversarial agents that defect sufficiently slowly. These results illustrate a fundamental tradeoff in multi-agent system control: either agents in a system cannot learn \textit{or} the system is susceptible to adversaries.

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.

When multi-agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error attribution remains a fundamental challenge due to the long interaction traces that large language model-based MAS generate. This paper presents a framework for error attribution based on conformal prediction (CP) which provides finite-sample, distribution-free coverage guarantees. We introduce new algorithms for filtration-based CP designed for sequential data such as agent trajectories. Unlike existing CP algorithms, our approach predicts sets that are contiguous sequences to enable efficient recovery and debugging. We verify our theoretical guarantees on a variety of agents and datasets, show that errors can be precisely isolated, then use prediction sets to rollback MAS to correct their own errors. Our overall approach is model-agnostic, and offers a principled uncertainty layer for MAS error attribution.

Multimodal large language models have made rapid progress in video temporal grounding, yet real-world applications routinely require localizing every event that satisfies compositional temporal and spatial conditions. Existing benchmarks fall short: they localize only a single moment per query, count without temporal conditions, or treat grounding and counting as disjoint tasks. We introduce CoMET-Bench for Conditional Multi-Event Temporal Grounding in long-form video, comprising 2,789 queries over 600 videos averaging 33.8 minutes across five real-world domains, with each query composed from 4 temporal conditions, 3 spatial conditions, and a dedicated negative-query subset. We further propose a unified evaluation protocol jointly measuring counting, grounding, and negative-query recognition, including a new Rejection-F1 metric that prevents trivial gaming by lazy "always-empty'' models. Benchmarking a broad suite of MLLMs, agent-based, and grounding-specialized methods reveals that existing approaches remain far from solving this task. Building on these findings, we propose CoMET-Agent, a training-free agentic framework that reformulates the task as structured search-and-aggregate, improving F1@0.5 by 6.1% over GPT-5 purely through structural reasoning. Failure analysis further surfaces three open directions: fine-grained entity tracking, position-uniform retrieval, and causal event pairing.