NeurIPS 2026 论文列表

Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents concisely communicate in a way that promotes aggregation of decision relevant information? Communicating only proposed actions can discard decision-relevant uncertainty: two agents may prefer the same action for different reasons. We study an alternative interface in which agents communicate calibrated beliefs over actions. We first analyze a simple synthetic setting that captures the structure of multi-round reasoning under partial information. In this setting, we show theoretically and empirically that communicating unbiased probabilities can be strictly more powerful than communicating actions. We then test the same principle in a collaborative maze-solving task with trained transformers and pretrained LLM agents. Our results demonstrate that probability communication outperforms action communication when the exchanged predictions preserve calibrated uncertainty. When communicated probabilities are biased we show that the benefits of probability communication can disappear. We give a post-hoc conversation calibration intervention that consistently improves decision-making by correcting these biases. These results suggest that probabilistic communication is useful for multi-agent collaboration not merely because it transmits more information, but because calibrated uncertainty gives other agents a reliable object to update on.

Reconstructing accurate camera poses and floor-plan layouts from sparse, unposed, wide-baseline RGB panoramas remains a challenging open problem. Direct multi-room floor-plan prediction is especially difficult in this setting: pose, geometry, semantics, and global layout structure are tightly coupled, yet a complete floor-plan is hard to learn as a single global output. Our key insight is to keep the model's predictions strictly local. Multi-view Ordered Wall Instance Segmentation (MOIS) is designed around this concept. Each panorama column predicts only the wall it observes plus the next two adjacent walls in clockwise order. The global wall partition, room layouts, and shared coordinates emerge from deterministic aggregation of overlapping local predictions. Building on MOIS, we propose Multi-view floor-plan Feed-Forward Network (MvFFN), which jointly predicts coarse camera poses, dense global geometry, and per-wall semantic outputs. A downstream Layout Chaining (LC) post processing pipeline then assembles these predictions into multi-room floor-plans with shared global coordinates, consistent wall identities, and connected room structure. The dense MOIS predictions recover cross-view wall identity and local within-room connectivity more accurately than composed modular baselines, and the assembled layouts substantially outperform prior methods on wall and junction level accuracy. We also introduce the ZInD-CrossView dataset, which augments ZInD with globally unique wall instances and cross-view segmentation labels for this task.

Recent advances in multi-agent systems have shown great potential for solving complex tasks. However, when multiple agents edit a shared codebase concurrently, their changes can silently conflict and inconsistent views lead to integration failures. Existing multi-agent systems address this through workspace isolation (e.g., one git worktree per agent), but this defers conflict resolution to a post-hoc merge step where recovery is expensive. In this paper, we propose STORM, i.e., STate-ORiented Management for multi-agent collaboration. Specifically, STORM manages agent states by mediating their interactions with the shared workspace, ensuring that each agent operates on a consistent view of the codebase and that conflicting edits are detected and resolved at write time.We evaluate STORM on Commit0 and PaperBench across multiple LLMs. STORM outperforms the git-worktree-based multi-agent baseline by +18.7 on Commit0-Lite and +1.4 on PaperBench, while achieving comparable or better cost efficiency. Combined with single-agent runs, STORM reaches highest scores of 87.6 and 78.2 on the two benchmarks respectively, suggesting that explicit state management is a more effective foundation for multi-agent collaboration than workspace isolation. STORM can also be plugged into any multi-agent system. Our code is available at \url{https://anonymous.4open.science/r/STORM-Mutli-Agent}.

Mathematical optimization drives decisions in supply chains, logistics, energy, and scheduling, but translating natural-language problems into solver-executable formulations remains a bottleneck. While frontier models show promise for this task, many deployments require small language models (SLMs) that run locally under privacy, latency, and cost constraints. Existing SLMs are limited by noisy data, ambiguous benchmarks, and one-shot inference that ignores the iterative, feedback-driven nature of optimization modeling. We introduce \textsc{OptiMind}, a framework for improving SLMs through formulation-centric data cleaning, class-conditional priors, and multi-turn reinforcement learning with solver feedback. \textsc{OptiMind} repairs ambiguous problems, regenerates solution trajectories, audits benchmark labels, constructs accepted objective-value sets, and distills class-level hints from recurring formulation errors. It then trains models to formulate, execute, and revise GurobiPy programs using solver-verified correctness as reward. On expert-cleaned IndustryOR, Mamo-Complex, and OptMATH benchmarks, a 20B \textsc{OptiMind-RL} model outperforms strong open-source baselines and reaches frontier-model performance under the same protocol. Our results suggest that optimization formulation is better treated as an interactive solver-grounded decision process than as one-shot code synthesis. We release our inference framework, cleaned benchmarks, and sample error analyses at \url{https://anonymous.4open.science/r/OptiMind-NeurIPS-1DB020}, with full data and model to follow.

Discovering object-centric representations from images can significantly enhance the robustness, sample efficiency and generalizability of vision models. Works on images with multi-part objects typically follow an implicit object representation approach, which fail to recognize these learned objects in occluded or out-of-distribution contexts. This is due to the assumption that object part-whole relations are implicitly encoded into the representations through indirect training objectives. We address this limitation by proposing a novel method that uses explicit graph representations for parts and introduce a new co-part algorithm for multi-part object discovery. The part representations are refined with a part-centric regularization objective. We then introduce three benchmarks to evaluate the robustness of object-centric methods in recognizing multi-part objects within occluded and out-of-distribution settings. Experimental results on simulated, realistic, and real-world images show marked improvements in the quality of discovered objects compared to state-of-the-art methods, as well as the accurate recognition of multi-part objects in occluded and out-of-distribution contexts. We also show that the discovered object-centric representations can more accurately predict key multi-part object properties in a downstream task, highlighting the potential of our method to advance the field of object-centric representations.

Realizing stable neural decoding over extended periods remains a significant challenge, primarily because invariant task-related neural dynamics are inextricably entangled with inherent signal non-stationarity and the physical drift of electrode-neuron interfaces. This work introduces LNDN (Long-term Neural Decoding Network), a hierarchical framework designed to decouple category-specific neural dynamic manifolds from representational drift, sustaining decoding precision without periodic recalibration. The proposed architecture addresses representational drift through an integrated tri-level alignment strategy. At the physical feature level, a decoupled representation and dynamic gating module isolates the physical identity of neurons from their transient states, adaptively filtering stochastic neural noise. This is further strengthened at the decision level by a Mixture-of-Experts (MoE) architecture, which employs a parallel soft-voting mechanism to suppress high variance induced by local representational distortions. Finally, at the manifold level, Adaptive Batch Normalization (AdaBN) and latent contrastive learning, combined with second-order correlation alignment, explicitly anchor the geometric consistency of the low-dimensional neural manifold over a period of months. Systematic evaluations on a longitudinal olfactory dataset, involving 9 mice performing a four-odor decoding task, demonstrate the efficacy of this approach. When trained exclusively on data from the initial four weeks, LNDN maintains an average accuracy of 83.67\% on completely unseen tests spanning the subsequent eight weeks, significantly outperforming mainstream domain adaptation and advanced neural decoding baselines. This framework offers a scalable, "set-and-forget" solution for robust, long-term invasive brain-computer interface applications.

Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the current state. In a multi-environment POMDP (MEPOMDP), the initial state is unknown, and assumed to be adversarially chosen. In this work we focus on computing the optimal value and policy in MEPOMDPs with finite-horizon objectives. That problem is known to be PSPACE-complete in POMDPs. Our main results are as follows: (1) we establish that it is also PSPACE-complete in the more general setting of MEPOMDPs; (2) we present a practical algorithm and evaluate it on classical benchmarks, significantly outperforming the only previously known algorithm.

Randomized Smoothing (RS) is a principled and widely adopted approach for certifying the robustness of black-box models, such as neural networks, against adversarial perturbations. We propose an optimization perspective that unifies several prior randomized smoothing certificates. We then show how to reduce the underlying high-dimensional worst-case optimization problem to an equivalent two-dimensional formulation under a double- or multi-sampling scheme, enabling efficient solutions via convex optimization. By exploiting additional information about the variance of the smoothed classifier across different smoothing distributions, we derive certified bounds that improve upon standard certificates. We show results on both classification and regression tasks.

Offline MARL increasingly relies on generative policies to model multimodal joint behavior, typically by distilling a centralized teacher into decentralized one-step actors under the CTDE. We identify a failure mode at the teacher training stage: standard flow-based teachers pair noise with replay targets independently, so nearby noise samples can be routed toward conflicting coordination modes. The teacher then produces samples between valid modes, and because the distillation loss regresses each local actor onto the conditional mean of the teacher's output given local input, this error is not absorbed but propagated to the student. To remove this teacher-side artifact, we propose Mode-Support Semi-Discrete Optimal Transport (MoSDOT), which summarizes multimodal replay into a finite mode support with prescribed capacities and uses conditional semi-discrete optimal transport to assign each noise sample to a single mode before teacher training. We additionally study a common-randomness variant that uses a shared noise component at execution to expose the residual gap intrinsic to strict-product execution. On controlled diagnostics and offline MARL benchmarks, MoSDOT improves endpoint quality and routing consistency, particularly on datasets exhibiting multimodal joint behavior.

Language models fine-tuned with reinforcement learning frequently learn to exploit their reward source rather than solve the underlying task. Existing fixes are typically reactive: detect a specific exploit, then patch the reward. We instead ask whether reward hacking can be prevented by constraining how the model represents the task. We introduce prompt KL regularization, a single auxiliary loss that adds a KL penalty between the actor's and reference model's per-token distributions on the prompt only, leaving the response distribution free. Across three reward hacking tasks of different sizes, prompt KL regularization maintains low hack rates (under 3\%) when standard training yields up to 100\% hack rates. Additionally, it matches or improves ground-truth accuracy, with no changes to the reward or environment. We aim to understand why prompt KL regularization is effective. Two independent mechanistic studies suggest that changes in prompt representations are key to models learning reward hacking behavior. First, we show that prompt-space activation steering Pareto-dominates response-space steering on hack rate and accuracy. Then, we demonstrate that swapping prompt activations between models can transfer and reduce reward hacking. Together, these results suggest that the way models internally represent the task is central to reward hacking, and that this representation can be directly constrained to prevent it.

We evaluate the autonomous offensive cyber capabilities of frontier AI agents on two purpose-built cyber ranges---a 32-step corporate network attack and a 7-step industrial control system attack targeting a simulated cooling tower---complemented by a private 82-task CTF suite spanning four difficulty tiers to measure narrow cyber skills. Estimated human-expert solve times are roughly 20 hours for the corporate range, 22 hours for the cooling tower, and from 1 minute to 40 hours across the CTF suite. Across twelve models released over a twenty-month period---from GPT-4o in August 2024 to early predeployment checkpoints of Mythos and GPT-5.5---we observe two trends. First, performance increases approximately log-linearly with inference-time compute, with no plateau observed for the strongest models up to 100M tokens. Second, newer frontier models generally complete more steps at fixed token budgets. GPT-5.5 and the Mythos Preview checkpoint each completed all 32 steps in at least one run, the first end-to-end completions of the corporate range; GPT-5.5 averaged 22.3 of 32 steps across ten 100M-token runs. On the control system range, the latest models reached 4 of 7 steps, primarily by directly probing the operational-technology protocol rather than following the intended chain of IT compromise and authenticated control. These results indicate rapid improvements in autonomous cyber capability and suggest that complex but undefended multi-step ranges are no longer reliably distinguishing the strongest frontier models. Future evaluations should emphasise hardened environments, active defences, defensive-control effectiveness, and operational security---priorities that grow more pressing as the threat of AI-driven cyber attacks becomes increasingly realistic.

We study Metrical Task Systems, a broad class of problems in online algorithms, under a model where input requests are generated by a stochastic process. We first consider the case of requests drawn IID from an unknown distribution, and then extend the model to requests generated by a finite-state Markov chain, where the algorithm observes both the current request and the current state of the chain before making its next move. This formulation captures practical settings in which requests are generated by an evolving environment. In both settings, we show how to find in polynomial time an algorithm whose expected cost per step is arbitrarily close to the cost of the best possible online algorithm. Our theoretical results are complemented by a brief empirical evaluation of our algorithms.

LLM multi-agent systems rely on division of labor to solve complex tasks, but their execution can be blocked by contention for shared resources such as GPU test environments, CPU execution slots, and exclusive tool sessions. Existing workflows, task allocation methods, and runtime schedulers usually arbitrate such access indirectly through workflow position, dependency structure, queue state, or resource metrics. These criteria do not fully capture the value of satisfying a specific request, because request value is distributed across agents' local execution states and changes with tests, retries, and downstream results. We propose a market-based protocol for access arbitration in LLM multi-agent systems. Each agent converts its current plan and local execution state into a structured, budget-constrained bid that signals relative urgency. For asynchronous requests, an online clearing method based on optimal stopping decides when to close the clearing window, allocate access rights, settle payments, and track resource occupation until release. Across three contention scenarios, Market reduces makespan by 5.2\%, 2.5\%, and 4.9\% relative to the strongest primary baselines, while improving task score by about two percentage points.

Multimodal Large Language Models (MLLMs) show strong visual perception capabilities, but existing evaluations largely focus on single-view or multi-view settings with shared camera parameters, leaving the challenge of {multi-agent collaborative} scenarios underexplored. In such settings, multiple agents observe a shared environment from independent viewpoints, and reliable decisions require reasoning across these independent observations of the same environment. To study this problem, we introduce **MaRiO**, a benchmark for evaluating multi-agent collaborative reasoning, and evaluate 23 state-of-the-art MLLMs on it. Our analysis reveals three key findings: (1) spatial tasks are generally easier than geometric reasoning tasks; (2) most models struggle to resolve cross-agent correspondences, with errors increasing with scene complexity; and (3) models show limited ability to leverage geometric cues present in the scene. As an initial step toward addressing these challenges, we explore geometry-guided synthetic view augmentation that generates intermediate transition frames between agent observations, providing auxiliary spatial context that improves performance in certain categories. Overall, our results highlight that multi-agent collaborative reasoning remains an open challenge and an important direction for multimodal embodied systems.

Modeling gene regulation in single cells requires jointly representing DNA sequence, chromatin accessibility, and gene expression across regulatory scales. Sequence-based models capture DNA-level regulatory grammar but typically lack cell-level resolution, whereas single-cell omics models capture cell-state semantics but treat genes and peaks as fixed vocabulary tokens, and thus lack a unified sequence-based view of regulation. Here, we present MUSS, a sequence-based multi-scale model for single-cell gene regulation that preserves both DNA-level and cell-level resolution. MUSS derives peak- and gene-level representations from DNA using a frozen pre-trained sequence encoder, integrates them with a multi-scale encoder combining peak-peak self-attention and distance-biased peak-gene interaction, and trains on paired scATAC-seq and scRNA-seq to align sequence-derived representations with cell-specific regulatory states. Across gene expression prediction, enhancer-gene linking, TF-target gene recovery, and zero-shot cell clustering, MUSS outperforms both sequence-based and single-cell omics baselines, and further generalizes to genes and peaks unseen during training. Together, these results demonstrate that MUSS learns biologically meaningful and broadly generalizable regulatory representations for decoding cell-type-specific regulatory programs from DNA sequence.

Document parsing converts visually rich documents into machine-readable structured representations, forming a crucial foundation for information systems. Although many benchmarks have been proposed for document parsing, they remain inadequate for realistic scenarios. Existing benchmarks either focus on specific tasks or assess only single-page, text-centric settings, making them insufficient for practical multi-page parsing. Moreover, they lack fine-grained evaluation of semantic continuity, hierarchical structure recovery, and visual content preservation. To address these gaps, we propose MPDocBench-Parse, a benchmark for multi-page document parsing in real-world applications. It contains 433 manually annotated documents with 3,246 pages, covering 15 document types in English and Chinese, with diverse layout styles, and supports document-level end-to-end evaluation. We further design a comprehensive protocol for content fidelity and logical structure, covering text, table, and formula recognition, truncated text and table merging, figure extraction, reading order, and heading hierarchy recovery. Experiments show that, while existing models perform well on basic text extraction, they still suffer clear limitations in semantic continuity integration, visual content parsing, and hierarchical structure recovery. MPDocBench-Parse provides a unified foundation for advancing document parsing toward more realistic scenarios.

Multimodal large language models (MLLMs) provide a promising alternative by taking object detection as a sequence generation task. However, applying MLLMs to multimodal object detection poses challenges in visual cross-modal (RGB-Infrared) fusion and coordinate prediction under Cross-Entropy supervision. In this paper, we propose a novel MLLM-based object detection framework that addresses these challenges through improving visual cross-modal token alignment and coordinate generation. Specifically, we formulate RGB-IR fusion as pre-decoding token-space modality routing, implemented by the Cross-Modal Complementary Adapters and the Visual Token-wise Router. We further reformulate coordinate-token learning as scale-adaptive neighborhood likelihood maximization through our proposed Geometry-Aware Loss, bridging discrete token prediction and continuous geometric localization. Extensive experiments show that our method achieves state-of-the-art performance, reaching 67.81\% mAP@0.5 under in-distribution scenarios, outperforming existing baselines by over 8\%, while also exhibiting strong generalization to out-of-distribution and referring object detection tasks.

Acoustic sensing is a promising technique for human-computer interaction, but existing learning-based systems still depend heavily on large task-specific labeled datasets. Compared with visual data, acoustic signals are difficult to interpret and annotate, making supervision costly and hard to scale. Meanwhile, existing unsupervised representation learning methods, particularly contrastive learning, are largely designed for natural images and rely on data augmentation to form positive pairs. Such strategies can destroy the temporal-physical structure of acoustic signals, causing shortcut learning rather than meaningful representation learning. To address this issue, we propose MCAS, a signal-processing-based multi-view contrastive learning framework for acoustic sensing. Instead of relying on artificial augmentations, MCAS constructs structure-preserving positive pairs from complementary representations derived from different signal processing pipelines of the same acoustic event. Moreover, MCAS is designed to address the heterogeneity of acoustic representations through progressive cross-view interaction enabled by Hierarchical Shared-Token Fusion and a multi-level pretraining objective with physical consistency regularization. Extensive experiments on two typical acoustic sensing tasks demonstrate the effectiveness of the proposed framework. Under low-label settings, MCAS consistently outperforms both supervised baselines and generic self-supervised baselines, while also showing strong cross-task transferability and robustness to corruptions and noise.

Deep clustering struggles when coarse-grained and fine-grained semantics coexist in the same dataset, as a unified partitioning strategy tends to either over-separate coarse categories or miss subtle fine-grained differences. To address this problem, we propose the adaptive multi-granularity clustering framework in a hyperspherical space (MC-H). The framework combines Dynamic Prototype-Center Assignment (DPCA), a Hyperspherical Determinantal Point Process (H-DPP) module that adaptively regulates repulsion according to local structure and promotes stronger separation in dense regions while preserving flexibility in sparse ones, and a Semantic Subspace Denoising (SSD) module that suppresses non-semantic variations and improves cluster compactness. Extensive experiments across datasets with different semantic granularities show that our method consistently outperforms existing approaches and effectively unifies clustering across the full granularity spectrum.