NeurIPS 2026 Papers

Multi-server MCP agents create an information-flow control problem: faithful tool composition can turn individually benign read/write permissions into cross-boundary credential propagation---a structural side effect of workflow topology, not necessarily malicious model behavior. We present MCPHunt, to our knowledge the first controlled benchmark that isolates non-adversarial, verbatim credential propagation across multi-server MCP trust boundaries, with three methodological contributions: (1) canary-based taint tracking that reduces propagation detection to objective string matching; (2) an environment-controlled coverage design with risky, benign, and hard-negative conditions that validates pipeline soundness and controls for credential-format confounds; (3) completion-requires-secret (CRS) stratification that disentangles task-mandated propagation (faithful execution of verbatim-transfer instructions) from policy-violating propagation (credentials included despite the option to redact). Across 3,615 main-benchmark traces from 5 models spanning 147 tasks and 9 mechanism families, policy-violating propagation rates reach 11.5--41.3% across all models. This propagation is pathway-specific (25x cross-mechanism range) and concentrated in browser-mediated data flows; hard-negative controls provide evidence that production-format credentials are not necessary---prompt-directed cross-boundary data flow is sufficient. A prompt-mitigation study across 3 models reduces policy-violating propagation by up to 97% while preserving 80.5% utility, but effectiveness varies with instruction-following capability---suggesting that prompt-level defenses alone may not suffice. Code, traces, and labeling pipeline are released under MIT and CC BY 4.0.

Large language model agents are increasingly deployed through the Model Context Protocol (MCP), which allows them to discover and invoke tools across multiple servers at runtime. Existing MCP benchmarks primarily measure task completion capability but provide limited insight into why agents fail, reporting aggregate success rates without attributing failures to specific reasoning, execution, or memory breakdowns. We introduce MCPHallu, a benchmark for diagnosing hallucination-prone agent failures in MCP environments along three axes: reasoning, execution, and memory. MCPHallu contains 358 tasks across five capability domains and runs on 36 production MCP servers in isolated containers. Each task is designed with a structural trigger that makes one of four subtypes the dominant failure risk: Branch Collapse, Unreachable Goal, Tool Misuse, or Context Forgetting. This design enables subtype-level analysis rather than aggregate task success alone. We evaluate fourteen frontier LLM agents from eight vendors on 5,012 trajectories. Current MCP agents remain vulnerable to hallucination-inducing tool environments, achieving an average score of 0.642. Failures concentrate on Unreachable Goal tasks, where the average score drops to 0.458 and 22.1\% of runs pass, indicating that agents often continue issuing tool calls or claim successful completion rather than recognizing infeasible goals. Performance across the four subtypes is largely independent: per-model ranks span on average 6.9 of 13 positions, and Branch Collapse is uncorrelated with Unreachable Goal across models ($r = -0.12$). MCPHallu is released at https://anonymous.4open.science/r/mcp_hallu.

The Model Context Protocol (MCP) is emerging as a standard interface through which large language model (LLM) agents discover and invoke external tools. However, existing MCP evaluations fall short along three key axes: realistic multi-step workflows with cross-server orchestration, breadth across authentic MCP servers rather than mocks, and structured, reproducible claim-level scoring disentangled from agent verbosity or style. We introduce MCP-Atlas, a benchmark for measuring tool-use competency against production MCP servers. MCP-Atlas contains 1,000 natural-language tasks written and verified by human experts spanning 36 real MCP servers and 220 tools. Prompts do not specify servers, tools, or parameters, requiring agents to identify relevant tools among semantically plausible distractors and to compose multi-step, cross-server workflows. Each task is scored with a claim-level rubric, where final answers are scored against atomic factual claims grounded in tool outputs. This answer-centric scoring permits valid alternative tool-call trajectories to receive credit. We pair this with an 11-category diagnostic taxonomy that disentangles tool-call failures from cognitive failures in task understanding, synthesis, parsing, and stopping. Evaluating 20 frontier models from six providers under matched task-level conditions, we find pass rates up to 82.2% at a 0.75 claim coverage threshold and a clear three-tier performance structure. Automated diagnostics show that 63.3% of diagnosed failures are cognitive rather than tool-call related. Notably, several high-performing models fail after successful tool execution due to premature stopping or incorrect synthesis. We release the task schema, containerized harness, claim evaluator, and a 500-task public split, while reserving a 500-task private split to preserve leaderboard integrity.

Spatio-temporal point processes (STPPs) represent a random collection of points where each point corresponds to the time and location of an event. STPPs are widely used to describe a wide range of phenomena, from earthquakes to wildfires to crime occurrence to disease outbreaks. Generative models have recently emerged as a new powerful paradigm for STPPs, demonstrating %due to their exceptional highly competitive generalization capabilities and promising potential for systematic uncertainty quantification. However, existing generative approaches primarily rely on unimodal numerical data, overlooking the geographic context in which events occur and failing to capture critical spatial and temporal patterns within the STPP. To address these limitations, we propose Morse-aware Cross-Modality learning within Diffusion Model, or \textbf{MCM-DM}. MCM-DM represents a cross-modality diffusion framework based on the discrete Morse and cobordism theories that augments STPP modeling with geographic scene understanding. MCM-DM consists of three key components: (i) a vision-language model-based geographic encoder that extracts semantic embeddings from satellite tiles at each event location; (ii) an attention-based fusion mechanism to integrate critical structure representation with spatio-temporal embeddings, and (iii) a Morse-theoretic topological aligner which aligns latent representations of critical events with spatio-temporal embedding space. We demonstrate the MCM-DM utility on 8 diverse STPP datasets from Earth sciences, epidemiology, urban mobility, and crime analytics, highlighting its cross-domain versatility. The code is available at~\url{https://anonymous.4open.science/r/MCM-DM-CF83}.

Graph-based Approximate Nearest Neighbor (ANN) search often suffers from performance degradation in high-dimensional spaces due to the Euclidean-Geodesic mismatch, where greedy routing diverges from the underlying data manifold. To address this challenge, this paper presents Manifold-Consistent Graph Indexing (MCGI), a geometry-aware and disk-resident indexing method that leverages Local Intrinsic Dimensionality (LID) to dynamically adapt search strategies to the intrinsic geometry of data. Unlike conventional algorithms that treat dimensions uniformly, MCGI modulates its beam search budget based on in-situ geometric analysis, which reduces sensitivity to data-specific hyperparameters by replacing a single scalar with a geometry-informed range that remains stable across datasets of varying dimensionality. Theoretical analysis demonstrates that MCGI provides robust approximation by preserving manifold-consistent topological connectivity. Extensive evaluations against five industry-standard baselines across five datasets up to billion scales confirm the advantages of the proposed approach.

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.

Large language models now produce text indistinguishable from human writing, which increases the need for reliable provenance tracing. Multi-bit watermarking can embed identifiers into generated text, but existing methods struggle to keep both text quality and watermark strength while carrying long messages. We propose MC$^2$Mark, a distortion-free multi-bit watermarking framework designed for reliable embedding and decoding of long messages. Our key technical idea is Multi-Channel Colored Reweighting, which encodes bits through structured token reweighting while keeping the token distribution unbiased, together with Multi-Layer Sequential Reweighting to strengthen the watermark signal and an evidence-accumulation detector for message recovery. Experiments show that MC$^2$Mark improves detectability and robustness over prior multi-bit watermarking methods while preserving generation quality, achieving near-perfect accuracy for short messages and exceeding the second-best method by nearly 30% for long messages.

Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost universally hand their reconciled directions to Adam~\cite{kingma2015adam}. We show this coupling introduces two systematic gaps between the solver's intent and the optimizer's execution. The first is a \emph{weighting mismatch}: Adam's second-moment denominator entangles the time-varying preference vector with gradient statistics, marginalizing the preference into a history average and collapsing distinct Pareto trade-offs toward a near-uniform mixture. The second is a \emph{geometric mismatch}: Adam's adaptive metric distorts the Euclidean geometry MOO solvers assume, turning aligned objectives into apparent conflicts. To resolve both jointly, we introduce \textbf{MAdam} (Metric-Aware Multi-Objective Adam), a drop-in wrapper that leaves both solver and optimizer unchanged. MAdam preconditions the reconciled direction by the preference-conditioned curvature of the scalarized objective; on this whitened input, Adam's second moment collapses to identity, so the realized update is governed by the preference-conditioned metric. Across multi-task learning, Pareto-front recovery, physics-informed neural networks, and medical imaging, MAdam consistently improves over Adam for every solver family.

Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing multi-objective alignment methods either rely on costly training or require pre-trained reward models for each preference, making it difficult for them to adapt to evolving preferences. Prompt-based personalization offers a training-free alternative, but prompting alone often provides limited steerability, as LLMs may overemphasize or overlook certain preferences and fail to give users reliable control over the relative importance of different objectives when conflicts arise, leading to suboptimal alignment. In this paper, we introduce MATO, a training-free framework for Multi-objective personalized Alignment with Test-time Optimization. MATO formulates personalization as a test-time optimization problem that steers the relative importance of multiple objectives through controllable weights during decoding, without modifying model parameters or requiring external reward models. Specifically, a reward discovery module recovers preference rewards directly from the backbone LLM for diverse objectives specified in natural language, while a weight optimization module dynamically adjusts objective weights based on the user's initial preferences and the partially generated response to balance competing objectives during generation. The resulting rewards and weights jointly guide an online optimization procedure over the token distribution, enabling better alignment with the target objectives. Extensive experiments across multiple datasets and backbone LLMs show that MATO consistently outperforms strong baselines, achieving Pareto-improving multi-objective alignment and stronger steerability. These results highlight test-time optimization as a promising direction for scalable, controllable, and model-agnostic personalized alignment.

The performance of offline Multi-Agent Reinforcement Learning (MARL) is limited by the quality of the fixed offline dataset, motivating trajectory augmentation. However, augmenting multi-agent trajectories presents a fundamental challenge of the diversity–coordination trade-off: independent generation improves diversity but lacks coordination, while joint generation induces coordination but lacks diversity, reproducing the training distribution. To address this challenge in multi-agent data augmentation, we introduce MASTARS, a novel diffusion-based framework that generates multi-agent trajectories sequentially across agents while enforcing cross-agent consistent coordination via the method of inpainting. MASTARS produces trajectories that are diverse, coordinated and globally coherent. Experiments on various benchmarks such as MPE, SMAC, and SMAC-v2 demonstrate significant performance gains across multiple offline MARL methods.

Scaling sequential recommendation to long user histories requires compressing diverse behavioral evidence into memories that can be scored efficiently against many candidates. We first provide evidence that real user histories exhibit multi-scale semantic structure: short-lived intent, medium-term interests, and long-term preferences can coexist in the same sequence. However, existing summarization-based models mainly optimize compact history compression and do not explicitly organize cached user memory across temporal scales, which can lead to \textit{temporal aliasing} where distinct behavioral signals become entangled before prediction. We propose MARS, a multi-resolution user memory that writes the full history into recurrent state tracks initialized with different half-life priors. A sparse routing reader then materializes compact seed memories by selecting the relevant temporal resolutions for each seed, preserving fixed-size candidate scoring. Experiments across recommendation datasets show that MARS outperforms strong recommendation baselines, with gains most pronounced for users with long histories.

Fine-tuning Vision Language Models (VLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation. However, imbalanced training dynamics across modalities often lead to suboptimal accuracy due to negative interference, a challenge typically addressed with inefficient heuristic methods such as manually tuning separate learning rates. To overcome this, we introduce **MARS** (**M**ultimodal **A**daptive **R**ank **S**earch), an approach to discover optimal rank pairs that balance training dynamics while maximizing performance. Our key innovation, a proposed framework of dual scaling laws, enables this search:~one law models module-specific convergence time to prune the search space to candidates with aligned dynamics, while the other predicts final task performance to select the optimal pair from the pruned set. By re-purposing the LoRA rank as a controller for modality-specific convergence speed, MARS outperforms baseline methods and provides a robust, automated strategy for optimizing VLM fine-tuning.

A central challenge for multimodal language models (MLLMs) in real-world tasks is to abstract visual observations into structured spatial states and reason over how those states change under actions over long-horizons. Existing multimodal benchmarks often focus on static question answering, where answers can be directly extracted from images, or assume symbolic states are available and bypass the visual-to-spatial abstraction process. We present MARBLE, a diagnostic benchmark for spatially grounded multimodal reasoning and planning across three agent environments: Cube, Maze and Blox. MARBLE spans 2D and 3D spatial puzzles under geometric constraints, dynamic layouts, and spatial transformations, with difficulty systematically controlled along two axes: observation format and planning horizon. Extensive evaluations show that frontier MLLMs struggle on MARBLE, with the strongest model GPT-5.4 achieving only a 7.3\% average success rate across the three environments in the hardest setting. Pairwise ablations on the open-weight models identify visual-to-spatial abstraction as the major bottleneck: when models must infer spatial states from images instead of receiving human-annotated states, success drops from 50.7\% to 3.7\% for Gemma-4-31B and from 87.9\% to 6.5\% for Kimi-K2.6. The results indicate that models often fail to construct precise spatial states from visual observations before planning, a task that is straightfoward for human. Overall, MARBLE reveals the gap of reliable visual-to-spatial for frontier MLLMs and provides a controlled testbed for measuring progress in spatially grounded multimodal reasoning and planning.

Large language models (LLMs) have shown strong reasoning capabilities in embodied planning tasks. However, they often fail to achieve good results on long-horizon and multi-robot tasks under strict physical constraints, and generate smooth but infeasible trajectories. In real-world robotic hardware deployment, systems also face two fundamental challenges: First, the fine-tuning method for open-source models, Direct Preference Optimization (DPO), typically only makes a binary true or false judgment on the planning results, without distinguishing the severity of the planning error. As a result, they fail to impose effective penalties for catastrophic violations of physical constraints. Second, methods that search over multiple candidate plans during inference do not filter out invalid plans in advance, often leading to combinatorial explosion and high inference latency, which makes them difficult to apply to real-time robotic operations. To address these limitations, we propose a complete training-to-inference architecture consisting of two modules. First, we introduce Margin-Aware Direct Preference Optimization (mDPO) during training, which distinguishes the severity of errors in negative samples, enabling the fine-tuned model to avoid generating severe errors that would render a task irreparable. Second, we deploy Verifier-Guided Search (VGS) during inference, using a symbolic verifier to hard-prune invalid search paths, thereby repairing infeasible task plans and reducing inference latency. In addition, latency analysis shows that our approach reduces the average end-to-end runtime from 12.814s to 10.969s compared with retry-based repair, while maintaining higher final success, making it more suitable for time-sensitive robotic deployment.

We introduce **MANGO** (Multi-Angle Neural Gated Operator), a spectral neural operator for chirp-perturbed PDEs whose solutions exhibit a local spectral content that rotates continuously with position. MANGO maintains multiple fractional Fourier transform (FRFT) branches whose angles are learnable parameters, optimized jointly with the spectral weights and a position-dependent softmax gate that combines branches at every spatial location. The architecture is matched to the structure of the problem class: the FRFT at angle $\alpha_0$ diagonalizes the chirp-perturbed PDE whose intrinsic angle is $\alpha_0$, and MANGO's learnable angles let the architecture discover $\alpha_0$ from data when it is unknown or varies across the domain—neither of which classical FRFT methods accommodate. We further introduce a Mihlin–Hörmander regularizer on the spectral weights of each non-Fourier branch, encouraging each branch to act as a bounded operator on $L^p$ for $1 < p < \infty$. We construct four chirp-perturbed PDE benchmarks with closed-form ground truth, positioned as identifiability tests. On these benchmarks, MANGO recovers the intrinsic spectral structure of each PDE from supervised solution data alone and achieves state-of-the-art performance against six neural-operator baselines.

Electroencephalography (EEG) data plays an important role in understanding human brain activity, with various major applications. While current approaches, which largely build on EEG foundation models, have demonstrated strong performance across datasets and tasks, they typically rely on a fixed temporal scale. This design choice limits their ability to capture characteristics of neural dynamic behavior, often spanning across multiple timescales, which are naturally captured in EEG recordings. To address this limitation, we propose MAGE (**Multi-timescale Adaptive Generalization for EEG**), a foundation model that dynamically adapts to capture and integrate information across different timescales. Specifically, MAGE combines two key mechanisms: (i) a novel multi-timescale attention mechanism, capable of learning representations across multiple temporal scales, and (ii) a trainable aggregation module that dynamically identifies and adapts to the most informative timescale of the input signal. Experimental results across seven datasets demonstrate that MAGE consistently outperforms existing EEG foundation models while using substantially fewer parameters. Moreover, our results support the hypothesis that different downstream tasks benefit from distinct temporal scales. All source code will be released.

Block diffusion LLMs are an emerging paradigm for parallel language generation, but their KV caching makes memory access the dominant bottleneck in long-context inference. Sparse attention, which attends only to a small KV subset per query, can reduce this latency with minimal accuracy loss. In block diffusion, however, the $B$ tokens of each block must share a single KV subset, and we show this per-block constraint degrades existing sparse KV estimators by up to 25% in recall. We address this challenge by exploiting a property that emerges from the block-diffusion training objective: it aligns the block-average query across denoising steps, so the All-[MASK] block at the first step already reveals the per-block KV subset for the entire trajectory. We exploit this in MAGE ([MASK]-Guided Sparse Attention), a training-free method that runs one exact attention pass at the first step and reuses its top-$k$ index sets for all remaining steps within the block. Across three block-diffusion families on LongBench, MAGE matches Exact Attention at $k{=}512$ with near-lossless accuracy, achieves up to $6.82\times$ end-to-end speedup at $128$K context, and runs up to $3.35\times$ and $2.28\times$ faster than Quest and SparseD, designed for AR LLMs and fully bidirectional diffusion LLMs, respectively.

We introduce the Massive Audio Embedding Benchmark (MAEB), a large-scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross-modal audio-text reasoning in 100+ languages. We evaluate 56 models and find that no single model dominates across all tasks: contrastive audio-text models excel at environmental sound classification (e.g., ESC50) but score near random on multilingual speech tasks (e.g., SIB-FLEURS), while speech-pretrained models show the opposite pattern. Clustering remains challenging for all models, with even the best-performing model achieving only modest results. We observe that models excelling on acoustic understanding often perform poorly on linguistic tasks, and vice versa. We also release MAEB+, the full 98-task collection from which MAEB is curated; to our knowledge, it is the largest publicly released collection of audio embedding evaluation tasks. MAEB preserves task diversity while reducing evaluation cost and integrates into the MTEB ecosystem for unified evaluation across text, image, and audio modalities. We release MAEB and all 98 tasks along with code and a leaderboard at https://anonymous.4open.science/r/mteb-2FEB/.