NeurIPS 2026 Papers

Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a sparse, comparable, and geometry-consistent panoramic training interface. Dense trajectories duplicate nearby views, source-specific rendering policies yield heterogeneous annotations, and sparse heuristics may miss important regions or introduce depth-inconsistent observations. *We study how to convert 3D assets into sparse panoramic RGB-D-pose data that preserves complete scene coverage with low redundancy and auditable provenance.* We propose **COVER** (**C**overage-**O**riented **V**iewpoint curation with **E**RP **R**ange-depth warping), a training-free ERP viewpoint curator that projects geometry observed from selected views into candidate ERP probes, scores incremental coverage, and penalizes depth conflicts. Under bounded proxy error, its greedy coverage proxy preserves the standard coverage-style approximation behavior up to an additive error term. Using COVER, we build **CM-EVS** (**C**overage-curated **M**etric **E**RP **V**iew **S**et), a panoramic RGB-D-pose dataset with 36,373 curated ERP frames from 1,275 indoor scenes across Blender indoor, HM3D, and ScanNet++, complemented by outdoor panoramas from TartanGround and OB3D re-encoded into the same schema. Each frame provides full-sphere RGB, metric range depth along ERP rays, calibrated world-to-camera pose, and provenance logs. With a median of only 25 frames per indoor scene, CM-EVS covers all 13 unified room types while maintaining compact scene-level coverage. Experiments show that COVER improves the coverage–conflict trade-off, making CM-EVS a sparse, compact, and auditable RGB-D-pose resource for geometry-consistent panoramic 3D learning.

Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in long-context streaming reconstruction due to error accumulation. In this paper, we revisit loop closure with streaming reconstruction foundation models to enable accurate, drift-free, kilometer-scale reconstruction. Specifically, our method detects loop candidates through global descriptor retrieval, and constructs loop-conditioned windows to estimate the relative poses between looped frames. Given the observation that our adopted streaming reconstruction backbone produces a globally consistent scale, we optimize all frame poses on the SE(3) manifold with sequential and loop closure constraints, avoiding the pose graph optimization on the Sim(3) or higher-dimensional SL(4) manifolds employed in prior works. Extensive experiments show that our method reduces drift and produces consistent geometry on kilometer-scale sequences, significantly outperforming the state of the art. The code will be made public.

Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects, layouts, or semantics from the style reference appear in the generated output. Although prior data-driven and training-free methods can reduce leakage, they often face a leakage--degradation dilemma: stronger content suppression may weaken style fidelity, while richer style preservation may reintroduce unwanted reference content. We identify this dilemma across the full style-transfer pipeline, including feature separation, feature-space grounding, and diffusion generation. To address these issues, we propose CLeaR, a training-free framework for content-leakage-resistant style transfer. CLeaR first uses Orthogonal Subspace Projection to define content-reduced style targets in each vision foundation model (VFM) feature space. It then performs Ensemble Inversion, which optimizes a shared pixel-space style anchor satisfying style constraints across multiple VFMs. Finally, Energy-Guided Calibration maintains style alignment during diffusion sampling by steering the denoising trajectory toward the ensemble-defined style manifold. We further provide a theoretical analysis showing that the style-anchor estimation error decreases with the number of VFMs. Experiments on StyleBench demonstrate that CLeaR improves style alignment, reduces content leakage, and achieves better LLM-as-Judge evaluation compared with existing methods.

We present Correlated Latent Uncertainty Estimation (CLUE), a single-pass deep uncertainty quantification framework that combines efficient amortized neural prediction with Bayesian-style information propagation across inputs. CLUE introduces a kernel-based prior, allowing cross-input dependence over latent variables while preserving test-time inference efficiency. CLUE requires neither posterior sampling nor ensembles, and avoids matrix inversion during inference, yet recovers posterior contraction behavior analogous to conventional Bayesian models. In the white-noise limit of the kernel, CLUE reduces to standard evidential deep learning (EDL). This limiting case reveals standard EDL as amortized variational inference with an independent latent structure, providing a probabilistic explanation for several pathologies of EDL identified in prior work. Empirically, CLUE exhibits consistent Bayesian-like uncertainty contraction and improved uncertainty quality across synthetic and real-world benchmarks, while maintaining competitive predictive accuracy and fast single-pass inference.

The widespread adoption of AI assistants has prompted the development of privacy-aware platforms designed to extract insights from real-world usage. Their privacy protections primarily rely on layering multiple heuristic techniques, such as PII redaction, clustering, and aggregation. In this paper, we put their privacy claims to the test by presenting CLIOPATRA, the first attack against privacy-preserving LLM-based insights systems. Our attack involves an adversary that carefully designs and inserts malicious chats into the system to break multiple layers of protections and induce the leakage of sensitive information from a target user’s chat. We evaluate CLIOPATRA on one such platform, Anthropic’s Clio, and target synthetically generated medical chats to show that an adversary can successfully and confidently (with nearly 100% precision) extract the medical history contained in these chats in up to 65% of cases. We also show that CLIOPATRA can stealthily extract information by obfuscating the private information in the generated insights. Finally, we demonstrate that existing ad hoc mitigations, such as LLM-based privacy auditing, are unreliable and fail to detect major leaks. Taken together, our findings indicate that, even when layered, current heuristic protections are insufficient to adequately protect user data, and that prompt injection has been an understudied risk in LLM-based insight systems.

Open-source web agents are now strong enough to execute realistic browser tasks, but training them with reinforcement learning still depends on weak supervision: binary task success is too sparse for credit assignment, while frontier-language-model judges are too expensive to call at every step and cannot be assumed available at deployment. We introduce CLIFT, a training and test-time scaling method built around conformal self-verification. During training, the agent answers natural-language verification questions about its own rollouts; a Compositional Conformal Certifier keeps only question signals whose URL-conditional evidence agrees with a training-time judge, assigns signed trust weights through polarity-aware lift, and blends the resulting verifier score into per-step rewards in a way that never subtracts from the judge baseline. At test time, the same certified bank is frozen and reused as structured evidence for Conformal Trajectory Selection (CTS): the agent samples a greedy rollout and one or more diverse retries, the self-verifier summarises each URL trace, and a conservative majority-vote rule chooses whether to swap away from the current incumbent without calling any external judge. This single mechanism supports three settings. On WebArena Infinity, CLIFT achieves state-of-the-art performance among open-source web agents. On VisualWebArena, a bank trained with the open model transfers to GPT-5.5 at test time and reaches state-of-the-art performance under the canonical harness. On Online Mind2Web, without training an agent on the benchmark, translating the certified question bank improves a live-web agent in zero-shot evaluation. Together these results position conformal self-verification as a way to turn costly judge feedback into a reusable training signal and a judge-free test-time scaling signal.

Clinical EEG interpretation requires reasoning over full EEG sessions and integrating signal patterns with clinical context. Existing EEG foundation models are largely designed for short-window decoding and do not incorporate clinical context. We introduce CLEF, a clinically grounded long-context EEG foundation model. CLEF represents EEG sessions as 3D multitaper spectrogram tokens, enabling tractable Transformer modeling at session scale, and aligns embeddings with neurologist reports and structured EHR data through contrastive objectives. We evaluate CLEF on a new 234-task benchmark spanning disease phenotypes, medication exposures, and EEG findings, with more than 260k EEG sessions from over 108k patients. CLEF outperforms prior EEG foundation models on 229 of 234 tasks, improving mean AUROC from 0.65 to 0.74. Reconstruction-only pretraining surpasses prior EEG foundation models, while report and EHR alignment yields further gains. Held-out concept and external-cohort experiments suggest that these representations transfer beyond observed alignment targets. These results support session-scale, clinically grounded representation learning as a promising foundation-model paradigm for clinical EEG.

This paper studies the problem of semi-supervised edge classification, which aims to identify edge relations using both labeled and unlabeled data. This problem is highly challenging due to the inherent asymmetry of edge relations and strong prediction biases from imbalanced homophilous and heterophilous edges. Towards this end, this paper proposes a novel approach named Complementary Tripartite Play with Bayesian CaLibration (CLEAR) for semi-supervised edge classification. The core of our CLEAR is to incorporate asymmetric semantic branches and a meta-teacher into a tripartite-play framework, enabling reliable guidance and optimization under label scarcity. In particular, our CLEAR introduces a topological branch and a contextual branch which extracts graph semantics, generating pseudo-labels in complementary views. To improve the reliability of pseudo-labels, we filter low-quality pseudo-labels using epistemic uncertainty, and then calibrate posterior distributions using the priors induced by neighborhood information. More importantly, we utilize a meta-consoler to guide the optimization of two branches by outputting the objective coefficients, ensuring a reliable pseudo-labeling process in a tripartite play framework. Extensive experiments on benchmark datasets validate the superiority of the proposed CLEAR in comparison to various baselines. Our code is available at https://anonymous.4open.science/r/CLEAR-D1D7/.

We introduce CIPHERGRID, a rule-inference and path-finding benchmark. In each task, models are given example grid-world images paired with encoded descriptions and encoded solutions. From this evidence, models then receive a new encoded grid-world to solve. To answer correctly, models must infer which symbols denote tiles, actions, row boundaries, and game mechanics. They must then decode the new world, and produce an encoded path from the start cell to the target cell. The benchmark is designed so that each component is individually solvable: rule inference is calibrated against human performance, and path-finding is solver-verifiable via exact shortest-path search. Although the underlying skills are individually solvable and verifiable, CIPHERGRID tests their interaction: whether models can infer an unfamiliar symbolic system, and carry it through rule application, and planning. CIPHERGRID remains difficult for current frontier models, which only achieve between 18.74\% and 41.89\% accuracy. CIPHERGRID offers a controlled test of whether models can coordinate multimodal grounding, rule inference, and sequential planning under unknown symbolic relationships.

Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated experience but ignore within-rollout redundancy; episodic rewards penalize intra-trajectory repetition but discard lifetime progress. Hybrid methods combine both signals through heuristic weights or require Gaussian-process dynamics that do not scale beyond low-dimensional state spaces. Trajectory-level information gain decomposes into per-step terms that condition on the replay buffer and rollout prefix simultaneously, but remains intractable for deep models. We derive the Conditional Information Gain (CIG) reward as a tractable surrogate: a log-determinant objective over an ensemble disagreement kernel whose Cholesky factorization yields causal per-step rewards that retain both conditioning sets while scaling to high-dimensional state spaces. We instantiate CIG in a model-based setting, where rollouts are short and within-rollout corrections remain largely unexplored. Across twelve tasks spanning discrete (MiniGrid) and continuous control (OGBench), in both clean and stochastic-distractor settings, CIG outperforms or matches prior exploration methods while remaining robust to stochastic distractors.

While Large Language Models demonstrate remarkable proficiency in high-level semantic planning, they remain limited in handling fine-grained, low-level web component manipulations. To address this limitation, extensive research has focused on enhancing model grounding capabilities through techniques such as Reinforcement Learning. However, rather than compelling agents to adapt to human-centric interfaces, we propose constructing interaction interfaces specifically optimized for agents. This paper introduces Component Interface for Agent (CI4A), a semantic encapsulation mechanism that abstracts the complex interaction logic of UI components into a set of unified tool primitives accessible to agents. We implemented CI4A within Ant Design, an industrial-grade front-end framework, covering 23 categories of commonly used UI components. Furthermore, we developed a hybrid agent featuring an action space that dynamically updates according to the page state, enabling flexible invocation of available CI4A tools. Leveraging the CI4A-integrated Ant Design, we refactored and upgraded the WebArena benchmark to evaluate existing SoTA methods. Experimental results demonstrate that the CI4A-based agent significantly outperforms existing approaches, achieving a new SoTA task success rate of 86.3\%, alongside substantial improvements in execution efficiency.

Chiplet-based GPUs are a promising alternative to monolithic designs for scaling compute and memory resources in machine learning (ML) workloads. However, distributing computation and memory across multiple chiplets introduces a hierarchical Non-Uniform Memory Access (NUMA) environment, where performance depends on the alignment between kernel execution and data placement. In deep neural network (DNN) inference, kernels execute repeatedly over shared weights and activations, and poor alignment leads to sustained inter-chiplet traffic that degrades performance. Existing approaches optimize scheduling or data placement in isolation, failing to coordinate these decisions across dependent kernel executions. We present CHoRD (Chiplet-Hierarchy-oriented Residency and Dispatch), a compiler-assisted, profile-guided framework for joint scheduling and data placement in chiplet-based GPUs. CHoRD reconstructs program structure from compiled kernels and host code, and supports two modes: a static mode that derives scheduling and placement decisions without profiling, and a profiling mode that performs a single lightweight run to collect execution statistics. Using this information, CHoRD determines chiplet-level kernel mapping, enables safe inter-kernel overlap, improves intra-chiplet parallelism, and assigns data to chiplets based on access patterns. These coordinated decisions reduce inter-chiplet communication and improve compute utilization. We evaluate CHoRD on 4-chiplet and 8-chiplet MGPUSim substrates across nine CNN and transformer-based inference workloads. On the 4-chiplet system, CHoRD-static and CHoRD-profiled achieve 2.57× and 2.94× geometric-mean end-to-end speedups over RR, and 1.46× and 1.66× over the placement-only CLAP baseline, respectively, with benefits increasing as chiplet count scales to 8.

Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Due to strong empirical performance, internal representation–based methods have emerged as the prevailing direction for detecting hallucinations, yet they remain largely model-specific and often fail to generalize to unseen LLMs. In this paper, we study an important yet underexplored problem, termed cross-model hallucination detection (CMHD), which aims to train hallucination detectors on source LLMs while ensuring performance on unseen target LLMs. The core challenge of CMHD lies in cross-model representation heterogeneity: hidden states from different LLMs exhibit severe semantic inconsistency, which limits the transferability of classical representation-based detectors. Through analysis and empirical validation, we show that relational structures over layers, tokens, and features capture transferable detection signals despite semantic inconsistency. Based on this observation, we propose cross-model hallucination detection via heterogeneity-oriented relational discrimination (CHORD), a relational graph discrimination framework that represents hidden states as joint relational graphs over layers, tokens, and features. It feeds these graphs into relation-aware attention to obtain structure-aware features, and uses meta-learning to shape these features toward transferable detection signals. Extensive experiments show that CHORD outperforms representative detection methods in cross-model generalization.

Spectral token mixers based on Fourier transforms provide an efficient way to model global interactions in visual feature maps. Existing designs often either apply filter-wise spectral responses along fixed channel axes, or learn adaptive frequency-indexed channel mixing without explicitly aligning the channel directions used across frequencies. We propose CHASM, a Cross-frequency Harmonized Axis-Separable Mixer, as a structured middle ground. CHASM separates what should be shared from what should remain frequency-specific: all frequencies share a learned channel eigenbasis, while each frequency retains its own positive spectral gains. The shared basis makes channel directions comparable across the spectrum, whereas the positive gains preserve local spectral adaptivity. CHASM applies this structured operator separably along the height and width axes and is used as a drop-in replacement mixer inside existing backbones. We provide a structural characterization of the shared-basis operator family and evaluate CHASM through controlled same-backbone comparisons. Across accelerated MRI reconstruction, undersampled MRI segmentation, and natural-image reconstruction, CHASM consistently improves over same-backbone spectral-mixer baselines. Ablations show that removing the shared-basis constraint weakens performance, and randomizing coherent sampling geometry substantially reduces the gain, supporting cross-frequency harmonization as a useful inductive bias for spectral token operators.

As sequence lengths continue to grow, distributed attention based on sequence parallel (e.g., Ring-Attention) has become a mainstream solution for large language models (LLMs). Nevertheless, inherent communication dependencies limit computation–communication overlap, reducing GPU utilization. Moreover, in cross-hardware (NUMA or node) architectures, due to imbalanced communication bandwidth, frequent communication synchronization will limit overall performance. Based on these insights, we propose CHARM+, Cross-Hardware Attention with Re-Merge Multistream Mechanism. We reorganize sequential communications into several levels and achieve intra-level parallelism and computation overlap. Furthermore, we decouple cross-hardware communication from intra-hardware communication, eliminating synchronization overhead caused by bandwidth imbalance. Forward and backward algorithms were evaluated on multiple hardware architectures and consistently outperformed state-of-the-art methods. Compared to Ring-Attention, our forward and backward algorithms achieve average speedups of $1.3\times$ and $1.7\times$ on a dual-NUMA PCIe architecture, and $2.8\times $ and $3.9\times$ on a dual-node NVLink architecture, respectively.

Efficient heterogeneous Multi-Agent Reinforcement Learning (MARL) in continuously changing environments is key to advancing MARL from simulation to the real world. However, it remains unclear whether current MARL methods can adapt under such dynamic scenarios. In this work, we show that existing methods face significant performance degradation in a novel continual heterogeneous MARL environment we construct, named slippery Multi-Agent Mujoco. We further demonstrate the connection between this phenomenon and *representation redundancy* as well as *insufficient policy heterogenization*. To address this issue, we propose **C**ontinual **H**eterogeneous Cooper**A**tion with **I**nformation Bottle**N**eck (CHAIN), equipped with Heterogeneous Policy Bottleneck (HPB). In HPB, we extend the traditional information bottleneck into three parts, fitting, compression and heterogenization. Our HPB encourages agents to learn an efficient latent representation to adapt to changed environments, as well as disentangles specializations of agents in state perception, thereby encouraging heterogenization. We evaluate CHAIN on standard MA-Mujoco, our slippery MA-Mujoco and a multi-task continual MARL benchmark, MEAL, with various challenging tasks. The experimental results indicate that our CHAIN outperforms state-of-the-art heterogeneous MARL methods across key continual learning metrics.

Accurate counts of fish populations are essential for monitoring ecosystems and making decisions about conservation, harvest limits, and river management. In many field deployments, these counts are obtained from sonar videos that experts must inspect manually, making large-scale monitoring slow, expensive, and difficult to reproduce. Automated counting systems have been proposed as a scalable alternative, but so far this promise has been difficult to realize in deployment. Developing evaluations that better reflect real world deployment conditions will close this gap, and drive improvements to counting methods that translate to reality. To achieve this, we propose Caltech Fish Counting 2026 (CFC26), a new dataset with improved evaluation protocols that simulate deployment to current and new rivers, testing in-distribution and out-of-distribution performance. We expand the CFC22 dataset to eleven deployment locations across nine Pacific salmon river systems from Alaska to Northern California, capturing a broader geographical and ecological range of data. We additionally introduce two new evaluation metrics for counting, nMNE and nMANE, that mirror the operational workflow that experts use and help surface errors that frame-level detection metrics do not capture. Using these metrics, we reveal previously-concealed directional bias in upstream vs downstream counts, which would have direct and significant ecological implications if undetected.

Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-world task: operating a startup for 500 days. Given diverse and realistic company management tools, business databases, and social media, an agent needs to design pricing strategies, allocate operating budgets, analyze business data, respond to unexpected competitor moves, and more. Our evaluation shows that most state-of-the-art models struggle to succeed in this environment, and only one model (GPT-5.5) finishes the simulation above its $1M starting balance. CEO-Bench reimagines the role of AI in the future, shifting from solving isolated tasks to driving sustained, adaptive progress over time. We open source full code and trajectory.

Convolutional neural networks (CNNs) are widely used for time-series classification, but their deployment in critical domains requires understanding the temporal and spectral patterns that drive their predictions. Concept extraction (CE) methods identify such patterns by analyzing representations within the models' latent space. However, existing time-series CE methods have three limitations: they operate only in the time domain and overlook frequency features, predefine the number of concepts, and produce localizations misaligned with the regions the model uses. We address these limitations by proposing CENDRe, a concept extraction method for CNNs. It first discovers concepts by clustering per-timestep latent representations in two stages, where silhouette-guided aggregation selects the number of concepts automatically. Then, it localizes each concept through gradients of a presence score that contrasts the latent representations with their prototypes, producing masks that concentrate on the regions driving the concept. These gradients, propagated through a differentiable invertible mapping of the input such as a Fourier transform, yield localizations for the same concepts in the frequency domain. Finally, each concept receives a relevance score that quantifies its contribution to each class. On synthetic benchmarks, CENDRe achieves representation correctness comparable to state-of-the-art CE methods and substantially higher importance correctness. On real bearing-fault data, CENDRe extracts the frequency bands driving the model's predictions, located in regions commonly inspected for fault diagnosis, producing evidence to assess the model that time-domain CE methods cannot.