NeurIPS 2026 Papers

Field trials use plot-level crop-state measurements to interpret how crops grow under real field conditions, but repeated measurements of canopy greenness, canopy amount, canopy closure, and growth stage require field visits, sensing campaigns, and post-processing. Modern wheat trials also collect weather records, trial metadata, field-camera images, UAV and satellite observations, proximal sensors, canopy products, and phenological surveys. Turning these records into a machine-learning benchmark is nontrivial because observations are sparse, asynchronous, unevenly collected, missing for structured reasons, and tied to measurement pipelines. We introduce INVITA-WheatFieldState, a benchmark derived from the INVITA wheat field-trial archive for estimating the crop state of a plot on a target date from observations available for that plot and date. Each plot-date example specifies a plot, date, and crop-state target, and is paired with available observations after temporal and source-provenance filtering. The derived dataset contains over 220k examples across NDVI, LAI, FCover, and Zadoks growth stage, covering canopy greenness, canopy amount, canopy closure, and phenological timing. LAI and FCover are released with provenance labels that distinguish product and proxy targets rather than pooled manual ground truth. The benchmark suite provides a plot-disjoint split matched to the plot-level experimental unit, validators, prediction schemas, regression representations, and prediction-level diagnostics. Results show that source, timing, metadata, and observation-availability structure are strong signals. Observation-set representations improve some all-example targets, while sensor-sequence, field-camera, and fusion representations remain target-dependent and coverage-bound. INVITA-WheatFieldState offers a concrete test case for ML methods that must learn from incomplete, asynchronous, and provenance-sensitive measurements used to study crop development in real fields.

Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce **INFUSER**, an iterative co-training framework with two co-evolving roles: a *Generator* that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a *Solver* that improves by training on them. The solver is trained with standard correctness rewards against the generator-provided answers, while the generator is rewarded by an *optimizer-aware influence score* that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose **DuGRPO**, a dual-normalized variant of GRPO, for generator training. Together, these turn the document pool into an *adaptive curriculum* that favors questions useful to the current solver, not just hard ones. On Qwen3-8B-Base, INFUSER outperforms strong self-evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co-evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary. Code is available at

In this paper, we introduce INEUS, a meshfree iterative neural solver for partial integro-differential equations (PIDEs). The method replaces the explicit evaluation of nonlocal jump integrals with single-jump sampling and reformulates PIDE solving as a sequence of recursive regression problems. Like Physics-Informed Neural Networks (PINNs), INEUS learns global solutions over the entire space-time domain, yet it offers a more efficient treatment of nonlocal terms and avoids the computationally expensive differentiation of full PIDE residuals. These features make INEUS particularly well suited for high-dimensional PDEs and PIDEs. Supported by a contraction-based convergence proof for linear PIDEs, our numerical experiments show that INEUS delivers accurate and scalable solutions for various high-dimensional linear and nonlinear examples.

Modeling Irregular Multivariate Time Series (IMTS) poses significant challenges due to asynchronous sampling and data sparsity. While existing methods focus on handling temporal irregularity after encoding, the tokenization stage itself remains underexplored. We propose IMTS-Tokenizer, a time aware tokenization framework built on the Irregular-time-aware Module (IRAM), which employs learnable temporal anchors and Gaussian-kernel attention to aggregate observations while preserving temporal information. To improve robustness under sparse and asynchronous sampling, the framework further incorporates Relative Time Feature (RTF) for pre-aggregation temporal encoding, Variable-Specific Anchor Initialization (VSA) for data-aligned anchor placement, anchor regularization for stable training, and multi-scale modeling. Experiments on four benchmarks demonstrate state-of-the-art (SOTA) performance, with the best results on seven out of eight metrics and statistically significant aggregate gains.

Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spatial foundation models have enabled generalizable feed-forward 3D reconstruction, most streaming methods remain geometry-centric and lack temporally consistent object-level understanding. Meanwhile, existing semantic reconstruction and 3D-aware vision-language methods largely rely on externally extracted 2D semantic cues or loosely coupled geometry inputs, limiting unified geometry-instance learning in long dynamic scenes. In this paper, we propose IGGT4D, a streaming instance-grounded geometry Transformer for online 4D scene understanding. IGGT4D processes video frames sequentially, reuses historical context through causal spatial-temporal modeling, and incrementally updates a unified representation of camera motion, geometry, and object identity. This enables long-sequence feed-forward reconstruction with geometry-instance consistency in dynamic environments. To address the lack of high-quality 4D supervision, we further construct InsScene4D-147K, a large-scale dataset spanning real/synthetic and static/dynamic scenes, with RGB images, depth, poses, and temporally consistent instance masks generated by an automated geometry-guided annotation pipeline. Experiments on 3D reconstruction, pose estimation, instance spatial tracking, and open-vocabulary segmentation demonstrate that IGGT4D outperforms existing streaming baselines while maintaining scalable online inference for long dynamic sequences.

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising approach to enhance Instruction Following (IF) capabilities of large language models (LLMs). However, RLVR for instruction following remains sample-inefficient and prone to reward hacking, where LLMs exploit verification shortcuts rather than fulfilling the core intent. To address these challenges, we frame RLVR for instruction following as an integrated environment that unifies dynamic task generation and robust training. We introduce Instruction Following Decorator (IFDecorator}, a framework coupling difficulty adaptation, intent alignment, and hack diagnostics. It features: (1) a cooperative-adversarial data flywheel that yields challenging yet solvable tasks for sample-efficient training; (2) IntentCheck, a gating module to mitigate reward hacking; and (3) TripWires, a proactive diagnostic tool for eliciting hacking behaviors and quantifying their prevalence. Extensive experiments show our Qwen2.5-32B-Instruct trained with IFDecorator using only 3,625 training examples achieves 87.43% on IFEval (outperforming GPT-4o) and improves FollowBench by 4.2%, while preserving general capabilities. Diagnostics confirm that our method effectively reduces reward hacking. The approach generalizes across model architectures and scales. We will release code and data for future research.

Large language models (LLMs) are increasingly used for tasks involving creative problem solving and idea generation. However, However, there is a lack of consensus concerning their creative capabilities:: some studies report superior performances compared to humans, while others highlight structural limitations such as fixation and the homogenization of outputs. Existing evaluation approaches either rely on narrow, decontextualized tasks that do not capture goal-oriented generation or on broader settings that confound multiple aspects of the creative process, making it difficult to isolate the effects of task formulation, prompting, and evaluation design. Significantly, the role of structured prompting strategies in shaping idea generation remains underexplored. Therefore, we introduce IDEAFix, an evaluation framework for analyzing divergent thinking in open-ended idea generation tasks. We prompt models to generate multiple original solutions to controlled variations of short design scenarios, task attributes, and defixation prompting strategies. This design enables systematic analysis of how structured guidance influences LLMs' idea generation. Our results show that both task formulation and attribute selection significantly affect models' performance, and that simple prompting strategies can boost the originality of solutions. However, we also observe persistent output homogenization across models, confirming inherent limits in their ability to generate diverse solutions. Overall, IDEAFix provides a controlled, extensible framework for studying the mechanisms underlying LLMs' creativity.

Concept-based explanation methods have emerged as a prominent framework for interpreting deep neural networks using human-understandable concepts. However, existing approaches are limited to assigning a scalar importance score to a concept, failing to capture the interaction between the concept and its visual context. We argue that overlooking this interaction leaves a predictor's reasoning only partially understood. To address this gap, we introduce IDEA, a novel global post-hoc explanation framework that explicitly models the interaction between a concept and its visual context. Through this modeling, IDEA decomposes concept-level predictive information into three atomic components: uniqueness (independent concept contribution), redundancy (information shared with the context), and synergy (information arising strictly from concept-context interaction). Consequently, IDEA extends interpretability beyond merely identifying influential concepts to diagnosing how a predictor reasons -- for instance, flagging potential context dependencies that scalar scores cannot reveal. IDEA requires no task-specific concept annotations and no access to classifier internals. Evaluations on controlled and real-world settings demonstrate that IDEA can surface reasoning patterns invisible to traditional scalar concept scores.

Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physical risk and severe consequences is rarely evaluated.We find that these models often downplay or omit key danger cues and severe outcomes for hazardous actions, which can induce unsafe preferences during planning and training on imagined rollouts. We propose ICAT, which grounds testing in real incident reports and safety manuals by building structured risk memories and retrieving/composing them to constrain the generation of risk cases with causal chains and severity labels. Experiments on an ICAT-based benchmark show that mainstream world models frequently miss mechanisms and triggering conditions and miscalibrate severity, falling short of the reliability required for safety-critical embodied deployment.

The Allen--Cahn equation is one of the standard phase-field models for mean-curvature flow (MCF), parameterised by the diffuse-interface width $\varepsilon$. Practical simulations span a working range of $\varepsilon$, so a single model covering this range is needed. PDE-specific neural operators are accurate but must be retrained at every $\varepsilon$; general neural operators cover $\varepsilon$ in one model but lose the phase-field structure of the target. We introduce Interface-Augmented Diffusion-Reaction (IADR), a neural operator that addresses both shortcomings: a target network with a diffusion-and-reaction structure preserves the phase-field structure of the Allen--Cahn equation, while a hypernetwork that maps $\varepsilon$ to the weights of this target network amortises the dependence on $\varepsilon$ across the working range. A single IADR matches the per-$\varepsilon$ specialists on in-distribution data, retains their cross-shape generalisation under both $\varepsilon$- and geometry-out-of-distribution shifts, and runs at the per-$\varepsilon$ specialist's per-step inference cost. We further establish a $K$-step rollout error bound that aligns with the long-horizon behaviour observed in experiments.

Recent diffusion models have advanced image-conditioned video generation, but their accessibility raises risks of unauthorized manipulation. Image immunization has emerged as a promising defense for Image-to-Video (I2V) protection, yet existing methods often rely on degrading visual quality or temporal coherence. In this work, we revisit I2V immunization as reducing source-content preservation, shifting the focus from output degradation to weakening source-conditioned content propagation. To this end, we propose I2V-DETACH, an image immunization method that prevents unauthorized I2V generation through source grounding detachment. I2V models preserve source content by propagating source-image cues into video latents during denoising. Accordingly, I2V-DETACH disrupts this process in two complementary ways: it suppresses source-video grounding to reduce source cue propagation, and strengthens intra-video interactions to shift denoising toward video-side dynamics with reduced reliance on the source image. To obtain informative optimization signals, we initialize auxiliary video latents with source-oriented states that remain within the source-conditioned generation regime while deviating from source-preserving trajectories. Extensive experiments across diverse diffusion architectures show that I2V-DETACH substantially reduces source-content preservation, improves over prior immunization methods, and exhibits strong black-box transferability.

Active perception—the ability of a robot to proactively select viewpoints to acquire task-relevant information—is essential for robust operation in real-world environments. However, existing approaches are typically limited to fixed objectives or constrained settings, and struggle to generalize to open-ended perception intents specified in natural language. We propose I-Perceive, a foundation model for language-conditioned active perception in large-scale indoor environments. Given a query image, a set of context images, and a natural language instruction, I-Perceive predicts a 6D camera pose that fulfills the specified perception intent. The model integrates a vision-language pathway for semantic grounding with a geometric reasoning pathway for multi-view 3D understanding, connected via multi-layer semantic fusion to enable language-conditioned geometric reasoning. To support scalable training, we construct a large-scale dataset of language-viewpoint pairs from both real-world scene-scanning data and simulated environments using an automated pipeline. Extensive experiments demonstrate that I-Perceive significantly outperforms strong baselines on prediction accuracy, viewpoint feasibility, and instructions alignment. The model exhibits strong zero-shot generalization to unseen scenes and instructions, and enables closed-loop active perception, progressively refining viewpoints over sequential interactions.

Large language models are increasingly capable of writing and executing code, suggesting a natural paradigm for tool-augmented agents: expressing tool use as programs rather than isolated calls. Programmatic tool calling (PTC) captures this idea through loops, batching, abstraction, and structured computation, but existing PTC approaches are largely stateless, limiting their ability to reuse intermediate results and adapt over time. This is problematic for real-world tasks, where tool environments and problem-solving processes are inherently stateful. We therefore present I-PTC, an interactive framework that executes stateful PTC: models issue code snippets that call tools, maintain reusable state, and evolve the tool interface through API corrections. To evaluate the effectiveness of I-PTC and the general PTC setting, we introduce PTC-BENCH, a benchmark of long-tail, production-style user requests whose difficulty comes from large initial environments, many dependent API interactions, extended planning, and recovery across follow-ups and transient failures. Experiments on PTC-BENCH show that I-PTC improves performance on complex multi-step tasks with large tool sets while substantially reducing token consumption over conventional tool-calling baselines and the original PTC baseline, highlighting stateful programmatic tool calling as a promising direction for scalable tool-augmented agents.

Temporal action detection in long untrimmed videos still suffers from inaccurate temporal localization and low-quality proposals, especially under sparse temporal annotations and ambiguous event boundaries. In the context of open-ended video understanding and LLM-driven event analysis, reliable temporal action proposals have emerged as a critical interface bridging temporal localization, event annotation, and semantic understanding. However, conventional temporal action detection (TAD) pipelines lack explicit modeling of proposal localization quality, cross-representation support, and inter-proposal relations, making it difficult to consistently provide high-quality proposals. We propose Hybrid Rank-Calibration of Action Proposals (HyrCap), a proposal-centric query-based TAD framework for temporal event understanding. In HyrCap, Hybrid denotes the use of complementary video features that provide different temporal localization cues, while Rank-Calibration refers to calibrating the ranking confidence of dense action proposals so that it better reflects proposal localization reliability and candidate competition. HyrCap treats dense query-based predictions as an over-complete temporal event hypothesis space and improves the ranking reliability, localization quality, and candidate discrimination of action proposals through modules such as Cross-Representation Consensus Calibration, Localization Quality Estimation, and Proposal Duplication Suppression. Furthermore, inspired by the strong capability of LLMs in fine-grained semantic recognition, event description, and reasoning, we introduce HyrCap for Semantic Action Understanding: calibrated proposals can be used for standard TAD detection and as inputs for LLM-based semantic understanding. We further define a staged evaluation protocol to separately measure event localization ability, segment-level semantic understanding, and their combined event understanding performance. Experiments on public benchmarks show that HyrCap achieves state-of-the-art performance across multiple metrics and provides a more fine-grained paradigm and evaluation protocol for query-based event segmentation and annotation.

Large language models (LLMs) increasingly help people solve problems, from debugging code to repairing machinery. This process requires generating plausible hypotheses from partial descriptions, then updating them as more information arrives. Yet how LLMs perform this form of inference, and how close it is to optimal, remains unclear. We study this question in the number game, a controlled setting in which a learner infers the hypothesis supported by a few positive integers, such as $\{16, 8, 2, 64\}$: a rule like powers of 2 or an interval like numbers near 20. We measure the posterior over hypotheses using three complementary probes: posterior prediction, hypothesis evaluation, and hypothesis generation. We then compare LLM behavior with an optimal Bayesian model and human behavior, and test whether the same posterior is expressed across probes. LLMs are often well described by a two-parameter Bayesian fit, but with systematic offsets: by default they show a strong-sampling assumption that creates an implicit Occam's razor, favoring narrower hypotheses, while thinking mode shifts them toward greater prior reliance. We also find a robust evaluation--generation gap: LLMs select more correct hypotheses during hypothesis evaluation but generate simpler, more rule-like hypotheses. Finally, this Bayesian-with-bias pattern does not extrapolate. Models can behave as if they hold rule-like hypotheses over observed examples, yet generalize poorly to parts of the hypothesis domain not covered by those examples. Our results highlight a limitation of LLMs as general problem solvers, especially for scientific inference, where hypotheses must go beyond the data.

Zero-shot machine unlearning aims to remove the influence of specified training instances from a pretrained model when the retain set is unavailable. Instance-wise unlearning further operates at the level of individual instances, where a forget instance may induce instance-specific representation and margin deviations that are entangled with class-relevant local structure. However, without retain instances, separating these deviations from the structure that retraining would preserve is difficult, exposing retain-forget entanglement in representation space. We propose HLMR, a novel zero-shot instance-wise unlearning approach that formulates unlearning as local margin retraction in hyperspherical representation space. HLMR uses hyperspherical geodesic probes to construct a local reference in representation space, preserving the structure supported by this reference while unlearning instance-specific margin excess. Empirical evaluation across multiple dataset--architecture settings shows that HLMR aligns more closely with the retraining oracle in utility and membership-inference risk than state-of-the-art zero-shot baselines.

Dense Associative Memory (DenseAM) is a promising family of AI architectures that is represented by a neural network performing temporal dynamics on an energy landscape. While hyperparameter transfer methods are well-studied for feed-forward networks, these methods have not been developed for settings in which weights are shared across layers and within the layer, which is common in DenseAMs. Additionally, DenseAMs utilize rapidly peaking activation functions that are rarely used in feed-forward architectures. The confluence of these aspects makes DenseAM a challenging framework for using existing methods for hyperparameter transfer. Our work initiates the development of hyperparameter transfer methods for this class of models. We derive explicit prescriptions for how the hyperparameters tuned on small models can be transferred to models trained at scale. We demonstrate excellent agreement between these theoretical findings and empirical results.

Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constraints. The exponential growth in the number of possible feature acquisition paths forces a DFS model to balance fitting specific scenarios against maintaining general performance, even when the feature space is moderate in size. In this paper, we study the structural limitations of existing DFS approaches to achieve an optimal solution. Then, we propose \textsc{Hyper-DFS}, a hypernetwork-based DFS approach that generates feature subset-specific classifier parameters on demand. We show that the use of hypernetworks compared to mask-embedding methods results in a smaller structural complexity bound. We also use a Set Transformer encoding to create a smooth conditioning space for the hypernetwork, so that functionally similar tasks are also geometrically close. In our benchmarks, \textsc{Hyper-DFS} outperforms all state-of-the-art approaches on synthetic and real-life tabular data. It is also competitive or superior across all image datasets tested, and shows substantially stronger zero-shot generalisation to feature subsets never seen during training than existing DFS approaches.

Hypergraphs record multi-way interactions among entities. Extracting information from the combinatorial structure underlying observed multi-way interactions is a central task in many real-world problems. Existing methods face several limitations. First, many deep architectures for hypergraphs do not explicitly exploit the potential low-rank structure, which can sacrifice parsimony and interpretability in the learned representations. Second, many low-rank-based methods operate on tensor representations, which typically require hyperlinks to have uniform sizes and thus limit their applicability to general hypergraphs with non-uniform hyperlink sizes. Third, many methods ignore the fact that hyperlinks often arise from heterogeneous mechanisms. For example, medical symptoms may co-occur in the profiles of patients with very different conditions, and such heterogeneity should be incorporated into the learning process. In this work, we develop a general framework for hypergraph representation learning using hyperlink random effects while exploiting the low-rank structure in hypergraphs. The proposed framework accommodates latent heterogeneity in hyperlink formation while preserving entity interaction patterns. We establish identifiability of the model parameters and leverage an expectation-maximization strategy for estimation. The framework allows flexible specifications for the hyperlink random effects; in this paper, we study three choices: categorical, Gaussian mixture, and score-based effects, and develop corresponding estimation algorithms. Through simulation studies, we demonstrate the effectiveness of the proposed method in recovering latent structure and capturing heterogeneous interaction patterns. Empirical studies on real-world hypergraph datasets further illustrate the practical utility of our approach.