NeurIPS 2026 论文列表

Hierarchical graph-level tasks that require reasoning over substructures that extend beyond local neighborhoods are posing challenges for standard Graph Neural Networks (GNNs). While graph pooling aims to address this by coarsening graphs into hierarchical representations, existing methods often fail to explicitly preserve the substructures they induce, leading to inconsistent empirical performance. Moreover, the evaluation of such approaches has proved difficult due to the lack of benchmarks with explicit hierarchical structure. We propose Pooled-Substructure-Aware GNNs, an approach to graph pooling that explicitly preserves and processes substructures uncovered by structure-based pooling schemes. We show that this design strengthens pooling in terms of Weisfeiler-Lehman (WL) expressivity. To enable principled evaluation of pooling-based models, we introduce two new synthetic datasets with controllable hierarchical structure and propose a metric to characterize the extent of hierarchical patterns in real-world graph-level benchmarks. On tasks with relevant hierarchical structure, our approach outperforms matched pooling baselines, while remaining competitive on structurally simpler benchmarks.

Text-guided point-cloud grounding enables robots to ground natural-language descriptions in 3D environments, making it important for embodied AI and human-robot interaction. Existing coarse-to-fine methods primarily rely on global descriptors for submap retrieval, but these descriptors often compress object semantics, spatial relations, and scene layouts into a single vector, thereby limiting discriminability in large-scale scenes. We propose HiGraLoc, a multi-level cross-modal alignment framework that improves the coarse retrieval stage through three complementary branches. The instance branch uses a Hyperbolic Instance Structure Encoder to model hierarchical object semantics, the relation branch aggregates reliability-weighted pairwise spatial relations, and the global branch employs a Global Spectral Graph Encoder to capture multi-frequency scene structure. A Text2Loc-style coordinate regression stage then refines the location estimate within the retrieved submap. Experiments on KITTI360Pose show that HiGraLoc achieves a 19\% improvement in Top-1 recall@10m over existing state-of-the-art methods. Our code and dataset are available at https://github.com/Anonymous09871745/HiGraLoc.

In fine-grained image recognition, semantic parts (e.g., wings, head) recur across classes while discriminative cues lie in within-part features (e.g., yellow wings, striped wings). The prototypical part network (ProtoPNet) is an interpretable model that provides case-based explanations for image recognition by measuring similarities between local patches in an images and their closest prototypes representing patches in a training set. However, existing ProtoPNet variants do not explicitly model this two-level structure, causing prototypes for the same anatomical part to fragment across classes. We propose a hierarchical prototype learning framework that separates shared prototypes capturing cross-class part concepts from class-specific prototypes capturing intra-part features. To model this hierarchy geometrically, prototypes are embedded in hyperbolic space and an entailment loss constrains each class-specific prototype to lie within the cone of its corresponding shared prototype. To prevent degenerate trivial solutions such as redundant prototypes, we introduce a patch-level contrastive learning using pseudo patch IDs from a vision foundation model, used only at training time, leaving the backbone choice free at inference. Unlike prior hyperbolic prototype methods where prototype hierarchies are uncontrolled, our framework defines explicit shared part to intra-part feature correspondences. Across four ProtoPNet variants and four backbones on CUB-200-2011 and Stanford Cars, our method improves classification accuracy by up to 10 points, raises hierarchical part hierarchy IoU from 11\% to 75\%, and consistently improves four interpretability metrics on CUB-200-2011.

Conformal prediction (CP) provides reliable uncertainty quantification by generating prediction sets with finite-sample coverage guarantees, yet it typically ignores hierarchical relationships between classes. We introduce Hierarchical Conformal Classification (HCC), a framework that integrates class hierarchies into the structure and semantics of prediction sets. HCC uses a constrained optimization problem formulation to produce prediction sets composed of nodes at various hierarchy levels while maintaining rigorous coverage guarantees. To ensure computational efficiency, we prove that a restricted subset of well-structured candidate solutions is sufficient to maintain both optimality and coverage. Empirical evaluations across audio, image, and text benchmarks, alongside a user study, demonstrate that HCC outperforms state-of-the-art methods and that hierarchical prediction sets align better with human preferences.

We propose a distributional theory of how hypernymy---the ``is-a'' relation between general and specific concepts---is encoded geometrically in language representations. Starting from the empirically verified assumption that words closer on the WordNet hypernym graph co-occur more often, we characterize theoretically the spectrum of the resulting embedding Gram matrix of word2vec embeddings. Under mild positivity and decay conditions on the co-occurrence kernel, we prove that the leading eigenvectors first separate broad taxonomic branches and then progressively finer sub-branches, producing a \emph{hierarchical splitting geometry} with a coarse-to-fine spectral organization that mirrors the tree. We confirm these predictions in word2vec embeddings across many sampled WordNet subtrees, and show that the same signature extends strikingly well to Gemma 2B unembeddings. Our results indicate that hierarchical concept geometry in LLMs need not reflect a hierarchy-specific functional mechanism, but emerges from the spectral structure of pairwise word statistics.

Due to their simplicity and interpretability, linkage methods are a central class of hierarchical agglomerative clustering (HAC). In particular, representative-based methods, such as centroid and medoid approaches, are appealing because their representatives provide intuitive summaries of the clusters. However, many representative-based linkage methods suffer from important drawbacks, such as high computational cost and the generation of inversions, which limit their broader applicability. We introduce *relaxed-representative clustering*, a principled generalization of representative-based linkages that preserves their interpretability while addressing these limitations. This framework yields (i) RMM*, a quadratic-time variant of the cubic-time Minimax method, and (ii) RC*, an inversion-free variant of the Centroid method whose representatives belong to the input set. On the theoretical side, we prove that for every $k$ our methods are $O(\min\{k,\log n\})$-approximation algorithms for the $k$-center objective. Empirically, experiments on tabular, biological, and textual datasets show that RMM* consistently outperforms several baselines and achieves clustering quality comparable to Minimax at a fraction of the computational cost. Meanwhile, RC* remains competitive with the Centroid method while producing inversion-free dendrograms and interpretable representatives.

Due to context-length constraints, most MLLMs cannot process full-length videos and therefore rely on sampling a subset of frames as input. However, existing sampling methods, ranging from uniform sampling to relevance-based selection, are often driven by a single sampling principle and thus struggle to accommodate the heterogeneous evidential requirements of different queries: some demand a holistic understanding of how the video unfolds over time, while others hinge on fine-grained events within a short temporal window. To address this limitation, we propose Hierarchical Adaptive Frame Sampling (HAS), a two-stage frame sampling framework. In the first stage, Backbone Frame Construction, we apply a Determinantal Point Process (DPP) to sample frames that are both query-relevant and non-redundant. These selected frames capture key moments across the video and form the backbone of the sampling set, providing a foundation for subsequent enrichment. In the second stage, Adaptive Contextual Enrichment, we analyze the temporal distribution of the backbone frames to infer the query type and adaptively allocate the remaining frame budget between Local and Global Context. The Local Context enriches the backbone with fine-grained temporal dynamics and short-range causal relations, whereas the Global Context connects temporally isolated evidence and provides a holistic view of the entire video to support global understanding. Through such a hierarchical two-stage process, HAS effectively addresses diverse query requirements. Incorporated into three leading MLLMs, it demonstrates consistently superior performance across the Video-MME, LongVideoBench, and MLVU benchmarks. We have released our code.

Open-vocabulary indoor scene synthesis aims to generate plausible layouts from arbitrary user instructions while ensuring physical feasibility and semantic consistency. Existing methods directly infer spatial relations between objects from instructions and solve for layouts based on predefined rules, achieving progress in physical feasibility. However, they often struggle to precisely align with complex instructions due to insufficient comprehension of spatial relations and limited capability to translate them into layouts. In this paper, we propose a Hierarchical Reasoning and Reflection agent framework (HierRR), which leverages a relational hierarchy to enable multi-granularity spatial relation comprehension and attributable layout reasoning, improving semantic consistency while maintaining physical feasibility. Specifically, HierRR introduces the spatial transformation chain‑of‑thought reasoning module that explicitly models the mapping from spatial relations to executable geometric placements, mitigating semantic drift during reasoning. Furthermore, the semantic- and vision-guided reflection module is employed to iteratively refine spatial relations and layout reasoning from local to global along the hierarchy, effectively bridging the gap between language instructions and geometric layouts. Extensive comparative experiments and ablation studies demonstrate that HierRR generates more plausible and semantically consistent indoor scene layouts than existing methods, while generalizing effectively to complex scenes with numerous objects and diverse spatial constraints.

Agentic AI systems enable LLMs to solve non-trivial tasks through structured workflows, but automatically generating such workflows remains challenging due to the discrete and combinatorial search space. Existing methods often rely on offline search or training, limiting query-level adaptability and incurring substantial data and engineering overhead. We formulate workflow generation as a coupled topology--execution search problem, where the upper-level topology induces subtask-specific code-search domains and lower-level execution feedback can revise the topology itself. Based on this formulation, we propose HierFlow, a training-free hierarchical test-time search framework for automatic agentic workflow generation. HierFlow couples feedback-driven topology refinement with an MCTS-inspired lightweight tree search for execution-level sub-workflow optimization, and uses an adaptive gating mechanism to selectively trigger execution-level search based on estimated necessity. We further provide a coupling-aware analysis characterizing when hierarchical decomposition and proxy-based gating are beneficial and when their advantages may degrade under stronger cross-subtask coupling. Experiments across QA, mathematical reasoning, and code generation benchmarks show that HierFlow achieves strong performance and favorable efficiency--quality trade-offs, outperforming competitive baselines without additional training.

Long-horizon visual search requires a vision-language policy to execute multi-step spatial actions over high-resolution images, where performance depends on the entire trajectory rather than any single decision. Existing supervision breaks at this horizon: outcome-only RLVR collapses many geometric decisions into a single delayed signal and discards the per-step geometry the environment already computes, while LLM-judged step rewards recover density at the cost of verifiability, scale linearly with trajectory length, and conflate distinct failure modes. We propose HSSP, a self-play framework in which a Hider adaptively weights challenging skeletons from a fixed trajectory pool and a Seeker learns to solve them through grounded spatial actions. Instead of relying on external judges, HSSP supervises every step with a geometry-verifiable process reward built from target-directed progress and exploration coverage, with trajectory length and spatial redundancy enforced as separate Constrained Markov Decision Process (CMDP) constraints. We pair HSSP with V-Trace, a trajectory-level companion dataset that augments skeleton seeds from established benchmarks with target-region annotations and explicit search-regime labels, ready for any trajectory-level training framework. The reward design admits closed-form guarantees on backtracking, find-versus-explore separation, and policy invariance, providing theoretical backing for the observed gains. Extensive empirical results on eleven challenging multimodal benchmarks, supported by controlled ablations and qualitative case studies, show that HSSP consistently outperforms advanced baselines and transfers zero-shot to OOD tasks.

Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that compromise reliability in real-world deployment. Detecting such failures during execution is therefore critical for the robust deployment of embodied systems. Existing failure detection methods either rely on expensive action resampling or external models, while alternatives propagate trajectory-level labels uniformly across every timestep, obscuring localized failure signals. In this paper, we propose \textbf{Hide-and-Seek}, a framework that formulates VLA failure detection as a coarsely supervised learning problem. By combining inter-trajectory and intra-trajectory contrastive objectives, Hide-and-Seek localizes failure-indicative actions and induces temporally structured failure signals from trajectory-level supervision alone, without any step-level annotation. We evaluate Hide-and-Seek on LIBERO, VLABench, and a real-world robotic platform across three representative VLA policies: OpenVLA, $\pi_0$, and $\pi_{0.5}$. Our method achieves state-of-the-art multi-task failure detection performance with a practical accuracy--timeliness trade-off under conformal prediction, and generalizes well to both seen and unseen tasks.

Recent think-answer approaches in VLMs, such as Qwen3-VL-Thinking, boost reasoning performance by leveraging intermediate thinking steps before the final answer, but their high computational cost limits real-world deployment. To distill such capabilities into compact think-answer VLMs, a primary objective is to improve the student's ability to utilize visual evidence throughout its reasoning trace. To this end, we introduce a novel think-answer distillation framework that encourages the student to anchor its thinking on visual information by masking the student's salient reasoning prefixes. To compensate for such masked textual cues, the student is encouraged to rely more on visual evidence as an alternative source of information during distillation. Our masking strategies include: 1) $\textit{token-wise salient reasoning-prefix masking}$, which masks high-influence reasoning prefixes selectively for each next-token prediction, and 2) $\textit{self-paced masking budget scheduling}$, which gradually increases the masking scale according to distillation difficulty, {measured by discrepancy between teacher--student distributions. In the distillation phase, the student is guided by our salient reasoning-prefix mask, which blocks both future tokens and salient reasoning cues, in place of the standard causal mask used for auto-regressive language modeling. Experimental results show that our approach outperforms recent open-source VLMs, VLM distillation, and self-distillation methods on multimodal reasoning benchmarks, while further analyses confirm enhanced visual utilization along the student thinking process.

Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but its effectiveness is often limited by exploration. For example, models often fail on hard problems, leaving little useful reward signal. External expert traces offer a natural source of guidance, yet they may also expose reward-relevant content along the critical path to the verifier target, such as final answers, intermediate values, executable implementations, or answer-related entities. This content can create an unintended reward hacking channel, allowing the policy to obtain reward by copying the trace rather than learning the underlying reasoning or agentic behavior. Existing guided-RL methods reduce this risk by using partial trajectories, but they mainly control how much expert information is shown heuristically rather than which parts should be hidden. To this end, we propose Semantic Masked Expert Policy Optimization (SMEPO), a fine-grained semantic masking strategy for expert-guided RLVR. Instead of truncating traces coarsely or revealing them unchanged, SMEPO masks reward-relevant semantic spans along the critical path while preserving the expert’s decomposition, plan, and procedural structure. This turns hard problems from reasoning from scratch into a fill-in-the-blank process: the policy can follow the expert’s problem-solving route, but must still reconstruct the missing values, code, or entities by itself. SMEPO is simple to apply and requires no changes to the reward function or RL objective. Across diverse domains, including math, code, and agentic search, SMEPO improves accuracy by up to 3.2 points over GRPO and reduces training time by up to 4.2$\times$.

Knowledge graph question answering (KGQA) systems are typically evaluated on questions whose surface tokens lexically expose the gold KG path. Real users, however, ask questions without knowing the underlying schema: "Which condition could Andrew have due to family history?" compresses a 4-hop chain (Andrew - father → father → medical condition → subclass of - hereditary disorder) into a single phrase (family history) whose tokens name none of those relations. We show that this collapse of path-question alignment is a blind spot of existing KGQA evaluation. The three dominant Large Language Model (LLM)-KG paradigms — hop-by-hop KG exploration, plan-then-retrieve, and similarity-based retrieval — all implicitly rely on the question's surface leaking the gold path, an assumption rarely tested in practice. We make three contributions. (i) HiddenPathQA, a human-validated KGQA benchmark of 1,055 multi-hop questions (2-6 hops, multiple domains) constructed so that the question surface does not leak the gold KG path's relations. (ii) Holistic Path Selection, a reasoning paradigm that exposes all candidate relation chains around the topic entity to the LLM at once, replacing per-hop relation scoring with question-against-full-chain alignment. Two implementations, TieredHolistic and FlatHolistic, outperform six strong baselines (ToG, PoG, FiDeLiS, R2-KG, KAPING, KARPA) on HiddenPathQA by +8.8 to +17.9 points over the strongest baseline across all four (backbone × split) settings. (iii) A pseudoword stress test that replaces entity surfaces with placeholder tokens. It both confirms HiddenPathQA items are solvable from KG structure alone — not from entity-level priors — and reveals that several baselines rely on entity memorization rather than KG-grounded reasoning.

High-stakes prediction systems are often evaluated only on labels revealed by past decisions. This selective observation can hide the losses that determine full-population tail risk: a predictor may appear safe on revealed labels while its worst compatible failures remain unseen. We study certification of conditional value-at-risk (CVaR) under selective labels. First, we prove non-identifiability: even with overlap, two predictors with identical observed selected-loss distributions can have reversed full-population CVaR rankings under compatible hidden-label laws. Under an outcome-dependent odds-ratio sensitivity model, we derive sharp upper and lower CVaR envelopes. A minimax interchange shows that worst-compatible hidden-label completion commutes with the Rockafellar--Uryasev threshold optimization, giving an exact certificate rather than a loose robust surrogate. We then define the tail-risk certification frontier: the minimum passive revelation cost needed to reduce hidden-tail ambiguity below a target tolerance. For a fixed predictor and threshold, this frontier is a fractional-knapsack problem whose value density, tail-identification value, pinpoints labels that can move the CVaR tail. Finally, we give finite-sample guarantees for cross-fitted conservative certificate learning and a lower bound governed by the effective number of revealed tail labels.

Text-code embeddings underpin modern code intelligence systems, but progress in this area is held back by a systemic problem: code retrieval benchmarks routinely treat valid alternative implementations as negatives, making cross-model comparison unstable and depressing measured performance. We document this problem with a detailed analysis of six widely used benchmarks and show that, depending on the dataset, between 44\% and 87\% of items a strong LLM judge marks as relevant are missing from the original labels. To address this, we adopt a unified evaluation protocol in which a single LLM judge re-annotates the top-10 retrieved candidates for every model under a fixed prompt, replacing fragmented benchmark heuristics with a consistent semantic standard. Applying the same idea at training time, we use a smaller LLM-supervised reranker to construct MegaCode, the largest semantically curated dataset for code search to date of 193M positive (text, code) pairs, roughly an order of magnitude larger than prior curated corpora, and train a family of embedding models on it at four scales (0.5B-7B) with contrastive learning. Under a unified LLM-judged protocol, our 0.5B model matches or exceeds all prior open code-retrieval embeddings up to 8B parameters, and our 7B model improves average MRR@10 by 3.5 points over the strongest baseline.

LLM evaluations drive which models get deployed, what safety standards get adopted, which research conclusions get published, and how projections of AI's labor-market impact get made. Yet standard confidence intervals ignore variability from judge model choice, model temperature, and prompt phrasing, producing under-coverage that worsens with more data. The omitted variance can shift results enough to reverse conclusions \citep{baumann2025llmhacking, huang2026dropping}; pipelines that fail to average over it leave the surface that ``benchmark hacking'' exploits \citep{singh2025leaderboard}. This paper decomposes LLM pipeline uncertainty into its sources, distinguishes variance that shrinks with more data from sensitivity to researcher design choices, and uses design-study projections to reduce total evaluation error (TEE). Across the demonstrations, naive standard errors are 40 - 60\% smaller than the TEE-corrected SE. Using Chatbot Arena data, we show naive 95\% CI coverage drops as $n$ grows while TEE-corrected coverage holds at 95\%, and TEE-guided pipelines restrict the benchmark gaming surface from 56 to 32 Elo ($K=27$), below the human-leaderboard baseline. We show further that a small pilot recovers honest CIs and projects which design changes most improve precision. Acting on those projections halves MMLU estimation error against the answer key at equivalent cost, and raises per-match agreement with human votes by 7.9 percentage points on Chatbot Arena.

Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined. We study this overlooked failure mode and ask whether a continually adapted MLLM can preserve not only what it answers, but also how it uses visual, textual, OCR, chart, and document evidence. We identify hidden evidence-use forgetting, where answer accuracy is retained while the model silently shifts toward different or less grounded evidence channels, and propose RCL, a replay-free reliance-constrained continual learning framework. RCL freezes the previous checkpoint as a behavioral reference, estimates teacher and student evidence-reliance profiles through counterfactual channel interventions, and jointly optimizes task learning, prediction preservation, and reliance preservation without adding inference-time cost. Across CoIN, COAST, MCITlib, and an evidence-sensitive multimodal stream, RCL consistently improves final performance and reduces forgetting over replay-free, PEFT, routing, and memory-assisted baselines, while substantially lowering modality reliance drift, dominant evidence flips, and hidden forgetting rates. These results suggest that robust continual multimodal learning requires preserving the evidence path behind correct answers, not merely the answers themselves.

Video generation models have achieved remarkable visual fidelity and have strong potential to become general-purpose world simulators. Despite this progress, they still fail to generate videos which adhere to laws of physics. The problem becomes even more apparent in realistic settings where multiple physical principles must work together within the same video; for example, "a balloon floating upward while steam rises from a pot" requires buoyancy and fluid dynamics to unfold coherently and simultaneously. Yet existing methods largely ignore multi-principle interactions, focusing on a single principle per video. We propose HiPhy (Hierarchical Physical Alignment), a reinforcement learning framework that grounds video generation in physical laws through a dual-level objective: locally enforcing the temporal dynamics of individual physical principles, and globally ensuring the physical and semantic coherence of the entire scene. To support multi-principle generation, we construct a 50K-prompt dataset and introduce a prompt benchmark MultiPhyBench, spanning a diverse range of co-occurring physical events. Our experiments show that HiPhy significantly outperforms prior methods and baselines, improving physical commonsense and semantic alignment significantly across various benchmarks, with the largest gains on scenes involving multiple concurrent physical principles where competing methods degrade most sharply.