NeurIPS 2026 论文列表

Adaptive volumetric finite element meshing is a critical step in computer-aided engineering and analysis that dictates the computational budget of a given problem. It traditionally requires iterative PDE solvers or heavily supervised, data-driven surrogates trained on large-scale simulation data. While Multimodal Large Language Models (MLLMs) excel in 2D visual tasks, their zero-shot capability to semantically ground regions based on geometric understanding and physics remains an open question. Overall, this study explores a significant question: can the high-level semantic understanding of off-the-shelf MLLMs serve as a viable, zero-shot geometric proxy for finite element mesh refinement? To investigate this, we introduce **GReFEM** (Geometric Reasoning Enhanced Multimodal LLMs for Finite Element Meshing), a framework that utilizes MLLMs to visually localize stress-critical regions based on physics-guided textual prompts. To bridge the gap between 2D MLLM pre-training and 3D geometries, we introduce *orthoViews*, a view-selection module that maximizes the observability of key geometric features. We conduct an in-depth empirical evaluation across diverse CAD geometries, loading cases, and SOTA MLLMs, comparing them against a tuned geometric heuristic under a strict, matched refinement budget. Our findings reveal that MLLMs demonstrate robust zero-shot capacity to accurately follow complex spatial-physical instructions, isolating stress-relevant features with higher precision than blind heuristics. By mapping both the successes and current limitations of MLLMs in physical grounding, this study defines the frontier of foundation models as semantic assistants in automated simulation workflows.

Autoregressive decoding with LLMs is primarily bottlenecked by GPU memory bandwidth, especially in edge-computing settings. While quantization is essential for mitigating this bottleneck, most existing methods treat inference as uniform process and fail to account for the asymmetry between the compute-bound prefill stage and the memory-bound decoding stage. We propose GRINQH (GRaded INput-based Quantization Hierarchy), a weight-only post-training quantization framework that accelerates decoding by unifying quantization and sparsification. GRINQH leverages activation magnitudes as a proxy for computational importance to dynamically assign weight channels to different precision levels, enabling flexible average bit widths during decoding. Evaluated on Llama3 and Qwen3 models, GRINQH outperforms state-of-the-art fixed- and mixed-precision baselines at comparable 3- and 4-bit settings, even enabling effective 2-bit generation. We experimentally verify theoretical speedups by leveraging a hierarchical nested memory layout for multi-precision storage in custom GPU kernels. Together, GRINQH establishes a new state-of-the-art Pareto frontier for LLM generation, enabling a dynamic trade-off between generation quality and inference speed.

Text-to-image flow matching transformers degrade sharply in long-tail settings: tail-class outputs collapse in fidelity and diversity, limiting their value as synthetic augmentation for rare conditions. We trace this to low head-versus-tail gradient alignment during fine-tuning, an optimization-level pathology that conditioning- and sampling-side interventions do not address. We propose GRASP (Guided Residual Adapters with Sample-wise Partitioning): a deterministic partition of the conditioning space, paired with group-specific residual adapters in the transformer feedforward layers, that leaves the flow-matching objective and the sampler untouched. In conditional flow matching, condition values index distinct sets of probability paths, so partitioning along the conditioning is the structurally correct factorization suitable as gradient alignment proxy. Because the partition is static, every tail sample is guaranteed to update its assigned expert, which bypasses extreme longtail failure modes. Crucially, GRASP is non-invasive and composable: on MIMIC-CXR-LT, combining GRASP with self-guided minority sampling at inference time yields the best all-labels IRS we observe, beyond either intervention alone. GRASP itself reduces overall FID by up to 80\% and lifts tail-class coverage by up to 44\% over full fine-tuning, learned-routing MoE, and minority guidance. Used as training data for a downstream DenseNet classifier on NIH-CXR-LT, GRASP synthetics significantly outperform every non-GRASP alternative on macro F1, match the macro F1 obtained from real training data, and yield nonzero F1 on $9$ of $13$ classes versus $3$ of $13$ from full fine-tuning. Results on ImageNet-LT confirm the mechanism is not tied to medical inductive bias.

Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification (UQ) with finite samples. However, applying CP to graph neural networks (GNNs) remains challenging. The combinatorial nature of graphs makes their encoding non-trivial, leading to insufficiently uncertain prediction logits and indiscriminative embeddings. Existing methods mostly rely on the embedding space for conformal prediction, which can be unreliable for graphs and often yields inefficient prediction sets. We propose GRAPHLCP, a structure-aware weighted conformal prediction framework that explicitly incorporates the graph topology and inter-node dependencies into localization and weighting. Based on experimental results that show a strong correlation between node homophily and sample coverage, we propose GRAPHLCP that utilizes a feature-aware densification followed by Personalized PageRank–based kernel computation to model structural proximity between nodes. Accounting for both structural and feature similarity allows GRAPHLCP to efficiently guarantee marginal coverage while attaining favorable test conditional coverage across extensive experiments on multiple datasets for both regression and classification.

Synthesizing Mixed-Boolean Arithmetic (MBA) expressions from input-output examples is central to program deobfuscation and also useful for compiler optimization, reverse engineering, and cryptanalysis. Existing MBA synthesizers are typically CPU-based and scale poorly on large specifications or complex targets. Recent GPU-accelerated synthesis methods achieve large speedups in qualitative settings, but they depend on caching observationally equivalent candidates; this strategy breaks down for MBA because candidate outputs are quantitative bitvectors and the behavioral space is enormous. We present SIMBA (Synthesis of Mixed-Boolean Arithmetic), a GPU-accelerated MBA synthesizer built around cache-free bottom-up enumeration. SIMBA avoids language caches entirely and uses a GPU-oriented enumeration design that keeps work local and highly parallel. In experiments, SIMBA is substantially faster than prior MBA synthesis tools, handles larger specifications, and reaches expression sizes that existing methods fail to solve. These results establish cache-free GPU synthesis as a practical and scalable approach for quantitative domains, and identify it as a strong alternative to cache-centric designs.

Linear-time sequence models such as State Space Models (SSMs) offer an efficient alternative to self-attention and have demonstrated strong language modeling performance at scale. In practice, however, their asymptotic advantage is often lost at common training and prefill lengths---optimized FlashAttention kernels remain faster than existing SSM implementations up to 8k tokens. In this work, we substantially narrow this performance gap with novel algorithmic improvements: First, we introduce a split-sequence algorithm that decouples the compute tile from the sequence-parallel split length, preserving efficient matrix multiplication tiles while substantially reducing boundary-state traffic and latency. Second, we resolve a longstanding *speed--stability tradeoff* in computing the 1-semiseparable (SS) decay mask, the main non-matmul bottleneck shared by all chunk-wise linear-time kernels (Mamba-2, Mamba-3, GDN, KDA). Existing kernels use the fast *diff segsum* (prefix subtraction), which suffers monotonicity violations and catastrophic cancellation---failures observed in real pretraining (e.g., Nemotron-H); the stable alternative (*direct segsum*) materializes the dense mask and is $\sim30\\%$ slower. Our *hierarchy-aware segsum* decomposes the SS mask into 1-SS diagonal blocks and rank-one off-diagonal blocks, computed via warp-level scans and cross-warp aggregation directly in registers, achieving the stability of direct segsum at the speed of diff segsum. We implement these algorithms using CuTe-DSL with warp specialization, asynchronous TMA, WGMMA, and layout-aware accumulator fragments. Across a large range of sequence length, our H100 kernels reach 91\% of measured memcpy bandwidth, improve prefill time by $4.1\times$ over the official Triton Mamba-2 kernel, and **outperform the highly-optimized FlashAttention-4 starting at 1-2k sequence length**---to our knowledge, the first time a linear-time sequence layer is faster than state-of-the-art attention at the 2-4k sequence lengths used in common pretraining and prefill workloads, where prior linear-time kernels only reached parity at $\geq 8$k. Our hierarchy-aware segsum is $1.35$-$1.40\times$ faster than a naive stable direct-segsum implementation.

We present GPT-IMAGE-EDIT-1M, a million-scale instruction-guided image editing dataset in which every sample includes an auditable curation decision and a traceable instruction trail. Open training data for image editing often suffers from instruction-image mismatch, weak edits, and opaque filtering; simply scaling raw data does not address these issues. To improve data quality, we regenerate edited outputs using GPT-Image-1, augment samples with complex multi-step edit instructions, and apply a policy-based quality control pipeline that assigns KEEP/RELABEL/DROP decisions with reason tags. RELABEL samples receive aligned instructions that better match the observed edit, and a second judge independently re-runs the same protocol to measure decision stability. After quality control, average quality improves from 6.90 to 7.67 under a shared rubric. Across two frozen encoder configurations, T5 and QwenVL+T5, models fine-tuned on GPT-IMAGE-EDIT-1M outperform the public FLUX.1-Kontext-dev baseline on key benchmarks, including GEdit-EN (7.22 vs. 6.26), ImgEdit (3.93 vs. 3.52), and KRISBench (66.23 vs. 49.54), while remaining competitive on Complex-Edit and OmniContext SINGLE. Controlled data-swap experiments show that GPT-Image-1 regeneration provides the dominant downstream gain, while quality control yields a smaller but consistent improvement over a size-matched random regenerated subset. Concatenating frozen QwenVL embeddings with T5 substantially improves inferential-complex benchmarks such as RISEBench and KRISBench without degrading general-editing performance. Finally, as an auxiliary diagnostic rather than a primary ranking protocol, inference-time instruction rewriting raises GEdit-EN to 7.75 and KRISBench to 78.55, suggesting that prompt specificity exposes additional instruction-following capacity in the fine-tuned editors.

Oracle-guided biological sequence design must improve predicted function without moving outside the sequence distribution where the oracle is trustworthy. We introduce Generative Population Annealing GPA, a test-time sampler for sequence design with a frozen generator and frozen oracle. GPA instantiates annealed Sequential Monte Carlo at population scale: particles are initialized from a pretrained sequence prior, reweighted by an oracle reward tilt, selectively upsampled as effective sample size falls, mutated with the pretrained generator, and returned as the design pool. The base sampler targets a reward-tilted prior; practical variants shape the proposal or modify the objective to trade activity, specificity, fidelity, and diversity. Across enhancer and promoter benchmarks, GPA scales to thousands of sequences and is competitive with inference-time samplers, gradient-based editors, tree-search diffusion, and RL-fine-tuned generators. The same inference loop is evaluated with masked discrete-diffusion and autoregressive DNA generators. GPA reaches high predicted activity while preserving strong motif fidelity and model-based likelihood, although specialized baselines retain higher diversity or stronger $k$-mer fidelity in some settings. Cross-oracle and sequence-level audits suggest fewer obvious reward-hacking pathologies, including shorter homopolymer runs than CTRL-DNA in the HepG2 audit (5.1 bp mean maximum run versus 20.9 bp), but all validation remains computational.

Transformers excel at sequence modeling, yet Softmax attention incurs quadratic complexity and unbounded KV cache growth. While linear attention offers a promising alternative, existing approaches lack systematic functional comparison with Softmax attention, rigorous error analysis, and a theoretically grounded improvement roadmap. We address this gap by framing linearization as KV cache approximation and establishing a principled pathway from Softmax attention to linear models. Our analysis identifies five critical components—redundancy elimination, token-level quantization with positional separation, positional compression, inter-layer similarity, and multi-state decomposition—each accompanied by theoretical justification and error bounds, with explicit connections to existing mechanisms. Building upon this framework, we introduce GPA, a linearized attention model that inherits pretrained weights and achieves state-of-the-art results. GPA outperforms strong baselines including MVA and GSA across multiple benchmarks, while requiring less fine-tuning resources. Our work provides both theoretical clarity and practical guidance for advancing linear attention, charting a principled course toward efficient, scalable alternatives to Softmax attention.

Gradient inversion reconstructs training data from shared gradients. However, existing methods target classification tasks and image diffusion, and do not address mixed tabular diffusion, where numerical columns follow Gaussian noising while categorical columns are corrupted by discrete masks. This mixed continuous-discrete training objective changes both the inversion target and the gradient structure: an attacker must recover not only rows, timesteps, and noise, but also the latent mask pattern whose density is tied to the diffusion timestep. We propose Goliath, the first gradient inversion attack designed for tabular diffusion models. Goliath employs a cyclic inversion loop that jointly recovers rows and diffusion latents across Gaussian and mixed diffusion regimes. We introduce a tabular-aware objective function that balances numerical and categorical gradient contributions while improving categorical recovery through enforced consistency between noise levels and mask structures. To accommodate large batches, we aggregate multiple reconstructions across epochs using a row-alignment heuristic. Evaluated across nine tabular datasets, Goliath achieves up to $88.9\\%$ per-cell reconstruction accuracy and consistently outperforms gradient-inversion baselines across Gaussian and mixed tabular diffusion. Code is available at https://anonymous.4open.science/r/fl-tab-diffusion-inversion-F4E7/README.md.

Designing RNA sequences that fold into specific 3D structures is a fundamental challenge in therapeutics. Current RNA inverse folding methods typically condition on 3D backbone coordinates but the generation is limited to 1D discrete sequences. This ignores the continuous side-chain orientations, which are essential for overall structural stability. To address this issue, we explicitly introduce side-chain geometry into the RNA generative process. Unlike co-design frameworks that diffuse across separate raw spaces, we propose Geometric Optimized Latent Diffusion (\textbf{GOLD}), which project sequence and structure into a unified latent manifold. GOLD employs a novel GeoVAE to elegantly integrate discrete RNA sequences with continuous side-chain angles. By mapping these modalities into a single, smooth latent manifold, GOLD naturally facilitates a synergistic co-diffusion process. To enhance structural validity, we further introduce a training-free, gradient-guided sampling mechanism. By decoding the generated sequence probabilities and side-chain geometries into structures during the sampling process, we compute analytical gradients from physical constraints. This dynamic feedback actively guides the generated sequence to a physically stable state. Extensive experiments validate the superiority of GOLD, achieving state-of-the-art performance across sequence recovery, secondary structure validity, and 3D foldability metrics.

Large language models (LLMs) exhibit pronounced position bias in long-context retrieval, systematically prioritizing information location over relevance. Existing mitigations modify positional encodings or attention internals, but in the common output-only deployment setting these are inapplicable. We introduce GOLD PANNING, a Bayesian framework for inference-time active search that (i) reorders documents to concentrate high-belief items in highly diagnostic positions (signal anchoring) and (ii) updates relevance beliefs from model outputs. Unlike active learning, which prioritizes uncertainty reduction, GOLD PANNING exploits anchoring---once flagged, keep it in sight---to preserve weak cues. A greedy assignment derived from the model's diagnosticity profile provably identifies a target among $N$ documents with high probability in $O(\log N)$ rounds. Across open-weight and closed-source models on multi-document QA, GOLD PANNING matches Permutation Self-Consistency's $F_1$ with $30$--$65\%$ fewer queries and remains effective under calibration mismatch, indicating that coarse positional ordering alone drives the gains. These results show that inherent model biases need not be failures, but can serve as exploitable signals.

Synthesizing interpretable controllers for multi-agent systems requires optimizing discrete symbolic programs from sparse, expensive, long-horizon simulator feedback. Evolutionary program search preserves interpretability but often spends many evaluations on weak random variation, whereas deep multi-agent reinforcement learning can learn effective policies that are difficult to inspect. We propose GNES, a neural-guided evolutionary program search framework for symbolic controller synthesis. GNES casts controller design as a program-edit decision process: gene growth trees are program states, validity-preserving genetic operators are actions, simulator fitness provides the return, and policy--value graph neural networks learn edit signals over controller topology. Monte Carlo tree search performs look-ahead planning in controller-program space before simulator validation, closing the loop between evolution, learned guidance, and verified rollouts. Experiments on three decentralized AirSim swarm-control benchmarks show that GNES improves search efficiency and final controller quality relative to evolutionary and reinforcement-learning baselines, while automatically evolving compact, inspectable distributed velocity controllers. The discovered policies expose coordination motifs, such as density-normalized radial gains and motion-compensating terms, that differ from standard hand-designed swarm rules. Ablations and learned-search comparisons indicate that graph-structured policy--value learning and MCTS look-ahead both contribute to the gains.

Moving Object Segmentation (MOS) aims to discover, segment, and track objects that move independently of the camera. Current MOS methods, however, exhibit two fundamental limitations: they rely on pre-computed 2D auxiliary modalities such as optical flow or point trajectories that lack 3D geometric information, and they treat motion as a sequence-level attribute, overlooking the instantaneous motion state of each object. We address both by grounding MOS in 3D space and time, and propose GMOS, a framework that operates directly on RGB video to produce 3D-aware, temporally fine-grained segmentation of multiple moving objects, alongside a foreground--background variant GMOS-S for faster deployment. To support training and evaluation in this regime, we curate GMOS-2K, a dataset of $2{,}210$ real-world videos with per-object temporal motion annotations drawn from five established Video Object Segmentation (VOS) benchmarks, and formalise MOS-I ("I" for *instantaneous*), a temporally fine-grained evaluation protocol with three complementary metrics. GMOS achieves state-of-the-art results across MOS, MOS-I, and Unsupervised VOS benchmarks, while running significantly faster than prior multi-object MOS methods and supporting online inference for streaming deployment.

Vision-language models (VLMs) for radiology have emerged as a scalable paradigm by leveraging image-report pairs naturally produced in clinical workflows. However, this pairing reveals a mismatch in scale: each finding occupies only a small region of the image, yet supervision is provided only at the global image-report level. This poses a central challenge: prior approaches spread weight densely across all patches rather than concentrating on the sparse subset relevant to a given query. To address this, we present GLINT (Gated Language-Image alignmeNT), a framework that explicitly models this sparse correspondence. On the alignment side, we introduce Sparsely Gated Alignment, a novel architecture in which a sigmoid gate over a separate gate embedding space activates only the patches relevant to each textual query, enforcing explicit sparsity. On the representation side, we add Dense Feature Regularization, which anchors the trainable encoder's intermediate features to a frozen self-supervised learning (SSL) teacher, preserving the fine-grained patch features that the gate relies on. The same recipe applies to both 2D chest X-ray (CXR) and 3D chest computed tomography (CT), built with DINOv3 and V-JEPA 2.1, respectively. GLINT enables zero-shot classification, grounding, and segmentation from free-text queries, and to our knowledge is the first to demonstrate zero-shot segmentation on 3D CT volumes without mask supervision. Notably, the most pronounced gains arise on zero-shot grounding and segmentation, where sparse, query-specific localization is required, consistent with our design intent. In downstream evaluation, GLINT outperforms both SSL encoders and medical VLMs on classification, report generation, and segmentation.

Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines. In this work, we revisit the problem through the lens of object detection on molecular graphs. Molecular fragmentation, a central step in MS/MS prediction, can be approximated as detecting a set of subgraphs (i.e., fragments) and their associated spectral contributions. Existing fragment-based models follow a two-stage paradigm—first generating candidate fragments and then scoring them—analogous to two-stage R-CNNs in computer vision. Towards higher accuracy and faster inference, we introduce GLACIER, a single-stage transformer-based fragment detection neural network for molecular graphs. This unified formulation eliminates the need for candidate enumeration, enabling scalable and globally consistent modeling of molecular fragmentation. GLACIER is faster and more accurate than existing state-of-the-art methods by a significant margin, achieving 62.4% and 62.1% Top-1 retrieval accuracy with and without contrastive finetuning on the MassSpecGym dataset (from 55.3%), and 55.2% and 38.2%, respectively, on the NIST'20 dataset (from 33.5%). Furthermore, GLACIER provides nearly 3-fold inference speedup over existing two-stage models.

Vision-Language-Action (VLA) models often struggle with precise manipulation due to their lack of explicit spatial awareness. To bridge this gap, we propose GIVLA, a framework that internalizes geometric priors into the VLA backbone through a deep-coupled architecture. GIVLA implements a two-stage gradient-informed training paradigm to resolve task-level interference and ensure precise execution, a design grounded in the perception-action duality identified through our analysis of training dynamics. Extensive experiments on LIBERO and physical robot platforms demonstrate that GIVLA achieves superior accuracy and robustness with high parameter efficiency.

Neural operators on irregular meshes face a fundamental tension. Spectral positional encodings, the natural choice for capturing geometry, require cubic-complexity eigendecomposition and inadvertently break gauge invariance through numerical solver artifacts; existing efficient approximations sacrifice gauge symmetry by design. Both failure modes break discretization invariance: models fail to transfer across mesh resolutions of the same domain, and similarly across different graphs of related structure in inductive settings. We propose GIST (Gauge-Invariant Spectral Transformer), a scalable neural operator that resolves this tension by restricting attention to pairwise inner products of efficient approximate spectral embeddings. We prove these inner products estimate an exactly gauge-invariant graph kernel at end-to-end $\mathcal{O}(N)$ complexity, and establish a formal connection between gauge invariance and discretization-invariant learning with bounded mismatch error. To our knowledge, GIST is the first scalable graph neural operator with a provable discretization-mismatch bound. Empirically, GIST sets state-of-the-art on the AirfRANS, ShapeNet-Car, DrivAerNet, and DrivAerNet++ mesh benchmarks (up to 750K nodes), and additionally matches strong baselines on standard graph benchmarks (e.g., 99.50\% micro-F1 on PPI).

The advancement of large language models~(LLMs) has significantly accelerated the development of search agents capable of autonomously gathering information through multi-turn web interactions. However, existing benchmarks often construct queries backward from answers, producing tasks misaligned with real-world needs. Moreover, these benchmarks tend to focus on either locating specific information or aggregating information from multiple sources, while relying on static answer sets prone to data contamination. To bridge these gaps, we introduce GISA, a benchmark for General Information-Seeking Assistants comprising 373 human-crafted queries reflecting authentic information-seeking scenarios. GISA features four structured answer formats (item, set, list, and table), enabling deterministic evaluation. It integrates both deep reasoning and broad aggregation within unified tasks, and includes a live subset with periodically updated answers to resist memorization. Notably, GISA provides complete human search trajectories for every query, serving as valuable references for analyzing and improving search agent behavior. Experiments on mainstream LLMs and commercial search products reveal that even the best model achieves only 19.30% exact match, with performance notably degrading on tasks requiring complex planning and comprehensive information gathering.