NeurIPS 2026 论文列表

Multimodal learning has become a prominent research area, with the potential of substantial performance gains by combining information across modalities. At the same time, model development has trended toward increasingly complex deep learning architectures, motivated by the assumption that multimodal-specific methods improve performance. We challenge this assumption through a large-scale empirical study by reimplementing 19 high-impact multimodal methods across nine diverse datasets with up to 23 modalities. Under standardized experimental conditions, including hyperparameter tuning, weight initialization, cross-validation, and statistical testing, increased multimodal complexity often yields confusion rather than effective fusion of data modalities. Accordingly, complex multimodal architectures do not reliably outperform unimodal baselines and a Simple Baseline for Multimodal Learning (SimBaMM). Through a focused case study, we further demonstrate concrete methodological shortcomings even in top-tier multimodal learning publications, underscoring the need for standardized evaluation practices. In summary, we argue for a shift in focus for multimodal learning: away from the pursuit of architectural novelty and toward methodological rigor.

Functional optimization problems are typically solved by optimizing the parameters of a fixed representation, such as a neural network, resulting in highly nonconvex losses that complicate both training and theoretical analysis. An interesting alternative is functional gradient descent (FGD) --- that is, gradient descent directly in function space --- which benefits from strong convergence results and admits a clean theory. However, FGD is difficult to implement in practice because functional gradients are infinite-dimensional, and thus cannot be fully computed nor stored in memory. Existing implementations therefore rely on fixed approximations, which introduce approximation error. We propose a new, theoretically-grounded FGD algorithm that adapts the representation of the functional gradients over the course of optimization. By explicitly incorporating this approximation into the analysis, we establish convergence to a stationary point (for smooth losses) and to a global minimizer (under smoothness + a Polyak-Łojasiewicz-type condition) regardless of our approximations. To the best of our knowledge, this is the first implementable FGD method with such guarantees in a general setting. We demonstrate the effectiveness of our method on regression, numerical solution of PDEs, and modern computer vision. Across settings, our method consistently outperforms both FGD with fixed approximations and neural network baselines in efficiency and accuracy.

Learning expressive representations for Boolean logic circuits is a fundamental challenge at the intersection of graph learning and Electronic $\mbox{Design}$ Automation (EDA). Existing Graph Neural $\mbox{Networks}$ (GNNs) primarily rely on topological message passing, which often fails to capture the strict causal dependencies and discrete functional semantics of logic gates. In this paper, we $\mbox{propose} \textbf{FuncFormer}$, a Graph Transformer that incorporates $\textbf{functional simulation}$ not as a proxy task, but as a fundamental inductive bias directly into the representation learning process. Unlike standard GNNs which typically rely on isotropic aggregation, FuncFormer $\textbf{encodes the intrinsic flow of functional propagation}$ by analyzing randomized simulation traces as they evolve through the network. This approach effectively aligns the continuous embedding manifold with the discrete Boolean function space, effectively mitigating structural aliasing. By integrating these deterministic signal trajectories with a scalable dual-path attention mechanism, our model preserves functional consistency across long-range dependencies in both combinational and sequential circuits. Empirical results demonstrate that FuncFormer significantly outperforms state-of-the-art models (e.g., DeepGate4) in Quality-of-Results (QoR) prediction and formal verification tasks, exhibiting robust generalization to unseen circuit scales.

In this work, we study nonconvex–strongly convex online bilevel optimization (OBO) using only first-order oracle. Existing OBO algorithms are mainly based on hypergradient descent, which requires access to a Hessian-vector product (HVP) oracle and potentially incurs high computational costs. By reformulating the original OBO problem as a single-level online problem with inequality constraints and constructing a sequence of Lagrangian function, we eliminate the need for HVPs arising from implicit differentiation. Specifically, we propose a fully first-order algorithm for OBO, and provide theoretical guarantees showing that it achieves regret of $O(1 + V_T + H_{2,T})$ with a total of $O(T\log T)$ iterations, where $V_T$ measures the variation in function values and $H_{2,T}$ characterizes the drift variation of the inner-level optimal solution. We also establish a sublinear regret bound under the single-loop structure by introducing additional gradient-variation terms. Furthermore, we develop an improved variant with an adaptive inner-iteration scheme, which removes the dependence on $H_{2,T}$ and achieves regret of $O(\log T + V_T)$. Finally, under the stochastic OBO setting, we establish the regret bound for the fully first-order algorithm, i.e., $O(T^{2/3}(1 + \sigma^2) + V_T + H_{2,T})$. Numerical experiments demonstrate the feasibility of our algorithm and support our theoretical findings.

We study price-adjustment dynamics for computing competitive equilibria (CE) in Fisher markets with chores. Unlike in classical goods markets, prices in chores markets are payments for taking on undesirable tasks, and natural excess-demand dynamics can fail; even the naïve analogue of Walrasian tâtonnement may diverge. Recent work of Chaudhury et al. (2025) overcomes this obstacle via \emph{relative tâtonnement}, which subtracts the average excess-demand signal from the excess demand vector. This recovers convergence, but at the cost of coupling the price updates across all chores. This leaves open whether such global coupling is inherent, or whether convergent tâtonnement can be recovered through a genuinely local update in which each chore reacts only to its own excess demand. We answer this question affirmatively through \emph{multiplicative tâtonnement}, a fully distributed dynamics in which each chore price is updated using only its current price and its own excess-demand signal. Although the update contains no explicit normalization term, Walras' law and the multiplicative form of the update implicitly preserve the relevant aggregate price geometry. We prove that multiplicative tâtonnement converges to a CE in any chores Fisher market with continuous, convex, and 1-homogeneous (CCH) disutilities. For convex CES disutilities, we further prove an approximate-CE convergence rate with the same O(1/\varepsilon^2) dependence as relative tâtonnement, but with improved dependence on problem constants. Experiments on real-world and simulated instances show that multiplicative tâtonnement is substantially faster in practice, often by an order of magnitude.

Autoregressive large language models have dominated generative recommendation by sequentially predicting the next item. However, this unidirectional paradigm suffers from error propagation, and limited ability to jointly optimize an entire recommendation list. In this paper, we introduce Full-Sequence Masked Diffusion for generative recommendation (FSMD), which extends masked diffusion models to enable global joint modeling over the entire user history and Top-$K$ recommendation list. We formulate recommendation as a unified sequence diffusion process, where the forward process randomly masks tokens across the full sequence and the reverse process simultaneously denoises all masked positions using bidirectional context. This formulation enables parallel generation of the entire recommendation list and naturally captures inter-item dependencies within a coherent global structure. To better align generation with ranking objectives, we further introduce a unified training objective that combines masked diffusion likelihood with a listwise ranking loss. Extensive experiments on multiple public benchmarks demonstrate that FSMD consistently outperforms state-of-the-art autoregressive and item-level diffusion-based generative recommendation methods across accuracy and list quality metrics.

We present DuplexMotion, a streaming, full-duplex speech-and-motion model designed for dyadic interaction. To capture the continuous and reciprocal nature of human communication, this full-duplex capability empowers the agent to simultaneously perceive and generate both speech and physical motion in a streaming fashion. At its core, our method leverages the strong priors of a foundational full-duplex speech model and integrates a novel motion pathway, thereby achieving fully synchronized multi-modal interaction. Specifically, we design a dual-tower Transformer architecture that preserves the zero-shot conversational reasoning of a frozen base speech model while constructing a deeply coupled, streaming motion pathway. By introducing a unified dyadic token interleaving mechanism and guiding cross-attention via a time-aligned speech-motion RoPE, our model effectively aligns autoregressive motions with rich latent speech features. Trained on the 4,000-hour Seamless Interaction dataset, our model effectively captures cross-speaker dependencies and establishes new state-of-the-art performance across both monadic and dyadic human interaction benchmarks.

Cyclic peptides are an increasingly important therapeutic modality, offering antibody-like binding specificity within compact and chemically tunable scaffolds. Recent deep generative models have advanced target-specific peptide design, where SE(3)-equivariant diffusion and flow-matching frameworks provide natural inductive biases for peptide-protein complex generation. However, existing SE(3)-based peptide generators mainly focus on linear peptides and do not explicitly control cyclic topology. As shown by recent cyclic peptide benchmarks such as CPSea, current generative baselines still have limited cyclization success and remain weak at controllably generating disulfide- and isopeptide-cyclized peptide distributions. In this work, we propose **CPFlow**, a test-time scaled autoregressive flow-matching framework for receptor-conditioned cyclic peptide sequence-structure co-design. CPFlow autoregressively unmasks residues and predicts full-atom sequence-structure variables with continuous flow matching. By decomposing generation into stepwise flow updates, CPFlow allows each step to balance target binding with structural closure. At inference time, sequence-geometry guided unmasking and multi-particle test-time scaling explore high-confidence autoregressive trajectories beyond the trained model without additional training. Cyclization-aware relative position encodings further enable stable control over head-tail, disulfide, and isopeptide cyclization. Experiments on the CPSea benchmark show that CPFlow outperforms strong generative baselines across *designability* (scRMSD **↓ 0.30 Å**), *self-consistency* (scRMSD **↓ 0.15 Å**), and *binding energy* (mean ΔG **↓ 3.0**), with **93.4%** *binding success* and **0.623** *diversity*. The code for CPFlow is available at https://anonymous.4open.science/r/CPFlow-F226.

Few-shot CLIP adaptation is often treated as a parameterization problem, where full fine-tuning is assumed to overfit, so parameter-efficient fine-tuning (PEFT) methods are preferred. In this paper, we show that this conclusion conflates parameterization with optimization. Across 11 datasets, 3 CLIP backbones, and 5 shot levels, dense full fine-tuning with plain SGD outperforms representative PEFT methods, whereas Adam/AdamW collapses. We trace this collapse to unreliable support-set preconditioning. The few-shot second moment systematically underestimates the corresponding full-data statistic, already at initialization. Adam’s inverse-square-root normalization converts this error into an oversized adaptive update. Matching Adam’s first-step update budget to SGD resolves most of the collapse, whereas numerator-side alignment does not explain the failure. Shampoo and SOAP also fail, showing that the problem is not merely Adam’s diagonal approximation but a broader mismatch between support-set and full-data preconditioning geometry. Theory formalizes how few-shot estimation error is amplified into unsafe updates. SGD succeeds because it avoids unreliable preconditioners, stays close to pretrained CLIP, and follows broad, connected, low-curvature corridors. These results identify optimizer-induced preconditioning, not dense full fine-tuning itself, as the main source of failure in few-shot CLIP adaptation. Our code is included in the supplementary materials and will be made public.

Both full fine-tuning (Full FT) and parameter-efficient methods like LoRA add weight updates without regard to the spectral structure that pretraining has established. This allows noisy gradients from a small fine-tuning distribution to freely perturb the robust features learned through pretraining. We first identify *spectral preconditioning* as the key missing ingredient: reparameterizing each weight $\mathbf{W}$ through its full-rank SVD and freezing one singular basis confines every update to the pretrained column space, yielding a preconditioned optimizer that outperforms unconstrained Full FT at the same parameter count. To make this insight practical, we propose FuRA (**Fu**ll-**R**ank **A**daptation), which factorizes $\mathbf{W}$ via a block tensor-train decomposition $\mathbf{W}=\mathbf{L}\mathbf{S}\mathbf{R}$: the large core $\mathbf{L}$ is frozen at the pretrained block-wise SVD basis while only the small core $\mathbf{R}$ and per-block singular values $\mathbf{S}$ are trained. This single design choice simultaneously delivers full-rank spectral preconditioning, full-rank update capacity, and parameter, step time, memory efficiency on par with LoRA. FuRA outperforms Full FT on LLM fine-tuning ($+1.37$ on LLaMA-3-8B commonsense reasoning), LLM math reinforcement learning, and VLM visual instruction tuning. The 4-bit quantized version QFuRA also outperforms QLoRA.

Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research often requires a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce **FrontierOR**, the first large-scale benchmark targeting LLM-based efficient algorithm design for realistic optimization problems. FrontierOR includes 110 tasks derived from methodologically diverse papers published in top-tier operations research venues, each with standardized instances and a hidden, human-verified evaluation suite. We evaluate seven frontier LLMs in one-shot and test-time evolution settings. The results reveal that frontier models still struggle to move from executable formulations to efficient optimization algorithms: the strongest one-shot model outperforms Gurobi in only **39\%** of cases in terms of both solution quality and runtime, and even strong coding agents with test-time evolution achieve only **50\%** on selected hard tasks. FrontierOR establishes a practical evaluation platform for LLM-based optimization algorithm design, which enables future LLMs and agents to be systematically tested on whether they can move beyond correct formulation toward feasible, high-quality, and runtime-competitive algorithms.

Engineering value is often created not by producing a single correct answer, but by improving a feasible artifact under hard constraints. We introduce **Frontier-Eng**, a benchmark for generative optimization agents that repeatedly edit executable engineering artifacts, receive frozen verifier feedback, and maximize the best feasible score found within a fixed budget. Frontier-Eng contains $47$ tasks across five engineering categories, spanning kernels, cryptography, quantum circuits, inventory and scheduling, robotics and control, optics and communication systems, structural design, reactions, and sustainable infrastructure. Each task provides a feasible starting solution, an editable artifact, and a read-only evaluator with verifier-parsed scoring, enabling cross-domain comparison without reducing engineering quality to binary pass/fail completion. We evaluate frontier models under OpenEvolve and compare search frameworks across the full task set. Current agents exhibit clear iterative improvement: **gpt-5.4** obtains the best average rank under OpenEvolve. Long-horizon runs reveal a dual **inverse-law pattern**: improvement events become rarer with iteration, and improvement magnitudes shrink with improvement count. At the same time, improvement is uneven across domains and remains sensitive to search depth, exposing a concrete frontier for engineering-oriented agents under realistic constraints and verifier-grounded feedback.

The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal. For instance, on LiveCodeBench, frontier models achieve over (99%) Pass@1 on easy splits and exceed (90%) Pass@1 on average across difficulty levels. Constructing new, sufficiently challenging datasets typically requires substantial human effort, creating a bottleneck for continued progress. We study a solution-centric evolutionary approach that automatically transforms existing programming problems into substantially harder variants. Rather than generating problems from scratch, the approach evolves reference solutions through structured transformations and derives corresponding problem statements and tests from the evolved solutions. This design grounds generation in executable semantics, enabling scalable construction of high-quality, diverse, and difficult tasks with verifiable correctness. Applied to LiveCodeBench and SciCode, it produces evolved tasks that are substantially more difficult while preserving validity, reference correctness, and diversity. Importantly, these tasks remain challenging even for the model that generates them, creating the prerequisite for self-improvement rather than merely expanding an evaluation set. We further show that RL on evolved tasks improves held-out coding performance: for \texttt{gpt-oss-20b}, seed+evolved training achieves (+8.7) and (+8.3) Pass@1 gains on LCB v6 Hard and LCB-Pro Easy, exceeding seed-only gains by (70.7%) and (34.8%), respectively. This closes the loop from self-generated challenges to capability improvement, demonstrating that saturated benchmarks can be converted into both stronger evaluations and reusable training signal.

LLM-based coding agents are usually evaluated in familiar software settings: mainstream languages, common libraries, and public repositories. These benchmarks remain important, but they can hide how agents behave when the language itself is unfamiliar. We evaluate six contemporary coding agents on four esoteric programming languages using a sequential setup with file editing, local execution, and hidden-test grading. Our protocol exposes capability differences between these agents that mainstream coding and agentic benchmarks such as SWE-Bench Verified and Terminal-Bench 2.0 compress into much narrower bands. We observe that the strongest agents, Claude Opus 4.6 and GPT-5.4 xhigh, often avoid writing the target language directly. On Brainfuck and Befunge-98, they write Python programs that generate target-language code and debug those generators locally. Forbidding this metaprogramming strategy causes large performance drops. Text guidance distilled from this strategy does not materially improve weaker agents. In contrast, Opus-derived Python helper code for building generators, with no solved benchmark programs or hidden-test answers, sharply improves Sonnet 4.6 and GPT-5.4 mini on the same problems, while Haiku 4.5 remains low. More interpreter calls and output tokens improve stronger agents but leave weaker agents near their original performance, indicating that these resources amplify useful strategies rather than create them. Together, these results show that strong coding agents adapt to unfamiliar languages by using tools, feedback, and workspace state to build a working model of the target language. Metaprogramming is the clearest case, but the broader gap is constructing and debugging a strategy that works under the target language's rules.

A hallmark of intelligent behavior is the ability to extract abstract relational structure from temporal sequences -- recognizing, for instance, that aab, ccd, and eef all follow the same underlying pattern, regardless of the specific elements involved. This capacity, observed across species and sensory modalities, is thought to underlie the formation of cognitive schemas: compressed internal models that support rapid generalization to novel experiences. Yet the neural circuit mechanisms by which such abstract, identity-independent representations emerge from sequential experience remain largely unknown. Here, we investigate this question using Recurrent Neural Networks (RNNs) as mechanistic models of neural circuits, trained to classify sequences based on their latent algebraic patterns (e.g., aab, aad $\to$ AAB; aba, aca $\to$ ABA) without supervision on intermediate transitions. We demonstrate that RNNs learn low-dimensional representations that mirror the hierarchical generative structure of the sequences, and that this abstraction is mechanistically supported by the emergence of low-rank recurrent connectivity. The leading singular component of the recurrent weights integrates relational transition information -- whether consecutive tokens are the same or different -- across time, driving the formation of a structured, tree-like geometry in the population state space. Through singular vector ablation, we establish a causal role for this component: removing it selectively erases memory for earlier transitions while leaving local, single-step sensitivity intact. Finally, while RNNs trained on next-token prediction do not acquire these abstract representations, transferring the low-rank scaffold learned from classification significantly accelerates learning and improves generalization -- an effect specific to the abstract structure of the scaffold rather than generic statistical pretraining. These findings offer a computational account of how task demands shape recurrent connectivity to support temporal abstraction, with implications for schema formation in biological brains.

Autoregressive world models have emerged as a powerful paradigm for interactive video generation, allowing users to navigate dynamically generated environments through actions. These models are typically conditioned on a text prompt and/or a single reference frame, from which the entire world is generated. Yet the moment the user navigates beyond what is visible in that frame, the unseen regions are populated by the base model's priors, with no mechanism for the user to specify what should appear and where. This is a fundamental limitation for applications such as gaming, interactive storytelling, and simulation, where controllable scene composition is essential. We refer to this missing capability as concept spawning; introducing a user-specified visual concept into a world model, analogous to spawning in a game engine. We introduce SPAWN (Swapping Pinned Anchor with Windowed iNjection), a training-free method for concept spawning. SPAWN exploits a structural property of image-to-video backbones: the first slot of the context memory is pinned to the reference frame and acts as a foundational anchor for every generated chunk. By swapping this anchor with an external concept latent over a short injection window and letting the original anchor return, we cause the concept to propagate naturally through the rollout via the model's own memory. SPAWN supports concepts from fine-grained entities such as characters and props to large-scale elements such as buildings and landmarks, and accepts either a concept image or a text description as input. Experiments show that SPAWN integrates concepts with consistent lighting, scale, and perspective while preserving identity and temporal coherence, demonstrating that controllable concept spawning is achievable in existing autoregressive world models without any training.

Reasoning-oriented post-training enables large language models (LLMs) to solve complex tasks via multi-step inference. Supervised fine-tuning (SFT) on high-quality reasoning traces is a particularly efficient approach, but its effectiveness depends critically on careful data curation. Existing pipelines rely on a costly generate-then-filter paradigm: large pools of long reasoning traces are produced by multiple teacher models and subsequently filtered for difficulty and diversity using additional LLMs, often requiring weeks of computation. In this work, we propose a fundamentally different approach that bypasses this pipeline by directly predicting the quality of reasoning data. We show that a brief LoRA-based adaptation, combined with evaluating loss on only 0.1–1% of each reasoning trace, suffices to estimate both difficulty and diversity. Our method is grounded in a geometric view of post-training, where pretrained LLMs lie in a low-rank, anisotropic loss basin. By probing this basin via low-rank perturbations, we construct compact loss signatures whose clustering captures diversity, while loss at curvature inflection points provides a robust proxy for difficulty. We also provide a simple criterion for teacher selection. Empirically, our approach reduces data curation time from weeks to hours while maintaining or improving performance. Remarkably, on mathematical reasoning, 5K generated examples match the highly filtered 89K-example in OpenThoughts. We further introduce a high-quality physics reasoning dataset.

Machine learning (ML) methods have achieved notable success in enhancing individual components of mixed-integer linear programming (MILP) solvers. However, most existing approaches focus on single-component enhancement, neglecting critical interactions among components and often yielding diminishing returns when multiple approaches are integrated. The emergence of large language models (LLMs) provides a promising paradigm for jointly optimizing multiple solver components within a unified framework. In this paper, we propose Jolly-MILP, an LLM-guided framework to jointly optimize presolving, cut selection, and branching variable selection in exact MILP solvers. Our approach introduces a synergistic Coordinator to align component-level generators and a hierarchical Controller to produce adaptive, multi-stage strategies. Extensive experiments on nine MILP datasets demonstrate that Jolly-MILP consistently outperforms both ML-based approaches and existing LLM-based methods. These results show that explicit joint optimization, rather than isolated component-wise enhancement, is crucial for acquiring further gains in exact MILP solving, offering a new pathway for more advanced solver design.

Vision-language models (VLMs) have achieved remarkable progress in 2D visual understanding tasks, yet the passive view-centric perception paradigm limits their capacity for 3D spatial reasoning. Such reasoning often requires actively acquiring spatial evidence beyond the currently visible views, such as camera poses and cross-view spatial relationships, to infer 3D relationships that are not explicitly represented in 2D images. However, existing methods either lack active exploration or merely provide additional views during inference, without explicitly acquiring such spatial evidence beyond 2D images. In this paper, we propose View-to-World (V2W), an active exploration framework that enables VLMs to actively explore 3D worlds and lifts VLMs from passive view-centric perception to active world-centric spatial reasoning. Given a spatial reasoning task with multi-view images, V2W first reconstructs an explicit 3D world with geometry, camera poses, and language-grounded semantics, and exposes it to VLMs through visual and linguistic interfaces. Through multi-turn interaction with these interfaces, VLMs iteratively acquire spatial evidence from the constructed 3D world, enabling active world-centric spatial reasoning. V2W can be integrated with existing VLMs in a training-free manner and can be further enhanced by optimizing the exploration policy via reinforcement learning. Experiments on spatial mental modeling benchmarks demonstrate that V2W substantially improves VLM baselines in the training-free setting and achieves state-of-the-art performance with the learned exploration policy, highlighting the importance of active exploration for spatial intelligence. The code will be available upon acceptance.