NeurIPS 2026 论文列表

Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited from early neural-network theory. We treat this as a normative architectural question. How should one split a fixed parameter budget $P$ between the number of units $N$, per-unit effective complexity $k_e$, and per-unit connectivity $k_c$? What controls the optimal allocation? This calls for a model in which per-unit complexity can be tuned independently of width and connectivity. Accordingly, we introduce the ELM Network, whose recurrent layer is built from Expressive Leaky Memory (ELM) neurons, chosen to mirror functional components of cortical neurons: multi-timescale memory, structured synaptic integration, and nonlinear internal computation. The architecture allows for individually adjusting $N$, $k_e$, and $k_c$ and trains stably across orders of magnitude in scale. We evaluate the model on two qualitatively different sequence benchmarks: the neuromorphic SHD-Adding task and Enwik8 character-level language modeling. Performance improves monotonically along each of the three axes individually. Under a fixed budget, a clear non-trivial optimum emerges in their tradeoff, and larger budgets favor both more *and* more complex neurons. A closed-form information-theoretic model captures these tradeoffs and attributes the diminishing returns at two ends to: per-neuron signal-to-noise saturation and across-neuron redundancy. Connectivity enters as a related mechanism that helps neurons learn distinct signals. A hyperparameter sweep spanning three orders of magnitude in trainable parameters traces a near-Pareto-frontier scaling law consistent with the framework, mapping the budget-constrained tradeoff surface between unit count, unit complexity, and connectivity. This suggests that the simple-unit default in ML is not obviously optimal once this surface is probed, and offers a normative lens on cortex's reliance on complex spatio-temporal integrators.

Despite surpassing human performance across mathematics, coding, and other knowledge intensive tasks, large language models (LLMs) continue to struggle to causally reason. A core obstacle is the target data itself: causal systems are complex and often expressed in non-executable forms, and ground-truth answers to causal queries are inherently scarce. We introduce, **CauSim**, a framework that turns causal reasoning from a scarce-label problem into a scalable, supervised one. It constructs \textit{increasingly complex causal simulators}: executable structural causal models (SCMs), incrementally built by LLMs, that scale to globally complex systems while maintaining verifiable answers to any causal query. **CauSim** operates across representations by formalizing non-executable causal knowledge into code, allowing for data augmentation, and informalizing executable SCMs into natural language, enabling supervision in previously unsupervisable representations. We structure our research into two parts: (1) how to construct increasingly complex causal simulators, and (2) a systematic study of what **CauSim** enables, demonstrating generalization across representations, consistent gains from curriculum scaling and data volume, LLM self-improvement through self-generated simulators, and data augmentation via formalization of existing domain knowledge.

Training neural networks at scale requires careful per-layer scaling of initialization, learning rate, and other optimizer hyperparameters, with the right rules depending jointly on the architecture, the optimizer, and which axis (width $D$, depth $L$, batch size $B$, etc.) is being scaled. Current practice is to derive each rule by hand, as in $\mu$P for width, CompleteP for depth, SDE-based and EMA-timescale arguments for batch size, and tailored prescriptions for matrix-preconditioned optimizers; as architectures, optimizers, and scaling axes continue to evolve, this hand derivation is increasingly a bottleneck to combining these advances at scale. We show that these derivations can be systematized by solving a linear system over hyperparameter exponents to satisfy the scaling constraints imposed by each primitive instruction of the training program, analogous to dimensional analysis in physics that enforces matched units on both sides of an equation. We develop \emph{automatic parameterization} (AutoP), a system that reads these rules off a traced training program and solves the resulting system to assign a scaling exponent to every hyperparameter, and provide a concrete JAX implementation. From a few primitive rules, AutoP recovers $\mu$P for width on Transformers, Monarch-structured Transformers, mixture-of-experts, and ResNets under Adam, SignSGD, Muon, and AdaMuon; CompleteP for depth; and the known batch-size prescriptions for the learning rate and AdamW weight decay. It also identifies new rules for the recurrence count in looped transformers, the context length in linear-attention and MLP-Mixer models, and the batch-size scaling for Muon's learning rate, all of which we demonstrate as empirically beneficial. Analogous to automatic differentiation, our results suggest that robust hyperparameter scaling rules can be automated, freeing practitioners to scale up new architectures and optimizers without rederiving the underlying theory or risking subtle errors that quietly cost compute at scale.

Post-training weight quantization is crucial for reducing the deployment cost of large language models (LLMs), yet maintaining model quality in the ultra-low-bit regime ($\le$ 3 bits) remains challenging due to highly non-uniform weight sensitivity and the lack of principled precision allocation strategies. In this work, we show that the weight sensitivity is inherently dynamic and depends on the current quantized model, rather than the full-precision reference used in existing methods. Leveraging this observation, we introduce a sensitivity estimate that better captures the changes in marginal loss during progressive quantization. We further uncover a structured pattern in sensitivity distribution, revealing a bi-directional concentration across input and output channels, which enables hardware-aligned yet expressive partitioning via channel reordering. Building on these insights, we propose ScaleBITS, a scalable framework that combines structured partitioning and an efficient approximation to greedy allocation. Our approach enables fine-grained, global bitwidth search while preserving hardware efficiency. Experiments show that ScaleBITS significantly improves over uniform-precision quantization (up to +36%) and outperforms state-of-the-art sensitivity-aware baselines (up to +13%) in the ultra-low-bit regime, without adding runtime overhead.

A post-hoc Bayesian procedure should not change its evidence or uncertainty estimates under a function-preserving reparameterization. Standard empirical-Bayes (EB) Laplace approximation violates this principle for ReLU networks. Positive homogeneity induces continuous scale orbits of equivalent parameter vectors, yet the standard isotropic objective depends on the chosen representative through the prior norm and curvature log-determinant term. Equivalent ReLU parameterizations can thus yield different marginal likelihoods, selected prior precisions, posterior covariances, and linearized predictive uncertainties. We fix this by introducing $\gamma$-canonicalization, selecting a canonical representative of each ReLU scale orbit and fitting Laplace there. This is equivalent to transporting the isotropic Gaussian prior from the canonical representative back to the original coordinates, producing a precision field that transforms compatibly with the curvature. For every $\gamma\in[0,1]$, the generalized evidence and linearized predictive distribution are invariant under ReLU rescaling. The parameter $\gamma$ indexes a family of orbit-consistent priors and is selected jointly with the prior precision by marginal likelihood, making EB Laplace a well-defined procedure on ReLU scale-equivalence classes. Empirically, we show that standard EB-Laplace varies substantially across functionally identical ReLU rescalings, while $\gamma$-canonicalized Laplace collapses this variation to zero.

Diffusion models have become the dominant paradigm in text-to-image generation, and test-time scaling (TTS) improves sample quality by allocating additional computation at inference. Existing TTS methods, however, resample the entire image, while generation quality is often spatially heterogeneous. This leads to unnecessary computation on regions that are already correct, and localized defects remain insufficiently corrected. In this paper, we explore a new direction -- Localized TTS -- that adaptively resamples defective regions while preserving high-quality regions, thereby substantially reducing the search space. This raises two challenges: accurately localizing defects and maintaining global consistency. We propose $\textbf{LoTTS}$, the first fully training-free framework for localized TTS. For defect localization, LoTTS contrasts cross-/self-attention signals under quality-aware prompts (e.g., ''high-quality'' vs. ''low-quality'') to identify defective regions, and then refines them into coherent masks. For consistency, LoTTS perturbs only defective regions and denoises them locally, ensuring that corrections remain confined while the rest of the image remains undisturbed. Extensive experiments on SD2.1, SDXL, and FLUX demonstrate that LoTTS achieves state-of-the-art performance:~it consistently improves both local quality and global fidelity, while reducing GPU cost by $2$-$4\times$ compared to Best-of-$N$ sampling. These findings establish localized TTS as a promising new direction for scaling diffusion models at inference time.

When deploying large language models (LLMs) to safety-critical applications, uncertainty quantification (UQ) is of utmost importance to self-assess the reliability of the LLM-based decisions. However, such decisions typically suffer from overconfidence, particularly after parameter-efficient fine-tuning (PEFT) for downstream domain-specific tasks with limited data. Existing methods to alleviate this issue either rely on a Laplace approximation based post-hoc framework, which may yield suboptimal calibration depending on the training trajectory, or variational Bayesian training that requires multiple complete forward passes through the entire LLM backbone at inference time for Monte Carlo estimation, posing scalability challenges for deployment. To address these limitations, we build on the Bayesian last layer (BLL) model, where the LLM-based *deterministic* feature extractor is followed by random last-layer parameters for uncertainty reasoning. Since existing low-rank adapters (LoRA) for PEFT have limited expressiveness due to rank collapse, we address this with Polar-decomposed Low-rank Adapter Representation (PoLAR), an orthogonalized parameterization paired with Riemannian optimization to enable more stable and expressive adaptation. Building on this PoLAR-BLL model, we leverage the variational (V) inference framework to put forth a scalable Bayesian fine-tuning approach which jointly seeks the PoLAR parameters and approximate posterior of the last-layer parameters via alternating optimization. The resulting PoLAR-VBLL is a flexible framework that nicely integrates architecture-enhanced optimization with scalable Bayesian inference to endow LLMs with well-calibrated UQ. Our empirical results verify the effectiveness of PoLAR-VBLL in terms of generalization and uncertainty estimation on both in-distribution and out-of-distribution data for various common-sense reasoning tasks.

Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are difficult to detect in reasoning-intensive tasks, where the content appears coherent but contains errors like logical flaws and unreliable intermediate results. While step-level analysis is commonly used to detect internal hallucinations, it suffers from limited granularity and poor scalability due to its reliance on step segmentation. To address these limitations, we propose TokenHD, a holistic pipeline for training token-level hallucination detectors. Specifically, TokenHD consists of a scalable data engine for synthesizing large-scale hallucination annotations along with a training recipe featuring an importance-weighted strategy for robust model training. To systematically assess the detection performance, we also provide a rigorous evaluation protocol. Through training within TokenHD, our detector operates directly on free-form text to identify hallucinations, eliminating the need for predefined step segmentation or additional text reformatting. Our experiments show that even a small detector (0.6B) achieves substantial performance gains after training, surpassing much larger reasoning models (e.g., QwQ-32B), and detection performance scales consistently with model size from 0.6B to 8B. Finally, we show that our detector can generalize well across diverse practical scenarios and explore strategies to further enhance its cross-domain generalization capability.

Learning-based safety certification offers a promising route for verifying complex dynamical systems when explicit models are unavailable, but existing data-driven methods often scale poorly: they treat the system as a black box, rely on dense discretizations or Lipschitz bounds, and consequently suffer from sample complexity that grows exponentially with dimension. This paper shows that a common structural property of dynamical systems---*monotonicity*---can be used to break this curse of dimensionality. We develop a data-driven framework for robust safety verification and safe controller synthesis for unknown monotone systems using only simulator queries. Our main theoretical result proves that, for monotone systems with upper-closed unsafe sets, safety is completely characterized by the existence of monotone inductive barrier certificates. This structure reduces certificate verification over continuous state spaces to localized boundary checks over finitely many cells. To learn such certificates, we introduce Max-Monotone Networks, a monotone neural architecture with universal approximation guarantees for monotone functions. We then propose a training and refinement algorithm that, upon successful termination, returns formally valid neural barrier certificates and, under mild partition assumptions, requires only $O(n)$ simulator queries. Across high-dimensional benchmarks, including oscillator networks with up to $13{,}659$ states and traffic networks with $1{,}000$ states, our method certifies safety in regimes where prior approaches either do not converge or exceed memory limits. These results demonstrate that exploiting order structure can make neural safety certification both scalable and formally sound.

Sparse rewards make cooperative multi-agent reinforcement learning difficult because useful feedback often appears only after multiple agents coordinate over long horizons. We introduce Multi-Agent Contrastive Reinforcement Learning (MACRL), a goal-conditioned method that trains parameter-shared decentralized actors with a centralized contrastive critic. Instead of learning from sparse task rewards, the critic contrasts joint state-action embeddings with future achieved goals from replay, turning every trajectory into dense self-supervised signal. Across tasks that vary in horizon length, coordination difficulty, and object interaction, our method is competitive with strong HER-based baselines when exploration is sufficient and substantially more robust when sparse rewards become uninformative. On the hardest tasks, it obtains substantial non-zero success while non-contrastive baselines remain near zero. Scaling from 4 to 64 layers further improves performance on several hard tasks, with gains up to 25 percentage points, while providing little consistent benefit to MA-SAC+HER. These results suggest that contrastive objectives and depth-scaled residual networks provide a promising foundation for scalable cooperative goal-conditioned control.

Image editing aims to modify specific attributes of an image while preserving all other aspects. In practice, however, applying even simple editing instructions to existing methods often leads to unintended changes. We identify the central issue: a fundamental causal principle of \emph{minimal change}, which requires that an intervention alter only the intended attributes in the output while leaving all others invariant, has not been explicitly incorporated as an optimization objective. To encourage minimal change, prior work rooted in causal representation learning typically imposes an $L_1$ regularizer on the latent difference between pre- and post-edit representations. However, this strategy does not scale to modern image editing models: $L_1$ regularization fails to induce true sparsity and instead tends to shrink all latent differences uniformly; sparsity in the latent space does not translate to localized change in the output of nonlinear deep image generation models; and the multi-domain or counterfactual supervision it requires is generally unavailable. To effectively leverage the minimal change principle for controllable image editing, we cast it as a reinforcement learning objective. Rather than constraining latent representations, we design rewards that directly capture minimal change over intervention outcomes and optimize them explicitly during training. To produce these rewards reliably, we further introduce an agentic reward model that leverages the chain-of-thought reasoning ability of vision-language models without requiring human annotations. The idea is that instead of emitting a single, unreliable scalar score, the model reasons explicitly about two complementary failure modes, unimplemented changes and unintended changes, and produces this supervision automatically. Experimental results demonstrate that our method produces significantly more localized and semantically consistent edits compared to existing approaches, reducing unintended changes.

A standard way to enforce group fairness in machine learning models is to add a fairness regularizer to the training loss. Existing dependence-based regularizers, however, are often computationally expensive, with per-batch costs that are typically quadratic or higher in the batch size $B$. We propose a novel group-fairness regularizer based on the Cramér-von-Mises (CvM) sensitivity index, which penalizes statistical dependence between model predictions and a sensitive attribute during training. Our method combines a rank-based CvM estimator with differentiable soft ranking, yielding a bounded training penalty with $\Oc(B \log B)$ per-batch complexity. This is the first sub-quadratic in-processing fairness method that targets genuine joint-distribution dependence. We further establish theoretical connections between the CvM regularizer and standard fairness metrics such as demographic parity and equality of opportunity. Experiments on tabular and image datasets show competitive fairness-utility trade-offs while substantially lowering training overhead compared to existing dependence-based regularizers.

In centralized, distributed, and federated learning with stochastic gradients and $n$ workers, it was recently shown that it is infeasible to find an $\varepsilon$-stationary point faster than $\tilde{\Omega}(\min[{\frac{d \kappa L \Delta}{\varepsilon}} + \frac{h L \Delta}{\varepsilon} + \frac{h \sigma^2 L \Delta}{n \varepsilon^2}, {\frac{h \sigma^2 L \Delta}{\varepsilon^2}} + \frac{h L \Delta}{\varepsilon}])$ seconds in both homogeneous and heterogeneous settings under standard assumptions: $L$-smoothness, $\sigma^2$-bounded unbiased stochastic gradients, and lower boundedness of the function, i.e., $f(x) \geq f^\star$ for all $x \in \mathbb{R}^d$, where $\Delta = f(x^0) - f^*$, $h$ is the computation time, $\kappa$ is the communication speed between the workers and the server, and $d$ is the dimension of the iterates and gradients. This result is pessimistic since it does not allow a complexity in which both ${\frac{d \kappa L \Delta}{\varepsilon}}$ and ${\frac{h \sigma^2 L \Delta}{\varepsilon^2}}$ improve with $n$, even when using random sparsification techniques; moreover, this lower bound can be matched by either non-distributed SGD or vanilla Synchronous SGD, which reduces the impact of recent progress in the design of compression-based methods. In this work, we challenge this limitation and propose new compressed methods, Inkheart SGD and M4, and show that under an additional structural assumption, which is necessary due to the lower bound and which does not restrict the class of considered problems, we achieve new state-of-the-art time complexities that break this pessimistic barrier and allow scaling with the number of workers $n$.

Geometric foundation models show promise in 3D reconstruction, yet their progress is severely constrained by the scarcity of diverse, large-scale 3D annotations. While Internet videos offer virtually unlimited raw data, utilizing them as a scaling source for geometric learning is challenging due to the absence of ground-truth geometry and the presence of observational noise. To address this, we propose SAGE, a framework for Scalable Adaptation of GEometric foundation models from raw video streams. SAGE leverages a hierarchical mining pipeline to transform videos into training trajectories and hybrid supervision: (1) Informative training trajectory selection; (2) Sparse Geometric Anchoring via SfM point clouds for global structural guidance; and (3) Dense Differentiable Consistency via 3D Gaussian rendering for multi-view constraints. To prevent catastrophic forgetting, we introduce a regularization strategy using anchor data. Experiments on applying SAGE to two architecturally distinct backbones (including both MV-DUSt3R and VGGT) show that Chamfer Distance is consistently reduced by 20-42% on unseen benchmarks (7Scenes, TUM-RGBD, Matterport3D). SAGE establishes Internet video as a viable and scalable adaptation source for geometric foundation models.

Multi-view 3D reconstruction is increasingly driven by feed-forward models, which are fast and robust but often imprecise or globally inconsistent across views, especially in sparse-view and low-overlap settings. Structure-from-motion (SfM) offers a complementary geometric signal through camera poses and sparse 3D points estimated by correspondence filtering and optimization. To propagate this globally consistent \emph{SfM scaffold} into dense geometry, we introduce \textbf{Scaffold3D}, an SfM-conditioned reconstruction framework that injects image patch-aligned SfM point tokens into a pairwise feed-forward pointmap predictor. Our approach preserves image-token reasoning while using explicit SfM structure to condition dense prediction. Since pairwise pointmaps are predicted in local frames, we fuse them with global alignment and an SfM anchoring term. Across established benchmarks (ScanNet++, ETH3D, and Tanks\&Temples) and out-of-domain data (4D-DRESS and MV-dVRK), Scaffold3D achieves stronger overall performance than feed-forward reconstruction models, point-token propagation, depth-completion baselines, and recent geometry-conditioned models. The gains are especially clear in low- and no-overlap evaluations. Our two-view SfM-conditioned pointmap predictor also outperforms several multi-view geometry-conditioned baselines, indicating that how geometric inputs are fused with image-token reasoning is as important as the number of conditioned views. Together, these results establish SfM-scaffold conditioning as a practical interface between learned pointmap prediction and classical geometric optimization.

Vision-language-action (VLA) policies increasingly generate natural-language rationales before executing embodied actions. These rationales are attractive as monitors for safety-critical behavior because users or automated checkers can inspect whether the model appears to understand the scene and intend a safe action before execution. We show that this signal can be misleading. In many reasoning VLAs, the rationale generator and action head share continuous hidden states, while the generated text reveals only part of that representation. This creates a Say the Same, Act Differently failure mode, where rationale text remains stable while the action output changes substantially. We first study this phenomenon by introducing a new statistical method to separate text-sensitive and action-sensitive directions in hidden-state space of a reasoning VLA. Across the driving VLA Alpamayo and the manipulation VLA InstructVLA, we show that the dominant action direction lies largely outside the active text span, with mean projection ratios of only 1.9% and 5.3%. We define the residual as a text-neutral action-subspace (TNAS) direction, which tests whether action-relevant directions remain after removing measured text-sensitive directions. We then use TNAS to guide white-box pixel-space projected-gradient-descent (PGD) attacks on Alpamayo. TNAS-guided PGD successfully uses a localized two-forward-camera patch to induce a 30.5 m mean ADE shift in trajectory planning while preserving the exact rationale in 86.7% of cases. Moreover, across multiple text-preserving PGD objectives, the optimization direction consistently aligns with TNAS. These results reveal a fundamental shared-cache monitorability gap in reasoning VLAs: stable rationale text should not be treated as sufficient evidence that the action output remains stable.

Urban uncrewed aerial vehicle (UAV) vision-language navigation (VLN) requires agents to follow instructions across extended urban spaces, inherently demanding long-term memory and geospatial grounding. However, scaling existing benchmarks remains difficult because of their reliance on costly reconstructed 3D assets, limiting geographic diversity and episode scale. To address this, we introduce SatNav, a scalable, long-horizon UAV VLN benchmark built from high-resolution satellite imagery. SatNav targets city-level navigation missions and uses satellite crops as approximations of UAV nadir views for visual observations. Through an automated cue-to-episode pipeline, SatNav constructs 118K episodes from 59 scenes across 18 cities, with an average trajectory length of 379 m. To stress-test long-horizon memory and geospatial reasoning, SatNav defines three task families: Boundary, Landmark, and Route, targeting loop progress tracking, landmark-based spatial grounding, and route following with counting cues. Benchmarking classical and recent large vision language model (LVLM) based VLN agents on SatNav shows that city-scale navigation remains challenging. We further introduce SwiftVLN, a modular framework with switchable memory components, and conduct systematic memory-design ablations. Finally, satellite-to-UAV transfer experiments show that satellite-trained navigation models can operate on real-flight UAV observations, showing the practical relevance of SatNav.

While foundation models have significantly advanced human recognition across diverse modalities, they predominantly rely on static, geometric feature extraction. This approach fundamentally diverges from human perception. Consequently, current models often suffer from ``semantic blindness,'' overfitting to transient noise while failing to leverage invariant soft biometrics, and struggle to capture temporal motion signatures. To bridge this gap, we propose SapiensID 2.0, a human recognition framework enriched with both semantic and temporal awareness. To overcome the lack of soft-biometric annotations, we transfer zero-shot semantic knowledge from Multimodal Large Language Models (MLLMs) into a discriminative embedding space. We resolve the dimensional mismatch between these spaces using Invariant Trait Alignment (ITA) to distill core persistent traits, and Transient Noise Disentanglement (TND) to decouple artifacts like clothing. Furthermore, we design a Kinematic Semantic Attention Head (K-SAH) that extends spatial attention across temporal windows. By tracking semantic patches over time, K-SAH captures rich kinematic signatures without requiring large-scale video datasets. Extensive experiments demonstrate that SapiensID 2.0 achieves state-of-the-art performance across image- and video-based person re-identification and gait recognition, while maintaining robust face recognition capabilities.

As multimodal LLMs increasingly emphasize video and audio, a common assumption is that solving such tasks calls for native omnimodal models. We show that this is not always necessary: coding agents equipped with only text+vision and a sandboxed tool-using interface can perform competitively with, and in several settings outperform, SOTA native omnimodal models and predefined multimodal agent scaffolds across multiple audio-video benchmarks. Our trajectory analysis suggests that their advantage comes from coding agents writing code and orchestrating tools to retrieve relevant content from transcripts, frames, and other non-textual modality signals from raw inputs. This effectively converts omnimodal tasks into evidence retrieval and information processing problems, avoiding the inefficiency of ingesting entire videos or audio streams into context. To characterize remaining limitations, we propose a failure taxonomy and a process-level analysis of tool-use traces, and find that simple skill injection, including human-written skills and self-distilled skills from execution logs, can substantially improve performance over the no-skill baseline. To examine whether such capability can be elicited in open-source models, we further introduce Code-X, a complete training recipe with the OmniCoding trajectory dataset and verifiable reward, providing an exploratory baseline on Qwen-3.5-9B and Qwen-3.6-27B. Finally, given the maturity of many-modality understanding, we argue that the more meaningful frontier lies in many-modality processing, and introduce TerminalBench-O, the first process-level benchmark designed for coding agents on real-world omnimodal processing tasks. Together, our findings open new directions for omnimodal content processing and evaluation.