NeurIPS 2026 论文列表

Generating accurate radiology reports from 3D medical volumes remains prone to hallucination. Existing grounding strategies often depend on external visual foundation models or on fixed medical phrase queues. The former passes segmenter-derived evidence to generation, where segmentation errors are hard to suppress. The latter has limited coverage of long-tail and compositional abnormalities. We therefore propose HeatRAD, a glance-before-tell framework that first routes suspicious slice evidence into an organ-ordered visual prefix, then generates reports from this localized evidence without explicit grounding supervision. HeatRAD uses a slice-centric signed heat-conduction encoder that performs efficient spectral mixing while separating smooth anatomy from lesion-like residues. \method{} then scores suspicious slices, binds their evidence to coarse organ contexts, and assembles an organ-ordered visual prefix, guiding the decoder toward localized abnormalities instead of redundant volumetric context. Experiments on three 3D radiology benchmarks show consistent gains in clinical fidelity and hallucination-related metrics, supporting anomaly-first evidence selection as an annotation-efficient paradigm for 3D report generation.

AI agents are increasingly embedded in continuous integration and continuous delivery (CI/CD) workflows to autonomously review pull requests, triage issues, and maintain codebases. We study fully automated configurations in which external contributions invoke agents without prior human approval. Because these agents ingest untrusted content while operating with repository credentials and tools, prompt injection can have software supply chain consequences. We present GitInject, an open-source framework for evaluating prompt injection in live GitHub Actions workflows. Unlike benchmarks that simulate tool calls, GitInject provisions ephemeral repositories and executes actual workflows, exposing platform semantics such as sandbox constraints, credential handling, and permission boundaries. We use GitInject to evaluate eleven named attack scenarios across five workflows from four AI providers, covering configuration-file injection, credential exfiltration, judgment manipulation, and availability. Every provider is susceptible to at least one high-severity attack in the tested configurations. The most consequential vulnerabilities are structural: attacker-controlled configuration files from an unmerged contribution are loaded as operator-level instructions, allowing the agent--infrastructure interaction to expose credentials or corrupt review judgments. Two configuration-file attacks replicate on GitLab and Hugging Face as well as GitHub. We evaluate workflow-level countermeasures and show their coverage and utility tradeoffs. We release GitInject and all attack scenarios.

Large language model (LLM) agents have demonstrated strong capabilities in long-horizon tasks by interleaving reasoning with tool use. However, as these agents scale to complex workflows such as software engineering and open-ended research, context management becomes a fundamental bottleneck: interaction histories grow unbounded, become costly to maintain, and are difficult to reuse across sessions and agents. We introduce \textbf{Git-Context-Controller (GCC)}, a structured context management framework inspired by software version control systems. GCC elevates agent context from a transient token stream to a persistent, navigable memory workspace with explicit operations---\texttt{COMMIT}, \texttt{BRANCH}, \texttt{MERGE}, and \texttt{CONTEXT}, that enable milestone-based checkpointing, isolated exploration of alternative reasoning paths, and hierarchical retrieval of historical context. By organizing agent memory as a versioned file system, GCC allows agents to manage long-term goals, recover and transfer reasoning across sessions, and coordinate multi-trajectory problem solving in a principled manner. Empirically, agents equipped with GCC achieve state-of-the-art performance on both SWE-Bench and BrowseComp benchmarks. On SWE-Bench Verified, GCC improves task resolution by over 13\% relative to strong long-context baselines and outperforms 26 existing open and commercial systems, reaching over 80\% success rate. The project will be open-sourced for the research community.

Layer pruning removes entire Transformer decoder blocks from large language models, but introduces a mismatch between the hidden state received by the next surviving layer and the distribution it was trained to process, leading to significant performance degradation. We propose Ghosted Layers, a training-free recovery module that addresses this issue by solving a boundary activation alignment problem. Our method derives a closed-form optimal linear operator from a small calibration set to reconstruct the activation discrepancy introduced by the pruned layers. We show that this solution corresponds to the unconstrained optimum of the alignment objective, whereas existing methods are restricted to constrained solutions over limited operator subspaces. Experiments across multiple LLM backbones and pruning strategies demonstrate that our method consistently improves accuracy and perplexity over prior training-free baselines, while preserving the efficiency gains of layer pruning.

Empirical claims about the connection between over-squashing and long-range interactions in GNNs, can only be trusted if the benchmarks used to validate them genuinely require long-range interactions. The de-facto standard, the Long Range Graph Benchmark, has been repeatedly shown to be saturated by tuned short-range models, with existing synthetic alternatives being tied to specific topologies. As such, there is a lack of principled certificate of long-rangedness on *arbitrary* graphs. This state reflects the absence of a precise characterization of long-ranged benchmarks. We address this fundamental gap by introducing four verifiable *axioms*: *Predictability*, *Tightness*, *Strictly $k$-Range*, and *Topology-Invariance*, that any task claiming to test $k$-hop interactions must satisfy. We formally prove that violating any one of them admits failure modes that undermine conclusions drawn from the task. Based on these axioms, we introduce TRIP (*Truly Ranged Interactions Problem*) and its generalisation GRIP (*Generally Ranged Interactions Problem*), constructive procedures that turn *any* graph into a provably long-ranged task by drawing features from *stable* distributions. Moreover, by construction, GRIP admits a closed-form, per-range Maximum-Likelihood oracle that yields the first *a priori per-range* lower bound on test error available on any benchmark. Using our framework, we: (i) audit 4 common long-range benchmarks and identify their failures modes with respect to our axioms; (ii) on TRIP-instantiated topologies, we find a popular notion of curvature is uncorrelated with GNN performance, supporting topological-vs-computational bottleneck distinction; and (iii) we show that a novel benchmark's over-squashing measures factors beyond pure long-rangedness. Code to use the framework and reproduce experiments is released anonymously https://github.com/ferranhernandezc/graph-grip.

Georeferenced 3D reconstruction from ground images requires recovering not only the local scene geometry, but also localizing every camera center and 3D point in a geographic coordinate frame. Existing cross-view geo-localization methods are able to estimate the GPS coordinate of a single ground camera, but do not reconstruct dense 3D geometry, while recent feed-forward multi-view 3D reconstruction models predict point maps and camera poses only in a local coordinate frame. We introduce X-VGGT, a feed-forward cross-view geometry transformer that unifies these tasks by jointly processing a set of ground-level images and a georeferenced satellite image to predict ground-view camera poses, dense 3D point maps, and a scene-level transform parameterized by gravity, scale, yaw and translation. This transform projects the local reconstruction into the satellite plane, resulting in the geo-registration of all ground images with the georeferenced satellite image. We further propose an evaluation protocol for this joint task and show that, across multiple cross-view datasets, X-VGGT produces accurate georeferenced reconstructions and outperforms prior cross-view geo-localization and multi-view 3D reconstruction models.

Knowledge distillation provides shared semantic supervision for local training in federated learning, reducing the tendency of client models to overfit their own data distributions. Existing methods construct a global knowledge repository from client-uploaded local information to establish consensus across clients. However, global knowledge that is fundamentally derived from client data inevitably contains local biases under heterogeneous settings. To address this issue, this study proposes a geometry-regularized cross-silo consensus enhancement method, termed RISE, which improves collapse resistance in federated learning by preserving pre-trained geometric consensus priors during local adaptation. Specifically, RISE introduces two complementary geometry regularizers. The Biased Low-dimensional Subspace Correction module preserves the spectral energy profile of pre-trained representations by constraining the energy distribution of adapted features within the principal representation subspace. Instead of enforcing direction-wise alignment, it discourages excessive energy concentration along a few dominant components, thereby mitigating biased low-dimensional feature collapse. The Manifold Relation Graph Alignment module regularizes the relational manifold geometry by constructing a transferable inter-class topology from pre-trained global class prototypes and aligning client-side class structures with this global relation, thereby mitigating class representation drift and stabilizing global aggregation. Extensive experiments on eight benchmark datasets across two tasks demonstrate that RISE improves cross-client consensus consistency and enhances the generalization ability of the global model, outperforming eight state-of-the-art methods.

Kolmogorov–Arnold Networks (KANs) replace fixed activations in deep architectures with learnable univariate edge functions, making the choice of edge parametrisation central. Existing variants rely on fixed bases such as splines, polynomials, or Fourier features, which impose a function-space geometry before data are observed. We introduce geometry-constrained KANs, a family of edge activations derived from Banach duality maps in which the geometry itself is learned through a scalar exponent $p > 1$ per edge. This exponent controls the qualitative response: sub-Euclidean values produce sharp threshold-like behaviour, $p = 2$ recovers the linear regime, and larger values produce flatter responses near the origin. Beyond expressivity, the exponent also modulates sensitivity: smaller values reduce amplification of perturbations, providing an implicit regularisation effect without introducing an explicit shrinkage hyperparameter. Across 50 Feynman symbolic regression equations, geometry-constrained KANs match strong fixed-basis baselines on clean data while substantially improving robustness under distribution shift. In particular, they outperform cross-validated spline baselines in 85 of 90 data-scarcity settings and degrade significantly less under noise (3.7× versus 21.6× for splines). Learned exponents are interpretable and stable, revealing consistent geometric structure across equation families and input dimensions.

When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than admitting being agnostic about the answer. Retraining models to reward admitting ignorance can lead to overly conservative behaviors and poor generalization due to scarce evaluation benchmarks. We propose a post-hoc framework, Conformal Abstention (CA), adapted from conformal prediction (CP) to determine whether to abstain from answering a query. CA provides finite-sample guarantees on both the probability of participation (i.e., not abstaining) and the probability that the generated response is correct. Importantly, the abstention decision relies on prediction confidence rather than the non-conformity scores used in CP, which are intractable for open-ended generation. To better align prediction confidence with the model's ignorance, we introduce a calibration strategy using representation geometry within the model to measure knowledge involvement in shaping the response. Experiments demonstrate that we improve selective answering significantly with 75% conditional correctness.

Zeroth-order optimization (ZOO) is a promising approach for memory-efficient fine-tuning of Large Language Models (LLMs). However, scaling ZOO to larger models on a single GPU necessitates aggressive quantization, which often destabilizes training. A primary cause of this instability is that existing methods treat strictly positive quantization scale parameters as Euclidean variables, potentially leading to boundary violations and optimization difficulties. To address this issue, we propose Hyper-Octant Zeroth-order Optimization (HoZO), a geometry-aware framework that formulates fine-tuning as Riemannian optimization on the positive orthant manifold. By constructing a novel geodesically complete metric and deriving the closed-form exponential map, HoZO inherently enforces positivity constraints without bias-inducing projections. On the theoretical side, we prove that HoZO achieves the optimal oracle complexity for zeroth-order methods. On the empirical side, HoZO outperforms baselines across six downstream tasks and three model sizes. Notably, it achieves a $34\times$ memory reduction compared to first-order fine-tuning, enabling the stable fine-tuning of a 4-bit quantized Llama-2-70b model on a single 48GB GPU.

Precise localization of recording electrodes is fundamental to systems neuroscience; yet, current methods depend on labor-intensive post-hoc histology, which is incompatible with real-time feedback or chronic implants. We test the hypothesis that the electrical signals being recorded themselves carry sufficient anatomical information to localize their recording site in 3D atlas coordinates, without the need for histology. To probe this, we introduce a self-supervised framework that learns geometry-aware representations of single-channel local field potentials (LFP), leveraging probe channel geometry as a self-supervisory signal. We compare speech-based SSL objectives (e.g., masked prediction) against geometry-aware objectives that constrain the embedding space to mirror physical channel layout along the probe. These representations outperform masked-prediction baselines, and the gains compound at larger data scales. We test downstream analysis with 3D coordinate regression and brain region classification, benchmarking three SSL backbones (Wav2Vec 2.0, Whisper, and Data2Vec) against supervised baselines (AnyNet, ViT, and classical spectral classifiers) across datasets, laboratories, species, and probe technologies. Our method achieves state-of-the-art results in brain region classification and 3D coordinate regression from raw single-channel LFP, outperforming all baselines, affirming our hypothesis.

The recent breakthrough Score-Repellent Monte Carlo (SRMC) method improves MCMC sampling by tilting the target density $\pi$ in $\mathbb{R}^d$ with the factor $\exp(-\alpha\,\theta_n^\top s(x))$, where the score function $s(x) = \nabla_x \log \pi(x)$, and $\theta_n$ is the score average from past samples. However, a single scalar repellence strength $\alpha\ge0$ cannot handle both the steep and the flat directions in anisotropic energy landscapes at once. We introduce Geometry-Aware SRMC (GA-SRMC), which generalizes the Euclidean alignment to $\theta^\top M s(x)$ for any symmetric positive-definite matrix $M$. A stochastic-approximation central limit theorem under a trace constraint on $M$ identifies the trace-normalized inverse Fisher $M_{\rm opt}\propto S^{-1}$ as the unique minimizer of the worst-direction covariance bound, elevating the Fisher information $S=\text{Cov}_\pi(s,s)$ from a proposal-side preconditioner (e.g., FisherMALA, natural gradient) to the optimal repulsion-side matrix, with a single online estimate $\widehat S_n$ serving both roles at no additional cost. On an $8$-target simulation spanning the condition number $\kappa\in[1.3,100]$ and dimension $d\in[2,785]$, GA-SRMC dominates SRMC by $4.4\times$ on anisotropic Gaussian ($\kappa=100$) and $6.8\times$ on MNIST under the MALA baseline, by $18\text{ - }32\\%$ on the well-conditioned Bayesian logistic-regression posteriors, and by up to $+48\\%$ under FisherMALA; the advantage scales monotonically with the condition number $\kappa$.

Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typically trained and evaluated under ideal, degradation-free imaging conditions, whereas real-world observations often contain various degradations that differ significantly from such settings. Improving robustness for multi-view 3D reconstruction under degraded conditions therefore remains an important challenge. We present Geometry-Aware Representation Denoising (GARD), a novel framework that performs diffusion-based multi-view restoration directly in the feature space of a feed-forward reconstructor. This design exploits the geometry-aware feature representations of the reconstructor to effectively recover accurate scene geometry. Furthermore, by employing a decoder, the refined representations can also be used to restore high-quality RGB images, thereby enabling the simultaneous recovery of 3D scene geometry and high-quality imagery. Comprehensive experiments on the DA3 benchmark demonstrate the effectiveness of the proposed GARD framework. Our code and weights will be publicly released for full reproducibility.

Neural operators provide fast surrogates for PDEs but their deterministic predictions limit their use in tasks requiring uncertainty quantification (UQ), especially under geometric variability. Existing approaches primarily model uncertainty in network parameters, largely overlooking the geometry-aware representations learned by the operator itself. We propose REEF-GP (Residual on Embedded Features Gaussian Process), a post-hoc UQ framework that fits a GP to the residuals of a frozen neural operator whose internal embeddings define the kernel feature space. Rather than learning a separate feature map, REEF-GP adapts the operator’s intrinsic coordinate-feature representations to construct geometry-aware uncertainties. To ensure stability and scalability on unstructured domains, REEF-GP incorporates spectral-normalized projections, heteroscedastic geometry-aware noise, and efficient subset-based training that avoids restrictive low-rank approximations. Across five PDE benchmarks with varying geometries, REEF-GP preserves predictive accuracy while achieving calibrated uncertainty estimates competitive with deep ensembles but at a fraction of their cost. Our approach remains robust under geometric distribution shift, with uncertainty concentrating in physically meaningful regions (e.g., shock fronts). Our results demonstrate that accurate and scalable post-hoc UQ for neural operators can be achieved directly in their learned feature space, offering a practical alternative to parameter-centric approaches.

Solving large scale Optimal Transport (OT) in machine learning typically relies on sampling measures to obtain a tractable discrete problem. While the discrete solver's accuracy is controllable, the rate of convergence of the discretization error is governed by the intrinsic dimension of our data. Therefore, the true bottleneck is the knowledge and control of the sampling error. In this work, we tackle this issue by introducing novel estimators for both sampling error and intrinsic dimension. The key finding is a simple, tuning-free estimator of $\text{OT}_c(\rho, \hat\rho)$ that utilizes the semi-dual OT functional and, remarkably, requires no OT solver. Furthermore, we derive a fast intrinsic dimension estimator from the multi-scale decay of our sampling error estimator. This framework unlocks significant computational and statistical advantages in practice, enabling us to (i) quantify the convergence rate of the discretization error, (ii) calibrate the entropic regularization of Sinkhorn divergences to the data's intrinsic geometry, and (iii) introduce a novel, intrinsic-dimension-based Richardson extrapolation estimator that strongly debiases Wasserstein distance estimation. Numerical experiments demonstrate that our geometry-aware pipeline effectively mitigates the discretization error bottleneck while maintaining computational efficiency.

The explosive demand for interactive Large Language Model serving has highlighted the management of the Key-Value cache's dynamic memory footprint as a critical area for performance optimization in inference engines. Modern inference systems overwhelmingly rely on time-centric scheduling heuristics, such as Shortest Job First. However, their theoretical optimality is rooted in traditional schedule modeling, failing to capture the highly dynamic, 2D spatio-temporal geometric growth specific to LLM inference mechanisms. To resolve this, we propose the geometry-aware online scheduling by introducing the Smallest Volume First (SVF) algorithm and its highly efficient variant, 1-bit SVF. Theoretically, we provide a rigorous mathematical foundation for our approach. Utilizing a novel proof methodology, we tighten the worst-case competitive ratio ($\text{CR} \le 48 \rightarrow \text{CR} \le 5$) for SVF with known output lengths. Building upon this core breakthrough, we complete a comprehensive theoretical taxonomy analyzing our algorithms across different traffic scenarios and information availability. Practically, we seamlessly integrate our approach as a plug-and-play layer in vLLM. Extensive evaluations on Llama-3.1 models demonstrate comprehensive performance gains: SVF delivers strong reductions in both average and tail latency, while 1-bit SVF, with merely a single bit information, achieves competitive throughput and latency. This work establishes a theoretically sound and empirically proven approach for resolving memory-constrained scheduling in modern LLM deployments. To facilitate future research, our code is available at \url{https://anonymous.4open.science/r/LLM-inference-scheduling-7970}.

Model merging has emerged as a practical paradigm for integrating multiple independently trained models into a single model without joint retraining. Prior work shows that parameter fusion techniques, such as parameter decomposition, coefficient optimization, and subspace learning, can achieve strong performance while avoiding the cost of joint training. However, these approaches largely treat merging as a parameter aggregation problem, implicitly assuming compatibility in the underlying directional structures. We argue that this assumption rarely holds. In practice, independently trained models often exhibit misaligned dominant directions in both parameter and feature spaces. Naïve merging can therefore disrupt structural coherence, leading to significant performance degradation, especially in heterogeneous settings such as healthcare, where models trained on tasks like medical QA or clinical text understanding develop distinct representational geometries. Moreover, coefficient-based methods rely on well-aligned feature directions, an assumption contradicted by insights from Neural Collapse, which suggest that class features follow structured yet model-specific directional patterns. In this work, we identify directional alignment as the key principle for effective model merging. We propose Merging with Directional Alignment (\method{}), a unified geometric framework that explicitly enforces consistency in directional structures across both parameter and feature spaces. Our analysis shows that directional alignment preserves structural coherence during merging, and extensive experiments across multiple benchmarks, including healthcare QA, demonstrate consistent improvements across model scales and task settings.

Mechanistic interpretability aims to explain a model’s behavior by identifying causally responsible internal structures. Dictionary-based explainers such as sparse autoencoders and transcoders are a primary tool, but their faithfulness under out-of-distribution (OOD) shift has received little systematic attention. We show that distribution shift rotates the subspace that the model actively uses, misaligning the explainer’s dictionary trained on in-distribution (ID) activations. We formalize this misalignment as the faithfulness gap, a geometric distance between the ID dictionary and the OOD-active subspace, and show that it controls OOD faithfulness degradation. To reduce this gap, we propose the Geometry-Adaptive Explainer (GAE), which realigns the explainer's dictionary with the OOD-active subspace while preserving the original feature structure. This requires only unlabeled OOD activations and no gradient updates. We prove that GAE improves over the unadapted ID explainer, with excess loss bounded quadratically by the second-moment shift. Empirically, GAE even matches or surpasses all training-based baselines in causal faithfulness across multiple models and OOD settings.

Out-of-distribution (OOD) detection identifies test samples that fall outside a model's training distribution, a capability critical for safe deployment in high-stakes applications. Standard OOD detectors are trained on a specific in-distribution (ID) dataset and detect deviations from that single domain. In contrast, we study few-shot cross-domain OOD detection: given a \emph{single} pre-trained model, can we perform OOD detection on \emph{arbitrary} new ID-OOD task pairs using only a handful of ID samples at inference time, with no additional training? We propose \textbf{UFCOD}, a unified framework that achieves this goal through information-geometric analysis of diffusion trajectories. Our key insight is that diffusion noise predictions are score functions (gradients of log-density), and we extract two energy features: \emph{Path Energy} (integrated score magnitude) and \emph{Dynamics Energy} (score smoothness), that form a discrete Sobolev norm capturing how samples interact with the learned diffusion process. The central contribution is a \textbf{train-once, deploy-anywhere} paradigm: a diffusion model trained on a single dataset serves as a universal feature extractor for OOD detection across semantically unrelated domains. At deployment, each new task requires only $\sim$100 unlabeled ID samples for inference: no retraining, no fine-tuning, no task-specific adaptation. Using 100 ID samples per task, UFCOD achieves 93.7\% average AUROC across 12 cross-domain benchmarks, competitive with methods trained on 50k--163k samples, demonstrating $\sim$500$\times$ improvement in sample efficiency. See our code in \url{https://anonymous.4open.science/r/UFCOD-22E8/README.md}.