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版本更新 · v2arXiv:2611.03577

Sonar-MoE: In-Backward Expert Updates for Memory-Efficient Mixture-of-Experts Training

Avni Mittal, Avinash Anand, Ashutosh Kumar, Dikshant Kukreja, Kritarth Prasad, Chaitanya Lakhchaura, Rishitej Reddy Vyalla, Erik Cambria 等 10 位作者

原文摘要

Most parameters of a Mixture-of-Experts (MoE) model belong to its experts, and standard training keeps every expert gradient in GPU memory until the optimizer step. This memory grows with the number and size of the experts, so it rises as MoE models scale. We introduce Sonar-MoE, which updates each expert tensor during the backward pass and frees its gradient right away. It brings optimizer-in-backward from dense models to batched MoE expert tensors, so the GPU never holds more than one layer of expert gradients. When we fine-tune OLMoE-1B-7B and Granite-3.1-3B-A800M in bf16 with two expert-kernel backends, Sonar-MoE lowers peak memory by 19% to 22%. Its updates are bitwise identical to standard training without expert gradient clipping, and held-out loss stays within 0.003 of all baselines. Each step also takes 17% to 23% less time than standard training with global-norm clipping. Updating whole batched tensors rather than individual experts is what keeps Sonar-MoE fast, as it avoids a 20% to 72% slowdown while using the same memory. Sonar-MoE matches optimizer-in-backward over all parameters in memory and speed while still clipping the dense parameters.

cs.LG · cs.AI · cs.CL · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07932

AttnSR: Attention Step Reward for Long-Horizon Agentic RL

Mengsong Wu, Tianchen Zhou, Zhaochen Su, Zhenhua Liu, Zhe Cui, Aimin Zhou

原文摘要

Long-horizon LLM agents make multi-step decisions, yet outcome-reward-only reinforcement learning provides little direct supervision for individual steps. We introduce AttnSR, a step-level reward augmentation method that derives reward allocations from policy attention extracted during training replay, without a separately trained reward model or additional rollouts. AttnSR traces backward through the policy model attention graph to estimate the relevance of earlier steps to the final response. The terminal step, measured relative to a running baseline, determines whether these rewards reinforce or penalize the selected steps. In a controlled Qwen2.5-7B-Instruct study, AttnSR consistently outperforms the PPO baseline across all evaluated checkpoints on ALFWorld, raising final held-out success from 91.2% to 97.1%. The ablation experiment shows that the benefit depends on how intermediate rewards are assigned, rather than simple accumulation of step rewards over time. AttnSR also improves ScienceWorld at every checkpoint while cutting environment interactions by 29.9%. These results support policy attention as a practical prior for step-level reward allocation in long-horizon agentic RL. Code is available at https://github.com/fairyshine/AttnSR .

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07929

Flux as a Codec: Zero-Shot Lossy Compression with Flux

Fuma Kimishima, Jinjia Zhou

原文摘要

We propose FluxC, a zero-shot codec that uses Flux as a compression prior. Although DiffC has shown that pretrained diffusion models can serve as zero-shot codecs, we ask how the choice of coding path and reconstruction method affects rate--distortion--perception behavior. We analyze FluxC along three axes: coding cost and bit allocation, reconstruction method, and low rate distortion. At matched signal-to-noise ratio (SNR), we show that the path choice does not affect the ideal information rate, but changes how bits are allocated across time steps. For reconstruction, we compare ancestral sampling, one-step ODE reconstruction, and many-step ODE reconstruction. Our analysis shows that ODE-based reconstruction can achieve lower distortion than ancestral sampling, especially in the low rate regime. Experiments on Gaussian sources and natural images support this prediction. Moreover, FluxC performs competitively with, and in some cases outperforms, recent neural image compression methods, suggesting that Flux can serve as a principled zero-shot codec.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07928

ZeroRAG: Adaptive Semantic Peer Routing for Decentralized Retrieval-Augmented Generation

Petru Neague, Quinten Stokkink, Martijn de Vos, Rafael Pires, Mathis Randl, Anne-Marie Kermarrec, Johan Pouwelse

原文摘要

Retrieval-augmented generation (RAG) is a standard approach to enhance the reliability of large language model (LLM) outputs by retrieving documents relevant to a query and supplying them in the LLM prompt. Today's RAG systems collect, index, and serve these documents from centralized stores. In practice, however, documents are owned by organizations, devices, and individuals who cannot or will not surrender custody of them to a central party, e.g., because of privacy, copyright or regulatory compliance. Existing modern decentralized retrieval techniques either reintroduce a coordinating authority, or rely on a global data structure that requires continuous network-wide coordination (e.g., DHTs). We present ZeroRAG, a decentralized routing layer for document retrieval in RAG that performs semantic query routing across an overlay of peers with- out global data structures or centralized orchestration. Documents remain with the peers that own them, and each peer keeps only lightweight local state: a bounded set of neighbor advertisements and a cache of recently routed queries. Using these, ZeroRAG steers each query through a best-first walk that progressively rewires the overlay toward semantically relevant neighbors, so recall improves over time as the network self-organizes. We evaluate ZeroRAG using three real-world workloads and two decentralized baselines. We find that ZeroRAG outperforms baselines in terms of recall and required network messages, approaches the recall and LLM answer accuracy of a centralized document store, self-heals after catastrophic peer failures, and scales to 100 000 peers.

cs.DC · cs.AI · cs.IR · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07926

AraEngage: Interpretable Linguistic Correlates of Engagement in Arabic Facebook News

Shimaa Ibrahim, Nour Elhouda Dalloua, Md. Rafiul Biswas, Wajdi Zaghouani

原文摘要

Engagement features developed for English are routinely applied to Arabic, but unvocalized Arabic changes what a lexical feature measures. We introduce AraEngage, a query-defined sample of 10,000 public Arabic Facebook news posts from 2,451 pages in 57 countries (2023-2024) that pairs engagement counts with interpretable linguistic features. We use it to examine which interpretable Arabic indicators are associated with follower-normalized engagement, and how far those associations can be trusted given publisher context, the definition of engagement, and lexical ambiguity. Religious expressions, message length, sentence length, and lexical density are the leading correlates in a Random Forest model ($R^2$ = 0.38 on a post-level split) interpreted with SHAP. Unlike English news, positive sentiment is associated with higher engagement in this Arabic news sample. Message length shows a length paradox: unadjusted raw engagement is highest for long posts, whereas the model's attributions for follower-normalized engagement favor short posts. A linguistic audit identifies why standard lexicon features are fragile in unvocalized Arabic: imperative lists contain homographs such as the form read as "express!" that is also the frequent preposition "via", hedge lists conflate the possibility marker qad + imperfect ("may") with the emphatic qad + perfect ("has indeed"), and religious lists mix devotional formulas with place names in political news. These are plausible sources of measurement error that may attenuate the near-zero associations of imperatives and hedges. All findings are observational associations with operationalized indicators. We release the derived linguistic features, engagement counts, refined lexicons, and analysis code; raw posts are not redistributed, in line with CrowdTangle terms.

cs.CL · cs.CY · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07925

Efficiently Estimating Cycle Statistics on Graphs via Spanning Trees

Josef Hoppe, Michael T. Schaub

原文摘要

Simple cycles encode feedback and alternative routes in networks and are important for routing problems, the convergence of dynamical processes on networks, message passing and belief propagation, or even static network characterizations such as structural balance in signed networks. Being able to assess and sample from the cycle structure of a graph is thus of interest in many analysis contexts. However, due to the combinatorial growth of the cycle space with graph size, the computational cost for such sampling is high. Therefore, we here introduce a sampling framework that enables us to sample from the cycle structure without enumerating all cycles. Specifically, the method draws uniform spanning trees and collects the fundamental cycles created by their non-tree edges, such that each tree provides an informative subset of simple cycles. Because cycles are included with unequal probabilities and are dependent within each subset, we formulate an inclusion-probability-based Horvitz-Thompson estimator, which we average over random subsets, a procedure we call Monte Carlo Horvitz-Thompson. For this, we sample multiple Horvitz-Thompson estimators and introduce a method to further subsample the individual estimators for increased computational performance. For sampling cycles, the estimator is unbiased if exact cycle inclusion probabilities are known. To extend the method to larger networks, we derive an efficient mean-field approximation for the inclusion probabilities and characterize the graph regimes in which it remains accurate. Experiments on synthetic and real-world networks demonstrate the computational scaling and statistical performance of the framework. We then illustrate the utility of Monte Carlo Horvitz-Thompson estimation by estimating cycle counts and cycle-level frustration statistics in signed spin systems. An implementation is available in the py-raccoon package.

cs.SI · stat.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07922

StitchEdit: Stitching Depth Priors into Image Editors via Per-Layer Gradient Probing

Qin Guo, Dan Xu

原文摘要

Text-guided image editing with diffusion models struggles with spatial edits (e.g. manipulating objects, changing viewpoints, manipulating occlusions) that depend on the 3D structure understanding of a scene. Marigold and follow-up work demonstrate that a pretrained diffusion backbone already harbors strong geometric priors that can be activated for depth estimation, yet how to redirect these priors toward editing remains unresolved. Through per-layer gradient probing, we discover that the editing and depth objectives are compatible in early transformer layers but actively conflict in late layers, explaining why naive depth-conditioned editing either ignores geometry or degrades visual quality. We present StitchEdit, which first activates the backbone's geometric capability via a lightweight depth-denoising adapter, then selectively fine-tunes this adapter for editing using a probe-guided per-layer learning rate schedule that preserves depth knowledge where it helps and prunes it where it conflicts. On the proposed StitchEditBench, a benchmark of 150 geometry-dependent edits spanning spatial transformation, occlusion manipulation, and viewpoint change, StitchEdit consistently surpasses both instruction-based and depth-conditioned baselines on perceptual quality and geometric fidelity.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2604.16565

Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models

Jiaoyang Ruan, Xin Gao, Yinda Chen, Hengyu Zeng, Liang Du, Guanghao Li, Jie Fu, Jian Pu

原文摘要

While Diffusion Large Language Models (dLLMs) offer structural advantages for global planning, efficiently verifying that they arrive at correct answers via valid reasoning traces remains a critical challenge. In this work, we propose a geometric perspective: Reasoning on the Manifold. We hypothesize that valid generation trajectories reside as stable attractors on the high-density manifold of the learned distribution, whereas invalid paths exhibit off-manifold drift. To operationalize this, we introduce Bidirectional Manifold Consistency (BMC), a training-free, unsupervised metric that quantifies the stability of the generated sequence through a forward-masking and backward-reconstruction cycle. Empirically, we demonstrate BMC's versatility across the full reasoning lifecycle: (1) in Diagnosis, it serves as a robust discriminator of solution validity without ground truth answer; (2) in Inference, it enables rejection resampling to effectively concentrate computational resources on complex reasoning tasks; and (3) in Alignment, it functions as a dense geometric reward that transforms sparse outcome supervision into fine-grained guidance, empowering models to self-evolve beyond standard baselines. Our results establish intrinsic geometric stability as a robust indicator of correctness for dLLMs.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07916

SymFC: Configurable Semantics for All Paths Symbolic Execution in Haskell via Embeddings

Robin Webbers, Wind Wong, Klaus v. Gleissenthall

原文摘要

Lightweight methods like QuickCheck have proven to be successful in functional languages and beyond, as they let users specify properties as Boolean functions directly in code. We can replicate some of their benefits for (bounded) proofs via symbolic execution. If we can execute arbitrary functions symbolically, we can specify properties just as easily. Unfortunately, existing symbolic execution tools for Haskell are restricted to a subset of the language that excludes many real-world applications like IO; they also have a hard time scaling to larger programs, making them a poor fit for general purpose lightweight verification. One reason behind these limitations is that existing techniques only offer fixed symbolic semantics for SMT base types. This makes it hard to cover more programming constructs and libraries; it also makes it difficult to fine-tune symbolic representations for better performance. We present SymFC, a new symbolic execution method for Haskell that offers configurable semantics via embeddings. An embedding of a function in SymFC is just another Haskell function that can use arbitrary code, as well as SMT primitives. Embeddings make it easy to support new programming constructs and fine-tune symbolic representations for speed. To integrate embeddings into Haskell's type system, we make use of Haskell's coercions, which let us transparently convert between a Haskell type and the type of its embedding. We realize embeddings in SymFC, an all-paths symbolic execution engine for Haskell, which lets us symbolically represent arbitrary types and values in GHC core. We implement SymFC as a GHC plugin, and evaluate it by checking properties of Haskell's container library, and the iproute library. We build embeddings for IORef, Haskell's Ptr/ForeignPtr interface, and the hardware library Clash, and our evaluation reports a 6.25x speedup on G2's own benchmarks.

cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07912

GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving

Xiyan Su, Finn Rasmus Schäfer, Hojun Lim, Frank Diermeyer, Johannes Betz, Markus Lienkamp

原文摘要

World action models (WAMs) for autonomous driving couple the driving policy and the visual world model in a single generative network whose objectives are fixed post-training. Conditioning on a new driving objective requires retraining. We study energy guidance as a general mechanism and present GuidedWAM, a model-agnostic framework for injecting driving objectives into frozen WAMs at sampling time. Our framework constructs each objective as differentiable energies on the sampler's intermediate clean-action estimate, which needs only forward access to the frozen model, and is stabilized by sampling-time guidance scheduling, per-energy normalization, and velocity-bounded correction. On the NAVSIM v2 hard-split, guidance improves all three frozen, publicly released WAMs by 1.8 to 3.6 EPDMS, averaged over three sampler seeds, and adds less than 10% to the sampling time per step. These results position energy guidance as a practical post-training control channel for WAMs.

cs.AI · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07909

EDGAR: Efficient Diffusion-Driven Gaussian Avatar Refinement via Synthetic Failure

Kwan Yun, Minhyeok Bang, Colette Suhjung Koo, Junyong Noh

原文摘要

Recent advances in Gaussian splatting have enabled efficient and photorealistic head avatar reconstruction, yet monocular video-based or image-based methods still suffer from under-constrained geometry and texture, leading to artifacts such as blurring, stretching, and temporal instability. We present EDGAR (Efficient Diffusion-Driven Gaussian Avatar Refinement), a conditional diffusion framework designed to enhance the quality of Gaussian avatars by correcting artifacts while preserving identity fidelity. To train EDGAR, we construct EDGAR-SET, a hybrid triplet dataset composed of real-captured and synthetic-rendered samples, which simulates realistic Gaussian avatar failure cases by pairing degraded renderings with ground-truth and reference images. EDGAR refines degraded frames through a fusion block that injects identity features extracted by the self-supervised Sapiens model into a one-step diffusion backbone, enabling effective artifact removal without sacrificing efficiency. Our EDGAR model incorporates a Mixture-of-Experts (MoE) architecture to address diverse degradation types and severity levels. The refined multi-view renderings are subsequently used as pseudo-targets to optimize a single underlying Gaussian avatar, so that the final avatar can be rendered without applying EDGAR to every output frame. Experiments on three different Gaussian avatar reconstruction methods, each evaluated under three different settings, show improvements in both perceptual and reconstruction measures over the raw outputs and an SDEdit-style refinement baseline. In addition, cross-view evaluation demonstrates improved calibrated consistency, while tests under large yaw and open-mouth expressions verify robustness under extreme settings. These results collectively indicate a useful operating regime for recoverable appearance artifacts. The code is available at https://kwanyun.github.io/edgar/

cs.GR · cs.CV · cs.LG · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06194

Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data

Zekai Wang, Yuxuan Li, Zhangyue Shi

原文摘要

Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few assets accumulate run-to-failure histories, while newly installed or rarely failing assets provide only normal-condition data. This paper asks how diagnostic knowledge can be transferred from a data-rich source asset to a data-scarce target asset when no fault data have been observed on the target. We formulate cross-asset early fault detection as a zero-shot domain adaptation problem, in which zero-shot refers to the absence of target fault samples, and propose a framework that requires only normal data from the target. The framework comprises (1) a statistical process control step that labels the source asset run-to-failure data with an exponentially weighted moving average chart, reducing manual annotation to verifying flagged segments, (2) an Adapted Domain-Adversarial Neural Network (ADANN) that combines domain-adversarial training and a Maximum Mean Discrepancy penalty with an Adapted Classification-Domain (ACD) loss, which anchors the classifier normal region to the target own normal data, and (3) an ahead-of-time labeling scheme with a prediction horizon that targets incipient faults. Under an exact-classification assumption, a theoretical analysis characterizes the domain-adversarial term for a normal-only target and the alignment state in which source fault features remain separated from target normal features. On a public run-to-failure bearing dataset, ADANN attains the highest mean F1-score among four transfer baselines, with a nominally significant precision gain over the strongest baseline, and its robustness is examined under synthetic noise.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07905

Tight Worst-Case Completion-Time Bounds for Dominant Resource Fairness

Mugen Blue, Sungjin Im, Alexander Lindermayr

原文摘要

Dominant Resource Fairness (DRF) is an influential policy for sharing multiple resources among jobs with heterogeneous demands. It combines sharing incentive, strategy-proofness, envy-freeness, and Pareto efficiency, making it particularly attractive for shared computing systems. DRF has been implemented in major resource-management frameworks, including Apache Mesos and Hadoop YARN. DRF is also nonclairvoyant: it requires no advance knowledge of job sizes, making it practical when processing requirements are difficult to predict. This motivates understanding its completion-time performance relative to an optimal schedule that knows all job sizes. However, elementary arguments can only give an $Ω(\sqrt D)$ lower bound and an $O(D)$ upper bound for total completion time, where $D$ is the number of resource types. In this paper, we close this gap by proving that DRF is $Θ(\sqrt D)$-competitive. More generally, for every fixed real $q\geq1$, we show that DRF is $Θ(D^{1/(q+1)})$-competitive for the $\ell_q$ norm of completion times, with larger $q$ placing greater emphasis on late-finishing jobs. These bounds hold with arbitrary release times. We also give a refined analysis of total completion time for small $D$.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2109.04867

Studying word order through iterative shuffling

Esmeralda S. Whitammer, Sameera Lanka, Pranav Goel, Nebojsa Jojic

原文摘要

As neural language models approach human performance on NLP benchmark tasks, their advances are widely seen as evidence of an increasingly complex understanding of syntax. This view rests upon a hypothesis that has not yet been empirically tested: that word order encodes meaning essential to performing these tasks. We refute this hypothesis in many cases: in the GLUE suite and in various genres of English text, the words in a sentence or phrase can rarely be permuted to form a phrase carrying substantially different information. Our surprising result relies on inference by iterative shuffling (IBIS), a novel, efficient procedure that finds the ordering of a bag of words having the highest likelihood under a fixed language model. IBIS can use any black-box model without additional training and is superior to existing word ordering algorithms. Coalescing our findings, we discuss how shuffling inference procedures such as IBIS can benefit language modeling and constrained generation.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06543

Using Experience Twice: Dynamic Process Rubrics for Privileged Token-Level Credit Assignment

Shumao Wu, Kongcheng Zhang, Jianqiang Huang, Baisheng Lai

原文摘要

Agent trajectories often contain both successful decisions and failed attempts, yet optimization with sparse trajectory-level rewards struggles to identify which critical decisions should be reinforced or suppressed. We observe that meaningful distinctions among on-policy trajectories can be distilled into natural-language process rubrics and used as process supervision signals. Based on this insight, we propose Online Rubric Self-Distillation (ORSD), which leverages self-derived process rubrics for dense token-level supervision, thereby augmenting outcome-centric reinforcement learning. Specifically, the current policy independently generates multiple rubric bundles at each step according to the same group of trajectories, extracting candidate process rubrics from cross-trajectory behavioral differences. A frozen verifier then evaluates all rubric items against each trajectory in the group, and selects one candidate bundle based on bundle-level alignment between rubric satisfaction and task success. ORSD treats the selected rubric bundle as privileged information during training, then compares the log-likelihoods of the original tokens with and without the rubric. The resulting likelihood gap provides token-specific process credit, which is added to the original trajectory-level GRPO advantage through additive advantage shaping. ORSD thus bridges trajectory-level outcome supervision and fine-grained process supervision, enabling token-level credit assignment using process signals. Extensive experiments on ALFWorld and WebShop show that ORSD accelerates policy improvement and boosts final task performance for both Qwen3-1.7B and Qwen3-4B under the same environment-interaction budget. Our code is available at https://github.com/AnnaXChun/ORSD.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07900

Structure from Collision via Hypothesis-Space Reasoning

Takuhiro Kaneko

原文摘要

Knowledge of the hidden interior structure of an object, including the presence and location of cavities, is essential for predictive simulation, robotic manipulation, and nondestructive inspection. However, such cavities leave no direct signature on the static visible surface, making collision-induced deformation one of the few cues used to infer their presence. Structure from Collision (SfC) addresses this problem by recovering the hidden interior structure of an object from multiview observations of a collision event. SfC is challenging for two reasons: hidden cavities are only indirectly observable through surface motion, and voids correspond to the absence of material, making direct optimization using standard density representations difficult. The density-based SfC baseline inherits both difficulties. Surface-matching gradients do not directly indicate the cavity location, while density gradients can be weak or uninformative inside or near void regions. Therefore, we reformulate SfC as hypothesis-space reasoning, representing hidden cavities as explicit structural hypotheses whose variables encode available structural assumptions rather than relying on implicit density updates. A global stage performs gradient-free optimization over this hypothesis space using forward-only material point method (MPM) simulation. A local stage then refines the selected hypothesis as a Lagrangian particle system through gradient-based optimization using differentiable MPM simulation. On the SfC benchmark, our method improves hidden-structure reconstruction and future prediction over the density-based SfC baseline. Interior-topology stress tests and robustness analyses characterize its scope and limitations. The project page is available at https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/sfc-hypothesis/.

cs.CV · cs.AI · cs.GR · cs.LG · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.22372

ASAP: Attention Sink Anchored Pruning

Jaehyuk Lee, Hanyoung Kim, Yanggee Kim, Donghun Lee

原文摘要

Vision Transformers (ViTs) face severe computational bottlenecks due to the quadratic complexity of self-attention at high resolutions. Existing token reduction methods rely on local metrics -- such as single-layer attention scores -- that are inherently vulnerable to the attention sink phenomenon, where uninformative tokens are paradoxically preserved over salient foreground objects. We propose ASAP (Attention Sink Anchored Pruning), a training-free framework that recasts this sink as a feature. Modeling ViT information flow as a Lazy Random Walk, ASAP identifies the sink as a dominant accumulator of probability mass. By computing the diffusion distance to the sink within the cumulative transition matrix, ASAP partitions tokens via Radial Diffusion Clustering and compresses background redundancy through Transition Weight Pooling in a single shot. Extensive experiments across image, video, and vision-language tasks demonstrate ASAP outperforms state-of-the-art methods, accelerating throughput by up to 48% while maintaining or even exceeding baseline accuracy.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13042

DynSHAP: Towards Explainable Dynamic Survival Analysis

Nastasya Anokhina, Jonas Jürß, Pietro Liò

原文摘要

Deep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their black box nature limits clinical trust and adoption. Existing explainability methods cannot handle longitudinal, irregular inputs and functional survival outputs simultaneously, which limits their usability in DSA. We propose DynSHAP, a SHAP framework suited specifically for dynamic survival analysis. It extends common marginal SHAP estimators to this setting by treating time--feature pairs as players in the Shapley game. We further introduce Temporal DynSHAP, which learns linear dependencies in features over time and uses conditional sampling to address them in explanations. When applied to synthetic data with known ground-truth attributions, Temporal DynSHAP recovers temporally dependent features more accurately than marginal estimators for a given state-of-the-art model. Applied to two real-world clinical datasets and two DSA architectures, DynSHAP produces attributions faithful to model learning, allowing medical experts to see which patient information drove the prediction and when.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07892

DISCERN: Distributional Synthetic Replay for Retain-Free Incremental Machine Unlearning

Nicolás Segura-Kunsagi, Juan Luis Suárez-Díaz, Iván Sevillano-García, Francisco Herrera

原文摘要

Machine unlearning aims to remove the influence of specific training data from a trained model without retraining from scratch. In real deployments, however, deletion requests arrive over time, calling for incremental class unlearning (ICU): classes are removed sequentially, previously forgotten knowledge must remain forgotten, and the remaining classes must be preserved. We present DIstributional SynthetiC rEplay for Retain-free iNcremental unlearning (DISCERN), an intervention-based class unlearning method that replaces access to the retain set with synthetic embeddings sampled from per-class parametric distributions. Unlike prototype-based replay, which assumes an isotropic Gaussian around each class centroid, DISCERN fits Euclidean (Gaussian, Laplace, Student's $t$) and directional (von Mises--Fisher, Fisher--Bingham) families to each class and selects among them through statistical tests and, when none of them accepts, cross-validated log-likelihood. This design is motivated by the architecture-dependent latent geometry: transformer embeddings lie approximately on a hypersphere and concentrate around class directions, unlike those of convolutional networks. We evaluate DISCERN against seven retain-free baselines on MNIST, CIFAR-10, and CIFAR-100 with two convolutional and two transformer backbones, under single-class, multi-class, and incremental unlearning. DISCERN is competitive when classes are removed at once and is the most stable method in ICU on transformers: on ViT-B/16, after 20 sequential deletions it retains a harmonic mean of $0.797$, whereas the strongest method in the simultaneous setting drops from $0.918$ to $0.048$. A paired ablation shows that per-class distribution selection substantially reduces the resurfacing of previously forgotten classes, acting as a stabilizer of accumulated forgetting rather than a uniform improvement.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.09258

Nexus: Same Pretraining Loss, Better Downstream Generalization via Common Minima

Huanran Chen, Huaqing Zhang, Xiao Li, Yinpeng Dong, Ke Shen, Jun Zhu

原文摘要

The foundational capabilities of large language models are acquired during pretraining on internet-scale, highly heterogeneous data mixtures. In this work, we investigate an interesting geometric question regarding the converged state of pretraining: Does the model converge to a common minimizer across all data sources (e.g., Fig. 2b), or merely a minimizer of the summed loss (e.g., Fig. 2a)? We hypothesize that the geometric "closeness" of task-specific minima is intrinsically linked to downstream generalization. We reveal that standard optimizers (e.g., AdamW) often converge to points where task-specific minima are distant from each other. To address this, we propose the Nexus optimizer, which encourages the closeness of these minima by maximizing gradient similarity during optimization. Experiments across models ranging from 130M to 3B parameters, various data mixtures and hyperparameter schedules, show that Nexus significantly boosts downstream performance, despite achieving the same pretraining loss (see Fig. 1). Notably, on the 3B model, Nexus reduces the out-of-distribution loss by 0.012 and yields up to a 15.0% accuracy improvement on complex reasoning tasks (e.g., GSM8k). This finding challenges the reliance on pretraining loss as the sole proxy for model evaluation and demonstrates the importance of implicit biases in unlocking downstream generalization.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07525

Unified Hallucination Fuzzing for Multimodal Large Language Models

Pengfei Zhou, Jiajun Song, Zhiwei Tang, Yixing Ma, Donghui Si, Yuhang Xu, Huiqi Song, Yiyuan Miao 等 14 位作者

原文摘要

Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applications. Existing evaluations, predominantly based on static benchmarks, suffer from narrow taxonomical coverage and rapid performance saturation, failing to reflect model robustness in evolving real-world scenarios. To bridge this gap, we present a systematic evaluation framework integrating a comprehensive benchmark with self-evolving stress testing. First, we introduce UniHall, a fine-grained dataset grounded in a unified taxonomy spanning Object, Instruction, and Knowledge dimensions. Second, to address benchmark saturation, we propose Self-Adaptive Multimodal Fuzzing (SAMF), a self-adaptive framework that employs evolutionary mutation strategies to explore the boundaries of model hallucinations. Crucially, to ensure reliable assessment of dynamic inputs, SAMF incorporates a structured metric suite driven by an ensemble of multi-modal oracles. Our extensive experiments reveal that state-of-the-art MLLMs exhibit significant performance degradation under fuzzing compared to conventional settings, exposing a dissociation between reasoning capabilities and factual grounding. Furthermore, we identify a helpfulness-hallucination trade-off, where reinforcement learning alignment inadvertently exacerbates sycophancy in instruction-following tasks. The framework, code and benchmark are available at https://github.com/LanceZPF/EvalHall.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07887

A Low Similarity Score Does Not Show Independence after Source-Conditioned Repository Rewriting

Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Tailia Malloy, Abdoul Kader Kaboré, Jacques Klein, Tegawendé F. Bissyande

原文摘要

Maintainers sometimes cite a low token-similarity score as evidence that an LLM-assisted rewrite of a copyleft project is independent of the original. We test what such a score can support under \emph{source-conditioned} rewriting: a coding agent keeps the original in its working copy, rewrites the repository, and iterates until a fixed behavioural harness passes, with no human edit to the code. On 69 rewrites of six copyleft Python projects we find that: \ding{182}~Agent-only rewriting is inexpensive and often yields low scores. Every controlled rewrite passes the harness after repair, usually within half an hour for the initial rewrite. Under a ground-up rearchitecture prompt, most rewrites fall below 25\% JPlag similarity and still pass the harness. \ding{183}~A single low score is an unreliable witness. Identical project--agent--prompt configurations produce wide spreads in similarity; run-to-run variation dominates project and agent effects, and in more than a third of configurations the three repeats straddle the threshold. Rewrites retain more of the original's imports and fingerprint facts than unrelated projects do, yet hand-written independent implementations of the same task often retain as much, and neither signal separates the two groups better than chance. Thus a low score on one rewrite is weak evidence of independence: another run of the same agent may score high. Claims based on similarity should report repeated generations, and candidate provenance signals should be checked against same task independent implementations, not only against unrelated projects.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07886

When, Where, How: Active In-Context Learning for Tabular Foundation Models

Christian M. M. Frey

原文摘要

Tabular Foundation Models (TFMs) adapt to a new task only through the labeled rows in their context. However, labels are expensive to acquire in real-world scenarios due to the need for highly specific domain knowledge to ensure high-quality data. Active in-context learning transfers classical acquisition rules to this setting, in which the model that guides acquisition changes with every label added. We ask how the value of diversity, predictive uncertainty and target-aware information gain changes as a frozen TFM acquires more context. For that, we study Target-Aware Nearest Neighbor (TANN) acquisition and the Expected Predictive Information Gain in Context (EPIC) and evaluate them against classical rules. Our study across twenty benchmarks with two well-established TFMs and classical GBDTs highlights three findings: (i) When: diversity-based acquisition is best during the first acquisition iterations; (ii) Where: labeling pool rows that serve the target inputs via a simple target-aware nearest-neighbor rule matters substantially; (iii) How: EPIC yields the best models at the label budget, retains on average 80--87% of the chance-corrected full-data performance with only 5-10% of the pool labeled on the eight benchmarks with the largest pools, and selects labels that also improve the predictive performance of gradient-boosted decision trees.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07883

InnerLogic-Surg: An Open Surgical Foundation Model via Continual Pretraining

Kun Yuan, Hao Ding, Yiqing Shen, Gasper Podobnik, Benjamin Killeen, Tito Porras, Axel Krieger, Mathias Unberath

原文摘要

Vision foundation models are the backbone of modern surgical image analysis, yet most surgical systems still rely on encoders pretrained on natural images or on specialist models trained from scratch on comparatively small datasets. Pretraining encoders on large amounts of surgery-specific data has the potential to generally enhance the performance of surgical image analysis methods simply by providing stronger backbones. Here we present InnerLogic-Surg, an open family of surgical vision encoders (21M--300M parameters) obtained by continually pretraining a transformer-based image encoder on millions of surgical images curated from 11,965 internet-sourced surgical videos spanning ~2,900 hours and more than fifteen procedure types. Our training recipe inherits DINOv3's pretraining stage to preserve representation ability after surgical adaptation, and additionally applies a learned feature-upsampling module for high-resolution dense prediction at low token cost. Across a benchmark covering workflow, instrument and anatomy recognition, segmentation, detection, and monocular depth, under the same architecture, the largest InnerLogic-Surg model achieves the best linear-probe performance in all 20 surgical tasks. We demonstrate that surgical continual pretraining yields transferable representations that generalize across procedures, anatomical regions, and task types, showing our surgical image encoders as a promising and scalable alternative to the procedure-specific representation learning approaches that have traditionally dominated surgical computer vision.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07882

Compositional Loop Invariant Inference for Multiphase Programs via Structured Neurosymbolic Reasoning

Mingxiu Wang, Jiawei Wang, Shuangxiang Kan, Angela Huo, Yuekang Li, Xi Zheng, Xiao Cheng

原文摘要

Loop invariants are essential for deductive program verification, yet automatically inferring them for {multiphase programs}, whose loops contain conditional branches that induce multiple execution phases with distinct behaviors, remains an open problem. Existing LLM-based methods either reason about loops as a whole or use explicit branch information to generate atomic assertions jointly across branches, without maintaining complete candidate sub-invariants for independent generation and targeted repair. We present CLOVER, a structured neurosymbolic reasoning framework for multiphase invariant inference built on two interlinked mechanisms. First, compositional inference: CLOVER statically decomposes a loop into symbolic {loop branch sequences}, conjunctions of the loop guard and branch conditions that characterize distinct execution scenarios, and guides the LLM to generate candidate sub-invariants for each one independently, so that each LLM query focuses on a single execution scenario rather than the entire loop. Second, counterexample-guided localized refinement: when SMT verification rejects a composed disjunctive invariant, CLOVER traces the counterexample back through the branch structure to identify which specific loop branch sequence caused the failure and repairs only that sub-invariant, preserving all correct components. Both mechanisms are unified by a shared symbolic structure derived from the loop's branch semantics, which guides what the LLM generates, how candidates are composed, and where refinement is directed. We evaluate CLOVER on 316 linear arithmetic, 50 nonlinear arithmetic, and 54 multiphase benchmarks. CLOVER solves 35 of 54 multiphase benchmarks, outperforming the best LLM-based baseline (29) by 20.7\% and the best traditional method (22) by 59.1\%. On linear/nonlinear benchmarks, CLOVER remains competitive, solving 355 of 366 programs overall.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07881

TactileLab: Sim-to-Real Dexterous Robot Manipulation with Efficient Shear-Sensitive Tactile Simulation

Yijiong Lin, Wenhao He, Toby Jack, Max Yang, Haoran Li, Nathan F. Lepora

原文摘要

Robotic simulators that represent shear within tactile sensing are becoming increasingly important for contact-rich manipulation. However, simulating contact shear with high-fidelity deformable or soft-body simulation is computationally demanding, limiting scalability to large-scale reinforcement learning. Here we introduce \textit{TactileLab}, a scalable multimodal tactile simulation framework built on IsaacLab, with a particular focus on a new optical-flow-based tactile representation termed \textit{simulated tactile flow}. Instead of relying on soft-body physics to model shear deformation, TactileLab uses rigid-body simulation to efficiently estimate dense tangential motion at the contact interface, producing tactile flow fields that encode shear-relevant contact dynamics. This flow field complements geometric features from a contact depth field. We further introduce a novel policy architecture for jointly processing long-horizon proprioceptive signals and rich tactile observations. Across multiple contact-rich tasks, policies trained using simulated tactile flow learn efficiently at scale and exhibit emergent contact-aware behaviours. To facilitate sim-to-real policy deployment, we then propose a novel real-to-sim tactile translation method that maps real tactile image streams to simulated tactile flow and depth fields, together with a new tactile data collection method, and validate it across diverse tactile sensor designs. Using this pipeline, we demonstrate real-world deployment on tasks encompassing dual-arm and dexterous in-hand manipulation. Overall, TactileLab provides an end-to-end sim-to-real framework for scalable tactile policy learning, enabling diverse shear-sensitive manipulation across heterogeneous tactile robotic systems.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07879

Network Effect Decomposition for Causal Effect Estimation under Network Interference

Zhirong Huang, Wenzhen Zhang, Guangquan Lu, Boyan Chen, Debo Cheng, Shichao Zhang

原文摘要

Estimating causal effects from observational network data requires accounting for both individual treatment and neighborhood exposure. Existing outcome models commonly incorporate these intervention variables into a joint predictor and estimate causal effects through prediction contrasts, leaving the corresponding response components implicit. We propose the $\underline{N}$etwork $\underline{E}$ffect $\underline{D}$ecomposition $\underline{Net}$work (NEDNet), which parameterizes the conditional outcome response as a query-invariant pre-treatment baseline plus separate treatment- and exposure-response pathways. Path-specific neighborhood contextualization supplies local pre-treatment information to the two pathways. We characterize the fitted response through a unique reference-normalized decomposition, which is equivalent to a zero mixed treatment--exposure contrast. Experiments on semi-synthetic benchmarks derived from BlogCatalog and Flickr show that NEDNet achieves the lowest PEHE among the compared methods for individual, peer, and total effects across homogeneous and covariate-heterogeneous response settings.

stat.ML · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.13977

WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL

Zhennan Jiang, Shangqing Zhou, Yutong Jiang, Zefang Huang, Mingjie Wei, Yuhui Chen, Tianxing Zhou, Zhen Guo 等 14 位作者

原文摘要

Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots. Recent work attempts to use learned world models as simulators for policy optimization, yet closed-loop imagined rollouts inevitably suffer from hallucination and long-horizon error accumulation. Such errors not only degrade visual fidelity, but also mislead policy optimization by providing unreliable learning signals. We propose WoVR, a reliable world-model-based RL framework for post-training VLA policies. Instead of assuming a faithful world model, WoVR explicitly regulates how RL interacts with imperfect imagined dynamics. It improves rollout stability through a controllable action-conditioned video world model, reshapes imagined interaction to reduce effective error depth via Keyframe-Initialized Rollouts, and maintains policy--simulator alignment through World Model-Policy co-evolution. Extensive experiments demonstrate that WoVR enables stable long-horizon imagined rollouts and effective policy optimization, achieving superior LIBERO performance and consistent real-world gains across multiple robotic platforms. These results show that world models can serve as practical simulators for RL when hallucination is explicitly controlled. Additional visualization results are available at https://wovr-rlinf.github.io.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07873

PVDepth: Panoramic Video Depth Estimation via Geometry-Aware Spatiotemporal Adaptation

Chuanxin Song, Peixi Peng

原文摘要

Panoramic video depth estimation is pivotal for applications such as Virtual Reality and World Models. However, advancements in this field are impeded by two primary obstacles: the scarcity of large-scale training data and the unique spatiotemporal challenges of Equirectangular Projection (ERP), which hinder the direct transfer of perspective models. In this paper, we first present PanoCARLA, a large-scale synthetic RGB-D panoramic video dataset, featuring natural motion trajectories and drone-like roaming perspectives. Building on this foundation, we propose PVDepth, an end-to-end framework adapted from perspective video depth models. To tackle ERP-specific geometric distortions and consequent non-linear temporal dynamics, we introduce two core mechanisms: (1) A Progressive Sphere-aware Noise Initialization strategy that anneals the noise distribution from planar to spherical, guiding the model to adapt to non-uniform information density; and (2) A Cube-rectified Temporal Modeling module that incorporates an auxiliary cubemap temporal branch to rectify non-linear temporal dynamics in the ERP domain. Extensive experiments demonstrate that PVDepth achieves superior performance, generating geometrically accurate and temporally consistent depth sequences. Code and data will be released at https:// github.com/ChuanxinSong/PVDepth.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07872

A Projection-Coupled 2.5D THB-GIFT and Quadtree-SBIGA Formulation for Moving-Load Analysis in Semi-Infinite Media

Hao Hong, Yanhui Zhong, Bei Zhang, Shuo Xu, Minchuan Tan, Quansheng Zang, Hussein Rappel, Stéphane P. A. Bordas

原文摘要

Moving loads in semi-infinite media generate extended wave fields while complex local structures require fine geometric and spatial resolution. This paper develops a projection-coupled two-and-a-half-dimensional (2.5D) THB-GIFT and quadtree-SBIGA computational formulation that assigns independent discretizations to these regions. The near-field THB-GIFT representation separates the geometry and analysis spaces and permits hierarchical local refinement, while the bounded propagation domain is discretized by quadtree-SBIGA with unified Bézier trace extraction and connected to the homogeneous unbounded exterior through a scaled-boundary impedance. On a common physical integration partition, an $L^2$ projection maps the nonmatching traces; Hermitian-adjoint force transfer then gives a virtual-work-consistent interface reduction. The regional dynamic operators and moving-load response reconstruction follow a single Fourier convention. Homogeneous half-space comparisons and a cylindrical-cavity analytical solution verify the components and the complete coupled system. In the cavity study, hierarchical THB refinement reduces the displacement error in the target inner band by a factor of approximately $41$. At a specified frequency--wavenumber point in the cavity benchmark, a comparison with matched outer-boundary discretization gives approximately $15\%$ lower sampled full-domain error for the coupled model, using $34\%$ fewer system degrees of freedom and $48\%$ fewer S-elements; the warm per-sample times are similar. A layered pavement with a buried conduit and twin modified-D tunnels demonstrate open, closed and multiple disconnected nonmatching interfaces and the analysis of local structural demand and dynamic interaction under moving loads.

cs.CE

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arXiv ↗PDF ↗