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新论文 · v1arXiv:2611.17191

最大化LoRA适配器容量利用率以缓解学习-遗忘权衡

Maximizing Capacity Utilization of LoRA Adapters for Mitigating the Learning-Forgetting Trade-off

Tomohiro Sawada, Kartik Goyal

AI 速读 基于摘要

作者提出一种无需SVD的Rényi-2有效秩正则项，通过提升LoRA适配器的有效秩来改善学习与遗忘之间的权衡。

cs.LG · cs.CL

arXiv ↗PDF ↗
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Low-Rank Adaptation (LoRA) often underperforms full fine-tuning, especially in heterogeneous and continual-learning settings in which substantial forgetting occurs. In general, it suffers from an undesirable learning-forgetting trade-off across diverse fine-tuning settings. We identify a potential contributor: LoRA adapters, especially high-rank ones, frequently fail to realize the available capacity afforded by the configured rank. Specifically, we find that standard LoRA updates have effective rank far below their configured rank, limiting how broadly the update energy is distributed. Therefore, we propose training mechanisms that optimize the original objective while maximizing adapter effective rank. Our main method adds a simple, stable, differentiable, and SVD-free Rényi-2 effective-rank regularizer to the language-modeling loss. We also connect capacity maximization to norm-bounded updates. In mixed-domain rank sweeps on three models evaluated with multiple random seeds, a penalty weight of $λ=0.01$ lowers mean Wiki perplexity at every tested rank of at least $16$ relative to unpenalized LoRA. Mean training perplexity improves or increases by at most $0.02$ in all but one model-rank comparison. Single-task and scheduling experiments further show that the balance between learning and retention depends on the configuration. We also observe that several LoRA variants in the literature also suffer from the identified capacity issue, and our approach provides a complementary axis of improvement to LoRA-based parameter-efficient fine-tuning. These results suggest that effective rank is not only a post-hoc diagnostic of adapter quality, but a trainable objective for improving the learning-forgetting frontier. Our code is available at https://github.com/tomohiro-sawada/lora-capacity-utilization

新论文 · v1arXiv:2611.16718

MusicRWKV: A Multimodal RWKV Framework for Music Representation Learning

Terry Tan

原文摘要

Recent music foundation models have achieved remarkable progress in music representation learning, yet most existing approaches rely on Transformer architectures whose quadratic attention complexity introduces substantial computational and memory overhead when modeling long-duration music recordings. In this work, we propose MusicRWKV, a multimodal music representation learning framework built upon the RWKV architecture. MusicRWKV jointly models waveform, spectrogram, and metadata information within a unified self-supervised learning framework. To improve efficiency for long musical sequences, we introduce an invertible compression module that reduces sequence length while preserving reconstruction capability. A FiLM-based conditioning strategy is employed to facilitate modality-aware representation learning without introducing expensive cross-attention operations. We further investigate the integration of Soft ROSA as a differentiable retrieval mechanism for neural-symbolic interaction. Experimental evaluation across a diverse collection of Music Information Retrieval (MIR) benchmarks demonstrates that the proposed RWKV-based architecture can be effectively adapted to heterogeneous music understanding tasks. A controlled pretraining ablation shows that Soft ROSA improves validation performance, while layer-wise telemetry analysis reveals a progressive transition from distributed retrieval to selective retrieval in deeper network layers. These results suggest that RWKV provides a promising architectural alternative to Transformer-based models for multimodal music understanding.

cs.SD · cs.AI · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16673

PIM: A Physical Intuition Model for Low-Latency Prediction of Object Interaction Outcomes

Tangwen Luo, Chuankun Zheng, Yuchi Huo

原文摘要

Predicting object interaction outcomes from initial conditions is a central problem in data-driven physical reasoning. Existing methods often use rigid-body-level states or predict the evolution through autoregressive rollouts. Coarse representations cannot represent local deformation, while rollout errors may compound over long prediction horizons. Instead, we introduce PIM, a Physical Intuition Model that directly predicts a physical system's equilibrium state from its initial mesh geometry, kinematics, and physical attributes. PIM combines local k-nearest-neighbor (KNN) attention to encode intra-object geometry, anchor-based cross-object attention to model interactions, and a dual-branch decoder that predicts object-level translations and per-vertex residual displacements. Across MuJoCo scenarios involving both rigid and deformable interactions, PIM achieves lower vertex and centroid errors than the evaluated baselines. By predicting complete equilibrium states in a single forward pass, PIM substantially reduces latency relative to autoregressive learned models and MuJoCo rollouts under the evaluated protocols, enabling low-latency inference when only the final outcome is required.

cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14782

Bayesian inference with a polymer probabilistic computer

Travis Wanless, Yuriy Bandera, Lubomir Kostal, Stephen H. Foulger

原文摘要

Bayesian inference weighs competing explanations of noisy data by sampling from a posterior distribution, a task that deterministic hardware performs only by simulating randomness. Probabilistic bits (p-bits) supply this randomness physically, but the few hardware networks of p-bits built so far use inorganic devices, address optimization or learning, and are judged without reference to the spread expected from finite sampling. Here we report a probabilistic computer built from solution-processed conjugated-polymer memristors. Four polymer p-bits, tracked online for drift and coupled in a closed Gibbs-sampling loop, sample competing, degenerate and cooperative posteriors over sixteen states with statistics that fall within the range of ideal p-bits, both in their distribution and in their autocorrelation, without any parameter fitted to the target. This agreement extends to new simulated sensor readings whose outcome was predicted before each run, including readings that point away from the true answer. Reprogrammed in software, the same circuit and controller reproduces the invertible AND gate of earlier p-bit networks, with its temperature fixed in advance rather than fitted. Polymer p-bits thus open a route to printed, large-area probabilistic computers that infer alongside their sensors.

cs.ET · cond-mat.dis-nn · cond-mat.mtrl-sci · cond-mat.soft

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07289

Patch-Based Generalized Neural Texture Upscaling

Shin Fujieda, Takahiro Harada

原文摘要

We propose a lightweight neural texture upscaling method. A single generalized MLP is trained on a diverse collection of texture patches and integrated into the texture sampling stage, upscaling low-resolution textures on-the-fly at rendering time. Because the model is generalized rather than overfit to specific content, no per-asset training is required - the same model applies to any existing scene without modifying texture data or the asset pipeline. Storing lower-resolution base textures reduces disk footprint, upscaling at sampling time eliminates the need to store high-resolution textures in VRAM, and sharing a single set of model weights across all textures ensures non-divergent GPU execution. We evaluate our method across multiple texture classes, demonstrating consistent improvement over analytic upscaling methods in perceptual quality.

cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36672

Learning Where to Trust: Reliability-Guided Expert Routing and Velocity Gating for 3D Deformable Registration

Chenyu Guo, Wenjie Dai, Yiqian Chang, Zhiyuan Zhang, Yunfan Wei, Shuhan Qi, Tianrui Xu, Dong Liu 等 9 位作者

原文摘要

Deformable image registration relies on accurate anatomical correspondences. However, explicitly coupling local matching reliability with spatial context selection and deformation update strength remains insufficiently explored in learning-based registration. This challenge is particularly pronounced in homogeneous, weakly textured, and severely deformed regions, where ambiguous matches may induce erroneous deformation updates. We propose ReliMorph, a reliability-routed symmetric framework for 3D deformable registration that couples correspondence assessment with spatial context selection and update strength.Local correlation distributions characterize correspondence ambiguity using shared anatomical features with modality-specific adaptation. These reliability cues guide spatial expert fusion and gate residual velocities during coarse-to-fine refinement. Updated inverse-inconsistency feedback guides routing and gating at finer pyramid levels. To our knowledge, ReliMorph is the first 3D deformable registration framework to jointly condition spatial expert routing and residual velocity gating on correspondence reliability. Extensive experiments demonstrate that ReliMorph consistently outperforms competing methods in anatomical overlap, highlighting its effectiveness across unimodal and multimodal registration tasks.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13708

FedVP: Federated Learning across Heterogeneous Models by Pooling Locally Trained Verifiers

Yifan Li, Xi Chen

原文摘要

Conventional federated learning relies on the shared model backbone to aggregate client updates, limiting collaboration among institutions that deploy large language models (LLMs) from different families and at different scales. We propose Federated Verifier Pool (FEDVP), a new federated learning paradigm that enables heterogeneous clients to collaborate through shared verification rather than parameter aggregation. Each client trains a verifier using local data and responses generated by its chosen LLM backbone. The server pools these independently trained verifiers and combines their judgments to select answers without merging model parameters. This allows both LLM backbones and verifiers to differ in architecture and size, while keeping the LLM backbones frozen and deriving collaborative gains from improved answer selection. We evaluate FEDVP across five public medical question-answering datasets treated as federated clients, using Qwen and LLaMA backbones ranging from 3B to 14B parameters. FEDVP consistently improves performance across all five clients and outperforms the strongest conventional federated baselines by 1.46-5.79 percentage points in mean accuracy.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.15507

Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference

Nitin Priyadarshini Shankar, Soham Lahiri, Sheetal Kalyani, Saurav Prakash

原文摘要

Federated Learning (FL) enables distributed clients to jointly train a global model without sharing their raw data. However, deploying the resulting deep neural networks on resource-constrained edge devices remains challenging due to their inference-time computational and memory requirements. Binary Neural Networks (BNNs) offer substantial efficiency gains by representing weights and activations using a single bit, but achieving competitive accuracy under such aggressive quantization remains difficult. Rotation-aware binarization reduces quantization errors by aligning real-valued weights with their binary representations. Despite its effectiveness in centralized learning, applying rotation-aware binarization in FL is non-trivial due to discrepancies between local and global models, particularly under heterogeneous client data distributions. To address this challenge, we propose FedBNN, a federated rotation-aware binarization framework that leverages global model information to guide local binarization while incorporating locally learned rotation information into server-side construction of the global binary model. Experiments across multiple benchmark datasets, model architectures, and degrees of data heterogeneity demonstrate that the proposed integrated methodology achieves accuracy competitive with full-precision federated models while generally outperforming existing federated binary learning approaches. Overall, FedBNN enables accurate binary inference in heterogeneous federated settings, with savings of up to $58\times$ in FLOPs and $32\times$ in memory over full-precision models, offering a practical balance between predictive performance and deployment efficiency on resource-constrained edge devices.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12568

Modeling and Estimation of a Towed Marine System

Josef Matouš, Simon Hoff, Damiano Varagnolo, Marius S. Rundhovde, Kristin Y. Pettersen, Martin Saska

原文摘要

This paper addresses the modeling and state estimation of a towed marine system consisting of two autonomous surface vehicles (ASVs), a pair of umbilical cables, towfish, and a trailing loop cable. Inspired by the existing literature, we employ a lumped-mass modeling approach for the cables and towfish, capturing the essential dynamics while maintaining computational tractability. A Lyapunov-based analysis is conducted to verify the stability and convergence properties of the proposed model. Building on this model, we propose two state estimation methods, namely a reduced-order observer and a perfect-measurement extended Kalman filter (PMEKF), using only the measurements from the ASVs' onboard sensors. Numerical simulations and experimental results are provided to validate the effectiveness of the proposed methods.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12346

ReVMap: Vectorized Global Mapping via Connectivity-Aware Local Map Fusion

Hongjae Shin, Jungho Kim, Donghyuk Kwak, Myeongjun Kim, Seunghoon Yu, Jiyong Oh, Jun Won Choi

原文摘要

Large-scale annotation of vectorized high-definition (HD) maps is labor-intensive and costly, motivating local-to-global auto-labeling approaches that incrementally construct global maps from local vectorized predictions. However, existing approaches often overlook prediction uncertainty and rely on heuristic map integration, potentially propagating errors and introducing topological inconsistencies. To address these limitations, we propose ReVMap, a reliable local-to-global framework that iteratively refines local map predictions and global map construction through uncertainty-guided prior utilization and connectivity-guided global map revision. Specifically, ReVMap leverages point-wise localization uncertainty to incorporate global map priors into local map prediction, improving robustness against inaccurate prior information. It then predicts local-to-global connectivity and selectively updates the global map through uncertainty-aware fusion based on the reliability of local predictions. By iteratively coupling local prediction and global revision, ReVMap progressively improves both local map accuracy and global map consistency. Extensive experiments on the nuScenes and Argoverse 2 benchmarks demonstrate that ReVMap achieves state-of-the-art performance in both local map prediction and global map construction.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12223

Mitigating the BICM Loss in MIMO Receivers with Correlation-Informed Logits

Christoph Studer

原文摘要

Bit-interleaved coded modulation (BICM) is widely used in multiple-input multiple-output (MIMO) wireless systems. However, separating MIMO data detection from channel decoding and only exchanging per-bit soft information discards correlation information among the bits within each transmit vector. We show that the resulting information loss limits the performance of practical one-shot receivers. Even though iterative detection and decoding (IDD) can recover most of this loss, IDD significantly increases receiver complexity and latency. In order to reduce this loss, we propose COrrelation-Informed Logits (COIL), a soft-output format that combines per-bit soft information with pairwise coupling logits for correlated bits. We show how to incorporate these coupling logits into a conventional belief-propagation-based channel decoder, which enables correlation-aware decoding in a one-shot receiver without IDD. We evaluate COIL in a MIMO-BICM system with a conventional low-density parity-check code. For a 4x4 MIMO orthogonal frequency-division multiplexing (OFDM) system, a one-shot linear minimum mean-square error (LMMSE) receiver with COIL outperforms a conventional one-shot unbiased LMMSE receiver by 1.3dB and operates within 0.8dB of four-iteration IDD, while requiring only a single pass through the data detector.

cs.IT · eess.SP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.13389

Logit-Conditioned Diffusion Decoding for Frozen Discrete-Token VLMs

Ji Woo Hong, Gwanhyeong Koo, Hee Suk Yoon, Eunseop Yoon, SooHwan Eom, Qi Dai, Chong Luo, Chang D. Yoo

原文摘要

Pre-trained discrete-token VLMs generate images by sampling sequences of codebook indices, but their visual fidelity is bounded by the VQ-VAE decoding stage. Prior work addresses this bottleneck by replacing the discrete tokenizer with continuous or hybrid visual token representations, or by integrating diffusion into the generation pipeline; both require foundation-scale VLM retraining or realignment. We show this is unnecessary: before each token is sampled, discrete-token VLMs already compute a full logit distribution over the codebook, but standard decoding discards it after selecting a single index. Under Otsu's threshold, the pre-sampling logits of capable VLMs assign statistically meaningful probability to at least two codebook entries per token, and recovering this distribution can substantially close the fidelity gap. We propose DCDD, a post-hoc framework that conditions a diffusion decoder on this discarded distribution while leaving the VLM frozen. At inference, DCDD converts the VLM's pre-sampling logits into distribution-weighted code vectors; during training, it uses VQ-VAE encoder-derived proxy logits calibrated to match the distributional statistics of the VLM's inference-time logits. The decoder maps these representations to high-fidelity images as an alternative to the native VQ-VAE decoder. Trained on ImageNet-1K for 50K steps, DCDD reduces reconstruction FID by 74% and Janus-Pro 7B's generation FID on MJHQ-30K by 34%, while semantic alignment across GenEval, DPG-Bench, and WISE remain consistent. Controlled comparisons with diffusion models from the same backbone family, applied either as an image-to-image refiner or as an identically trained decoder on the sampled codes, confirm that the gain stems from conditioning on the VLM's pre-sampling logit distribution rather than from the diffusion model itself.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11973

Beyond Prediction Residuals: Conformal Regret Calibration for Robust Contextual Linear Optimization

Lingjie Zhao, Hansheng Jiang, Wei Qi

原文摘要

Predict-then-optimize systems convert contextual forecasts into decisions, yet conformal robust optimization typically calibrates prediction residuals rather than downstream loss. For linear optimization, this parameter-space geometry can be badly misaligned with decision risk: large objective perturbations may preserve the optimizer, while small boundary-crossing perturbations may cause substantial regret. We introduce Conformal Regret Sets, a post-hoc, model-agnostic framework that conformalizes an optimization-derived regret surrogate and uses its calibrated level set for minimax-regret refinement. We first show that directly conformalizing exact regret, although sufficient to certify the nominal predict-then-optimize decision, yields a degenerate robust problem: absent an exogenous bounded support, every alternative decision has unbounded worst-case regret. We therefore use SPO+, a pointwise upper bound on exact regret, as the conformal score, obtaining distribution-free finite-sample marginal regret guarantees under exchangeability. For contextual linear programs, the resulting set decomposes into polyhedral pieces aligned with the normal fan of the feasible polytope. A vertex is active if and only if its predicted suboptimality gap is at most half the calibrated threshold $η$, enabling local, output-sensitive generation of all relevant pieces. Moreover, if $Γ$ is the predicted gap to the closest competing vertex, the nominal and refined decisions admit the sharper worst-case-regret certificate $(η-2Γ)_+$, with an exact early exit when $η\leq 2Γ$. Experiments on contextual fractional knapsack validate the geometry and certificates: continuous refinement reduces the $0.95$-quantile and $\operatorname{CVaR}_{0.95}$ of regret by $9.1\%$ and $13.1\%$ relative to nominal PTO, while vertex-restricted refinement changes only $10.0\%$ of nominal decisions.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.17739

GeneZip: Region-Aware Compression for Long Context DNA Modeling

Jianan Zhao, Xixian Liu, Zhihao Zhan, Xinyu Yuan, Hongyu Guo, Jian Tang

原文摘要

Long-context DNA modeling is computationally expensive, motivating existing approaches to reduce sequence length through uniform downsampling. However, functional information and sequence redundancy are unevenly distributed across the genome, calling for finer resolution in functionally dense regions and stronger compression of redundant sequence. We introduce GeneZip, a region-aware DNA compression framework that learns where to allocate a limited token budget. Building on H-Net-style dynamic routing, GeneZip combines a Region-Aware Ratio objective, which uses regional labels derived from GENCODE gene annotations to specify compression targets during training, with bounded routing for stable token counts. At inference, it compresses raw DNA without annotations. GeneZip provides three main benefits. First, it preserves predictive utility under high compression: GeneZip-70M achieves 137.6-fold compression with a validation perplexity increase of 0.31 relative to uncompressed Mamba2-84M, and the best average rank across four DNALongBench tasks among compression models with leakage-controlled pretraining. Second, it learns region-aware allocation with emergent repeat-aware compression. Alternative regional priors yield task-dependent performance trade-offs. Without repeat-specific supervision, GeneZip compresses transposable-element-derived and tandem repeats more strongly than matched non-repeat sequence; its tandem-repeat compression preference transfers to a nine-species evaluation panel without further training. Third, it enables efficient long-context modeling: on 450-kb inputs, GeneZip-70M achieves 36.1-fold higher inference throughput than JanusDNA-7.7M on a single H100 GPU.

q-bio.GN · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11500

ForeAct: Geometry-Guided Action Learning from World Representations

Wei Lu, Jiaxi Zhang, Yunheng Wang, Taowen Wang, Yuetong Fang, Zhenhui Zhang, Shuning Zhang, Zizhao Yuan 等 11 位作者

原文摘要

Robotic manipulation requires representations that capture both scene geometry and its evolution during interaction. Existing approaches have largely treated these two aspects separately. World action models capture such dynamics by coupling action prediction with future video generation, but their reliance on iterative diffusion or rectified-flow inference leads to high computational cost, while their representations lack explicit spatial structure. Explicitly modeling scene geometry typically requires auxiliary modules that increase both parameter count and computational cost. Conversely, geometry-centric policies primarily recover observed spatial structure and often lack explicit supervision for anticipating future geometric changes during interaction. To address these limitations, we introduce ForeAct, which repurposes a pretrained video world model as a single-pass representation backbone for low-latency control. ForeAct directly processes the clean latent of the current observation in one forward pass, eliminating future-frame synthesis and iterative denoising while retaining the backbone's pretrained spatiotemporal priors. Rather than introducing a separate geometry backbone, ForeAct uses learnable tokens supervised by next-frame relative depth to shape the shared video representation through bidirectional self-attention. This compact interface turns the spatial knowledge already encoded in the backbone into a future-aware representation for action generation, with little parameter overhead. The lightweight geometry readout is used only during training and removed at deployment, enabling low-latency, geometry-aware control without depth input or an external geometry backbone. Without additional large-scale pretraining, ForeAct achieves 96.6% average success on LIBERO, 63.5% on SimplerEnv-Bridge, and a 74.4% success rate across 90 real-world rollouts.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.27769

Smoothed Score Queries and the Complexity of Sampling

Jingbo Liu

原文摘要

We study the query complexity of sampling from high-dimensional Gaussian distributions using gradient information. In the standard oracle model, exact gradients expose only matrix-vector products with the precision matrix, leading to polynomial approximation barriers and a characteristic $\sqrtκ$ dependence on the known spectral range. We show that this barrier disappears when the sampler is allowed to query smoothed scores, namely gradients of the logarithms of the Gaussian-convolved densities. For a centered Gaussian target with precision matrix $Λ$, a smoothed-score query at noise level $τ$ gives access to the resolvent $(Λ+τ^{-1}I)^{-1}$. Combining geometrically spaced noise levels with sinc-quadrature rational approximation, we obtain a sampler with $q=O((\logκ+\log(e\sqrt d/δ_{\rm TV}))\log(e\sqrt d/δ_{\rm TV}))$ smoothed-score queries for total variation error $δ_{\rm TV}$, improving the condition-number dependence from $\sqrtκ$ to logarithmic. We also study finite-bit gradient oracles. Using coordinatewise quantization of the transformed smoothed-score answers and a final dithering step, we obtain a sampling scheme whose total communicated gradient information is polylogarithmic in $κ$; in particular, for fixed dimension and accuracy, the bit complexity is $O(\log^2κ)$. To complement these upper bounds, we introduce a channel-synthesis, or reverse-Shannon, converse technique for sampling lower bounds. This converts total-variation simulation guarantees into communication requirements and yields an $Ω(d\logκ)$ bit lower bound for $d\ge2$ and fixed total variation error below one, as $κ\to\infty$. Together, these results identify smoothed scores as a provably more informative oracle for sampling and give nearly matching upper and lower bounds for its finite-bit complexity.

cs.DS · cs.IT · cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.12412

Contrastive Nonmyopic Objective Cost-Tradeoff Acquisition for Longitudinal Data

Dzung Dinh, Boqi Chen, Yunni Qu, Marc Niethammer, Junier Oliva

原文摘要

In many critical domains, features are not freely available at inference time: each measurement may come with a cost of time, money, and risk. Longitudinal prediction further complicates this setting because both features and labels evolve over time, and measurements missed at earlier timepoints may become permanently unavailable. We propose CAPE (Contrastive Acquisition-Plan Embeddings), a partitioning acquisition policy for longitudinal active feature acquisition. CAPE first defines NOCT, an oracle-based objective that scores a set of future feature-time acquisitions by its expected predictive loss together with its acquisition cost. Based on NOCT, CAPE learns a contrastive acquisition-utility manifold of masked partial observations, where proximity reflects the expected utility of future acquisitions. Offline, training states are embedded and partitioned at each rollout timepoint; candidate future plans are scored within each partition using plug-in NOCT, and the best partition-level acquisition plans are cached. During inference, CAPE retrieves the cached plan via nearest-centroid lookup, enabling low-latency adaptive acquisitions. Experiments on synthetic and real-world healthcare datasets demonstrate that CAPE outperforms nonparametric, greedy, and RL-based alternatives, achieving higher accuracy at lower acquisition costs. Code is publicly available at https://github.com/dzungdinhh/CAPE.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.15058

MEDR: Query-Independent Frame Selection via Multi-Signal Event Modeling and Dynamic Rescoring

Xinlei Pu, Weijie Shi, Wen Yang, Yi Cao, Yao Zhao, Hao Chen, Yuanjun Liu, Wenwei Ding 等 10 位作者

原文摘要

Frame selection is a fundamental component of multimodal large language models, enabling long videos to be processed under limited visual-token and computational budgets. Uniform sampling preserves temporal coverage but may miss informative content that appears only briefly. To alleviate this limitation, query-dependent methods can retrieve question-relevant frames. However, because the selected frames depend on the current question, the same visual input cannot be directly shared across different questions, and frame selection must be repeated in multi-turn video dialogue. This motivates us to seek a query-independent frame selection method that preserves the reusability of a fixed visual input while improving the coverage of informative events beyond uniform sampling. We propose Multi-Signal Event Modeling and Dynamic Rescoring (MEDR), a training-free and query-independent frame selection method. Multi-Signal Event Modeling organizes complementary visual, motion, and text signals into signal-specific temporal events. Dynamic Rescoring then iteratively reevaluates each candidate relative to the current selected set, updating its score according to frame-level signal strength, additional event coverage, and balanced temporal coverage. The resulting fixed frame set is constructed without observing the query and can be reused across different questions. Experiments on Video-MME and LongVideoBench demonstrate that MEDR achieves competitive performance across two video MLLMs. Under the 64-frame budget, MEDR improves Video-MME accuracy over the strongest evaluated query-independent baseline by 1.33-1.78 percentage points. We further evaluate the reuse of a fixed frame set in multi-question video dialogue.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.09568

EvoDesign：通过设计专长演化实现智能体式可编辑图表创建

EvoDesign: Agentic Editable Diagram Creation via Design Expertise Evolution

Tianfu Wang, Leilei Ding, Ziyang Tao, Yi Zhan, Zhiyuan Ma, Wei Wu, Yuxuan Lei, Junyang Wang 等 15 位作者

AI 速读 基于摘要

提出一种基于可操作画布中间表示与设计专长演化记忆的智能体框架，用于生成可编辑图表，并给出 CanvasBench 评测基准。

cs.HC · cs.CL · cs.CV

arXiv ↗PDF ↗
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High-fidelity diagram creation requires coordinated visual-spatial decisions over semantic topology, visual styling, and geometric layout. Existing methods face a representation gap: pixel-based generation offers limited object-level control, while code-based synthesis provides executable structure at the cost of intuitive manipulation. We introduce EvoDesign, an agentic framework for editable diagram creation through an intermediate canvas schema that is both machine-actionable and directly manipulable by users. EvoDesign first constructs a diagram manifest through coordinated structure, style, and layout agents, and then renders the manifest in a diagnostic verification environment. This environment produces objective defect signals and VLM-based critique, enabling localized refinement of the current diagram while also providing evidence for long-term design expertise evolution. The evolution mechanism distills refinement traces into a three-tier design expertise memory, where candidate strategies are linked to the verification evidence that supports them and are promoted into broader guidelines and principles only through repeated cross-context support. We further define CanvasBench, a canvas-recoverable benchmark and evaluation protocol with content, visual, and cognitive dimensions. Experiments across pixel, code-based, agentic, one-shot schema, and canvas-native methods show that EvoDesign provides the strongest overall balance across content fidelity, visual organization, and cognitive usability. Our code is available at https://github.com/GeminiLight/EvoDesign.

新论文 · v1arXiv:2611.08147

MCUFlowNet：面向微控制器光流的硬件感知神经架构搜索

MCUFlowNet: Hardware-Aware Neural Architecture Search for Optical Flow on Microcontrollers

Enmin Lin, Jona Beysens

AI 速读 基于摘要

提出宏到微的神经架构搜索框架 PACE-NAS，在 Grove Vision AI V2（Ethos-U55）上为微控制器设计稠密光流网络 MCUFlowNet，并报告了速度与精度权衡。

cs.CV · cs.AR

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Dense optical flow allows small drones to avoid obstacles. However, few dense optical flow networks are designed for microcontrollers (MCUs) with neural accelerators, whose on-chip SRAM limits the input resolution. We present PACE-NAS, a macro-to-micro neural architecture search framework that decouples memory from latency: a macro design fixes the backbone and its peak SRAM, and a micro design searches operators and depths to trade accuracy against compiler-estimated latency. We apply it to EdgeFlowNet on a Grove Vision AI V2 board (Ethos-U55), where profiling locates the peak SRAM at the full-resolution output head. Replacing transposed convolutions with bilinear upsampling and convolutions lowers the peak SRAM by 18%, and two lightweight context modules lower the endpoint error (EPE) without raising the peak. All searched subnets share this peak, so the search needs no memory constraint. We name the Pareto-optimal models MCUFlowNet; the fastest variant, MCUFlowNet-S, runs 1.76x faster than a retrained EdgeFlowNet at a 1.6% higher EPE and, in simulated forest flights at 2 m/s, reaches the goal without collision in 51.5% of trials, against 41.8%. PACE-NAS also applies to semantic segmentation, where a searched PIDNet-S variant runs 1.17x faster at similar accuracy. Code is available at https://gitlab.kuleuven.be/emedia-research-lab/esat-stadius/mcuflownet/mcuflownet.

新论文 · v1arXiv:2611.07605

The Gating Mechanism of KV Cache Eviction

Hao Gu, Xintong Yang, Zhiwei Li, Lei Zhu, Jian Yang, Qiyuan Zhu, Dezhi Li, Jingyuan Zhang 等 13 位作者

原文摘要

KV cache eviction must decide which entries carry information that the rest of the cache can no longer supply. A natural way to estimate this is a learnable gate: give each cached entry a gate on its attention weight and train the gate on the model's own objective. The gate belongs inside the normalizing softmax, where suppressing one entry hands its mass to the others rather than discarding it. That same normalizer absorbs any per-query constant, so a constant gate cancels and reproduces the frozen model exactly. Trained on the language-modeling loss the gate stays near a constant score while its loss falls throughout: optimization succeeds, but a frozen backbone was fit for dense attention, so its loss prefers the no-op, and a near-constant score ranks nothing. The no-op is not a floor the gate improves from but what its objective asks for. A second obstacle survives once the gate moves, since the same gradient charges the gate for errors the frozen model already makes, which no reweighting of positions separates out. Much of the machinery in learnable eviction, from sparsity penalties to relaxations of the discrete decision, works around the first obstacle rather than removing it; both are removed by putting dense attention beyond the gate's reach and making the dense model's predictions its target. Under these two conditions capacity is never the missing ingredient: neither a larger scorer, nor explicit access to the past, nor a trainable backbone helps. The same holds for what eviction unavoidably discards, which is recovered by parameter-free centroids read inside the same softmax far better than by any memory read outside it, since the mass they must return is a share of that normalizer and available nowhere else. At 75\% KV cache compression, this gate reaches 90.14 on RULER at 8K and 46.03 on LongBench with Qwen3-8B, 3.72 and 1.36 points below full-cache inference.

cs.CL

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新论文 · v1arXiv:2611.07358

A Synchronized Tri-Modality Wireless System for Multi-Point Non-Contact Cardiac Monitoring

Wei Ren, Kaili Zhao, Huansheng Yi, Miaoyang Hu, Jianqi Wang, Canhua Xu, Fugui Qi

原文摘要

Cardiac physiological monitoring is fundamental to evaluating cardiovascular health and diagnosing related diseases. Non-contact radar sensing can capture chest-wall vibrations with spatial specificity, enabling localized physiological monitoring and cardiac assessment. Progress, however, has been limited by the absence of a reliable, research-grade hardware platform for rigorous validation of radar-based methods. Prior systems typically pair radar, namely Doppler cardiography (DCG), with only electrocardiography (ECG) or other coarse references, providing no direct mechanical ground truth and thus constraining fine-grained evaluation of radar outputs. We present a tri-modality wireless system that integrates ECG, seismocardiography (SCG), and DCG under a unified acquisition and control framework for time-aligned recording and analysis. The tri-modality system combines miniaturized wearable ECG/SCG modules with a high-resolution mmWave radar module, supporting spatially resolved sensing and subsequent feature extraction. To our knowledge, this is the first integrated ECG--SCG--DCG hardware system for comprehensive cardiac monitoring; it enables multi-point thoracic acquisition, achieves precise cross-modal synchronization, and maintains specifications suitable for wearable operation. Experiments on a linear-motion platform show close agreement between DCG- and SCG-derived displacements, confirming accurate displacement measurement. Human-subject studies further demonstrate cross-modal consistency and robust extraction of feature points, establishing ECG as the electrical anchor and SCG as the mechanical ground truth for DCG. Overall, the system provides a rigorous, spatially resolved validation platform that advances non-contact cardiac sensing and supports next-generation radar-based monitoring.

cs.AR · eess.SP · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2507.17892

DiNAT-IR: Exploring Dilated Neighborhood Attention for High-Quality Image Restoration

Hanzhou Liu, Binghan Li, Chengkai Liu, Mi Lu

原文摘要

Transformers, with their self-attention mechanisms for modeling long-range dependencies, have become a dominant paradigm in image restoration tasks. However, the high computational cost of self-attention limits scalability to high-resolution images, making efficiency-quality trade-offs a key research focus. To address this, Restormer employs channel-wise self-attention, which computes attention across channels instead of spatial dimensions. While effective, this approach may overlook localized artifacts that are crucial for high-quality image restoration. To bridge this gap, we explore Dilated Neighborhood Attention (DiNA) as a promising alternative, inspired by its success in high-level vision tasks. DiNA balances global context and local precision by integrating sliding-window attention with mixed dilation factors, effectively expanding the receptive field without excessive overhead. However, our preliminary experiments indicate that directly applying this global-local design to the classic deblurring task hinders accurate visual restoration, primarily due to the constrained global context understanding within local attention. To address this, we introduce a channel-aware module that complements local attention, effectively integrating global context without sacrificing pixel-level precision. The proposed DiNAT-IR, a Transformer-based architecture specifically designed for image restoration, achieves competitive results across multiple benchmarks, offering a high-quality solution for diverse low-level computer vision problems.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2310.07884

候选人数随机的秘书问题：先验分布信息如何发挥作用

Secretary Problems with Random Number of Candidates: How Prior Distributional Information Helps

Junhui Zhang, Patrick Jaillet

AI 速读 基于摘要

研究候选人数 N 为随机变量且已知其分布 p 的秘书问题变体，分析单阈值策略的近似保证与采样设定下的样本复杂度。

cs.DS

arXiv ↗PDF ↗
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We study variants of the secretary problem, where $N$, the number of candidates, is a random variable with distribution $\mathbf p$. The decision maker, given prior distributional information about $\mathbf p$, wants to maximize the probability of success -- picking the largest number among the $N$ candidates -- using only the sequentially revealed relative ranks of the candidates. When $\mathbf p$ is fully known (full information setting), we show that single-threshold type of strategies can achieve $1/e$-approximation to the optimal probability of success among all possible strategies. In the sampling setting, we give an algorithm that finds a strategy with additive suboptimality at most $ε$ and provide its sample-complexity guarantee. We also give a sampling lower bound; for small $ε$, the upper and lower bounds match up to a logarithmic factor in $1/ε$.

版本更新 · v3arXiv:2610.32057

HyScale：面向可学习稀疏注意力的长上下文训练扩展系统

HyScale: Scaling Long-Context Training for Learned Sparse Attention

Yangshen Deng, Xinjing Huang, Yulong Wang, Yunzhuo Liu, Jun Hu, Shuyong Tan, Chun-Chih Tseng, Mingji Han 等 11 位作者

AI 速读 基于摘要

HyScale 将可学习稀疏注意力中的 top-k 选择视为索引集合而非激活，通过 FlashTopK 与选择重放加速百万级 token 的长上下文训练。

cs.AI · cs.DC

arXiv ↗PDF ↗
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We present HyScale, a long-context training system for learned sparse attention. Learned sparse attention is a promising architecture for million-token contexts, widely adopted in production since DeepSeek-V3.2. Selection, however, grows into the training bottleneck. For each query, it scores every previous token whereas sparse attention reads only $k$; worse, current systems materialize a full score table and repeat selection during recomputation. Our key insight is that the output of selection is not an activation, but rather a set of indices. Building on it, HyScale introduces FlashTopK and selection replay. FlashTopK finds each query's exact top-$k$ on the fly without a score table, while selection replay reuses forward-pass selection in recomputation. Together with holistic system optimizations tackling end-to-end challenges, HyScale speeds up GLM-5.1 training on 1M-token sequences by $2.07\times$ over Megatron-LM on 512 H20 GPUs. It also speeds up the CSA layers of DeepSeek-V4-Flash and the QSA layers of Qwen3.8-Flash-Next by $2.01\times$ and $1.57\times$ at 1M tokens on 32 H800 GPUs.

版本更新 · v2arXiv:2611.01466

Resolve, Clear, or Defer: Training-Free Active Prototype Triage for Ecological Detection Pipelines

Anirudh Potlapally, Brijesh Nanda Brunda Yogananda, Jianyang Gu, Maanav Jajodia, Luke Meyers, Wei-Lun Chao, Hari Subramoni

原文摘要

Ecological monitoring increasingly relies on camera traps and aerial surveys that generate large image collections. High-recall animal detectors can localize candidate animals at scale. However, they also produce many detections that still require expert review, including false positives, ambiguous cases, and animals difficult to identify at the species level. Since exhaustive review requires substantial effort, we propose training-free active prototype triage, where reviewed detections become reusable evidence to (1) Resolve supported foreground classes, (2) Clear likely background false positives, or (3) Defer insufficiently supported cases for expert review. Across camera-trap and aerial datasets, foreground recognition and false-positive rejection benefit from different pretrained representations: BioCLIP2 provides foreground evidence while DINOv3 provides background evidence, reducing erroneous foreground clearing while improving automatic foreground recovery. Deferring unsupported cases avoids the higher error rates caused by forced decisions. Compared with confidence-thresholded multiclass k-NN and uncertainty-sampling logistic regression, prototype triage improves foreground recovery while maintaining competitive or lower review burden. Acquisition goes beyond class discovery: even after classes are represented, prototype choice determines how reliably evidence propagates through the remaining detections. Our results suggest that once animal localization reaches high recall, the challenge shifts from finding animals to deciding which examples to review, how to reuse them, and when evidence does not support an automatic decision.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.06770

Quantum Information Transmission Over Quantum Relay Channels

Yigal Ilin, Uzi Pereg

原文摘要

Quantum relays are central to both quantum communication and distributed quantum computing, enabling long-distance transmission and modular architectures. Unlike classical repeaters, quantum repeaters preserve coherence without amplifying quantum information, relying on entanglement swapping and quantum error correction to overcome loss and decoherence. In this work, we investigate the transmission of quantum information via quantum relay channels. Our three-terminal relay model captures the trade-off between repeater-assisted and repeaterless communication strategies. Specifically, we propose a partial decode-forward strategy, in which quantum ``message system" consists of two components. The first component is decoded by the relay and then sent to the destination receiver, whereas the second component is decoded by the destination receiver without the relay's help. We analyze both entanglement-assisted and unassisted scenarios. As a special case, the full decode-forward strategy is recovered, with the relay decoding, re-encoding, and forwarding the entire message. Our framework allows for different entanglement topologies between the transmitter, the relay and the destination receiver, recovering known results on entanglement-assisted and unassisted communication. Furthermore, we discuss the interpretation of coding with quantum side information. These findings provide a foundation for designing secure, efficient, and reliable quantum networks and for realizing practical quantum repeaters and long-range quantum key distribution.

quant-ph · cs.IT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.06332

LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing

Shaofeng Qin, Li Wang

原文摘要

Constructive neural routing solvers usually score the next action by matching a decoder context against candidate embeddings computed once per instance from static node features such as coordinates, demands, and time windows; deterministic one-step consequences such as travel, waiting, slack, and capacity changes are not provided as inputs and must be inferred implicitly by the model. We propose LINC, a decoder-side candidate decision architecture that decouples computing these consequences from learning how much they matter. The environment computes the consequences explicitly at each step; candidate-level consequences are scored by a state-conditioned shared linear comparator, while feasible-set summaries modulate the decoder context. This keeps the cached embeddings and standard global matching while giving the scoring function direct access to deterministic transition quantities. The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) serves as the main constrained-routing testbed, and the same interface extends to the Capacitated Vehicle Routing Problem (CVRP) and Traveling Salesman Problem (TSP). Across external benchmarks and no-retraining scale-transfer settings, LINC consistently improves strong neural baselines, with the advantage becoming more pronounced as test size moves further beyond the training scale, especially on constrained routing problems.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05776

Closed-Loop Adaptive Sampling on a Generative Ocean Model

Shayesteh Hafezi, Alan Papalia

原文摘要

Adaptive sampling can substantially improve the value of sparse ocean observations by directing mobile robots toward measurements that are most informative for an evolving environment. Recent learned generative ocean models can produce high-resolution forecasts and ensembles at substantially lower computational cost than traditional numerical models, creating an opportunity to use rich predictive forecasts directly in closed-loop robotic sampling. However, these models do not natively provide the explicit uncertainty representations assumed by classical informative-planning methods, and their forecasts often evolve in learned latent spaces rather than directly in physical ocean variables. We develop a multi-robot closed-loop adaptive sampling framework that uses a single learned generative model for forecasting, uncertainty estimation, planning, and measurement assimilation. The model forecasts an ensemble in latent space, decodes it to physical ocean states, and uses ensemble statistics to construct A- or D-optimal sampling objectives. We select sampling locations subject to robot reachability under predicted currents. The collected measurements are assimilated back into the latent state using observation-conditioned diffusion sampling. Across 120 test scenarios A- and D-optimal sampling generally outperforms static, random, and systematic-coverage baselines while adding little planning overhead. Our experiments uncover several scenarios in which model uncertainty diverges from forecast error, motivating future work in uncertainty quantification.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05023

HiLo-WAM: Decoupling Long-Horizon Task Evolution and Local Dynamics in World Action Models

Xingyu Wang, Yanjiang Guo, Zhijing Ding, Jiaxin Fang, Zeyu Lai, Jiayu Dong, Weikang Wan, Jianyu Chen 等 10 位作者

原文摘要

World Action Models (WAMs) improve robotic manipulation by jointly modeling visual dynamics and robot actions. However, most existing WAMs model dense short-horizon visual dynamics, capturing local interactions but providing limited long-range task context. Extending prediction horizons increases uncertainty and computation while entangling task-level evolution with fine-grained local dynamics, obscuring their dependencies and distinct roles in action generation. We propose HiLo-WAM, which decouples long-horizon task evolution from local visual dynamics while jointly modeling their interaction with robot actions. A sparse long-horizon visual stream captures historical task context and future evolution, while a dense local dynamics visual stream models short-horizon robot-object interactions. The two streams operate at different temporal scales within a unified dual-DiT architecture and guide actions through structured cross-stream attention. Progressive inference further reduces unnecessary computation through stream-wise partial denoising, joint denoising, and dynamic skipping. Experiments demonstrate strong long-horizon manipulation performance: HiLo-WAM achieves a 93.70% average success rate on RoboTwin 2.0 and a 17.43 progress score on RoboDojo long-horizon tasks, with robust in-domain and OOD performance on the Fourier GR-3 robot. Ablations confirm the complementary contributions of long-horizon task evolution and local interaction dynamics, while temporal-scale analyses highlight the importance of balancing temporal coverage and resolution. Progressive inference further achieves a 2.93x speedup while improving performance.

cs.RO

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arXiv ↗PDF ↗