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新论文 · v1arXiv:2611.01902

Training-Free Motion-Corrected 3D Brain MRI Reconstruction via Event-Guided References

Wei Jiang, Hongli Chen, Tianyi Ding, Feng Liu, Nan Ye, Hongfu Sun

原文摘要

Head motion causes artefacts in 3D brain MRI and can bias structural measurements. Model-based correction alternates between image and motion estimation, but artefacts in the reconstructed reference can reinforce motion errors. Learned priors can reduce these artefacts but require pretraining and may add test-time computation. We propose EventMC (event-guided motion correction), a training-free method for interleaved Cartesian acquisitions. It iteratively detects motion events and estimates motion using reference images reconstructed from stable intervals. It then refines the image and motion from low to high spatial frequencies. In a fair comparison in simulation (same reconstruction), EventMC's motion estimates improve mean PSNR by up to 4.0 dB over those of the learning-based MotionTTT. A single run of EventMC is up to 7.5 times faster than MotionTTT with less than half the GPU memory. On in vivo motion-corrupted scans, EventMC achieves similar mean image quality to MotionTTT without any training. Code is available at https://github.com/bayesbreeze/EventMC.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2608.17050

Frozen Memory Is Not Enough: Rethinking External Memory as Extraction

Mingyuan Li, Guangsheng Yu, Xu Wang, Shaoxiong Ji

原文摘要

Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer. Engram-style hashed memory occupies a middle regime: it stores learned information in an external, addressable table, yet consumes that table through a small learned reader. This raises a basic question: when such a memory is moved across backbones, what matters more, the frozen memory itself or the target-side reader? We study this question through cross-model frozen-memory extraction, in which a memory trained on a source model is frozen and attached to a different target model, with only a lightweight reader trained. Ablations show that learned memory content and correct addressing both matter, while the amount of usable signal depends on how well the reader is aligned to the target model. In downstream question answering tasks, a dual-layer, four-branch reader nearly closes the gap between same-model and cross-model reuse, achieving an average score of 38.8 under our controlled evaluation protocol. Moreover, when the provider reader is directly compatible with the target interface, the frozen artifact can provide substantial utility without target-side training, while optional reader adaptation yields further improvement. These results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface; target-side adaptation can further improve alignment when direct reader reuse is insufficient.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01900

Systematic Evaluation of Robustness in Fine-tuned Models

Angela John, Hamayoon Behmanush

原文摘要

Our study systematically evaluates the robustness of fine-tuned pre-trained models across different datasets and attack types, aiming to provide insights for researchers in selecting pre-trained models for image classification tasks while prioritizing robustness against potential attacks. Our work explores the influence of fine-tuning pre-trained models with different datasets and attack types on their robustness and overview of the appropriate pre-trained model for image classification based on factors such as computation resources, memory usage, and accuracy on clean data. We fine-tune the models with weights. Evaluate the models on data before any attack and after different attack cases. The study utilizes publicly available image datasets such as MNIST, CIFAR10, CIFAR100, and SVHN to assess the robustness of pre-trained models. The results indicate that the robustness of fine-tuned models varies across different data distributions and sample sizes, emphasizing the need for tailored selection based on specific task requirements. The findings also highlight the varying performance of ResNet50, DenseNet, and MobileNet in defending against attacks on different datasets. Overall, this research provides valuable insights into the robustness of fine-tuned pre-trained models and their suitability for real-world applications in image classification.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01898

Robust Remote ECG Monitoring under Energy Constraints: Anomaly Filtering versus Predictive Uncertainty

Mustafa Fuad Rifet Ibrahim, Maurice Meijer, Alexander Schlaefer, Peer Stelldinger

原文摘要

Machine learning for remote electrocardiogram monitoring must handle noise and diseases absent from training under tight resource constraints. We investigate whether upstream unsupervised anomaly detection can improve monitoring decisions more efficiently than classifier-derived uncertainty. We evaluate five detectors on PTB-XL and BUT QDB and compare a two-stage filter-classifier system with Monte Carlo Dropout and deep ensembles under unseen-disease and noise conditions. Performance is averaged over five training seeds. We measure single-pass energy, latency, and graph memory on a microcontroller's central processing unit (CPU) and neural processing unit (NPU). Per-score neural network costs account for repeated evaluations but exclude scoring outside deployed graphs. Energy equations relate filtering and rejection to avoided classifier execution. Across four disease scenarios, Deep Support Vector Data Description (Deep SVDD) achieves the highest mean area under the receiver operating characteristic curve within all tested finite CPU energy budgets. On the NPU, it leads at 1 mJ, while Monte Carlo Dropout has slightly higher means at 10 and 100 mJ. With a narrow CPU classifier, the Deep SVDD two-stage system increases the fraction of correctly classified or correctly rejected inputs by 15.0-19.4 percentage points over classification without rejection. Estimated energy per input is 1.93-6.99 mJ, compared with 52.53 mJ for Monte Carlo Dropout and 8.76-17.51 mJ for deep ensembles, at each method's retrospectively selected test accuracy peak. These results support upstream filtering for efficient remote monitoring, while independent validation and complete-system measurements remain necessary.

cs.LG · cs.PF

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37470

Autonomous fixed-wing interception using reinforcement learning

Daan van Hamersveld, Ewoud Smeur, Iurii Kriachko

原文摘要

Medium altitude, long endurance unmanned aerial systems such as Shahed-type strike drones are an unanswered threat in modern conflicts. Conventional air defense systems are too expensive to provide a feasible large-scale solution. Quadcopter interceptors are a cheap, battle-proven alternative, but their extremely limited flight time restricts them to low altitude targets at short range, whereas fixed-wing platforms have the endurance to reach higher altitude targets from longer distances. However, such interceptors are still operated manually. Recent developments in learning-based controllers show potential for automation. In this work, a fixed-wing interceptor is presented that is controlled by a neural network that maps filtered monocular vision observations directly to actuator commands. The policy was trained in a custom Python environment using reinforcement learning and proximal policy optimization. The policy was then deployed on a fixed-wing platform and tested in real interception scenarios. As a baseline, two classical guidance controllers were also developed and tested in simulation. The policy is able to intercept targets both in simulation and in real flight. In simulation, the policy outperforms the classical controllers under most conditions. The interceptor was ultimately successfully validated in real flight by intercepting maneuvering fixed-wing targets. This novel system shows the potential of artificial intelligence for autonomous control of fixed-wing interceptor drones.

cs.RO · cs.AI · cs.NE · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01893

Rethinking Model Aggregation for Federated Learning with Domain Skew: A Knowledge-Preserving Perspective

Haotian Wu, Xu Ji, Qiheng Li, Li Xiong, Zibin Pan

原文摘要

Domain skew challenges federated learning by requiring a shared model to accommodate distinct visual distributions. Domain-specific experts can capture these distributions separately, creating an opportunity to integrate their representations in a unified predictor. We propose Federated Knowledge-Preserving Learning (FKPL), a two-stage framework that combines domain-oriented expert training with prototype-based classifier calibration. First, a one-shot subspace-based clustering procedure groups clients for intra-cluster training. The resulting expert encoders are concatenated and frozen, preserving each learned feature mapping during global integration. Second, clients interpolate their concatenated features with class-matched domain prototypes and upload the resulting virtual features. The server trains a global linear classifier on these features, concentrating classifier optimization at the server after a single feature upload. FKPL achieves average accuracies of 73.01\%, 67.00\%, and 64.51\% on Office-Caltech-10, PACS, and a four-domain subset of DomainNet, respectively. On DomainNet, its cross-domain standard deviation is 7.50 percentage points, the lowest among the evaluated methods. With the same concatenated extractor, prototype-based calibration improves average accuracy over federated classifier training by 9.99 and 14.94 percentage points on Office-Caltech-10 and PACS. These results identify classifier calibration as an effective way to combine preserved domain representations into a global predictor.

cs.DC · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01888

Prior-Guided Physics-Informed Deep Learning for Fast 4D MRI Reconstruction

Mojtaba Safari, Parag J. Parikh, Shansong Wang, Mingzhe Hu, Philip Connell, Stanley L. Liauw, Ralph R. Weichselbaum, Zhen Tian 等 9 位作者

原文摘要

Background and purpose: We propose PGPI4MR (prior-guided, physics-informed 4D MRI reconstruction), a deep learning method that leverages information from other respiratory phases or a setup scan, the first 30 s of the same scan, to improve 4D MRI reconstruction without extra scan time. Materials and methods: PGPI4MR is an unrolled network that reconstructs 16 respiratory phases from 60, 30, or 15 s of data, and single 3D volumes, each from 0.29-0.49 s of data, using a setup-scan image. The networks were trained once, on other volunteers, and were not retrained for new volunteers. We evaluated PGPI4MR on measured k-space from three datasets: free-breathing MR-linac (1.5T) and abdominal (3T) scans, and thigh scans with cuff compression (1.5T). All methods, including comparative models, received identical samples and were scored similarly. Results: In vivo, PGPI4MR had the lowest held-out error for 4D MRI at every scan time, and its 16 phases were ready a median of 7.5-13 s after the scan on the 2 GPUs tested. Coupling the 16 respiratory phases reduced the error at 60 s by 9.1% compared with reconstructing each phase alone. For sub-second MRI, the error at 0.49 s was 15.5% lower than that of the best comparison method, classical reconstruction toward the same prior, and on the faster GPU, every 0.29 s window was acquired and reconstructed within 0.5 s. Without retraining, PGPI4MR matched the best methods on the 3T abdominal scans but not on the thigh scans; after retraining on thigh data, it reduced the held-out error by 6.6% relative to the best comparison method. Conclusion: Prior information allowed PGPI4MR to reconstruct 4D and sub-second MRI more accurately than compressed sensing and a published 4D network, within seconds and without extra scan time. This could support 4D MRI for treatment planning and 3D motion monitoring during MR-guided radiotherapy.

cs.CV · physics.med-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.21647

Riemannian Neural Hamiltonian Flows: From Interpretable Potentials to Exact Surrogate Sampling

Vincent Souveton

原文摘要

Hamiltonian normalizing flows are attractive generative models because their phase-space maps are invertible and volume preserving, but most neural constructions are formulated in Euclidean space. We introduce Riemannian Neural Hamiltonian Flows, which combine the fixed kinetic energy of a Riemannian manifold, a learned scalar potential, and an explicit geodesic integrator. We then study the interpretability of the learned potential through a thermodynamic reading in which the momentum variance plays the role of a temperature. The matched potential, whose Boltzmann distribution is the data, is the potential whose force balances the pressure of the data. The output of one pass of the flow is the Boltzmann distribution of the learned potential multiplied by a memory factor, so that the learned potential is the matched potential whenever the pass forgets its starting point. Numerical experiments on Euclidean, hyperbolic, and spherical spaces show competitive sample quality and numerical cost against a Riemannian continuous normalizing flow, and confirm the interpretability of the learned potential. We then show that this reading has a computational use. When the energy of the data is known but expensive, the landscape learned from existing samples is a surrogate energy for Hamiltonian Monte Carlo, allowing exact sampling with significant acceleration. We test this approach on the localization of a neutrino source from public IceCube data.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01878

Don't Guess, Ask Wisely: Benchmarking and Training Proactive Elicitation in Open-Domain LLM Conversations

Rongyu Zhang, Mutian Bao, Jinjian Zhang, Yuetian Du, He Xu, Wei Zhou, Xiaojun Zhu, Ming Kong 等 11 位作者

原文摘要

When user queries are incomplete, large language models should ask targeted questions before committing to an answer, a capability we call proactive elicitation, yet evaluation on real user-assistant queries across seven LLMs (8B to frontier) shows that fewer than 11% of underspecified queries trigger a follow-up question. To benchmark and train this capability, we introduce VagueBench, where each seed query is annotated with structured intent and detail units then degraded into intent-vague and fully-vague variants via controlled removal of linguistic cues, yielding known hidden information sets for evaluating both when and what to ask. We propose DialogGRPO, an offline reinforcement learning method that organizes multi-turn elicitation as a dialogue-state directed acyclic graph, where candidate questions at each node receive dense turn-level rewards combining immediate information gain with lookahead value through group-relative comparison, enabling fine-grained credit assignment without runtime simulation. DialogGRPO elicits missing information on over 88% of vague queries with substantial gains in both recovery efficiency and answer coverage, without over-asking on complete queries, outperforms prompting and test-time-scaling baselines, and generalizes zero-shot to MultiWOZ 2.4 and AmbigQA.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01874

Efficient Lempel-Ziv Prediction with the Run-Length BWT

Ohad Avnery

原文摘要

Lempel-Ziv Plurality (LZP) is an algorithm of Kosoy that predicts the next symbol of a string by using its Lempel-Ziv factorization, and makes at most $O(z\log n)$ mistakes on every string of length $n$ with $z$ LZ77 phrases. We give efficient online implementations of LZP and of a new variant of it. Our first implementation maintains a run-length encoded Burrows-Wheeler transform (RLBWT) of the reversed history. It implements LZP, uses $O(z\log z\log^2 n)$ bits, and has worst-case update and prediction times $O(\log z+\log\log n)$ and $O(σ(\log z+\log\log n))$, respectively, where $σ$ is the size of the alphabet. This is an exponential improvement over Kosoy's implementation, whose update time is $O(z\log^7 n)$. We then introduce the Lempel-Ziv Majority (LZM) predictor, which has the same mistake bound and update time, and predicts in $O(1)$ time independently of $σ$. These two predictors accept new symbols at any time, without knowing the alphabet in advance. For an alphabet known in advance, a different data structure gives $O(σ(1+\log z/\log\log n))$ time per symbol, which is constant when $σ=O(1)$ and $z=\mathrm{polylog}(n)$.

cs.DS · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01870

Automated Stateful Specialization for Adaptive Agent Systems

Myan Vu, Harrish Ayyanar, Jiang Pang, Anwiketh Reddy, Mayank Goel

原文摘要

Current automated agent design frameworks produce either static workflows that lack adaptability or per-query optimizers that prevent the accumulation of deep, agent-level task expertise. We propose a new direction that reconciles these paradigms: creating stateful teams of specialist agents that accumulate knowledge over time and can be reconfigured for novel tasks entirely without human intervention. To this end, we introduce ASpec, a framework that manages this full agent lifecycle by first autonomously discovering specialist archetypes via evolutionary search and then cultivating their expertise through experience, mirroring how human experts learn through practice and reflection. We further introduce a lightweight hierarchical control policy, "retain-then-escalate," which governs when to leverage the established agent system versus when to adapt its structure. Through comprehensive experiments, we demonstrate that this approach leads to significant performance gains on expert-level scientific benchmarks like GPQA while matching the state-of-the-art on broader domain tasks, demonstrating a promising path toward agent systems that are simultaneously expert, adaptive, and efficient. We will release the code at https://github.com/myanvoos/ASpec.

cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01868

A Self-Generated Floor on Adam's $\varepsilon$: Evidence from Power-Law Matrix Sensing

Lngel Kyo

原文摘要

In nonconvex Burer--Monteiro low-rank matrix sensing of $U^\ast \in \mathbb{R}^{V \times D}$ under power-law covariance $Σ= \mathrm{diag}(j^{-s})_{j=1}^V$, the precision-wall approximation predicts that Adam stalls when the smallest non-gauge Hessian eigenvalue drops below its denominator floor, giving $s^\ast(V,D) = [\log(1/\varepsilon_{\mathrm{eff}}) - \log V]/\log(VD) + δ_{\mathrm{Adam}}$. If $\varepsilon_{\mathrm{eff}}$ were set by $\varepsilon_0$, lowering $\varepsilon_0$ from $10^{-10}$ to $10^{-12}$ would raise $s^\ast$ by $0.95\text{--}1.33$. Instead, across six values of $\varepsilon_0$ two decades apart, $10^{-2}$ to $10^{-12}$, the boundary follows the $\varepsilon_0$ slope down to $10^{-6}$, bends near $10^{-8}$, and is flat below $10^{-8}$, while warm-started L-BFGS and Gauss--Newton reach machine precision on stalled instances. We trace this saturation to a self-generated dynamical floor $\varepsilon_{\mathrm{dyn}} \propto α$: high-curvature modes oscillate persistently near the adaptive edge of stability with amplitude $\propto α(1-β_1)/(1+β_1)$ and leak gradient fluctuations of scale $κα$ into rare modes via measurement coupling, pinning $\sqrt{\hat{v}_V} \approx κα\gg \varepsilon_0$. Fitting a single threshold-crossing wall parameter $\varepsilon_{\mathrm{dyn}} = 4.76\times 10^{-9}$ (with the detection lag frozen at its companion calibration) predicts the saturated failure boundary for $D \ge 4$ across five $(V,D)$ scales to RMS $0.098$ (breaking down at $D = 2$), while shrinking $\varepsilon_0$ below $10^{-8}$ is ineffective and lowering the step size $α$ reduces the floor directly.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01867

Fairness-Aware Design for Contextual Experiments: Guaranteeing Reliability and Equity in Heterogeneous Subgroups

Guangyan Gan, Ling Zhang, Yanhua Cheng, Yongxiang Tang, Kaiyuan Li, Xialong Liu, Peng Jiang

原文摘要

Experimental design is critical for evidence-based decision-making in healthcare, marketing, and public policy. However, designing efficient experiments across heterogeneous subgroups presents significant challenges. Existing methods often optimize for statistical power or overall sample efficiency, overlooking crucial fairness considerations across these different subgroups. To address this gap, we introduce a Fairness-Aware Contextual Track-and-Stop Design (F-CTSD) algorithm. The proposed F-CTSD algorithm provides statistical guarantees on subgroup fairness while minimizing required sample sizes. We quantify the fairness-efficiency trade-off and derive the exact sample complexity for the proposed F-CTSD algorithm under its fairness constraints. We further theoretically prove that the proposed F-CTSD algorithm consistently produces accurate treatment effect estimates even under fairness requirements, enhancing statistical reliability. Numerical experiments show that the proposed F-CTSD algorithm outperforms existing methods, achieving higher sample efficiency while reducing subgroup fairness violations by 4.95%.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01866

The (Butter)fly Dream: Linear Separability of World-Model Rollouts in a Connectome-Constrained Visual Model

Junichiro Niimi

原文摘要

Zhuangzi dreamt he was a butterfly and, on waking, could not tell which was the dream. We ask a version of his question of a model of the fly brain: placed inside a world generated by a world model, can a fixed brain tell that the world is a dream? We treat it as a question about what a connectome-constrained model trained on one task makes available beyond that task: do its responses make the defects of a dream linearly decodable? The observer is a published model of the fly visual system trained only for optic-flow estimation; reality is the MPI-Sintel movie on the model's 721-column retina; dreams are continuations of five real frames by three small world models (a persistence random walk, an MDN-RNN and an RSSM) at three temperatures. The judge is a classifier two-sample test on temporal statistics of cell-type responses, with sequence-level cross-validation. In the trained observer's responses all nine dreams are separable from reality (AUC 0.85-0.99, mean over 50 trained models), in those of the same network before training much less so (0.58-0.75 for the learned world models; paired difference 0.20-0.37), equally on sequences the observer never saw, and in an order unrelated to pixel error. Because these AUCs are near ceiling, we add graded probes made of each window's own real frames with only their order broken. Swapping one adjacent pair of the 14 generated frames gives 0.73 on the trained responses against 0.54 before training and 0.56 for pixel statistics; the trained curve reaches 0.75 at 1.1 swapped pairs of seven, the pixel statistics at 4.7, and the untrained network not at all. A detection horizon, the test on the first k generated frames, orders the world models where the full-length AUC saturates.

cs.LG · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01865

A Compute Center Anyone Can Join: A Peer-to-Peer System for AI Inference

Yimo Lin, Zhen Zhang

原文摘要

Today, most people obtain AI inference by buying it from a small number of organizations that run large data centers, while many GPUs owned by individuals and small institutions sit idle for much of the day. Bitcoin showed that strangers can contribute resources under shared rules and receive checkable rewards, but it relies on an asymmetry: proof-of-work is expensive to produce and cheap to verify. Useful inference has no such asymmetry. We propose the Open Inference Market (OIM), which replaces the missing asymmetry with an economic mechanism. A worker delivers a result without knowing whether it will be audited; its unsettled earnings serve as collateral during a challenge window; and confirmed cheating forfeits all of it. We call this principle earnings as collateral. In a simplified model we show three things. The number of audits needed for deterrence is a constant per worker per day, independent of the worker's throughput, so the audited share falls as the network gets busier. A new worker can join with a short period of intensive auditing and a very small bond. And whether the mechanism works at all depends only on the ratio of the audit judge's false-flag rate to its detection rate. Under example parameters, about 0.3 replays per worker per day are enough to make cheating unprofitable for a rational worker. On this basis we lay out a path that hands four powers back to participants one at a time: owning compute, writing history, judging work, and releasing value. A compute center need not be a building; it can be a protocol that anyone can join.

cs.AI · cs.CR · cs.DC · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01864

Quantizing Recurrent Language Models Is Easier Than You Think

Philipp Nazari, Neehal Tumma, Daniela Rus, T. Konstantin Rusch

原文摘要

Linear recurrences are becoming an increasingly common building block in large language models, replacing the growing KV cache of softmax attention with a fixed-size recurrent state that enables efficient decoding. However, the quantization stack has largely been developed around softmax Transformers, whose numerical properties and inference state differ from those of recurrent models. Our goal is to understand these differences and study the design space of quantization methods for recurrent models. In our analysis, we differentiate between two targets. First, extra-state quantization concerns weights and activations outside the recurrent state, whose distributions are shaped indirectly by the sequence mixer during training. In controlled 1.3B parameter models, we find that decayed recurrences develop weaker residual outlier channels than softmax attention and are therefore easier to quantize without incoherence processing. Second, intra-state quantization concerns the recurrent state itself during decode, where repeated updates create a different numerical problem from KV cache quantization. We instantiate our findings as LoReS, a state quantization pipeline combining value-axis scale grouping to match output channel magnitude, decay-based mixed-precision to protect slowly decaying heads or channels, and stochastic rounding to limit error accumulation under repeated requantization. Across Qwen-3.5 9B, Nemotron-H 8B, and Kimi-Linear 48B-A3B, LoReS supports robust 8-bit state quantization and lower effective precision on shorter decode horizons. We implement LoReS in vLLM and demonstrate end-to-end decode speedups.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2601.15500

Low-Dimensional Adaptation of Rectified Flow: A Diffusion and Stochastic Localization Perspective

Saptarshi Roy, Alessandro Rinaldo, Purnamrita Sarkar

原文摘要

In recent years, Rectified flow (RF) has gained considerable popularity largely due to its generation efficiency and state-of-the-art performance. In this paper, we investigate how well RF automatically adapts to the intrinsic low dimensionality of the support of the target distribution to accelerate sampling. We show that, using a carefully designed \blue{end-point adapted} time-discretization scheme and with sufficiently accurate drift estimates, the RF sampler enjoys an iteration complexity of order $O(k/\varepsilon)$ (up to log factors), where $\varepsilon$ is the precision in total variation distance and $k$ is the intrinsic dimension of the target distribution. In addition, we show that the denoising diffusion probabilistic model (DDPM) procedure is equivalent to a stochastic version of RF by establishing a novel connection between these processes and stochastic localization. Building on this connection, we further design a stochastic RF sampler that also adapts to the low-dimensionality of the target distribution under mild requirements on the accuracy of the drift estimates, and also with a specific time schedule. We illustrate the efficacy of newly designed time-discretization schedules with simulations on the synthetic data and text-to-image (T2I) data experiments.

stat.ML · cs.AI · cs.LG · math.ST

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01860

Advances in Agentic Computer Graphics

Hengyu Liu, Jihyeon Je, Ruiqi Wang, Mingxuan Wu, Haven Feng, Qixing Huang, Georgios Pavlakos, Hao Zhang 等 14 位作者

原文摘要

Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and iteratively refine it through various feedback. This capability opens the door to a potentially new paradigm of computer graphics, with graphics algorithms increasingly built around agentic systems to automate progressively complex tasks. Over the past years, research on agentic graphics has expanded rapidly across a diverse range of tasks. This rapid growth makes it increasingly difficult for manual literature reviews to keep pace with new developments. At the same time, similar ideas are often described using different terminology across these tasks, further complicating efforts to develop a systematic understanding of the field. In this report, we address these challenges through two contributions. First, we introduce a unified taxonomy for characterizing agentic systems across diverse computer graphics tasks. Second, we develop an agentic literature system that can assist human researchers in analyzing existing and newly published papers at scale. Building on these contributions, we analyze emerging research trends and discuss open challenges in coordinating agents and tools across graphics tasks, designing interfaces and representations for human-agent collaboration, sustaining long-horizon planning and refinement, and reliably evaluating interactive graphics systems.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01858

Learning-to-Memorize: Dynamic Context Management for Long-Horizon Autoregressive Video Generation

Haowei Zhu, Qijie Wang, Jia Li, Xing Wang, Tianyu Zhao, Jiexi Wang, Xurui Peng, Xinglong Wu 等 10 位作者

原文摘要

Autoregressive video generation has achieved impressive results on short clips, yet generating long videos remains challenging due to error accumulation over extended horizons, where each predicted frame depends on potentially imperfect previous frames. Existing approaches typically rely on static context management strategies, such as sliding-window caches that discard early frames or fixed sink frames that permanently preserve initial content. These designs either lose critical long-range context or anchor generation to outdated information, which can lead to degraded fidelity, motion stagnation, and reduced diversity. In this paper, we propose Learning to Memorize (L2M), a learned context memory management framework that dynamically retains, evicts, and evolves historical frames according to predicted importance. The framework consists of an importance prediction router that estimates the relevance of each historical frame, degradation-aware training that improves robustness to noisy or corrupted context, and a dynamic memory initialization and evolution mechanism that updates long-term memory using exponential moving average (EMA) importance scores. This allows high-quality frames to replace stale anchors and enables memory to adapt naturally to scene evolution. Trained with a teacher-student distillation objective and sparsity regularization, L2M makes efficient use of a fixed KV-cache budget while preserving the most informative contextual information. Extensive experiments demonstrate that L2M improves long-horizon video generation quality and temporal consistency over existing baselines, providing a learned strategy for context memory management in long autoregressive video generation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16834

ReLATE: Learning Relative Representations for Post-Hoc Multimodal Alignment

Shiwon Kim, JaeSeong Hong, Yu Rang Park

原文摘要

Post-hoc multimodal alignment connects independently pretrained vision and language encoders while keeping them frozen, offering an alternative to joint pretraining when paired data are scarce. A central design challenge is to construct comparable representations across heterogeneous feature spaces while retaining local information relevant to distinct semantic concepts within each input. We propose Relative Alignment with Learnable Anchors and Token Evidence (ReLATE), which learns modality-specific anchors that jointly define relative representations and determine token contributions to each relative coordinate. Image patches and text tokens are represented by their similarities to anchors within their respective frozen feature spaces. Anchor-wise Similarity Pooling (ASP) then weights and aggregates these similarities separately for each anchor to construct the corresponding relative coordinate. A sample-level symmetric contrastive objective aligns the resulting image and text representations by optimizing only the anchors, without updating the encoders. Experiments on 13 general-domain benchmarks across four vision-language tasks demonstrate improvements over the compared post-hoc alignment methods under matched paired-data budgets, with further evaluations across data scales and encoder combinations. Beyond the general domain, we evaluate ReLATE in pathology as a specialized-domain application. By aligning frozen pathology image and biomedical language encoders, ReLATE matches or exceeds the evaluated pathology-specific vision-language models in balanced accuracy on three zero-shot classification benchmarks.

cs.CV · cs.AI · cs.LG

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新论文 · v1arXiv:2611.01853

Privacy-Preserving ECG-HRV Emotion Classification for Mood Swing Detection using Federated Learning on Constrained Edge Devices

Chidike Vincent, Muhammad Aslam Jarwar, Najam Ul Hasan, Laurence Hirsch, Jonathan Zasada-James, Hem Raj Dadra

原文摘要

This paper presents a privacy-preserving mood monitoring framework that classifies emotional states from ECG-derived heart rate variability (HRV) features. The framework is evaluated on three public datasets comprising 74 participants and further validated using a wearable device in a pilot study. Leave-One-Participant-Out cross-validation (LOPO-CV) is used to assess generalization to unseen individuals. A series of experiments investigates the impact of temporal windowing, HRV feature selection, and class balancing on both four-class valence-arousal and binary valence classification. Results show that class balancing substantially improves minority-class recall, while Logistic Regression consistently outperforms five alternative classifiers and achieves competitive performance against HRV-based baselines. Pilot validation using a Polar H10 wearable achieves 75% binary valence accuracy on an unseen individual without calibration, demonstrating the feasibility of real-world deployment. To preserve privacy, the framework is also implemented using federated learning on a six-node Raspberry Pi cluster. Federated Logistic Regression achieves performance comparable to centralized training without sharing raw physiological data. These results demonstrate the potential of interpretable and privacy-preserving mood monitoring for continuous mental health assessment in clinical and community settings.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.01851

Missingness-Aware Prior-Data Fitted Network for Irregular Clinical Time-Series

Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora, Pietro Bosoni, Simon Lebech Cichosz, Arianna Dagliati, Arijit Khan, Riccardo Bellazzi

原文摘要

Clinical time series are irregularly observed with laboratory measurements recorded at uneven intervals and observation patterns often reflecting patient state, clinical concern, and site-specific practice. Consequently, missingness is not merely a nuisance factor but an informative signal for prediction. We propose a Missingness-Aware Prior-Data Fitted Network (MA-PFN) for irregular clinical time-series prediction. MA-PFN first learns a diffusion-based prior over joint laboratory value and observation-mask trajectories from source-domain electronic health records, capturing both physiological dynamics and measurement processes. Synthetic trajectories sampled from this prior are then transformed into context-query tasks to train a PFN-style conditional predictor. Once trained, the model performs prediction for new admissions in a single forward pass, requiring neither retraining nor parameter adaptation on target datasets. The framework naturally supports both imputation and forecasting through different context-query constructions. We conduct internal validation on a held-out MIMIC-III test set and external validation on eICU, evaluating both imputation and forecasting and assessing cross-domain generalization under shifts in patient populations, clinical practices, and observation mechanisms. MA-PFN achieves the best performance across all evaluated settings, with Overall/Macro nRMSD ranging from (0.193/0.186) for internal imputation to (0.297/0.297) for external forecasting, without target-domain adaptation. These results demonstrate that learning missingness-aware task priors enables accurate and transferable clinical time-series prediction under informative missingness.

cs.LG

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新论文 · v1arXiv:2611.01850

Biological Congruence: Evaluating Microscopy Generation Across Representations

Duway Nicolas Lesmes-Leon, Shumpei Takezaki, Fabian Schmeisser, Ryoma Bise, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed

原文摘要

Generative models increasingly produce realistic microscopy images, yet conventional evaluation primarily characterizes visual fidelity and distributional coverage rather than the biological information represented by synthetic data. We introduce biological congruence as an evaluation principle describing whether biologically relevant information forms a coherent description across representations and remains consistent with its empirical biological context. To study this principle, we develop a text-conditioned diffusion framework that jointly generates microscopy images and instance masks, exposing the same generated biological state through conditioning, visual appearance, and instance structure. Across prompt--mask, prompt--image, image--mask, and population-level analyses, we find strongly feature-dependent behavior: density and confluency are consistently preserved, morphology exhibits recurrent disagreement, while nearest-neighbor distance reveals that strong image--mask agreement can coexist with weaker conditioning and population fidelity. Stress testing increasingly unsupported phenotype combinations further shows that target controllability can increase while untargeted phenotypes reorganize and the complete generated state moves toward configurations better supported by real data. These findings demonstrate that generative quality, controllability, cross-representation agreement, and population fidelity capture complementary properties, motivating biological congruence as a relational perspective for evaluating scientific generative models.

cs.CV

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版本更新 · v14arXiv:2509.04154

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation

Peter Racioppo

原文摘要

We introduce Robust Filter Attention (RFA), a formulation of self-attention as a robust state estimator. Each token is treated as a noisy observation of a latent trajectory governed by a linear stochastic differential equation (SDE), and attention weights are determined by consistency under this model rather than static feature similarity. Under isotropic noise and decay assumptions, RFA matches the computational complexity of standard attention. On language modeling benchmarks, RFA matches or improves on RoPE's perplexity within the training window while remaining stable under zero-shot extrapolation to longer contexts, where RoPE degrades sharply. The framework also provides a dynamical interpretation of standard positional mechanisms, connecting rotational embeddings and recency biases to transport and uncertainty propagation induced by dynamics.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.03731

Complete Donor-Set Guidance for Frozen Diffusion Docking Models

Hyunwoo Kim, Insoo Park, Ziseok Lee, Wooyeol Lee, Yoorim Gang, Kyungsu Kim

原文摘要

Metalloprotein docking requires a ligand pose to satisfy both global pocket fit and the identity and geometry of metal-coordinating atoms. General-purpose diffusion docking models do not explicitly condition generation on externally specified complete ligand donor sets, and their predicted poses can exhibit incorrect metal-donor relationships. Metal-aware models address coordination during training and generation; here we ask whether external donor information can correct already-trained general-purpose models without retraining. We propose Complete-Identity Contract Guidance (CICG), which applies a supplied complete donor set through translation, rotation, and torsion updates, and Historical Contract Transfer (HCT), which supplies donor identities from earlier complexes at compatible receptor sites. We compare HCT with trajectory-local controls retaining the historical donor count with or without elemental composition. On 86 assessable HCT-covered queries, HCT-guided CICG increases frozen-confidence-selected joint success from 21.5% to 36.6% on SurfDock and from 25.0% to 34.9% on DiffDock-L. The joint criterion requires correct global ligand placement, complete native donor recovery, coordination geometry, preserved stereochemistry, and absence of fatal receptor clashes. Composition-matched guidance reaches 33.14% and 34.30%, leaving the additional benefit of fixed historical membership unresolved. Our results therefore support donor-set guidance as a conditional correction for frozen docking models, while identifying donor-source reliability and physical-quality preservation as the main barriers to broader use.

physics.chem-ph · cs.LG

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新论文 · v1arXiv:2611.01849

FADE: A Flow-Based Approach for Multichannel Tail Estimation

Parmida Valiahdi, Niloofar Mehrnia, Mustafa Furkan Beker, Ubeydullah Erdemir, Ibrahim Hokelek, Sinem Coleri

原文摘要

Ultra-Reliable Low-Latency Communication (URLLC) is vulnerable to rare low-received-power events, and the spatial-diversity benefits of multiple receivers can be significantly reduced when they experience simultaneous deep fading. Multivariate Extreme Value Theory (MEVT) provides a principled framework for characterizing such joint tail behavior. However, conventional MEVT methods often rely on restrictive parametric assumptions or discrete representations of the dependence structure, which may not fully capture complex multivariate tail behavior. This paper proposes Flow-based Angular Density Estimation (FADE), a flow-based approach for characterizing the dependence between extreme events across receivers within the MEVT framework. FADE transforms multivariate extremes into a standard extreme-value representation and uses a flow-based density estimator to learn the dependence structure from the transformed data without imposing a predefined parametric form. The proposed framework is evaluated using experimentally collected data from a UAV communication system, in which the UAV serves as the transmitter and communicates with two ground receivers. FADE provides closer agreement with the empirical joint CDF, achieving reductions of up to 22.4% in MAE compared with the state-of-the-art Logistic and Maximum Empirical Likelihood (MEL) methods, while also improving distributional agreement and reducing absolute bias.

eess.SP · cs.AI · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.02416

Capacity-Aware Multi-UE Handover Optimization for LEO Satellite Networks Using Dueling DDQN

Po-Heng Chou, Chiapin Wang, Chung-Chi Huang, Kuan-Hao Chen, Chien-Yu Chen

原文摘要

Frequent handovers and finite satellite capacity make multi-user mobility management challenging in low Earth orbit (LEO) satellite networks. This paper develops a capacity-aware multi-user handover framework based on a double deep Q-network (DDQN) with a dueling architecture. Both training and evaluation employ sequential user equipment (UE) processing with shared satellite occupancy, while action masking excludes invisible or capacity-infeasible satellites. The state incorporates satellite visibility, achievable link quality, remaining capacity, previous association, short-term visibility, and time information. A fixed multi-criteria reward balances throughput, blocking, handover cost, connection persistence, and short-term visibility, while expert trajectories are used only for replay-buffer initialization. Simulations under different UE densities and satellite capacities show that the proposed framework maintains high average throughput per UE and low blocking rates with moderate handover frequency. Ablation results show that removing the remaining-capacity vector has only a limited effect on performance when sequential occupancy tracking and feasible-action masking are retained.

cs.IT · cs.LG

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版本更新 · v2arXiv:2512.12663

PerNodeDrop: A Method Balancing Specialized Subnets and Regularization in Deep Neural Networks

Gelesh G. Omathil

原文摘要

Deep neural networks possess strong representational capacity yet remain vulnerable to overfitting, primarily because neurons tend to co-adapt in ways that, while capturing complex and fine-grained feature interactions, also reinforce spurious and non-generalizable patterns that inflate training performance but reduce reliability on unseen data. Noise-based regularizers such as Dropout and DropConnect address this issue by injecting stochastic perturbations during training, but the noise they apply is typically uniform across a layer or across a batch of samples, which can suppress both harmful and beneficial co-adaptation. This work introduces PerNodeDrop, a lightweight stochastic regularization method. It applies per-sample, per-node perturbations to break the uniformity of the noise injected by existing techniques, thereby allowing each node to experience input-specific variability. Hence, PerNodeDrop preserves useful co-adaptation while applying regularization. This narrows the gap between training and validation performance and improves reliability on unseen data, as evident from the experiments. Although superficially similar to DropConnect, PerNodeDrop operates at the sample level. It drops weights at the sample level, not the batch level. An expected-loss analysis formalizes how its perturbations attenuate excessive co-adaptation while retaining predictive interactions. Empirical evaluations on vision, text, and audio benchmarks indicate improved generalization relative to the standard noise-based regularizer.

cs.LG · cs.AI · cs.CV

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新论文 · v1arXiv:2611.03729

Learning to read residual images for single-pixel photography

Hongjun An, Shuaizhe An, Tong Tian, Zhe Sun

原文摘要

Continuous video scales in cost with camera count, resolution and recording duration, yet many monitoring tasks seek events rather than complete images and can operate on a compact single-pixel measurement stream. Recovering a chosen interval as video is then desirable, but at 0.24\% of the pixel count the scene correlation image looks like noise and per-clip optimization takes minutes. Correlating the residual of an estimate with the coding patterns nevertheless gives a residual image, the gradient of a centered measurement loss. Lifted through a pretrained video decoder, its agreement with the true error exceeds that of control decoders. Video-latent inversion (VLI) then outperforms frame-wise image-latent reconstruction at 1{,}000 bucket values. Measurement-Gradient-Conditioned Video Flow (MGV-Flow), trained once to read the lifted residual image with preceding frames, reconstructs five frames in 1.55\,s at 22.9\,dB median PSNR, against 398\,s and 18.1\,dB for VLI, with the largest gains in high-motion scenes. Directional fidelity ranks 30 coding configurations and selects one needing a third fewer bucket values. On a physical system, MGV-Flow outperforms per-frame reconstruction without retraining. A sliding window over the bucket-value stream yields 3.2 frames per second on one RTX~4090 and, in theory, 20 frames per second on a data-centre accelerator. Demonstrations are available at https://cavedweller509.github.io/MGV-Flow.

eess.IV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01846

Z-Cache: Accelerating Diffusion Transformers via Self-Reflection

Zegang Cheng, Zhikai Wang, Jiacheng Liu, Xiaobing Tu, Chang Zou, Junjie Chen, Yue Ma, Zhiyuan Ma 等 11 位作者

原文摘要

Diffusion transformers have become the most powerful models for visual generation, but still suffer from massive computation costs. To solve this problem, feature caching has been proposed to cache the features of diffusion models in the previous computation steps and then reuse them in the following caching steps, which brings significant acceleration but also degradation in generation quality. To address this problem, this paper proposes Z-cache as a feature caching method that can maintain high-quality generation through self-reflection. Concretely, we observe that the error from feature caching tends to be sharply reduced after each full computation. Based on this observation, Z-Cache is designed to first predict the features in the future caching steps and then perform a full computation. After that, Z-Cache returns to the caching steps and re-predicts them based on the previous and the current computation steps, which brings correction in features. Experiments demonstrate that with Z-Cache, diffusion transformers achieve comparable generation quality to the original model but with faster inference speed, for instance, 6.22X acceleration on FLUX-dev for text-to-image generation. Our code is released on GitHub.

cs.CV

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