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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:2611.01891

Interface Quilt: Modifying Applications with Computer Use Agents and Generative UI

Leping Qiu, Jo Vermeulen, Tim Zindulka, George Fitzmaurice, Fraser Anderson

原文摘要

Modifying an existing application is difficult when it does not provide programmatic access for that change. Recent advances in computer-use agents (CUAs) enable richer understanding and operation of existing interfaces, while generative UI (GenUI) makes it practical to generate new ones. We examine how these advances expand software modification through a design space of what can be changed and a framework for how modifications can be implemented. We then introduce Interface Quilt, a system that combines CUAs and GenUI to generate interface patches, which are new interfaces displayed over an existing application to modify its presentation, interaction, and capabilities. Through demonstrations across a variety of applications, we show that interface patches support a range of modifications without source code, APIs, or accessibility data.

cs.HC

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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 ↗
新论文 · v1arXiv:2611.01884

The Runtime-Moment Frontier for Universal Sampling

Sebastian Ben Daniel

原文摘要

Can one sampler give every string at least a polynomial fraction of the probability that any source in a class gives it, while keeping a bounded moment of its own runtime? We answer this in terms of runtime moments. For every oracle and every rational $a>0$, one sampler with polynomial $a$th runtime moment dominates every source with polynomial $r$th moment with power $B(a,r)=1+(a-\min\{r,1\})_+/r$, and the Rényi divergence of order $B/(B-1)$ of the source from it is $O(\log n)$, a sharp quantitative form of Gurevich's domination on average. Relative to an oracle, $B(a,r)$ is optimal in both senses, with failures at every large input length. So the first moment is a threshold: one sampler of order one is universal for expected polynomial time, but none of higher order is universal even for polynomial-space sources with all polynomial moments. For expected-polynomial-time sources the threshold also holds relative to a random oracle and, without an oracle, under a sequential-hardness hypothesis on iterated functions or a parallel-sequentiality hypothesis on verifiable delay functions with short seeds. The frontier governs algorithms as well. For rational orders we determine the exact exponent by which polynomial runtime moments of deterministic deciders transfer from one fixed sampler to every source, and show, relative to oracles, that all correct solvers of a sparse language in $\mathsf{UP}\cap\mathsf{coUP}$ meet it. Where $B(a,r)=1$, actual running times transfer better than arbitrary costs, but only above a critical moment.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01881

Green Learning-Based Signal-Level Detection of RIS Spoofing Attacks in Vehicular ISAC Systems

Po-Heng Chou, Tingyu Shui, Pin-Qi Fu, Lien-Sheng Chang, Walid Saad, Li-Chun Wang

原文摘要

Integrated sensing and communication (ISAC) enables wireless infrastructure to jointly support communication and environmental sensing, but also introduces new physical-layer security threats. A malicious reconfigurable intelligent surface (RIS) can manipulate its time-varying reflection phases to generate spoofing echoes and distort the sensing results of a roadside unit (RSU). Existing trajectory-level detectors rely on reconstructed sensing parameters and vehicle trajectories, making their performance dependent on upstream estimation accuracy. In this paper, we propose a signal-level Green Learning (GL) framework that directly detects RIS spoofing from matched-filtered RSU echoes. The proposed method combines two-stage Subspace Approximation with Adjusted Bias (SAAB), Discriminant Feature Test (DFT), and eXtreme Gradient Boosting (XGBoost) for spatial-temporal representation learning, feature selection, and classification, respectively. Simulation results show 99.335\% detection accuracy for a 32-element malicious RIS at $-80$ dBm receiver noise power. GL remains competitive with Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) baselines while requiring only 0.1119 million floating-point operations (FLOPs), and also generalizes to previously unseen RIS sizes.

eess.SP · cs.IT

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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:2611.01880

Ray-Tracing-Parameter-Based Lightweight Hybrid Precoding via Cascade XGBoost for Wideband Massive MIMO-OFDM Systems

Po-Heng Chou, Wei-Chen Lin, Wan-Jen Huang, Walid Saad, Ronald Y. Chang

原文摘要

Hybrid precoding reduces the radio-frequency (RF) hardware burden in wideband massive MIMO-OFDM systems, but conventional optimization and deep neural network (DNN)-based approaches can incur high online complexity. This paper proposes XGB-SVD, a lightweight hybrid precoding framework that maps compact ray-tracing path parameters directly to analog beamforming codeword indices using cascade eXtreme Gradient Boosting (XGBoost). The cascade captures sequential beam-selection dependencies and transmit--receive propagation correlation, while the digital precoder and combiner are obtained from the reduced-dimensional equivalent channel using singular value decomposition (SVD). Evaluations over five DeepMIMO scenarios spanning sub-6-GHz and millimeter-wave bands show that XGB-SVD consistently outperforms the considered DNN baselines and approaches simultaneous orthogonal matching pursuit (SOMP) in several environments. Cross-scenario results further indicate improved transfer from propagation-diverse training environments, while XGB-SVD provides lower online computational and training costs than the DNN baselines.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01879

Graphs Have Structure: Declarative Manifolds, 3D Z-Order Monoid Roll-ups, and Zero-Simulation Visual Analytics in a Graph DBMS

Madhulatha Mandarapu, Sandeep Kunkunuru

原文摘要

Every real-world graph is embedded in an ambient structural manifold: proteins fold in Euclidean space ($R^3$), telecom networks sit on a planetary surface ($S^2 \times R$), and ontologies and calendars form discrete subsumption posets ($(V, \sqsubseteq)$). Yet property graph databases treat node coordinates as opaque payload scalars, leave spatial proximity edges unindexed, and force visualizers into $O(V^2)$ client-side force-directed simulations that destroy true geometry and truncate hierarchy roll-ups. We elevate ambient manifold structure to a first-class primitive in Samyama Graph. First, we introduce a declarative and auto-detected structural convention contract (samyama.structure/1) persisted in .sgsnap headers that binds node labels to Euclidean 3D, Geo-Spherical, Causal Spacetime, or Poset Hierarchy manifolds. Second, we show that both discrete posets (via Order-Embedded Hierarchy $[tin, tout]$ DFS intervals) and continuous 3D manifolds (via a 63-bit 3D Morton Z-order Structural-Geometric Index, SGI) reduce regional aggregation to 1D rank-interval monoid folds answered directly from index-resident Fenwick trees (SUM, COUNT) and Segment Trees (MIN, MAX). At $100,000$ 3D nodes, SGI builds in $28.14$ ms and answers Morton-interval SUM and MAX roll-ups in $0.048\,μ$s and $0.104\,μ$s---$8,345.1\times$ and $3,824.4\times$ faster than property scans. Third, we introduce 3D voxel-hashed virtual contact edges weighted by Topological--Geometric Stress $σ(u,v) = d_{graph}(u,v) / d_{manifold}(u,v)$ to isolate long-range tertiary folds, plus manifold-guided $A^*$ shortest-path search that cuts explored states by up to $3.80\times$ over Dijkstra. Fourth, we replace client layout simulation with server-computed 3D Cone-Tree/Ribbon coordinates and a non-WebGL 2.5D Isometric projection with painter's-order depth sorting and sub-millisecond Level-of-Detail (LoD) macro-cuts.

cs.DB · cs.CG · cs.HC

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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:2611.01876

Acoustic and Attentional Challenges Shape the Balance Between Attended and Competing Speech Tracking Differently

Johanna Wilroth, Martin A. Skoglund, Emina Alickovic, Martin Enqvist

原文摘要

Selective attention enhances cortical tracking of attended speech, but the role of ignored speech remains less clear. In particular, it is unknown whether attended and ignored speech contribute to cortical tracking in a fixed balance or whether this balance changes with listening conditions. We addressed this question across six electroencephalography (EEG) datasets spanning acoustic and attentional challenges, hearing impairment, and hearing-aid processing. We systematically varied the relative weighting of attended and competing speech and studied how their combined contribution affected cortical speech tracking. Across most listening conditions, cortical tracking was improved when we included competing speech, compared with attended speech alone. Importantly, the balance between attended and competing speech changed across conditions. Increasing acoustic difficulty increased both the benefit and the optimal contribution of competing speech, with the same general pattern observed in normal-hearing and hearing-impaired listeners. In the hearing-aid datasets examining noise-reduction conditions, the contribution of competing speech was more limited. Attentional challenges also changed the contribution of competing speech, but differently from acoustic challenges. These findings show that attended and competing speech do not contribute to cortical tracking in a fixed balance. Rather, their balance depends on the listening challenge. Although these results do not define a single optimal ratio, they provide a step toward hearing devices that adapt not only to the attended talker, but also to how competing speech is balanced in complex listening environments.

cs.SD · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01875

Approximation Algorithms for Maximum-Weight Degree-Bounded Forests

Theophile Thiery, Vera Traub, Nicola Widmer

原文摘要

In the maximum-weight degree-bounded forest problem, we are given a graph with nonnegative edge weights and a degree upper bound on each vertex. The objective is to find a maximum-weight forest satisfying the degree bounds. We show that this problem admits a simple $2$-approximation via the greedy algorithm. This already improves over the best-known guarantees obtained via a reduction to weighted $3$-matroid parity, and sets a baseline to improve upon. Our main result is an $\frac{8}{5}$-approximation algorithm for the maximum-weight degree-bounded forest problem. For the special case of uniform degree bounds, our algorithm achieves an approximation ratio of $\frac{3}{2}$. It computes two sets of edges, each of which we then restrict to a feasible solution in an optimal way, and finally returns the better of the two resulting solutions. To obtain the initial two edge sets, we first relax the degree bounds and leverage an algorithm by Lau and Singh [JACM'15] for degree-bounded spanning trees, and second we relax the forest constraints and employ an algorithm for maximum-weight $b$-matchings.

cs.DS · math.CO

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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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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 ↗
版本更新 · v3arXiv:2606.16061

Coresets for Continuous $k$-Center in Hyperbolic Space

Eunku Park

原文摘要

We study coresets for the continuous $k$-center problem in fixed-dimensional hyperbolic space $\mathbb H^D$, where centers may lie anywhere in the space. For an input set $P$ of $n$ points, we construct an unweighted coreset of size $(k/\varepsilon)^{O(kD)}$ in $O(nkD(k/\varepsilon)^{O(kD)})$ time, independent of the enclosing radius. Every exact optimal solution for the coreset is a $(1+\varepsilon)$-approximation for $P$. The same construction also yields a strong coreset after adjusting the accuracy parameter. Our method combines local Euclidean grids at bounded scales with radial shells at larger scales. Hyperbolic balls induce spherical caps on representative spheres, whose multiplicative enlargement can be controlled by additive hyperbolic distance errors. A recursive ball-coverage certificate then selects a radius-independent set of input witnesses. We establish lower bounds of $Ω_D(k\varepsilon^{-D})$ for $k\ge 8D$ and sufficiently small $\varepsilon$, and $2^{Ω(k)}$ for $D\ge2$ when $\varepsilon=Θ(1/k)$, ruling out bounds polynomial jointly in $k$ and $1/\varepsilon$. For $k$ sufficiently separated clusters, we obtain simultaneous additive and multiplicative weak coreset guarantees, with optimal worst-case size $Θ_D(k\varepsilon^{-(D-1)/2})$ when both errors equal $\varepsilon$. In contrast, even under the same separation condition, no radius-independent additive weak coreset bound exists in the Euclidean plane for $k=2$.

cs.CG · cs.DS

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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:2611.01862

Interoperability Assessment of Fronthaul and E2 Interfaces for Multi-Vendor Open RAN Systems

Farhad Mehran, Charles Turyagyenda, Dritan Kaleshi

原文摘要

Open Radio Access Networks (RANs) aim to transform the existing wireless network architectures by adopting open interfaces, disaggregating and virtualising RAN components, and enabling dynamic control of the system via data-driven methods. To accelerate the development of interoperable RAN solutions, rigorous assessment of Open Fronthaul and E2 interfaces based on O-RAN Alliance Interoperability Testing (IOT) specifications plays a key role as the integration of these interfaces seems to be very challenging, e.g. due to the need for alignment of Fronthaul IOT profiles and unforeseen challenges for E2 interface integration as Near-Real-Time RAN Intelligent Controller (Near-RT RIC) is relatively a new component introduced within the RAN architecture. In this paper, selected parts of the latest findings and lessons learned from interoperability assessment of Open Fronthaul and E2 interfaces for 13 multi-vendor Open RAN systems deployed in SmartRAN Open Network Interoperability Centre (SONIC) Labs are presented.

cs.NI · eess.SP

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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:2611.01859

Apto: Self-Adaptive CPU-GPU Hybrid MoE Inference on Shared PCs

Zihang Zhong, Jiali Wang, Rong Chen

原文摘要

CPU/GPU hybrid inference enables Mixture-of-Experts (MoE) models to run on memory-constrained PCs by keeping all expert weights in host memory and executing activated experts across both processors. Existing hybrid systems, however, assume exclusive resource access and determine an upfront CPU/GPU partition, so CPU contention from colocated applications can severely imbalance the two execution paths. This paper presents Apto, a self-adaptive hybrid inference system that applies dynamic scheduling to expert execution. The CPU and GPU executors claim experts from opposite ends of a dual-ended queue as they progress, allowing the CPU/GPU work split to adapt to their execution progress without predicting contention. Apto further uses affinity-aware scheduling to expose each executor's preferred experts at its queue end and cancellable tail racing to mitigate final-expert stragglers. Compared with state-of-the-art GPU-only and hybrid baselines, Apto achieves average prefill and decode speedups of 2.96$\times$ and 1.52$\times$ under standalone execution, and 5.97$\times$ and 3.45$\times$ under concurrent CPU workloads. It also accelerates real-world coding-agent applications by 2.02$\times$ on average.

cs.DC

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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 · v3arXiv:2609.01465

Mobile Backscatter Communication for the Battery-less Internet of Things

Weining Song, Thiemo Voigt, Stefanos Kaxiras, Yuan Yao, Luca Mottola

原文摘要

We enable backscatter communication in the battery-less mobile Internet of Things (IoT). Backscatter communication is extensively studied in static settings. Existing designs are, however, fundamentally mismatched with mobility and time-varying energy patterns. Channel conditions rapidly fluctuate, impacting the achievable data rates and thus transmission costs. Energy availability varies unpredictably, possibly forcing devices to remain quiescent to recharge energy buffers. The two issues compound each other: while recharging, a battery-less mobile IoT device may miss more favorable channel conditions. We design a lightweight decision system that dynamically determines when to transmit by checking short-term trends in signal strength, while using Non-volatile Memory (NVM) to retain packets in unfavorable channel conditions and across energy failures. Using a prototype we built and real-world mobility and power traces, we compare our design against a rate-adaptive baseline that only considers the instantaneous channel conditions. Experimental results show that our system improves throughput by up to 5.16x while reducing transmission energy consumption by up to 47.3%, with only 0.23% - 7.3% additional energy overhead.

cs.NI

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arXiv ↗PDF ↗