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新论文 · v1arXiv:2611.06338

Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves

Félix Martinez-Jiménez, Sander Miller-Murphy, Tobie Ondoa-Essomba, Emmanuel Witrant

原文摘要

Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent. At each iteration, two closed-loop experiments are run on the plant: the first one measures the tracking error, and the second one, driven by this error, provides the sensitivities from which the gradient of the cost is estimated. This estimate is unbiased for linear plants only. We study the biased case on automotive electronic throttle valves, whose friction and stiction vary between valves and runs, controlled by an intelligent proportional (model-free) controller, which is an incremental PI controller. We show that the mismatch of the controller state at the start of the two experiments, which stiction makes unavoidable, adds an error inversely proportional to the amplitude of the second experiment. On digital twins of ten valves, the IFT gradient is a descent direction in only 69\% of the low-cost region, friction being the cause, and all update rules plateau and then drift. On two hardware valves, the best update rule depends on the noise structure of the gradient. On the first valve, where one gradient component is buried in noise, Adam outperforms Gauss--Newton, its normalisation and its momentum each improving one parameter. On the second valve, where both components are consistent, the same normalisation amplifies a direction that Gauss--Newton rightly discounts, and Adam drifts to a controller worse than the initial one. The twins reproduce the mechanism but not this ranking.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2606.10829

Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models

Yusuf Sahin, Ahmed Rockey Saikia, Volkan Cevher, Paolo Favaro

原文摘要

Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration, but this parallelism is fragile: positions that are individually confident may be unsafe to commit together when their predictions are coupled. Existing training-free samplers such as Top-$k$, Fast-dLLM, and EB-Sampler mainly control how many tokens to reveal, while often ranking candidates by token-wise scores that ignore interactions within the selected set. We propose ADAS, a training-free reranking rule that leaves the base sampler's stopping rule unchanged and greedily discounts each token-wise confidence score according to its attention to already selected positions, weighted by their prediction uncertainty. Across LLaDA-8B-Base and Dream-7B-Base on the reasoning benchmarks GSM8K and MATH500 and the code benchmarks HumanEval and MBPP, plugging ADAS into all three samplers improves low-NFE performance at matched denoiser evaluations by $9.11$ and $10.46$ percentage points on average, respectively, with $3.1\%$ per-forward runtime overhead. Code is available at https://github.com/yusufsahin99/ADAS.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06328

EvoCodeAgent: Project-Level Code Generation via Architecture-Code Co-Evolution and Experience-Guided Repair

Qianhui Zhao, Li Zhang, Fang Liu

原文摘要

Project-level code generation aims to automatically synthesize complete software repositories from high-level requirements, producing coherent multi-file systems with well-structured modules and consistent cross-file dependencies. Despite the strong capabilities of large language models (LLMs) in specification understanding and code synthesis, existing approaches still exhibit several critical limitations. First, many methods rely on static architectural designs that cannot adapt during iterative generation, leading to a growing misalignment between architecture and implementation. Second, they lack mechanisms to accumulate and reuse experience across iterations, resulting in recurring errors and reduced efficiency. Third, they often depend on standalone LLM-based evaluation of intermediate artifacts, which suffer from limited transparency and potential unreliability under long-context reasoning. To address these challenges, we propose EvoCodeAgent, a state-centric framework for project-level code generation. EvoCodeAgent maintains an evolving architectural state initialized from structured project specifications and continuously reconciles predicted relations from architecture and skeletons with realized relations extracted from generated code. It further uses tool-grounded verification evidence to support reliable state updates and abstracts reusable repair experiences from previous revision attempts to guide subsequent generation. Extensive experiments on project-level benchmarks of different scales, including DevBench and CodeProjectEval, demonstrate that EvoCodeAgent consistently outperforms baseline approaches. Under the GPT-5 setting, it achieves unit test pass rates of 60.48% and 27.73%, respectively, yielding up to a 53.5% improvement over the strongest baseline.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06324

Mass Producing Training Data with Surprising Side Effects

Yaowen Ye, Jacob Steinhardt, Sarah Schwettmann, Cassidy Laidlaw

原文摘要

Training data can have unexpected side effects on language model behavior outside the training domain: insecure code data can induce broad misalignment, and outdated bird names can induce a 19th-century persona. Understanding such generalization is hard because only dozens of isolated examples exist, each constructed by hand or discovered by accident. We propose automatically discovering such datasets with surprising side effects (DSSEs). We define three objectives for DSSEs: they should (1) elicit a target behavior, (2) read like ordinary post-training data, and (3) be surprising to human auditors reading the data. We operationalize each with LLM judges, and search for high-scoring datasets via evolution, reinforcement learning, and subliminal learning approaches. After re-scoring and filtering with held-out metrics, our pipeline produces a collection of 6,575 DSSEs across 8 behaviors, many of which generalize across model families and sizes. We find our automated metrics track ML researchers' judgments, and frontier models outperform humans at predicting side effects. These datasets form a testbed for studying generalization and lay the groundwork for predicting training side effects before model deployment. Explore them at https://dsse.transluce.org.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06321

EnzymeRSI: A dual-loop recursive self-improvement system for enzyme design

Genoria AI Team, Yuejie Hou, Lixiang Yang, Yisi Liu, Weiting Tang, Yueyuxiao Yang, Meng Yang

原文摘要

Improvement in enzyme engineering is usually pursued by optimising which candidates to assay within a fixed model and workflow; when the bottleneck arises from a mismatched method or a failing workflow, that route is difficult to escape. We present EnzymeRSI, a dual-loop system for recursive self-improvement (RSI). The outer loop evolves harness assets without changing model weights, the inner loop develops prediction and selection algorithms under controlled conditions, and a cloud laboratory system is specified to close the physical loop with quality-controlled feedback. By updating its prediction strategy through iterative comparison, the outer loop raised the mean development-set Spearman correlation on ProteinGym by 0.044 across five assays, reaching 0.335 on CD19 and 0.683 on OPSD (rhodopsin); of the two first places, only CD19 was produced by the outer loop itself. In the self-improvement of the ThermoMPNN algorithm the inner loop reached a PCC of 0.849, and on TadA-Bench it reached a Spearman correlation of 0.307 in 1.13 h. In three prospective wet-lab tests of the system's experimental decision-making, 10 of the 32 candidates that had not been pre-specified for the luciferase RLuc showed at least twice the wild-type activity; a recommended variant of the cytidine deaminase A3A raised 5mC conversion to 90.2%; and path selection in evolution raised the catalytic efficiency of the DNA polymerase KOD-E on a modified substrate to 31.1 times that of the parent. By treating the organisation of the research workflow and the development of algorithms as a single falsifiable dry-to-wet loop, this work offers an evolutionary paradigm for moving beyond the blind spots of a fixed method and for autonomous enzyme engineering.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06320

Conformal Prediction for Distribution-to-Distribution Regression

Trung-Khang Tran, Tuan A. Hoang, Viet-Hoang Tran, Tan M. Nguyen

原文摘要

Conformal Prediction (CP) provides a model-agnostic framework for constructing finite-sample prediction sets with coverage guarantees and has become a tool for reliable decision-making in high-risk settings. Recent advances have extended Split Conformal Prediction to increasingly complex learning problems, including multilabel, high-dimensional, and functional outputs. In this work, we study CP for Distribution-to-Distribution Regression, a setting of growing interest across many application domains. We show that a naive application of Split CP yields prediction sets that, while marginally valid, are non-interpretable, e.g., lacking a well-defined volume or a tractable sampling procedure, and of limited practical value. To overcome this, we propose a new conformal framework that operates in an orthogonal basis space, producing interpretable prediction sets while preserving coverage guarantees. We further develop an adaptive variant with asymptotic conditional coverage under mild assumptions. Empirical results on synthetic and real-world datasets validate the effectiveness of the proposed approach. The code is publicly available at https://github.com/tuanhacs/conformal_d2d.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06319

Verified Implementation of the Next-gen Borrow Checking Algorithm for Rust via Abstract Interpretation

Jinhua Wu, Yuting Wang, Zhemin Chen

原文摘要

Borrow checking is a central mechanism for Rust to ensure temporal memory safety. Its correctness is based on the intuition that the Aliasing XOR Mutability discipline guarantees that programs without borrow errors inherently could not access invalid references (i.e., pointers). However, existing work on formalizing borrow checking treats it either as a component of type systems or as symbolic execution describing resource transfer. None provides a direct explanation of the correctness of borrow checking algorithms such as Non-Lexical Lifetimes (NLL) or Polonius in the official Rust compiler that function more like static pointer analysis. We take a first step toward solving this problem by formalizing Polonius, the next-generation borrow- checking algorithm for the Rust compiler, on a core subset of Rust. We prove its correctness by showing that any program accepted by Polonius does not access invalid references at runtime. The key observation is that, when borrow checking is performed lazily, Polonius could be treated as a forward abstract interpretation on abstract domains recording pointer aliasing (borrowing) relations. By providing a concrete semantics that formally describes the Aliasing XOR Mutability discipline, we prove the soundness of this abstract interpretation, from which we derive memory safety of checked programs. This formulation of Polonius is implemented and formally verified in the abstract interpretation framework of CompCert. The correctness proofs are generated by an agentic AI under human guidance using paper proofs as proof outline. To the best of our knowledge, this is the first comprehensive formalization and verification of a borrow checking algorithm as manifested in the Rust compiler.

cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06315

Exact certification for conic optimization over quadratic modules: partitions of unity and recession directions

Didier Henrion, Alaa Ibrahim, Victor Magron

原文摘要

We consider linear conic problems whose cone is a product of truncated quadratic modules, the sum-of-squares (SOS) side of the moment-SOS hierarchy. A floating-point solution is turned into an exact rational certificate in three steps: rounding, repair of each block residual by a constant, and absorption of the constants along a recession direction of the problem. The repair relies on strict partitions of unity in the quadratic modules. We characterize their existence, relate the canonical one to the analytic center of the dual pseudo-moment cone, Christoffel-Darboux polynomials and Pell equations, and we derive closed-form rational partitions for balls and boxes. The loss incurred by the certification is the optimal value of a recession problem, equal to a support function of the masses of the dual feasible pseudomoments. The method is illustrated on polynomial optimization, volume computation and positivity of polynomials along D-finite trajectories.

cs.SC · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04108

Separating Common and Unique Directions for Model Merging

Shiting Wang, Yingjie Zou, Ke Li

原文摘要

Model merging combines multiple fine-tuned models without additional training, and arithmetic methods have emerged as a simple yet effective way to merge models without data. However, existing arithmetic methods treat task vectors in parameter space and apply a single element-wise rule to directions that can differ in mergeability. In a shared directional basis, we find a consistent two-regime structure: a low-dispersion common bulk shared across tasks and a high-dispersion task-dominated tail that carries disproportionate energy. These findings motivate Common--Unique Subspace Decomposition (CUSD), a data-free pre-merging method that separates task vectors into common and unique directional components, merges the common components with a downstream arithmetic operator, and retains unique residuals separately. Experiments across $11$ base models, four model families, and four arithmetic-based merging operators show that CUSD improves 42 of 44 model--operator combinations and achieves up to 22.5% improvement over the corresponding baselines. Our code is publicly available at https://github.com/stwang666/CUSD

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06311

The Exact Memory-Rate Tradeoff for Three-User Secure Coded Caching

Han Fang, Nan Liu, Wei Kang

原文摘要

We determine the exact zero-error memory-rate tradeoff for secure coded caching with three users and any number $N\ge2$ of independent files. Each user recovers its requested file, while its cache and the broadcast jointly reveal no information about the complete tuple of unrequested files. For $N\ge4$, two converse inequalities establish the optimality of the known achievable points and the memory-sharing segments between them. For $N=3$, two additional bounds identify a new corner at $(M,R)=(3/2,2)$, where memory and rate are measured in file units. An explicit linear code over $\mathbb{F}_3$ attains this corner and reduces the rate by one quarter of a file relative to memory sharing between the known points. The converse links the information held by cache pairs and triples to delivery under repeated and distinct requests. Its equality conditions guide the construction: users cancel the keys to recover their requested files, while uniform masking protects the complete tuple of unrequested files.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2310.19385

Gradient-free online learning of subgrid-scale dynamics with neural emulators

Hugo Frezat, Ronan Fablet, Guillaume Balarac, Julien Le Sommer

原文摘要

In this paper, we propose a generic algorithm to train machine learning-based subgrid parametrizations online, i.e., with $\textit{a posteriori}$ loss functions, but for non-differentiable numerical solvers. The proposed approach leverages a neural emulator to approximate the reduced state-space solver, which is then used to allow gradient propagation through temporal integration steps. We apply this methodology on a chaotic two-timescales Lorenz-96 system and a single layer quasi-geostrophic system with zonal dynamics, known to be highly unstable with offline strategies. Using our algorithm, we are able to train a parametrization that recovers most of the benefits of online strategies without having to compute the gradient of the original solver. We found that training the neural emulator and parametrization components separately with different loss quantities is necessary in order to minimize the propagation of approximation biases. Experiments on emulator architectures with different complexities also indicates that emulator performance is key in order to learn an accurate parametrization. This work is a step towards learning parametrization with online strategies for climate models.

physics.comp-ph · cs.LG · physics.flu-dyn

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06308

Scale Anchoring via Implicit Semantics and Sequence-Consistent Loop Closure for VGGT-Based SLAM

Ziyi Jin, Kangjin Yu, Beichen Wang, Can Jiang, Zhongpu Xia, Jie Xu

原文摘要

Feed-forward geometric foundation models enable dense monocular reconstruction without requiring camera calibration, but independently reconstructed submaps can accumulate scale drift, and even a valid revisit can yield an inconsistent cross-submap measurement. We extend VGGT-SLAM 2.0 with fixed-root scale anchoring and short-sequence-assisted direct loop connections. SALAD selects an image pair between the initial submap and each eligible new submap; joint VGGT inference then uses learned implicit semantic-geometric cues to supply a scale-only anchor while preserving the original overlap chain. For loop closure, neighboring image pairs support a consistency check and translation-scale alignment. Each accepted candidate contributes one robust Sim(3) factor between existing center-frame nodes, without an auxiliary loop submap. A 17-sequence comparison lowers mean Sim(3) ATE on nine TUM sequences from 0.1018 to 0.0451 m relative to the VGGT-SLAM 2.0 baseline. Anchoring improves all seven no-loop module ablations. On three Clio scenes, a fixed-loop comparison favors the direct connection pipeline over the tested auxiliary-submap pipeline. A separate candidate audit exposes additional rejections of visually overlapping pairs by the short-sequence check. These comparisons distinguish the gains from long-range scale anchoring and loop connection choices from the limitations of candidate verification.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06307

When Does Risk-Controlled Throughput Forecasting Actually Control Risk? A Reproduction Study on LEO Satellite Link

Mayan Sharma, Kriti Saini, Devansh Behl, Suganya R, S. Vijayaragavan

原文摘要

Risk-budgeted throughput forecasting converts a point forecast of LEO satellite capacity into a conservative bound whose overestimation rate is held at a user-specified budget, so that admission control can act on it safely. We reproduce the state of the art, BG-CFQS, on the three Starlink traces its authors used and on an independent fourth dataset, and report four findings. First, its risk guarantee is reproducible but conditional on the calibration split: under a random split we recover every published quantity, including risk-pass on 3 of 3 datasets, but under a contiguous temporal split, which is what a deployed terminal faces, it holds on 1 of 3. Second, independent of any split, the method cannot serve a risk budget below 0.15, the low end of its candidate quantile set. On all four datasets the achieved rate is identical at budgets of 0.05, 0.10 and 0.15, overshooting by 1.4x to 3.7x at the tightest. Third, static conformal calibration cannot improve a ranking, only shift an operating point; we confirm this exactly on latency spike detection, where online calibration improves AUPRC by up to 51%. Fourth, group-conditional online recalibration splits into two mechanisms. The online part replicates: it tracks the budget within 1% from 0.05 to 0.35, where static calibration is 16 to 21% off. The group-conditional part does not: on three of four datasets, conditioning on any available covariate, including measured serving-satellite geometry, is worse than not conditioning, and our attempt to predict which datasets benefit failed. What is new here is empirical, not algorithmic: we claim the findings, not the methods.

cs.LG · cs.AI · cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.33387

From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?

Daisy R. Bradley, Nathan A. Hinchliffe, Daniel J. Pitchforth, Matthew R. Jones, Elizabeth J. Cross

原文摘要

Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost. Physics-informed machine learning or "grey-box" models have been developed to overcome some of the limitations of traditional black-box learners, utilising the physical insight that an engineer would have about the structure they are modelling and have shown promising results in the structural engineering field among many others. This work explores whether an additional advantage could be a reduced environmental impact, considering the relationship between training data quantity and training time, linking this duration to carbon emissions from computing. In a structural health monitoring context, four physics-informed machine learning approaches - spanning Gaussian processes and neural networks - are evaluated: residual modelling, input augmentation, hybrid modelling, and constrained learning. The emissions for training each of the models to reach a given error threshold is compared, and in most examples, shown to be lower for the physics-informed models (with input augmented models being an exception). This reduction in training emissions further compounds the environmental savings achieved by collecting and storing less data. Although promising results, we cannot expect a silver bullet and the case studies demonstrate that a trade-off is needed between the increased complexity that comes from introducing physics into a machine learner, against the gain from reduced training data requirements.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.17441

Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure

Moritz Schlager, Friederike Jungmann, Samuel Schmidgall, Philipp Raffler, Franziska Hartl, Eva Wende, Paula Roßm"uller, Conrad Ketzer 等 18 位作者

原文摘要

Realistic patient simulation is a key requirement for developing and testing clinical LLMs at scale without costly user studies. Existing simulators lack realism and controllability: they overshare information and miss the variability of real patient behavior. Here, we introduce PatientsWithPersonality (PWP), a patient simulation framework that generates realistic yet diverse patient responses through explicit personality parametrization over a latent patient state. Grounded in the six-dimensional HEXACO personality model, it gives fine-grained control over conversational style, cooperativeness, and information disclosure. We evaluate PWP in the first direct comparison of different patient simulators under a shared protocol, anchored in transcripts of real physician-patient encounters. Clinicians judge PWP conversations as realistic at least as often as the recorded encounters themselves and far more often than prior simulators, and almost never flag them as "too informative". Our simulator also reproduces the disclosure pacing of real patients, and its HEXACO-conditioned personality traits can be recovered by clinicians and an autorater. PWP further shows a substantially wider behavioral range than the closest baseline. In a downstream benchmark, LLMs interviewing patients simulated by PWP perform closest to how they perform on the real encounters, whereas prior simulators make the task easier. PWP thus provides a realistic, steerable, and diverse patient population for improving and benchmarking clinical LLMs.

cs.HC · cs.AI · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06306

Spatial Path Superposition: A No-Cloning Perspective on MIMO-Inspired Spatial Diversity

Angela Sara Cacciapuoti, Claudio Pellitteri, Marcello Caleffi

原文摘要

Spatial diversity is a key mechanism in classical Multi-Input Multi-Output (MIMO) communications, where multiple propagation alternatives provide redundant observations that improve reliability in noisy environments. A direct translation of this principle to quantum communications is forbidden by the no-cloning theorem, since an unknown quantum state cannot be copied and transmitted through independent links. In this Perspective article, we argue that spatial path superposition provides a no-cloning-compatible analogue of the spatial-diversity principle of MIMO. Rather than duplicating the transmitted quantum state, a quantum carrier is coherently delocalized over multiple communication links, so that alternative spatial evolutions are experienced in quantum superposition. In this framework, classical redundancy is replaced by spatial coherence, and the communication link itself becomes part of the quantum dynamics. Indeed, spatial coherence can enhance the fidelity of the received quantum state, providing the quantum counterpart of the classical diversity gain, and can be converted into bipartite and multipartite entanglement during propagation. This implies that, under suitable coherent path control, noisy channel evolutions can contribute constructively to entanglement generation. This Perspective identifies spatial path superposition as a quantum-native primitive for the Quantum Internet, linking no-cloning-compatible spatial diversity, fidelity enhancement, entanglement generation, and coherent path control.

quant-ph · cs.NI

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2307.16694

Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging

M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun, Peter H. N. de With, Fons van der Sommen

原文摘要

Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. In image segmentation, latent density models can be utilized to address this problem. The most popular approach is the Probabilistic U-Net (PU-Net), which uses latent Normal densities to optimize the conditional data log-likelihood Evidence Lower Bound. In this work, we demonstrate that the PU-Net latent space is severely sparse and heavily under-utilized. To address this, we modify the training objective to introduce the entropy-regularized Sinkhorn Divergence in the latent space, which promotes homogeneity across all latent dimensions, effectively improving gradient-descent updates and latent space informativeness. Applied to public datasets of various clinical segmentation problems, our proposed methodology achieves the best Empirical Wasserstein Distance on every dataset compared against preceding latent variable models for probabilistic segmentation. The results indicate that encouraging a homogeneous latent space significantly improves latent density modeling for medical image segmentation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.27002

IMFuse: Instance-Aware Multi-Layer Fusion for LLM-Enhanced Sequential Recommendation

Yuheng Zheng, Yu Cui, Bin Wu, Jian Zhang, Ye Feng, Can Wang, Jiawei Chen

原文摘要

Leveraging large language models (LLMs) to enrich item embeddings with textual semantics has become a prevailing paradigm for enhancing sequential recommendation (SR). However, existing methods typically rely solely on the final-layer LLM embeddings, leaving the semantics encoded in other layers largely unexploited. Through an empirical analysis, we uncover three key findings: 1) item embeddings from deeper LLM layers suffer from increasingly severe spectral collapse, which reduces their information richness and hinders the optimization; 2) intermediate LLM layers encode complementary item semantics beyond the final layer; 3) different items exhibit heterogeneous layer-wise semantic patterns. These findings suggest that the final layer alone is insufficient and that multi-layer LLM semantics should be exploited adaptively for each item. To this end, we propose IMFuse, a novel Instance-Aware MultiLayer Fusion strategy for LLM-enhanced SR. IMFuse adaptively aggregates embeddings from all LLM layers with learnable layerwise fusion weights, and further customizes the fusion for each item via an instance-aware Mixture-of-Experts (MoE) module. As a plugand-play strategy, IMFuse can be seamlessly integrated into various SR backbones and LLM-based enhancement methods. Extensive experiments on four real-world datasets demonstrate that IMFuse consistently improves state-of-the-art methods, achieving an average relative improvement of 6.72% with limited computational overhead.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.14581

Online learning of subgrid-scale models for quasi-geostrophic turbulence in planetary interiors

Hugo Frezat, Thomas Gastine, Alexandre Fournier

原文摘要

Machine learning approaches to subgrid-scale (SGS) modeling are now well established in atmospheric and oceanic applications. Among these, online end-to-end learning, where the differentiable solver participates in the training, has shown particular promise. Yet existing studies are largely restricted to idealised periodic domains, with no mechanical boundaries, precluding them from addressing the dynamics of bounded rotating flows relevant to planetary interiors. Here we consider two-dimensional quasi-geostrophic turbulence in a rapidly rotating annular bounded domain. We examine three configurations varying the geometry of the container and the rotation rate. The system exhibits key features such as zonal jets, Rossby waves, and, in the spherical shell geometry, a slow quasi-periodic inward drift of the jets. The spectral properties of the zonal and non-zonal flow can be understood in the framework of zonostrophic turbulence theory. We develop a differentiable solver for this system, which allows us to train SGS models online, over a time span of one turnover time, using coarse-grained data from direct numerical simulations. In all cases, a SGS model trained on a single turnover time accurately reproduces global integrated diagnostics, energy spectra as well as long-term dynamical behaviours, such as jet migration, occurring on timescales which exceed the training window by one order of magnitude. The online-trained model further outperforms classical hyperdiffusivity and Leith closure schemes, for which reducing the radial resolution is impractical. The resulting speed-up paves the way to further investigations of long-term geophysical fluid processes beyond the reach of direct numerical simulations.

physics.flu-dyn · astro-ph.EP · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00815

VCoT-SQL: Enhancing Text-to-SQL Generation through Dual Chain-of-Thought Validation Training

Rui Zhang, Yujia Su, Ruixuan Li, Yuhua Li, Yixin Su

原文摘要

Open-source LLMs provide a flexible alternative to closed-source models for Text-to-SQL but still struggle with complex queries. Finetuning with chain-of-thought (CoT) data improves reasoning, yet standard datasets lack CoT annotations, requiring synthetic generation. Existing synthesis methods only verify the final answer, allowing flawed reasoning steps to corrupt the training data. We propose VCoT-SQL, a framework that improves Text-to-SQL generation through intermediate-step validated CoT supervision and a dedicated SQL validator, which enables small open-source models to outperform frontier closed-source models. VCoT-SQL trains two complementary models on curated reasoning traces: an SQL generator using clause-level CoT templates where reasoning steps are verified via execution and schema alignment checks, and an SQL validator using diagnostic CoT templates to detect syntactic, semantic, and type level errors. We further design a hierarchical schema filtering pipeline that integrates and refines complementary retrieval strategies to distill relevant database elements. VCoT-SQL achieves 66.82% execution accuracy on challenging dataset BIRD dev and 86.8% on Spider test, improving the base 7B model by 7% on BIRD. Notably, our CoT synthesis relies on 7B models yet outperforms methods that use 72B-scale or even proprietary closed source teachers for data generation, demonstrating that rigorous step-level validation can be more effective than scaling up teacher model size. Our dataset and code are available at https://github.com/INTREBID/VCoT-SQL.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.26684

MuLaCover: Flexible Cover Song Generation via Symbolic Tonal Control

Lynn Yi, Zihan Xiong, Junjie Cao, Xiaolong Weng, Yingzhe Ma, Gus Xia, Jinliang Liu, Yuxin Xie 等 20 位作者

原文摘要

A cover song re-renders an existing piece, most commonly by preserving its tonal content -- melody and chord progression -- while reshaping other musical attributes. This suggests a natural formulation for generative cover synthesis with pretrained music language models: explicitly control tonal content, while specifying lyrics and style through text. Existing approaches condition on frame-level features, tightly coupling generation to the reference's timing and structure, and limiting flexibility such as tempo variation, structural rearrangement, or partial conditioning. To this end, we present MuLaCover, a cover generation model that conditions a pretrained text-to-song foundation model on an editable symbolic lead sheet. Our design enables alignment-free control: generated outputs preserve the tonal signature of the source without frame-level alignment, allowing flexible timing, structure, and segment-level generation. We further introduce a training curriculum with partially mismatched audio-symbolic pairs, improving diversity and robustness. We evaluate MuLaCover with objective metrics, standard subjective ratings, and in-depth expert interviews that provide fine-grained diagnostic insights. Experiments show that MuLaCover achieves state-of-the-art performance among open-source systems and is competitive with leading commercial models such as Suno-v5.5. Audio samples and code are available at our project page: https://mulalabs.ai/mulacover/

cs.SD · eess.AS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.25256

PanoDent-KD: From Generalist Knowledge Distillation to Cross-Dataset Specialist Detection and Embedded Deployment in Panoramic Radiographs

Muhammad Waqas Ashraf, Imad Ali Shah, Ali Hassan

原文摘要

Reliable panoramic-radiograph detection requires consistency across datasets with different class definitions and deployment software. We present PanoDent-KD, an auditable 1280-pixel YOLO26m pipeline linking a 12-class source generalist, feature-level knowledge distillation (KD), exact classifier-row transfer, teacher-free five-class specialization, and embedded deployment. A common training and evaluation pipeline compares three source-training arms across three seeds: a supervised baseline (B1), single-teacher KD (STKD), and multi-teacher KD (MTKD). Five shared classifier rows initialize nine OralXrays-9 specialists, while the backbone, FPN-PAN neck, and dual-detection-head topology are retained unchanged. On the 2,686-image test subset, all three arms achieve macro box F1 near 0.82. B1 achieves the highest three-seed arm-level mean macro box F1 (0.8239$\pm$0.0027) and $mAP_{50:95}$ (0.6130$\pm$0.0067). STKD seed 1, selected after development but before final testing, was used for deployment. Its Jetson Orin Nano Super FP16 TensorRT engine achieves 77.03-ms median and 80.86-ms P95 prediction-call latency, throughput of 12.90 images/s, and gross total-module energy of 1.39 J/image, with high numerical agreement between within-device PyTorch and TensorRT FP16 inference. These findings demonstrate the feasibility of an auditable, device-specific deployment workflow, but do not establish clinical validity or improved target-domain accuracy from KD.

cs.CV · cs.PF

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.08407

Who Needs DRAM? We Have Fiber

Hannah Atmer, Yuan Yao, Thiemo Voigt, Stefanos Kaxiras

原文摘要

The rising pressure on DRAM availability and contract pricing reflects generative AI's massive high-performance memory requirements. This pressure is heavily compounded by hyperscale data center expansion, which now consumes a significant portion of global DRAM output. In this work, we propose a new architecture, Fiber Memory, which reimagines the role of optical fiber in a hyperscale data center, deploying it as an active, recirculating delay-line memory for immutable data, such as large language model weights. We present a hybrid data- and pipeline-parallel optical broadcast delay-line memory architecture that accounts for fiber's physical realities. By incorporating space-division multiplexed multi-core fibers, passive optical tap-and-amplify interfaces, co-packaged optics, and centralized in-ring 3R (re-amplify, reshape, retime) regeneration, our case study evaluation suggests that Fiber Memory can eliminate redundant weight storage across tens of thousands of AI accelerators and reduce weight-delivery power by over 75% compared to traditional HBM3e configurations.

cs.AR · cs.DC · cs.ET · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06299

STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation

Jing Wang, Shiyuan Piao, Fugee Tsung

原文摘要

Retrospective battery characterization across heterogeneous cells is challenging because cycling records vary substantially with chemistry, protocol, cell format, capacity, and operating conditions. An endpoint charge--discharge curve captures the local condition of an evaluated cycle, while a cell-level lifecycle terrain summarizes structural evolution across the archived trajectory. We propose STEK, a structural-terrain-enhanced framework for retrospective battery multi-target estimation. STEK combines a cell-level lifecycle (dQ/dV) terrain with an endpoint-derived structural proxy through quality-proxy-conditioned gating. Bounded FiLM and a validity-gated residual head refine the endpoint-curve representation while preserving a curve-based prediction pathway. Battery-type and optional material-text representations further condition an identity-centered low-rank linear map, providing a Koopman-inspired cross-channel consistency objective without modeling temporal dynamics or performing rollout. Under the fixed seven-output MIX-battery protocol, the full retrospective setting improves aggregate performance over endpoint-only learned baselines across multiple temporal backbones; this comparison reflects both the additional archived context and the modeling design. Under matched auxiliary inputs, STEK outperforms direct concatenation and quality-unaware FiLM, while descriptor-removal tests show graceful fallback to the endpoint pathway. Deterministic rules remain preferable for directly recoverable capacity summaries. The method targets retrospective archive harmonization rather than online estimation from cycle t alone.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06297

Speaker-Consistent Grounding for Confusion-Resilient LLM-Based Target Speech Extraction

Hanyu Meng, Hao Shi, Zhanhong He, Qiquan Zhang, Yang Xiao, Sungwoo Cho, Haowei Lou, Haizhou Li

原文摘要

Deterministic target-speaker extraction (TSE) tends to preserve observed linguistic content but may extract the interfering speaker. In contrast, autoregressive generative TSE improves perceptual quality, but is prone to content hallucination and speaker identity drift, which can be mitigated by model grounding with the evidence from the deterministic TSE. However, grounding such a generative TSE model with speaker-confused evidence may therefore reinforce the error. To address this gap, we proposed a repair-before-grounding framework with a training-free evidence-repair stage. Conditioning-diverse candidate selection (CDCS) selects target speaker-consistent evidence from complementary frozen TSE outputs. The selected waveform can then be used by code-space grounding (CSG) to constrain LLM-based TSE decoding. On Libri2Mix, CDCS reduces wrong-speaker extraction errors from 6.87% to 0.75% on clean mixtures and from 18.95% to 10.46% under noise. With the LLM-based TSE checkpoint unchanged, CSG further reduces noisy content- and acoustic-switch rates from 12.79% and 1.68% to 9.10% and 0.51%, respectively. Despite this stronger grounding, generative TSE retains its predicted perceptual-quality advantage over direct deterministic extraction. These results show that repairing evidence identity before grounding improves the reliability of generative TSE. Audio demos and code are available at https://hanyu-meng.github.io/LLM-TSE-Grounding-Demo/.

eess.AS · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06296

Multi-Resident Data Association in Smart Homes via MDP Modeling and Behavior Cloning

Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz

原文摘要

Ambient sensors offer a privacy-preserving solution for activity recognition in smart homes, yet they face a critical challenge in multi-resident environments known as Data Association, which aims to identify the resident triggering a specific anonymous sensor event. Existing approaches fundamentally fail to maintain global consistency by often yielding conflicting associations for silent residents. Additionally, these methods frequently suffer from severe trajectory fragmentation caused by the ontological mismatch of applying continuous physical motion models to discrete logical behaviors. To address these limitations, we propose a novel framework that reformulates data association as a sequential decision-making task within a Markov Decision Process (MDP). We solve the defined MDP via Behavior Cloning (BC) to avoid complex reward engineering by directly learning from expert demonstrations. We instantiate the policy using two complementary architectures, including a lightweight Transformer that treats association as a multi-label classification task over linearized event histories, and a fine-tuned Large Language Model (LLM) that reformulates the problem as conditional text generation to leverage pre-trained semantic knowledge. Extensive evaluations on three diverse datasets demonstrate that our approach significantly outperforms state-of-the-art baselines, particularly in resolving concurrent multi-resident events. Furthermore, the LLM-based policy shows preliminary cross-environment transfer potential under benchmark settings, while its robustness in real deployments still requires further validation.

cs.AI · cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.34249

Evolving Support Priorities in Empathetic Reinforcement Learning

Pengyu Huang, Zhiyuan Han, Wenwen Tong, Hewei Guo, Jiangnan Chen, Sirui Chen, Lewei Lu, Beier Zhu 等 9 位作者

原文摘要

We identify a fundamental mismatch in empathetic reinforcement learning: support priorities evolve with the dialogue state, yet existing methods typically optimize predefined reward specifications that remain fixed across turns. To model these evolving support priorities, we organize empathetic support along cognitive, affective, and proactive empathy, and propose Context-Adaptive Rubric Evolution (CARE). At each turn, CARE generates a context-adaptive rubric by adjusting both the weights of these three empathy dimensions and their fine-grained evaluation criteria. The rubric generator is trained with turn-level rubric supervision and human preference data through supervised fine-tuning followed by preference-based reinforcement learning, and then serves as an adaptive reward interface for online empathetic RL. Integrated with both RLVER and MICA, CARE achieves state-of-the-art performance across SentientBench, EQBench3, and EMPA under three independent LLM judges. Notably, on EMPA, CARE improves EPM-Idx over the strongest baseline by at least 13 points under all three judges, including an increase from 28.11 to 83.54 under Gemini-2.5-Pro. Further analyses show that learned rubric priorities systematically vary across dialogue stages and user emotions, demonstrating that CARE adapts what is rewarded as support needs evolve. Code is available at https://asa13opp.github.io/CARE-project-page.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.18086

Gap-Induced Sidelobes in Multiband Wi-Fi CSI and Their Impact on Multipath Delay Estimation

Ana Jeknić, Mihael Mohorčič, Carolina Fortuna, Aleš Švigelj, Tomaž Javornik, Andrej Hrovat

原文摘要

Leveraging channel state information from multiple Wi-Fi bands has the potential to improve delay resolution for ranging and sensing when wide contiguous spectrum is unavailable. However, non-contiguous spectrum access reshapes the spectral window, producing sidelobes and secondary peaks that can mask closely spaced multipath components. Prior work either designs the bands, which fixed Wi-Fi channelization does not allow, or addresses only the first multipath component. To characterize these secondary peaks, multipath delay estimation is studied in Wi-Fi-compliant multiband settings using channel state information. For a two-path model with unknown complex gains and delays, the Cramér-Rao lower bound for delay separation is derived and analyzed, confirming the benefit of larger frequency aperture at every separation considered, while revealing pronounced, separation-dependent oscillations driven by gap geometry and coupling of the two multipath components. Increasing aperture sharpens the mainlobe but also intensifies sidelobes and multipath interference, yielding competing peaks and a dominant peak shifted from the true delay. For the single-path matched filter, this shift is a deterministic offset governed by the slope of the autocorrelation function (ACF) of the allocation at the separation. A closed-form expression is derived for this offset, predicting it to within 21 ps for the analyzed channel combinations. Since the spectrum allocation enters both the bound and the offset, the impact of its gaps can be assessed for any legal aggregation before a measurement is taken.

eess.SP · cs.ET · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06290

The Split Protocol Decides the Result: Re-Evaluating Encrypted Traffic Classification

Mayan Sharma, Kriti Saini, Devansh Behl

原文摘要

Encrypted traffic classifiers are routinely reported at 95-99% accuracy. We show that on one of two public corpora, the same model and code score macro-F1 0.992 under the evaluation protocol standard in the literature and 0.567 under a protocol that keeps flows from one packet capture on one side of the split. Nothing changes but the partition (gap 0.426 over eight seeds, p = 0.008 after Holm-Bonferroni correction). The inflation is not a property of random splitting as such. On a month of backbone QUIC traffic, we evaluate five split protocols at eight seeds each: random, temporal and client-disjoint splitting are statistically indistinguishable from session-disjoint, while server-disjoint splitting costs 0.192 macro-F1 (d = 17.0, p = 0.008). Which axis matters depends on how the corpus was collected, in a way invisible from its label space. We give a diagnostic that identifies the right axis before any model is trained: single-identifier probes. A decision tree on the capture identifier alone reaches 0.983 under a random split and exactly 0.000 under session-disjoint splitting. Under this evaluation, a metric-trained flow embedding leads four deep baselines but does not beat gradient-boosted trees on twenty packet sizes. Five of our own pre-registered architectural claims fail: distance-based rejection of unknown applications beats softmax thresholding, but ablating every metric objective makes rejection better, because class separation correlates at r = -0.88 with rejection quality. Few-shot enrollment is flat within a corpus but, across corpora, five labelled flows per class recover 91% of the deficit left by failed zero-shot transfer. All splits, results, code and negative findings are released.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06286

Clear Human Web: Where a Person Vouches for Every Client

Lorenzo Porcelli

原文摘要

The web cannot tell whether a request comes from a person or from a program. Sites have checked the network address and puzzles in its place, and both have given way: automated traffic exceeds human traffic, and puzzles are solved for pay and by agents. This paper proposes the Clear Human Web, the part of the public web that sites open only to clients a person vouches for. A person's wallet presents their person identification data (PID) to one issuer, the only party that registers as a relying party, and receives a bounded number of blind-signed tokens, proofs of personhood that carry no attribute. The browser spends one token at each site that runs the gate, and the site learns that a valid token of a trusted issuer was spent, and nothing else. No field is added to the PID, no protocol is introduced, no site registers, and the publisher of a site holds the same proof as its readers. We define the Clear Human Web through five properties and seven adversaries, and state which properties the construction satisfies against each adversary. The identities a person can hold on one site in an epoch are bounded to k. The bound holds once the wallet limits a person to one pseudonym at the issuer. In the optimum k = 1, and a zero-knowledge proof of PID possession and a pseudonym per site replace the tokens.

cs.CR · cs.CY · cs.NI

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arXiv ↗PDF ↗