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新论文 · v1arXiv:2611.11220

EbbFlow: PINN-Inspired Progressive Curriculum for MeanFlow Training

Mengfan Yuan, Xunheng Kuang, Jianbin Chen, Lei He

原文摘要

MeanFlow learns interval-average velocity for few-step generation, but its fixed temporal sampler mixes diagonal and interior supervision throughout training. We interpret temporal coordinates as collocation points: diagonal samples provide a stochastic boundary target, whereas finite intervals supply a differential-consistency target. The diagonal fraction controls how often the consistency component appears, while interval length enters its gradient through an explicit factor. Motivated by this distinction, EbbFlow uses Temporal Supervision Allocation (TSA) to schedule the diagonal fraction and interval geometry. It starts with diagonal supervision, then admits interior samples and expands their maximum interval; a left-anchored interval law preserves coverage across time. The per-sample loss, target, backbone, and inference procedure remain unchanged. In matched class-conditional ImageNet $256\times 256$ runs without classifier-free guidance (DiT-B/2, one training seed per method, 400k updates), EbbFlow achieves 50k-sample FID of 39.65 at one network evaluation and 36.02 at two-evaluation ODE sampling, versus 44.82 and 39.86 for MeanFlow. Gradient probes show a mid-training difference in component geometry. Ablations support allocation scheduling and left-anchored intervals, although a fixed full-horizon variant performs better at one evaluation. These results suggest that temporal sampling is a useful training control whose benefits depend on the inference protocol.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11217

scBIG++: Learning Across Genes and Cells to Predict Single-Cell Perturbation Responses

Jiafa Ruan, Zican Qiao, Zongxin Yang, Ruijie Quan, Yi Yang

原文摘要

Predicting single-cell transcriptional responses to genetic and chemical interventions supports gene-function discovery and regulatory analysis. Existing studies often focus only on highly variable genes, restricting the expression changes available for downstream analysis. Cells exposed to the same intervention can also show heterogeneous expression responses, with coordinated gene programs active to different degrees. We propose scBIG++, a full-gene perturbation prediction framework that models this heterogeneity by connecting expression-adaptive gene modules with population-conditioned generation. To preserve shared gene structure while accommodating cellular variation, Gene-Relation Clustering organizes genes into common windows, within which Dynamic SoftSub learns expression-dependent submodule memberships. This shared organization enables Set-GCAE to exchange information about corresponding gene groups across control cells, placing individual representations in their source-population context. The cell representations initialize a latent flow, while their pooled summary and the perturbation representation condition response generation. Generated states are decoded into expression profiles covering all assayed protein-coding genes retained after preprocessing, supporting comprehensive analysis of perturbation-induced expression changes. Experiments across six genetic and chemical settings spanning 6,539--19,451 genes demonstrate competitive perturbation-effect recovery and full-gene expression reconstruction under unseen targets and combinations, cross-cell-line transfer, and held-out compounds. Our implementation is available at https://github.com/ttruan2426-dot/scBIG.

cs.LG · cs.AI · q-bio.GN

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36617

Balanced State Subspaces with Spectral Rank Allocation for Mamba-2 State Compression

Shang Tingyu, Cao Wenfei, Han Zhi

原文摘要

Mamba-2 compresses all context into a fixed-size recurrent state that dominates decoding memory and bandwidth. Existing compressors quantize state values or prune coordinates with per-channel scores. Trained states are nearly rank-one and bias-like, aligned with the mean write; but the output's energy spans an order of magnitude more directions. Per-channel scores see only the diagonal of the write--read operator, so they cannot select the subspace that governs the input--output map, which is a rotation of the coordinates. We therefore propose \method{}, which keeps that subspace by applying balanced truncation exactly via Mamba-2's scalar-identity transition. Layers differ just as sharply: most are bias-dominated, while the few that carry long-range associations are the flattest and need most of their state. We therefore introduce \spar{}, which redistributes a fixed total rank budget across layers according to the Hankel spectrum. On Mamba-2 checkpoints from 130M to 2.7B, \method{}+\spar{} halves the state at an average cost of $0.3$ WikiText-2 perplexity points, against $1.6$ for GHOST, while retaining $97\%$ of the dense model's five-digit in-context recall. At a quarter of the state, giving every layer the same rank keeps $5\%$ of that recall and \spar{} $77\%$, for a $7\%$ perplexity cost.

cs.LG · cs.AI · cs.CL · stat.AP · stat.ME

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11214

Disentangling Where and When:Factored Spatio-Temporal Explanations for Video Action Recognition

Bhavan Vasu, Giuseppe Raffa, Prasad Tadepalli

原文摘要

Post-hoc explanation methods for video action recognition typically conflate spatial appearance and temporal dynamics into a single saliency or importance map. We propose a perturbation-based framework that disentangles these contributions into a spatial appearance mask ($M_a$) and a temporal motion mask ($M_m$). The two masks are jointly optimized against a frozen action recognition model through factored perturbation architecture, with nearest frame replacement to identify important temporal dynamics. A separation penalty discourages the appearance mask from spending budget on frames that the motion mask has already discarded. A ReLU-based area constraint penalizes explanations larger than an assigned budget. To evaluate these explanations, we introduce a separation protocol that sweeps spatial pixels and temporal frames independently, backed by proofs characterizing the proposed method. Across EPIC-Kitchens-55, EGTEA Gaze+, and Something-Something V2, our method attains the lowest deletion AUC against leading baselines while producing masks roughly five times sparser.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.14448

NeuroActiSep: Detecting Factual Hallucinations from Feed-Forward Neurons in a Single Pass

Ali Derogar Odolou, Reza Nazari, Mostafa Salehi

原文摘要

Hallucination in large language models reduces their reliability and slows adoption. Various white-box studies have used internal representations to detect patterns of truthfulness and factuality. A less-studied approach is to identify feed-forward neurons correlated with hallucination. In this empirical study, we propose a method to rank feed-forward neurons at the final prompt token and use it to investigate the feasibility of utilizing them for hallucination detection in the first forward pass. Using a neuron selection dataset built from CounterFact for identifying neurons correlated with hallucination, we transfer the selected neuron identities to train hallucination classifiers on other factual question-answering datasets. Our work provides empirical evidence that probes trained using the features from sparse sets of selected neurons perform on par with probes trained on internal representations. We also analyze the distribution of selected neurons and the effects of layer depth and neuron budget on detection performance. Our results show that randomly drawn neuron sets are also competitive at larger budgets, suggesting that signals correlated with hallucination are distributed across many neurons, while selection can help in extremely sparse sets.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06700

PINR-ESI: Physics-Informed Self-Supervised Implicit Neural Representation Framework for EEG Extended-Source Imaging

Zhiyong Zhou, Sheng Liao, Xiaolin Li, Youshen Xiao, Bin Dai, Surui Liu, Yakang Dai

原文摘要

EEG extended-source imaging aims to localize active cortical regions and characterize their spatial extent, yet supervised inverse networks typically depend on synthetic EEG-source pairs and may be sensitive to synthetic-to-real mismatch. We propose a physics-informed self-supervised implicit neural representation (INR) framework for spatial source reconstruction without paired source labels or simulation-based supervised pretraining. The lead field matrix encodes the source-to-sensor volume-conduction model and constrains the INR through agreement between predicted and measured EEG. Cortical current density is modeled as a continuous coordinate-conditioned field. A classical inverse estimate provides warm initialization and spatial constraints on source activity, while patch-integrated projection maps the field to discrete source coefficients for forward modeling. Simulations across varying noise levels, source extents, and source counts showed consistently low spatial dispersion and good localization performance. Quantitative and qualitative evaluations using real EEG recordings from epilepsy patients and intracranial stimulation experiments further supported the feasibility of the framework for source imaging. These findings suggest the potential of physics-informed self-supervised INR for extended-source imaging.

cs.LG · q-bio.NC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.10219

Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses

Yuhan Ye

原文摘要

We study the parameterized complexity of testing approximate first-order stationarity at a prescribed point for continuous piecewise-affine (PA) functions, a basic task in nonsmooth optimization. PA functions form a canonical model for nonsmooth stationarity testing and capture the local polyhedral geometry that appears in ReLU-type training losses. Recent work of Tian and So (2025) shows that testing approximate stationarity notions for PA functions is computationally intractable in the worst case, and identifies fixed-dimensional tractability as an open direction. We address this direction from the viewpoint of parameterized complexity, with the ambient dimension $d$ as the parameter. In this paper, we give XP algorithms in fixed dimension for the tractable sides, and prove W[1]-hardness for the complementary sides. Moreover, lower bounds under the parameterized $k$-Clique hypothesis rule out deterministic algorithms running in time $ρ(d)s^{o(d)}$ for any computable function $ρ$, where $s$ denotes the total binary encoding length of the instance. As a further consequence, our results yield the corresponding parameterized complexity picture for testing local minimality of continuous PA functions. We also extend our hardness results to a family of shallow ReLU CNN training losses, tested in the trainable weight space. Thus, the same parameterized-complexity picture also appears for simple CNN training losses. Notably, our lower bounds assume that $k$-Clique on $N$-vertex graphs admits no deterministic $ρ(k)N^{o(k)}$-time algorithm for any computable function $ρ$. The recent refutations of the Exponential Time Hypothesis (ETH) (OpenAI, 2026) and the stronger fine-grained $k$-Clique hypothesis (Vassilevska Williams and Zamir, 2026) do not contradict this assumption.

math.OC · cs.CC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11204

Not Yet in the Profile: Uncertainty-Aware Dynamic User Profiling by Learning from Future Interactions

Jieyong Kim, Neil Shah, Dong Wang

原文摘要

On the Web, user profiling builds compact user representations from interaction histories for efficient personalization, and dynamic user profiling updates them as users' behavior and preferences change. However, new user behavior can be ambiguous, and its interpretation differs across users. To address this, we introduce Uncertainty-aware Adaptive Profile Manager (UAPM), which separates the claims in a user profile from interpretations of new behavior that are not yet confirmed, through a Profile, a Buffer, and a Rulebook. UAPM records user-specific interpretation and update rules in the Rulebook, keeps ambiguous evidence in the Buffer outside the Profile as evidence for later decisions, and writes supported general and situational characteristics to the Profile. We train UAPM with Privileged On-Policy Distillation with Dual Specialization (POPD-DS), which learns from recorded future interactions during training without access to them at deployment. Its first stage, Dual Specialization, distills a stronger model into a current-only Student and a future-informed model that becomes the Teacher. In the second stage, privileged on-policy distillation (POPD), only the fixed Teacher receives future interactions and guides the Student's own management decisions alongside task rewards. Experiments on three Web personalization tasks show that UAPM outperforms multiple baselines under the same downstream input budget, improving on the best same-budget baseline by up to 5%.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.12449

Computationally sufficient statistics for Ising models

Abhijith Jayakumar, Shreya Shukla, Marc Vuffray, Andrey Y. Lokhov, Sidhant Misra

原文摘要

Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms for learning Gibbs distributions rely on access to full sample configurations generated from the model. For many systems of interest that arise in physical contexts, expecting a full sample to be observed is not practical, and hence it is important to look for computationally efficient methods that solve the learning problem with access to only a limited set of statistics. We examine the trade-offs between the power of computation and observation within this scenario, employing the Ising model as a paradigmatic example. We show that the couplings of the model can be learned to a desired accuracy from statistics whose order grows linearly with the $\ell_1$ width of the model and only logarithmically with the inverse of the accuracy. In particular, for a fixed accuracy, the required order is proportional to the width. This allows us to infer the model's structure, using statistics whose order grows logarithmically with the inverse of the weakest coupling, and also to learn its magnetic fields. When the width and the accuracy are fixed, the running time is polynomial in the number of variables. We also discuss a setting where prior information about structure of the model is available and show that the learning problem can be solved efficiently with even more limited observational power.

cs.LG · cond-mat.stat-mech · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11189

TEAM: Topology-Aware, Semantic-Sensitive, General Transformer for Multimodal-Attributed Graphs

Yushuo Li, Xunkai Li, Zhengyu Wu, Ronghua Li, Guoren Wang

原文摘要

Multimodal-Attributed Graphs (MAGs) organize heterogeneous data in graphs whose nodes are characterized by modalities such as text and images rather than traditional unimodal attributes. However, many representative MAG methods organize propagation around node-level states and topology-defined neighborhoods, which can obscure fine-grained modality contributions: a bounded neighborhood may contain semantically weak evidence, while compatible context need not be confined to it. To address both limitations, we propose TEAM, a modality-centric framework that constructs and encodes graph context with node-modality tokens rather than treating each node as a single interaction unit. Concretely, TEAM unfolds the local topology into a Modality Encoding Tree and performs tree-anchored semantic evidence acquisition, organizing local structural context while expanding the accessible evidence space outside the constructed local Tree. We further introduce the Modality-Source Context Transformer (MSCT), which encodes the selected node-modality tokens and coordinates interactions among modality-source groups. By retaining textual and visual attributes as separate interaction units until readout, it supports fine-grained cross-node cross-modal exchange without first collapsing them into whole-node features. Across the evaluated baselines, TEAM attains the highest reported mean on all graph-learning metrics, all three G2Text metrics, and both G2Image metrics. On Toys, it surpasses the strongest baseline by 3.37 and 2.93 percentage points in Accuracy and Macro-F1, respectively.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.09895

SE-Diff: Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation

Xiaoda Wang, Kaiqiao Han, Yuhao Xu, Xiao Luo, Yizhou Sun, Wei Wang, Carl Yang

原文摘要

Cardiovascular disease (CVD) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well-annotated ECG corpora are scarce due to cost, privacy, and workflow constraints. Generating ECGs can be beneficial for the mechanistic understanding of cardiac electrical activity, enable the construction of large, heterogeneous, and unbiased datasets, and facilitate privacy-preserving data sharing. Generating realistic ECG signals from clinical context is important yet underexplored. Recent work has leveraged diffusion models for text-to-ECG generation, but two challenges remain: (i) existing methods often overlook the physiological simulator knowledge of cardiac activity; and (ii) they ignore broader, experience-based clinical knowledge grounded in real-world practice. To address these gaps, we propose SE-Diff, a novel physiological simulator and experience enhanced diffusion model for comprehensive ECG generation. SE-Diff integrates a lightweight ordinary differential equation (ODE)-based ECG simulator into the diffusion process via a beat decoder and simulator-consistent constraints, injecting mechanistic priors that promote physiologically plausible waveforms. In parallel, we design an LLM-powered experience retrieval-augmented strategy to inject clinical knowledge, providing more guidance for ECG generation. Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text-ECG semantic alignment over baselines, proving its superiority for text-to-ECG generation. We further show that the simulator-based and experience-based knowledge also benefit downstream ECG classification.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12541

PPG2ECG: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation

Xiaoda Wang, Minxiao Wang, Kaiqiao Han, Defu Cao, Ching Chang, Yidan Shi, Runze Yan, Xiao Luo 等 13 位作者

原文摘要

Electrocardiography (ECG) is the clinical standard for cardiac assessment but requires dedicated hardware that does not scale to daily-life monitoring. Photoplethysmography (PPG) is ubiquitous in wearables but lacks ECG-specific diagnostic morphology and is corrupted by motion and sensor noise. PPG-to-ECG generation aims to bridge this gap by recovering electrical morphology and timing from peripheral pulse signals. However, existing methods largely rely on statistical alignment and data-driven generation. They fail to explicitly structure the latent space around physiology-aware electro-hemodynamic factors and lack constraints from forward physiological dynamics. To address these challenges, we propose PPG2ECG, a physiology-guided latent rectified flow framework. PPG2ECG introduces an electro-hemodynamic simulator that co-models ECG and PPG through shared cardiac phase dynamics. Guided by this simulator, a Physiology-Aware AutoEncoder learns a structured electro-hemodynamic latent space. Then we integrate this simulator guidance into a PPG-conditioned latent rectified flow, enforcing ECG-side morphology consistency and ECG-to-PPG forward hemodynamic consistency during generative transport. Experiments on the large-scale MC-MED dataset demonstrate that PPG2ECG significantly improves PPG-to-ECG generation and downstream cardiovascular disease classification, proving its ability to generate ECGs that are both signal-faithful and physiologically plausible under the ECG-to-PPG hemodynamic pathway

eess.SP · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12492

Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation

Kexuan Shi, Hanxuan Li, Zeju Qiu, Yandong Wen, Simon Buchholz, Weiyang Liu

原文摘要

We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam and Muon, Pion updates each weight matrix through left and right orthogonal transformations, preserving its singular values throughout training. This yields an optimization mechanism that modulates the geometry of weight matrices while keeping their spectral norm fixed. We derive the Pion update rule, systematically examine its design choices, and analyze its convergence behavior along with several key properties. Empirical results show that Pion offers a stable and competitive alternative to standard optimizers for both LLM pretraining and finetuning.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.21160

FISolver: Data-Driven Discovery of First Integrals via Backward Generation and Guided Reinforcement Learning

Jingfeng Zhong, Zhengxiang Liu, Zhijie Wang, Shuai Li

原文摘要

The discovery of first integrals is of profound scientific significance for understanding physical conservation laws in dynamical systems. However, existing symbolic computation systems and Large Language Models (LLMs) struggle with this task due to the scarcity of high-quality training data and the reliance on mathematical intuition for finding solutions. This paper introduces FISolver, an LLM-based solver designed to bridge this gap. First, we propose a novel "Backward Generation" algorithm that systematically constructs large-scale datasets of (differential equations, first integrals) pairs by deriving equations from sampled integrals, effectively overcoming the data scarcity bottleneck. Second, we perform supervised fine-tuning on a compact mathematical model and further enhance its capabilities using reinforcement learning with a Levenshtein Distance-based shaped reward function. Additionally, we introduce data synthesis and blending techniques that enable effective adaptation to specific hard instances with sparse examples. Experiments demonstrate that FISolver, with significantly lower computational cost, substantially outperforms larger mathematical LLMs and commercial solvers like Mathematica on complex benchmarks, establishing a new data-driven paradigm for automated mathematical discovery.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11177

When Are Nearby Experts Composable? Cross-Task Model Merging in Neural Thickets

Suxin Ji, Hungtao Wan, Mingjun Liu, An Zhang

原文摘要

Random perturbations around a shared checkpoint can produce experts for distinct tasks, but shared origin does not ensure that their gains survive composition. We formulate expert-pair selection for joint base-relative retention, measured by strict joint success and worst-task gain with unconditional abstention accounting. An exact finite-difference decomposition and an arbitrarily small orthogonal counterexample show why proximity and orthogonality alone are insufficient. We instantiate this formulation with quality-constrained spectral selection, a fixed-budget framework combining cross-task screening, layerwise pair geometry, separate validation, and Base fallback. Across six checkpoints, 21 task pairs, and five independent perturbation pools, the procedure improves strict joint success over Top-1 averaging throughout and leads the four-method model-scale comparison on four of six checkpoints. For the five models with at least 1.5B parameters, matched quality-only, global-cosine, and spectral ranking attain 49.1%, 50.5%, and 53.1% joint success. Under this matched protocol, the spectral selector also yields the highest worst-task gain and Weak Pareto rate and the lowest regression rate. Mean worst-task gain reaches +0.61 percentage points, while 17.1% regression and 13.0% abstention show that composability remains conditional. Project page: https://anonymous.4open.science/r/Thickets-Merge/.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.02068

Sequential Capacity of Quantum Processes with Finite Memory

Yibin Wang

原文摘要

How complex can the responses of a quantum process become as it runs longer with fixed internal memory? We measure this complexity by sequential response capacity, the depth of an adaptive tree of experiments whose acceptance probabilities separate possible processes by a prescribed gap. Each experiment uses a fresh complete run, and one fixed process must realize every response along a path. For any fixed common visible dimension of at least two and internal memory cap, we prove that capacity has order $K\log((K+1)/γ)$ for $K$ transitions per run and probability gap $0<γ\leq1$. Time-dependent single-qubit phase channels without internal memory already attain the fixed-gap $K\log K$ growth, realizing each path with zero-or-one response probabilities. Under the same tester permissions, classical stochastic processes that measure in a fixed basis at every transition have only linear capacity at fixed dimensions and gap. For phase sequences selected by a classical address stored inside the process, we then quantify how known independent Pauli noise changes this logarithmic enhancement. With ideal tester controls and weak residual phase noise after correction, we prove matching capacity bounds at a fixed small gap. At fixed address size and fixed positive residual noise, the logarithmic enhancement saturates once the phase-sequence length exceeds the inverse noise scale, and capacity then grows linearly with sequence length.

quant-ph · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12190

Information-Theoretic Generalization Bounds for Sequential Decision Making

Futoshi Futami, Masahiro Fujisawa

原文摘要

Information-theoretic generalization bounds based on the supersample construction are a central tool for algorithm-dependent generalization analysis in the batch i.i.d. setting. However, existing supersample conditional mutual information (CMI) bounds do not directly apply to sequential decision-making problems such as online learning, streaming active learning, and bandits, where data are revealed adaptively and the learner evolves along a causal trajectory. To address this limitation, we develop a sequential supersample framework that separates the learner filtration from a proof-side enlargement used for ghost-coordinate comparisons. Under a row-wise exchangeability assumption, the sequential generalization gap is controlled by sequential CMI, a sum of roundwise selector--loss information terms. We also establish a Bernstein-type refinement that yields faster rates under suitable variance conditions. The resulting bounds specialize to online learning, streaming active learning with importance weighting, and stochastic multi-armed bandits.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11170

ClusLoad: Cluster-Guided Model Sharing for Scalable Distribution Transformer Load Forecasting

Fangzhou Lin, Guoshun He, Yi Wang, S. Supraja

原文摘要

Large-scale device-level load forecasting for distribution transformers is challenged by substantial cross-device heterogeneity and the deployment burden of maintaining device-specific forecasting models. Existing approaches typically rely either on device-specific models, which are difficult to scale to large transformer populations, or on global models, which reduce deployment complexity but may overlook heterogeneous load patterns across devices. To address this trade-off, this study develops a trend-clustered forecasting framework that enables model sharing among transformers with similar temporal behaviors. Representation Learning for Time Series using Time-Embeddings (T-Rep) is employed to learn compact temporal representations of transformer load series, which are subsequently grouped using a Gaussian mixture model (GMM). Representative transformers are sampled within each cluster to train cluster-specific Patch Time Series Transformer (PatchTST) models while retaining device-level predictions. The framework is evaluated using real-world 15-min operational data from nearly one thousand distribution transformers. The proposed framework reduces mean absolute error (MAE) by approximately 20% relative to the direct PatchTST baseline, achieves a high-load warning accuracy of 95.66%, while requiring only seven cluster-specific forecasting models. These results demonstrate that behavior-based device organization provides an effective balance between forecasting accuracy, deployment scalability, and risk-oriented operational support for distribution transformer management.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11165

The Race Is Decided Early! A Kernel Law Predicts Lock-In in Majority-Vote Test-Time RL

Jaeho Hwang, Sehong Park, Daejin Choi, Uiwon Hwang

原文摘要

Test-time reinforcement learning with majority-vote rewards trains a language model on unlabeled questions by rewarding the samples that agree with its most frequent answer. During training, each question locks into one answer, on hard questions often a wrong one, but which answer it locks into is known only after training. In this paper, we show that this outcome is decided early, in a short race driven by sampling noise between the answers that are close at the start, and we derive the probability of each outcome. With a separate logit for each answer, the runner-up wins with a normal tail probability of the initial gap, scaled by an explicit noise constant. In a network, however, the answer logits depend on shared parameters, and through the kernel of these parameters the updates of the trailing answers also change the gap of the two leaders. This spillover shifts the boundary between the starts won by each leader, and a closed form computes the shifted probability from a kernel probe and the start logits. In LoRA experiments with three models from two families, predictions made before training with the kernel frozen at its start value forecast which answer each question locks into better than a logistic model refitted on the outcomes, although they use no fitted constants. The closed form matches these predictions on the runs in which a check before training finds that the two leaders stay above the other answers, and it explains why a law that ignores the spillover needs a strongly inflated step.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2401.07174

On the (In)Compatibility between Group Fairness and Individual Fairness

Shizhou Xu, Thomas Strohmer

原文摘要

We study the compatibility between utility-optimized statistical parity and individual fairness. While individual fairness seeks to treat similar individuals similarly, optimal statistical parity aims to provide similar treatment to individuals who share relative similarity within their respective sensitive groups. Although both fairness perspectives are desirable, they often conflict in practical applications. Our goal in this work is to analyze the existence of this conflict and its potential solution. In particular, we establish sufficient (sharp) conditions for the compatibility between the optimal (post-processing) statistical parity $L^2$ learning and the ($K$-Lipschitz or $(ε,δ)$) individual fairness requirements. When a conflict arises, we first relax the optimal statistical parity solution to the Pareto frontier, representing the optimal trade-off between $L^2$ error and statistical disparity, and then examine its compatibility with individual fairness. Our analysis identifies regions along the Pareto frontier that satisfy individual fairness constraints. Additionally, we provide individual fairness guarantees for the composition of a trained model with the optimal post-processing step, enabling practitioners to assess the compatibility of post-processed models. This provides practitioners with a principled approach to achieving Pareto-optimal statistical parity while maintaining individual fairness constraints.

math.ST · cs.CY · cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.08315

Reusable Conditional Resampling via Flow Matching for Constraint-Based Causal Discovery

Shunyu Zhao, Yanfeng Yang, Shuai Li, Kenji Fukumizu

原文摘要

Constraint-based causal discovery requires many conditional independence (CI) tests. Generative CI tests are attractive in nonlinear and high-dimensional settings because they can model complex conditional distributions, but often suffer from high per-test costs and limited reusability across query-dependent conditional resampling tasks in graph search. We propose a generative nonlinear CI testing and graph search framework for PC-style causal discovery. We introduce Langevin-RePaint for conditional imputation. Building on Langevin-RePaint and joint flow matching modeling, we propose the Flow Matching based Conditional Independence Test (FMCIT), which reformulates generative CI testing from query-specific conditional modeling into a reusable conditional resampling framework. We establish theoretical guarantees for FMCIT's type-I error control. FMCIT reuses a single trained generative model to generate randomized copies throughout graph search. We then introduce GPC-FMCIT, which embeds FMCIT into a guided PC pipeline with screening, guidance, refinement, and orientation. By using low-cost screening to shrink the search space, constructing edge-specific candidate conditioning sets from local graph structure, and invoking FMCIT only in a budget-controlled refinement stage, GPC-FMCIT reduces both the deployment cost of individual nonlinear CI queries and the overall query complexity. On synthetic nonlinear structural equation models, GPC-FMCIT improves CPDAG recovery over PC-stable baselines and requires far fewer FMCIT queries than direct FMCIT-based PC search, and on hESC gene-expression data it yields smaller graphs than PC/FisherZ in a descriptive comparison. Overall, our results suggest that, through the joint design of reusable conditional resampling and controlled graph search, generative nonlinear CI testing can become a practical component of PC-style causal discovery.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.01472

Causal Optimal-Transport Attention: Positional Priors and Length Extrapolation

Ming Lian

原文摘要

Causal masking obstructs the column balancing used by optimal-transport (OT) attention. We study an unbalanced OT operator that uses only current and earlier rows and characterize its induced positional prior. For bounded scores and any fixed balancing strength below one, its fixed point assigns vanishing mass to every fixed window as context grows. With constant scores, the prior has an exact scale-invariant form. We classify positional encodings by whether they prevent this dilution. We also derive finite-length window-mass bounds for log-distance priors with exponents near one. Preregistered experiments use 25M- and 100M-parameter language models. The OT operator improves loss extrapolation over softmax, while exploratory comparisons favor an explicit distance bias at a fraction of the cost. Changing only the bias beyond the training length moves long-context loss in the predicted direction on all 30 tested checkpoints. During training, learned exponents approach one even when constrained above it. A margin chosen from prior calculations controls distant-mass growth in the tested setting.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.39383

From Search to Signal: Search-Value Credit for Evolutionary Automatic Heuristic Design

Yilun Yuan, Tianyu Zhou, Zhenzhou Tang

原文摘要

Large language models generate candidate programs for automatic heuristic design, while evolutionary search evaluates and selects them to guide further generation. Online post-training uses rewards from candidate evaluations to adapt the generator. Retained programs can also guide later generation as parents. This motivates rewards that reflect their reuse opportunities beyond immediate fitness. We propose Search-value credit (SVC), which constructs response-level rewards from actual admission to the retained set and a short-term parent-exposure proxy. Admission determines reward eligibility; the proxy weights eligible responses by reuse opportunities under existing parent-selection rules. Rewards are computed after candidate evaluation, without evaluating descendants. SVC retains the search and learning procedures of the Co-Evolution of Algorithms and Language Model (CALM) framework. Across four combinatorial optimization tasks with the same base model and response budget, SVC achieves the best mean final search performance on three tasks among six conditions. Under identical prompts and parents on traveling salesman and capacitated vehicle routing tasks, SVC-trained generators yield a higher mean proportion of responses whose programs outperform the best prompting parent than those trained with CALM's native reward. These findings support reward construction using search retention and selection information.

cs.LG · cs.AI · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11152

Full Fine-Tuning is not the Problem: Why Adam Fails and SGD Succeeds in Few-Shot CLIP Adaptation

Nairouz Mrabah, Ismail Ben Ayed, Eric Granger

原文摘要

Few-shot CLIP adaptation is often treated as a parameterization problem. Full fine-tuning is assumed to overfit, so parameter-efficient fine-tuning (PEFT) methods are preferred. In this paper, we show that this conclusion conflates parameterization with optimization. Across 11 datasets, 3 CLIP backbones, and 5 shot levels, dense full fine-tuning with plain SGD outperforms representative PEFT methods under the reported evaluation protocol, whereas Adam/AdamW collapses. We trace this collapse to unreliable support-set preconditioning. The few-shot second moment systematically underestimates the corresponding full-data statistic, already at initialization. Adam's inverse-square-root normalization converts this error into an oversized adaptive update. Matching Adam's first-step update budget to SGD resolves most of the collapse, whereas numerator-side alignment does not explain the failure. Shampoo and SOAP also fail, so richer preconditioning alone does not resolve the problem. Theory formalizes how few-shot estimation error is amplified into unsafe updates. SGD succeeds because it avoids unreliable preconditioners and stays close to pretrained CLIP. Interpolation probes show low-loss connections along tested directions, and curvature probes show favorable directional curvature. These results identify optimizer-induced preconditioning, not dense full fine-tuning itself, as the main source of failure in few-shot CLIP adaptation.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11151

Fast Approximate $\ell_p$ Chamfer Distance via Lopsided Embeddings and Structured JL

Ying Feng, David P. Woodruff

原文摘要

For two $d$-dimensional point sets $A, B$ of size $n$, the Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A,B)=\sum_{a \in A} \min_{b \in B} \mathsf{dist}(a, b)$, where $\mathsf{dist}$ is some underlying distance measure. We present efficient algorithms for approximating the Chamfer distance under the $\ell_p$ norm for $1 < p \le 2$. For the general case $1 < p < 2$, we utilize lopsided $\ell_p$ to $\ell_1$ embeddings with weak guarantees. We show that they are sufficient for preserving the Chamfer distance. In the high-dimensional regime, we use Fast Matrix Multiplication techniques to speed up the lopsided embeddings. These lead to $O(nd\cdot \mathrm{poly}(\log \log n, \log(1/\varepsilon))/\varepsilon^2)$ total time. For the specific Euclidean case ($p=2$), we leverage the Fast Johnson-Lindenstrauss Transform based on Toeplitz matrices and re-analyze the previous $\ell_1$-specific Chamfer algorithm in $\ell_2$. These achieve a runtime matching the recent state-of-the-art $\ell_1$ result of $O(nd(\log \log n + \log (1/\varepsilon))/\varepsilon^2)$, improving upon the previous $O(nd \log n/\varepsilon^2)$ bound for $\ell_2$. We also give additional results for the angular distance and upper and lower bounds in the streaming setting.

cs.DS · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11149

ZarrFM: A Canonical Tensor Storage Architecture for EEG Foundation-Model Pretraining

Dheeraj Chintapalli, Sunkalp Chandra

原文摘要

Statistical models for brain-computer interfaces (BCIs) increasingly require large, heterogeneous EEG corpora, yet existing datasets vary in sampling rates, channel layouts, window lengths, and storage formats, creating substantial preprocessing and I/O overhead. We propose ZarrFM, an execution-oriented representation that complements BIDS by storing a corpus as a single Zarr v3 hierarchy with indexed metadata, canonical channel spaces, explicit masks, hash-stable subject splits, verified event tables, and block-aligned sharding. On real PhysioNet recordings and synthetic corpora, we compare ZarrFM with EDF, FIF, NPY, HDF5, and Parquet. Across 200 recordings, its two-level index reduces metadata queries from 601 requests to 14. Preserving native signal codes reduces storage from 42.4 MB/hour in EDF to 22.7 MB/hour, while block-aligned sampling reaches 3,131 windows/s, up to 11 times faster than Parquet. Reads remain bit-identical across independent Zarr implementations, and concurrent failure-injected appends recover without data loss. These results show that ZarrFM can provide a compact, interoperable, and execution-efficient storage layer for large-scale EEG pretraining.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.17162

OraclePhys: A Systematic Framework for LLM Fine-Tuning on Structural Mechanics

Mingyu Li, Guorui Song, Jing Lin, Haoqian Wang

原文摘要

What fine-tuning teaches a model is usually diagnosed after the fact. We make it an experimental variable. OraclePhys has three parts. OraclePhys-Bench is a structural-mechanics benchmark whose finite-element oracle grades every answer and counterfactual edit. OraclePhys-30K holds seven answer forms over byte-identical structure descriptions. A controlled training study compares the forms and verifier roles. Two findings follow. First, within this task family the answer form of the label determines what fine-tuning teaches. Ranking supervision lifts held-out localization of the most-deforming story on taller structures from 0.25 to 0.86, a peak-value label yields at best a partial predictor, and a pass/fail label none. This ranking-versus-peak gap replicates on a second domain, a second model family and a paraphrased surface. Second, the gap between our standard SFT and GRPO runs is mostly a recipe effect. With learning rate and step budget matched, the two reach held-out accuracies within 0.03 of each other. At higher rates, GRPO falls below its start while SFT does not. Both also teach the model to apply edits given as instructions. The trained 8B matches a specialist and exceeds a frontier LLM. Both domains are linear, steady-state systems with synthetic text inputs.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.03014

MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency

Saptarshi Mitra, Yifan Zhang, Rachid Karami, Sreetama Sarkar, Phyo Pyae Moe Aung, Nazmul Takbir, Souvik Kundu, Sitao Huang

原文摘要

Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs. Efficiently executing this workload on limited GPU resources has bottlenecks. Skill-based routing creates skewed expert demand, and combining instruction-tuned LLMs with long-reasoning models results in extreme variability in generation lengths. Consequently, traditional scheduling strategies suffer from significant GPU idling and throughput collapse due to load imbalances. We present MOSAIC, a scheduling framework for accelerating batch-oriented MoA workloads. MOSAIC uses two-stage workload-aware scheduling: an expert placement stage, where an integer linear program (ILP) decides expert placement, followed by a query-sequencing stage that schedules requests based on predicted output lengths. Additionally, MOSAIC uses confidence-aware adaptive aggregation, leveraging inter-expert agreement to bypass the heavy aggregator LLM for consensus queries. On a 4-GPU system, MOSAIC achieves 2.7-3.4x end-to-end speedups over round-robin scheduling and reduces the mean time-to-answer by 3.9-7.8x, while matching accuracy within +/-1pp.

cs.LG · cs.AR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.07365

Private Sparse Sketches for Linear Regression

Shrutimoy Das, Anirban Dasgupta

原文摘要

Sketching compresses a large dataset into a smaller summary, allowing regression on the sketch to approximate the full-data objective. A sketch released under differential privacy can be reused with any non-private regression solver, any number of times, without further privacy loss. We study private sparse sketches for $\ell_2$ and $\ell_1$ regression. We first prove lower bounds on the noise required by common sparse sketches for both problems when the sketching matrix is public. Since regression on a sketch can be solved without revealing the sketching matrix, we construct sketches whose randomness is kept secret. Hiding how individual records enter the sketch provides an additional source of privacy, allowing less noise for the same privacy guarantee. We prove differential privacy guarantees for both constructions, and experiments on real datasets show improved utility over public-sketch baselines.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11145

DISCOVER: Online Variance-Guided Data Discovery for Budgeted Multimodal GRPO

Peyman Gholami, Shivam Chandhok, Saber Malekmohammadi, Shayan Shams, Robert Xiao, Leonid Sigal

原文摘要

Recent advances in reinforcement learning have made verifiable rewards a central post-training paradigm for improving multimodal reasoning. However, methods such as GRPO remain data- and compute-inefficient, spending rollout and annotation budget on prompts that provide little learning signal. We propose DISCOVER, an online data-discovery framework for budgeted multimodal GRPO that enables vision-language models to dynamically identify prompts that are most useful for the current policy. DISCOVER first organizes the multimodal sample pool into semantic clusters and queries a small set of density-diverse representative samples to obtain initial reward observations. During training, it estimates the utility of unannotated samples by transferring sparse reward feedback from queried samples. Since sample utility changes as the policy evolves, DISCOVER dynamically updates these estimates with recency-weighted rollout observations. It then selects only high-utility prompts for annotation using a policy-conditioned score that accounts for reward variance and difficulty. Once the discovery budget is exhausted, DISCOVER continues with utility-based replay. Experiments on ViRL39K and VLAA-Thinking with Qwen3-VL-2B show that DISCOVER substantially improves multimodal mathematical reasoning under a strict 10% unique-sample budget, outperforming budget-matched random selection, hard-example sampling, full-pool GRPO under the same training-step budget, and online selection baselines. Ablations further validate the importance of semantic coverage, online reward transfer, and stable discovery cadence, establishing DISCOVER as a practical framework for data-efficient multimodal RL post-training.

cs.LG · cs.AI

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arXiv ↗PDF ↗