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Min Wu

Publications and source records attributed to Min Wu.

At least 19 recordsLinked to original sources
arXiv2026

UniFlow: Unified Multi-Scale Flow Matching for Time Series Generation

Real-world time series exhibit complex dynamics across multiple temporal resolutions, yet effectively incorporating multi-scale information into generative modeling remains challenging without increasing generative complexity. We propose UniFlow, a unified framework that integrates multi-scale representation learning with efficient non-autoregressive generation through a shared-semantic discrete interface. Specifically, a multi-scale tokenizer employs shared-index voting to consolidate complementary temporal information into a single public-ID sequence, while scale-specific codebooks preserve the information required for reconstruction. Building on this unified representation, we develop geometry-aware categorical flow matching, which initializes transport from learned low-rank anchors and generates public identities non-autoregressively. This design captures multi-scale temporal structure without requiring multiple generative token streams or sequential token decoding. Experiments across four benchmarks and both short- and long-term settings demonstrate improved generative fidelity, strong cross-scale consistency, and substantial inference speedups over representative autoregressive and diffusion-based baselines. Code is available at https://github.com/William-Liwei/uniflow

stat.ML↗
arXiv2026

Random-Sign IBLTs: Calibration-Free Self-Sizing After Decoding Failure

We propose the random-sign Invertible Bloom Lookup Table (IBLT) for calibration-free self-sizing after decoding failure. In a two-round set reconciliation protocol, the receiver uses the failed first-round table to estimate an upper bound on the unknown difference size $d$ and size the second-round table. In standard IBLTs, failure selection couples the overlap bias with the sign composition of the set difference, making practical sizing depend on offline simulation calibration. A shared pseudorandom sign independent of the position mapping removes this dependence while preserving cell fields, field widths, and ideal peeling recovery. We prove exact unbiasedness on every fixed mapping, a Gaussian limit after failure for $d\gg\sqrt M$, and a discrete limit dominated by duplicate edge pairs at smaller scales, where $M$ is the number of cells. These results yield a Skellam upper-bound rule whose conditional miss rate is asymptotically at most the target risk, uniformly over all $d\ge2$, without offline calibration. More than 500 million paired decoding trials and end-to-end protocol experiments support the design. For small differences, the Gaussian rule's conditional miss rate exceeds the target by more than a factor of 8. The Skellam rule meets the target within sampling error across all 189 tested load configurations. Total two-round communication approaches that of an oracle using the true $d$ in the same protocol. With fixed deployment parameters, precomputed table lookup reduces sizing to less than one microsecond, with total protocol time within 0.5 percent of the Gaussian rule.

cs.DS↗
arXiv2026

From Retrieval to Recognition:Knowledge-Enhanced Post-training of Time Series Foundation Models for Classification

Time series foundation models (TSFMs) have achieved notable progress in classification tasks through large-scale pretraining. However, their post-training practice mainly focuses on implicit parameter fine-tuning, where task-relevant information is entangled with abundant irrelevant knowledge in vast parameter spaces. This may hinder TSFMs from precisely activating useful knowledge for downstream classification. To this end, we propose Retrieval-to-Recognition (R2R), a plug-and-play post-training paradigm that adapts pretrained TSFMs to time series classification. Rather than pretraining a new foundation model, R2R improves how existing TSFMs are post-trained: it introduces a decoupled query encoder to extract discriminative patterns and retrieve instance-level numerical knowledge from a constructed knowledge base. The retrieved explicit knowledge is then fused with the TSFM's implicit parametric embeddings, enabling joint knowledge transfer at both the parameter and feature levels. In this way, R2R allows TSFMs to recall task-relevant patterns learned during pretraining, improving the efficiency of knowledge utilization in downstream classification transfer. Experiments on 158 time series datasets show that R2R consistently improves the classification performance of state-of-the-art TSFMs. Ablation and knowledge-enhanced studies further validate the effectiveness of the proposed framework.

cs.LG↗
arXiv2026

Decoding Failure Biases IBLT Count Measurements: A Gaussian-Selection Law at the Peeling Threshold

In a self-sizing set-reconciliation protocol, a small first-round Invertible Bloom Lookup Table (IBLT) sketch attempts peeling decoding; after failure, the protocol estimates the difference count $d$ from the count energy and sizes the second round from a lower quantile of the measurement. This quantile must be computed conditionally on failure: the measurement and peeling success are determined by the same random mapping, so conditioning can shift the measurement law. In the ideal peeling model, inside the critical window where failure has nondegenerate probability, the standardized energy and a statistic of the unsigned mapping that governs the peeling outcome (the precritical score) have a joint Gaussian limit with correlation $η_kρ^2$, $ρ$ being the sign imbalance and $η_k$ a closed-form constant. The measurement conditional on failure is thus a bivariate normal truncated to a half-plane, which we call the Gaussian-selection law. Since $η_k$ changes sign between $k=4$ and $k=5$, under one-sided imbalance the unconditional threshold undercovers exactly when $k\ge5$ and is conservative for $k=3,4$. The key tool is a quenched CLT for a signed quadratic form on a random overlap graph; under asymptotically balanced signs, conditioning on any unsigned mapping event that is permutation-invariant and has probability bounded away from zero leaves the standard normal limit unchanged. After failure the protocol observes the total sign; inverting the selection law yields an upper confidence bound for $d$, asymptotically conservative in the critical window and, when $d/\sqrt M\to\infty$, along the whole load axis. The experiments use analytic constants, none fitted; the unconditional threshold carries a selection error that does not vanish with scale, while the corrected selection-law threshold matches per-configuration empirical quantiles.

cs.DS↗
arXiv2026

SDFlow: Similarity-Driven Flow Matching for Time Series Generation

Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. However, such models are fundamentally limited by exposure bias: during inference, errors can accumulate across sequential predictions, leading to pronounced quality degradation in long-horizon generation. To address this, we propose SDFlow ($\textbf{S}$imilarity-$\textbf{D}$riven $\textbf{Flow}$ Matching), a non-autoregressive framework that operates entirely in the frozen VQ latent space and enables parallel sequence generation via flow matching. We tackle three key challenges in making this transition: (1) eliminating exposure bias by replacing step-wise token prediction with a global transport map; (2) mitigating the high-dimensionality of VQ token spaces via a low-rank manifold decomposition with a learned anchor prior over the latent manifold; and (3) incorporating discrete supervision into continuous transport dynamics by introducing a categorical posterior over codebook indices within a variational flow-matching formulation. Extensive experiments show that SDFlow achieves state-of-the-art performance, improving Discriminative Score and substantially reducing Context-FID, particularly for challenging long-sequence generation. Moreover, SDFlow provides significant inference speedups over autoregressive baselines, offering both high fidelity and computational efficiency. Code is available at https://github.com/William-Liwei/SDFlow

cs.AI↗
arXiv2026

IBLTs Measure Before They Decode: Self-Sizing Set Reconciliation for Database Consistency Verification

Cross-system data replication pipelines cannot confirm end-to-end consistency from the local guarantees of each hop, so the two endpoints must be compared directly on a periodic basis. Once the rows of a fixed snapshot are normalized into fingerprints, the task reduces to finding the symmetric difference of the two sets. An Invertible Bloom Lookup Table (IBLT) reconciles the sets with communication that grows only with the difference cardinality $d$, independent of table size, but its capacity must be fixed while $d$ is still unknown. Across 41,603 production reconciliations over 90 days, nonzero $d$ spans about seven orders of magnitude, and no reliable empirical constant exists. We show that the count array of an IBLT has already measured $d$ before decoding. The measurement is in-band: it is carried by the recovery sketch itself and adds no bytes dedicated to estimation. A mapping-aware theorem extends the construction to Irregular, Rateless, and MET IBLTs. The protocol reads the estimate only after a decoding failure; we prove that the failure-conditioned lower quantile bounds the risk of underestimation, which gives the second-round capacity a configurable success-probability guarantee. The resulting self-sizing protocol attempts recovery with a small first-round sketch and stops on success; on failure it reads $d$, sizes the second round, and completes reconciliation in at most two rounds. Against a controlled oracle, communication is 1.29-1.47 times that of a scheme given $d$ in advance. Production workload characterization, relational-database replay, and a cross-city KV deployment confirm the end-to-end mechanism. In production on an Oracle-MySQL link, all completed runs succeeded within two rounds, over 90% on the 1-RTT fast path with a single 16 KB sketch.

cs.DB↗
arXiv2026

Fluidization in Growth-Induced Morphogenesis

Elastic buckling has explained shape formation in growing tissues, yet the role of tissue fluidity remains elusive. We derive a minimal fluidized growth-elasticity model as a nonlinear analogue of Maxwell rheology. Analysis of a growing strip reveals a different picture of growth-induced morphogenesis: rather than emerging at a critical stress, symmetry breaking develops continuously during growth. Fluidity regulates stress evolution, the rate of shape-symmetry breaking, and flow patterns, establishing it as an active regulator of morphogenesis beyond its intuitive role in stress relaxation.

physics.bio-ph↗
arXiv2026

Deep Divide-and-Reduce in Symbolic Regression

Symbolic regression (SR) aims to discover underlying mathematical expressions from data while preserving interpretability. Most existing learning-based SR methods primarily optimize expressions from observations without explicitly exploiting their structural mathematical properties. AI Feynman introduced a complementary paradigm that leverages such properties to recursively decompose complex expressions, but its decomposition criteria cover only restricted structural forms and its treatment of nested composition can require brute-force search over candidate sub-expressions. Building on this paradigm, we propose Deep Divide-and-Reduce in Symbolic Regression (DDRSR), a mathematically grounded framework that systematically generalizes expression decomposition and variable reduction. DDRSR extends translational symmetry to coefficient- and exponent-interfered forms, enables variable separation under overlapping variables and additive constant offsets, and generalizes the identification of nested compositional structures. We further characterize an intrinsic non-identifiability limitation of decomposition when no effective variable separation is induced. Experiments across multiple symbolic regression algorithms and benchmark datasets show that DDRSR identifies a broader range of decomposable structures than AI Feynman and overall improves downstream regression accuracy and exact-expression recovery.

cs.LG↗
arXiv2026

PAGR: Proof-Carrying Algebraic-Geometric Retrieval: A Quiver-, Provenance-, and Sheaf-Theoretic Framework for Grounded LLM Retrieval

Retrieval-augmented generation is usually formulated as a statistical information-retrieval problem. Graph-based variants add relational structure, but the mathematical status of that structure is often left underspecified. Three distinct questions tend to be conflated: which statements are certified as knowledge, which latent representations are useful for retrieval, and which multi-hop compositions are semantically admissible. We propose Proof-Carrying Algebraic-Geometric Retrieval (PAGR), a framework that separates these questions mathematically. Its symbolic layer is a many-sorted relational theory generated by a typed quiver, path equations, and positive Horn inclusions. A quiver representation assigns inner-product spaces to entity types and linear operators to relations. A cellular sheaf measures local-to-global consistency. Semiring provenance records derivations and supports machine-checkable certificates. The central principle is epistemic separation: learned geometry may rank and organize evidence, but cannot promote a hypothesis to certified ground truth. We show the certification criterion is invariant under arbitrary replacement of learned components. Further results include a conditional completeness bound, identification of the isometry group as the relevant symmetry for residual-based retrieval, a cohomological consistency diagnostic, and a bounded-bisimulation index for admissible-path expansion. PAGR is a mathematical architecture for separating where a system should look from what it is allowed to treat as knowledge.

math.RT↗
arXiv2026

Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment

Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks. However, their performance remains constrained by two fundamental challenges: the inherent heterogeneity of temporal patterns and the modality gap between continuous numerical signals and discrete language representations. In this work, we propose \textbf{TALON} (Temporal-heterogeneity And Language-Oriented Network), a unified framework that enhances LLM-based forecasting by modeling temporal heterogeneity and promoting representation alignment. Specifically, we design a Heterogeneous Temporal Encoder that partitions multivariate time series into structurally coherent segments, enabling localized expert modeling across diverse temporal patterns. To bridge the modality gap, we introduce a Representation Alignment Module that projects temporal features toward LLM-compatible representations, making time series more amenable to LLMs and thereby unlocking their modeling potential, while eliminating the need for handcrafted prompts during inference. Extensive experiments on seven real-world benchmarks demonstrate that TALON achieves superior performance across all datasets, with average MSE improvements of up to 11% over recent state-of-the-art methods, while maintaining higher efficiency. These results underscore the effectiveness of incorporating both pattern-aware modeling and representation-level alignment when adapting LLMs for time series forecasting. The code is available at: https://github.com/syrGitHub/TALON.

cs.CL↗
arXiv2026

Continuum modeling of fluidic and elastic flow during growth-driven wound closure in partial-EMT cell monolayers

Large-scale circular gap closure occurs over a time scale on which cell growth and proliferation become important. Growth is the main driver of the closing process, while cell dynamics such as elongation and intercalation reflect elastic and fluidic contributions to tissue deformation. We develop a novel fluidized growth-elasticity framework as a nonlinear analogue of a Maxwell fluid with growth. The framework decomposes the experimentally observable strain rate into the additive sum of the growth, elastic, and fluidic strain rates, thus enabling the separate quantification of these contributions from tissue kinematics and allowing the roles of tissue elasticity and fluidity (the inverse of viscosity) to be characterized. We apply the model to large circular gaps ($\sim$1.7 mm in diameter) in confluent monolayers of mouse embryonic epicardial cells (MEC1) under two conditions, without and with TGF-$β$ treatment. We show that both tissue fluidity and the elastic properties associated with fiber reinforcement are critical for reproducing the closure kinematics. Specifically, we predict that the treated condition has lower fluidity, associated with a lower fluidic deformation rate and a higher elastic deformation rate than the untreated condition, in agreement with the experimental observations.

physics.flu-dyn↗
arXiv2026

ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person forgot, is confused, has side effects, or deliberately refused, nor what support is appropriate. This reveals a structural gap in Agentic AI: Digital Agents primarily transform software states, while Embodied Agents transform physical states; neither makes a person's evolving state and agency the primary object of modeling, intervention, and evaluation. We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time, using software tools, sensors, wearables, robots, and human services as action channels rather than end goals. We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop: event-based multimodal perception reconstructs meaningful personal events; longitudinal, correctable memory provides temporal context; Personal World Models estimate future personal states and outcomes under alternative decisions and interventions; and an admissible intervention policy selects proportionate support under consent, uncertainty, safety, reversibility, and user control. Feedback from the person and environment updates the loop. Rather than requiring an exhaustive Human Digital Twin, the framework uses purpose-bounded, uncertainty-aware, user-correctable representations. We organize the design space by human-state targets, relational contexts, and agent roles, and propose scenario-centered evaluation, agency-preservation metrics, benchmark requirements, edge-native personal models, and governance directions. Combodied Agents shift Agentic AI from external task completion toward sustained human benefit.

cs.AI↗
arXiv2026

Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems

Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations. Current forecasting models can capture temporal dependencies with low average errors, but global error metrics may conceal poor reproduction of prolonged high-water plateaus that are relevant to flood early warning. Because hydrometeorological and operational signals are distributed across heterogeneous gages, single-site records do not fully represent high-water dynamics. Nevertheless, unconstrained fusion of cross-site signals can degrade the stability of local temporal forecasts. This work proposes an anchored forecasting framework that incorporates cross-site information through state- and lead-dependent bounded residual corrections. A multi-source regime representation constructed from hydrometeorological and operational observations adaptively calibrates inter-site relationships and correction scales, enabling targeted cross-site adjustment while preserving the local temporal forecast as a stable anchor. Beyond conventional global error statistics, we evaluate event-scale high-water characteristics through the temporal alignment of forecasted and observed high-water processes. Experiments demonstrate that selectively integrating multi-station dynamic conditions improves the prediction reliability of sustained high-water plateaus while maintaining high accuracy during routine hydrological conditions, supporting flood early warning and water-management decision support.

cs.LG↗
arXiv2026

Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer

Integrating complex, multi-omics data presents significant challenges. Existing approaches often face a trade-off between model interpretability and representational capacity, with most either relying on post-hoc interpretation or use linear models that may overlook complex interactions. We report Pathway Activity Autoencoders for the multi-omics setting, which embed prior knowledge via pathway-informed architectural constraints, fostering interpretability, while preserving representational power. Our multi-omic framework is applied in the context of breast cancer and is evaluated in survival prediction and subtype classification with results indicating a positive effect of integration. We conduct analysis of individual omics layer impact on end-task performance, revealing that gene, protein, and microRNA expression layers provide the strongest contribution. Repeatability studies indicate that, while dropout improves model robustness and consistency, excessive regularisation can reduce predictive performance. Finally, visualizations of the learned feature space illustrate the framework's intrinsic transparency and clinical relevance. The results underscore the value of multi-omic integration and delineate the impact of individual omics layers, establishing practical guidelines for integration within our framework. Overall, our pathway activity autoencoder frameworks yield superior latent representations that are biologically meaningful and are directly translatable into clinically relevant insights.

cs.LG↗
arXiv2026

A phase-field model for viscoelastic compressible tumor growth

It is well known that growing tumors generate and respond to stress in their local microenvironment. Tissue re-arrangements can relax these mechanical stresses and make the tissue more fluid-like. Further, intricate coupling between mechanotransduction and biochemical signaling leads to complex patterns of growth. To predict the outcomes of these nonlinear interactions, we develop a phase-field model to simulate tumors growing into a surrounding medium taking into account their elastic and viscous properties as well as their compressibilities. We couple continuum modeling of the viscoelastic mechanics to the concentration of a diffusible growth-promoting nutrient in a mass conservative way. The phase-field method is a stable and flexible way to describe the dynamics of arbitrarily shaped tumors. We demonstrate convergence of the phase-field model to a sharp interface model in radially symmetric geometries and can observe progression to stationary tumors. However, our results show that these stationary symmetric tumors are subject to symmetry-breaking instabilities in 2D and 3D driven by two primary mechanisms: (i) elastic buckling instabiliies due to differential growth induced by the nutrient gradient and (ii) instabilities generated by apoptosis-related volumetric loss. Further, tissue fluidity and compressibility can lead to changes in tumor topologies. Our modeling framework provides a robust methodology for investigating how tissue mechanics and growth factor signaling influence the progression and invasive potential of solid tumors.

cond-mat.soft↗
arXiv2026

Connectivity Estimation using Stochastic Graph Heat Modelling

A growing number of techniques leverage the spatial structures that underlie many real-world datasets. Despite these advances, the complementary task of estimating spatial structures and understanding their role within these techniques has often been overlooked. In neurophysiological data analysis specifically, numerous methods exist to estimate brain connectivity, but most are not explicitly model-based, dynamic, multivariate, or directed. To address these limitations, we previously introduced noise-driven heat modelling on graphs for neurophysiological connectivity estimation. In this study, we extend this framework by relaxing earlier noise assumptions and adding regularisation to improve robustness. We also develop a simulation procedure to characterise and evaluate our technique in a controlled setting. Finally, we demonstrate that the technique is able to capture meaningful spatial structure across two experiments, each using two real-world datasets. The explicit model formulation of our connectivity estimator has the potential to improve the interpretability of graph-based techniques across a wide range of applications. The code implementing our method is available at https://github.com/sgoerttler/Heat_Connectivity.

stat.ML↗
arXiv2026

A Comprehensive Survey on World Models for Embodied AI

Embodied AI requires agents that perceive, act, and anticipate how actions reshape future world states. World models serve as internal simulators that capture environment dynamics, enabling forward and counterfactual rollouts to support perception, prediction, and decision making. This survey presents a unified framework for world models in embodied AI. Specifically, we formalize the problem setting and learning objectives, and propose a three-axis taxonomy encompassing: (1) Functionality, Decision-Coupled vs. General-Purpose; (2) Temporal Modeling, Sequential Simulation and Inference vs. Global Difference Prediction; (3) Spatial Representation, Global Latent Vector, Token Feature Sequence, Spatial Latent Grid, and Decomposed Rendering Representation. We systematize data resources and metrics across robotics, autonomous driving, and general video settings, covering pixel prediction quality, state-level understanding, and task performance. Furthermore, we offer a quantitative comparison of state-of-the-art models and distill key open challenges, including the scarcity of unified datasets and the need for evaluation metrics that assess physical consistency over pixel fidelity, the trade-off between model performance and the computational efficiency required for real-time control, and the core modeling difficulty of achieving long-horizon temporal consistency while mitigating error accumulation. Finally, we maintain a curated bibliography at https://github.com/Li-Zn-H/AwesomeWorldModels.

cs.CV↗
arXiv2026

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models

Post-training quantization (PTQ) is critical for the efficient deployment of large language models (LLMs). Recent ultra-low-bit PTQ methods rely on rigid weight-saliency assumptions or position heuristics, introducing substantial hidden scaling overhead. We propose SAGE-PTQ (Saliency-Aware Graph-guided Efficient PTQ), a novel ultra-low-bit quantization framework for LLMs that minimizes hidden scaling cost. SAGE-PTQ separates salient and unsalient weights using distributional statistics, then models subsampled unsalient weights as a sparse graph to estimate the optimal number of groups per layer. SAGE-PTQ applies dual-mode quantization, assigning multi-bit precision to salient weights and binarizing unsalient weights. To reduce scaling overhead, SAGE-PTQ uses one per-channel scale for salient weights and one scalar per unsalient group. Finally, SAGE-PTQ implements adaptive saliency thresholding to select the optimal saliency ratio per matrix. SAGE-PTQ achieves 1.03 weight bits and only 0.004 scaling bits per matrix on average, outperforming state-of-the-art methods such as BiLLM and PB-LLM. On LLaMA-3-8B, SAGE-PTQ achieves 6.74 WikiText2 perplexity, compared to 55.8 for BiLLM, while using less than 50% of BiLLM's GPU memory. On LLaMA-2-70B, SAGE-PTQ provides 1.5x faster decoding on one NVIDIA L40 GPU, demonstrating practical inference efficiency.

cs.AI↗