arXiv subjects

Ming Liu

Publications and source records attributed to Ming Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

To Blend In, First Decouple: Rethinking Camouflage Image Generation via Context-Decoupled Representations

Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds. Existing methods typically follow either background-guided paradigms that adapt object appearance via style transfer, or foreground-guided strategies that outpaint surrounding regions conditioned on object features. However, they still suffer from appearance discrepancy and background artifacts. We attribute these limitations to cross-context representation leakage, where object and background cues are entangled in a coupled conditional space, resulting in ambiguous control and degraded camouflage fidelity. To tackle this, we propose a new context-decoupled generative paradigm, termed CamoDreamer, which aims to isolate contextual conditional guidance and explicitly decouple latent camouflage features into coordinated object and background control streams. First, a Contrast-aware Contextual Bridge is designed to model cross-context discrepancies and construct contrast-aware dual conditional guidance. Second, Context-Decoupled Assimilation Streams are employed to separate generative interactions conditioned on the dual guidance, while facilitating background rendering with target-aware cues in the latent space. Finally, a Frequency-Adaptive Contextual Blend module integrates complementary high-frequency textures and low-frequency structures from decoupled features to improve holistic coherence. Extensive experiments demonstrate that CamoDreamer consistently outperforms existing methods with a substantial margin, while maintaining a relatively lightweight design.

cs.CV↗

arXiv2026

From Bench-to-Bedside: A Review of Clinical Trials in Drug Discovery and Development

Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical trial phases (Phase I [safety assessment], Phase II [efficacy evaluation], Phase III [large-scale validation], and Phase IV [post-marketing surveillance]), highlighting the distinct characteristics and interconnections. Major challenges are identified, including ethical compliance, participant recruitment, and ensuring diversity and representativeness in trial populations, while proposing evidence-based mitigation strategies. To address these challenges, innovative technologies, such as artificial intelligence, big data analytics, and digital health tools, are transforming trial design and implementation, enhancing efficiency and data quality. Looking forward, the review explores how emerging therapies, including gene therapy and immunotherapy, are reshaping trial design requirements and emphasizes the growing importance of regulatory harmonization and global collaboration. Clinical trials remain central to advancing innovative drug development and improving patient outcomes.

cs.CY↗

arXiv2026

From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings

Translating computational predictions into experimentally validated biological knowledge remains one of the central challenges in modern bioinformatics. This review critically examines how in silico findings can be prioritized, tested, and interpreted through experimental validation. We organize the validation process around three recurring challenges: the specificity gap between genome-wide computational predictions and single-target experimental assays; the reproducibility-translatability tension, in which results validated in one model system may not generalize to another; and the scale-depth trade-off between high-throughput candidate discovery and the low-throughput nature of mechanistic validation. Rather than presenting an encyclopedic catalogue of techniques, we compare the strengths, limitations, and common failure modes of major validation approaches, including qPCR, RNA-seq, Western blotting, co-immunoprecipitation, luciferase reporter assays, CRISPR perturbation, and functional phenotypic assays. We also provide structured comparison tables for gene expression, protein-protein interaction, non-coding RNA, regulatory element, and pathway validation, together with decision-making frameworks to guide method selection according to prediction type, biological context, evidence stringency, throughput, and resource constraints. Case studies from cancer genomics, drug target discovery, miRNA regulation, and neurological disease illustrate how multi-step validation workflows can strengthen causal inference and reduce false-positive interpretation. Finally, we discuss how emerging technologies, including CRISPR screens, single-cell and spatial multi-omics, and AI-assisted experimental design, may reshape validation practice by improving scalability, context specificity, and reproducibility.

q-bio.GN↗

arXiv2026

MOSAIC: A Universal Agent-Level Interface for Cross-Paradigm Agent Mixing and Human-AI Collaboration

Existing infrastructure cannot deploy agents from different decision-making paradigms within the same environment, making fair cross-paradigm comparison under identical conditions impossible. We present MOSAIC, an open-source platform that enables heterogeneous agents (RL policies, LLMs, VLMs, and human operators) to act within shared reinforcement learning environments in ad-hoc team settings with reproducible results. MOSAIC introduces three contributions. (i) IPC-based worker protocol that wraps native and third-party frameworks as isolated subprocess workers, each executing its own training and inference logic unmodified and communicating through a versioned inter-process protocol. (ii) An operator abstraction that forms an agent-level interface by mapping workers to agent slots: each operator, regardless of whether it is backed by an RL policy, an LLM, or a human, conforms to a minimal universal interface. (iii) A deterministic cross-paradigm evaluation framework with two complementary modes: a manual mode that advances up to $N$ operators in lock-step under shared seeds for fine-grained visual inspection of behavioural differences; and a script mode that drives automated, long-running evaluation via declarative Python scripts for reproducible experiments. Our documentation is released at: https://mosaic-platform.readthedocs.io.

cs.LG↗

arXiv2026

A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving

Reinforcement learning (RL) has demonstrated considerable potential for autonomous driving decision-making. However, its deployment in urban autonomous driving, particularly at highly interactive unsignalized intersections, remains challenging, as learned policies may struggle to maintain both safety and robust decision-making in complex traffic situations. Conventional safety-filtering approaches typically employ fixed conservative constraints, which may improve safety at the cost of excessive intervention and degraded traffic efficiency. To address these limitations, we propose a Risk-sensitive and Uncertainty-aware Decision-making and Control (RUDC) framework for safe and robust autonomous driving. RUDC couples risk-sensitive distributional RL with ensemble-based policy uncertainty quantification, jointly accounting for tail risks in return distributions and uncertainty in learned policies. An uncertainty-aware high-order control barrier function (HOCBF)-based safety correction mechanism adaptively adjusts constraint strictness according to policy uncertainty, while a learnable residual predictor compensates for CBF model mismatches and discretization errors. Extensive simulations at unsignalized intersections demonstrate that RUDC achieves a favorable balance among safety, efficiency, and robustness, outperforming representative safe RL baselines under both nominal and challenging OOD and long-tail scenarios while satisfying real-time requirements.

cs.RO↗

arXiv2026

PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians

Efficient, fully automatic, and physically plausible 4D Gaussian synthesis is an important goal for dynamic scene generation. Recent physics-based methods couple 3D Gaussians with the Material Point Method (MPM) to generate physically driven motion, but extending this paradigm to heterogeneous multi-part objects and interacting multi-object scenes remains challenging. Object-level physical assignment collapses distinct parts into a single material state, while one-shot predictions from large language models, vision-language models, or agents neither reliably bind different materials to identified parts nor verify that the resulting MPM configuration is executable. Score Distillation Sampling (SDS)-based parameter optimization, meanwhile, requires repeated per-scene score evaluations and gradient backpropagation, incurring lengthy optimization and potentially yielding suboptimal or unstable solutions. We therefore present PhysMAS, a physics-grounded multi-agent framework. From a motion prompt and four scene views, an Object-Part Scene Agent establishes persistent identities and calls a Material Reasoning Agent for part-wise profiles. It invokes solver-aware skills to bind these identities and profiles to per-particle MPM fields and execute all objects in a shared domain; the framework then screens candidate forward-simulation results. This supports heterogeneous multi-part and interacting multi-object scenes without per-scene diffusion-score backpropagation. Extensive experiments demonstrate that, compared with recent physics-based 4D Gaussian baselines that rely on SDS, PhysMAS achieves better semantic alignment and perceived physical plausibility while requiring less runtime.

cs.AI↗

arXiv2026

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are invalidated by tomorrow's models and systems, forcing perpetual refactoring of performance-modeling frameworks. Meanwhile, AI coding agents have become fast and capable enough that regenerating an entire library is cheaper than paying down the tech debt of incrementally patching it. We describe SMART, a rigorous symbolic performance-modeling library for ML systems whose main branch contains almost no code: the repository is a DAG of self-contained natural-language design docs, coding sub-agents regenerate the implementation from only the docs on new version updates, and every human change is a natural-language edit to a doc--self-documenting by construction. Two ingredients make regeneration reliable: (i) a design-doc style built around step-by-step worked examples that act as in-context demonstrations for the generating agents, and (ii) a minimal, recursively defined operator IR with symbolic (SymPy) cost expressions, a fast analytical roll-up mode for large sweeps, and a slow modulo-scheduling mode for fine-grained schedule studies. Regenerated implementations reproduce hand-audited reference models--including DeepSeek-V3 serving on a TPU pod slice--to round-off precision, suggesting that design docs--not code--can be the durable artifact for ML-systems co-design tools.

cs.PL↗

arXiv2026

DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation

Industrial anomaly detection benefits from anomaly samples, yet newly deployed products typically provide only normal images, making anomaly samples difficult to collect. Zero-shot anomaly generation offers a promising solution which avoids collection of target-product anomalies. However, existing methods mainly rely on texture images or text descriptions as anomaly sources, which often produce unrealistic anomalies. Observing that similar anomalies can recur across different products, we propose anomaly transfer-based zero-shot generation, which reuses real anomalies from existing source products, making target-product anomalies no longer necessary to generate realistic anomalious samples for unseen target products. Since not every anomaly type suits the target product, an anomaly type filtering mechanism first selects plausible source types. To transfer selected anomaly, we propose DPA, a diffusion-based framework that decouples product-agnostic anomaly representations. Instead of directly extracting anomaly representations, DPA learns product-irrelevant anomaly embeddings through training with the mismatched data pair, enabling transferable anomaly concept learning across products. Furthermore, we design an adaptive mask-guided pipeline that leverages adaptive masks to control the positional and geometric plausibility of generated anomalies during generation. A training-free anomaly labeling module is further introduced to produce pixel-level annotations aligned with generated anomalies. Extensive experiments on MVTec-AD, VisA, and a dedicated anomaly-transfer benchmark demonstrate that the proposed setting and DPA generate more realistic anomalies and significantly improve downstream anomaly detection performance under both zero-shot and few-shot settings. Source code and models will be released.

cs.CV↗

arXiv2026

Measuring Memory and Generalization as Separable Geometric Channels: The Topo^2 Framework

Deep networks trained on noisy labels simultaneously generalize on clean data and memorize flipped labels. These are usually conflated as pressures on one capacity. We present Topo^2, a measurement framework that makes them causally separable, measurable, and law-governed. Persistent-homology H1 structure of the representation space separates into a within-class manifold channel (a function of the training stopping point) and a cross-class channel (a monotone readout of memorized flipped samples). An intervention, the FM0 prescription (zero loss on flipped samples from epoch 0), reaches each setting's generalization ceiling while memorizing essentially nothing. Within the framework we establish a law set with graded evidence: (L2) FM0 separation prescription (9/9); (L1) the within-channel as a training-position function (mid-rise 6/6; convergence-back CIFAR 3/3, SVHN 2/3); (L3) a ring-construction identity (definitional, not a law); and TLS (memory-generalization topological layering): memory is causally additive, anchored (silencing clean collapses the representation), invertible (stripping memory restores near-ceiling generalization), and quantitatively billable (the memorization cost law, effective slope coefficient C ~ 0.38 at the reference capacity: CIFAR-10 0.3801 / SVHN 0.3806 / CIFAR-100 0.384 / VGG 0.3715, capacity-dependent in general and traced to clean-sample feature displacement). We also publish the framework's boundaries: a falsification ledger of nine dead ends, and an instrument-vindication section that excludes six families of global statistics as explanations of the within-channel. The framework turns "memorization" from an ill-defined capacity into a measurable, separable, invertible topological layer.

cs.LG↗

arXiv2026

GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL

Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formulation. While deterministic graph tools are invariant to such shifts, extracting topological structures from noisy text is highly fragile for LLMs, which often overfit to surface patterns. Moreover, mitigating these parsing failures via multi-agent systems incurs prohibitive latency. To address this, we propose GRAIN, a single-agent framework optimized via reinforcement learning. GRAIN models reasoning as a semantic parsing and tool-execution pipeline, guided by a Structure Invariance Reward. By validating extracted intermediate graphs against ground-truth topologies, this reward forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts. We also introduce GRIT, a benchmark evaluating sensitivity to such linguistic shifts. GRAIN outperforms multi-agent baselines by 16.45\% in accuracy with approximately 24\% lower latency. Furthermore, it demonstrates superior structural generalization, halving the out-of-distribution (OOD) gap of SFT models (from 15.77\% to 7.80\%) and maintaining robustness on large-scale graphs beyond the training distribution.

cs.AI↗

arXiv2026

One Model, Many Minds: Unlocking Multi-Agent Synergy in a Single Agent via Mixture of Roles

Specializing Large Language Models (LLMs) toward distinct abilities underpins successes ranging from personalized assistants to multi-agent systems (MAS). Single-agent paradigms rely on pre-defined personas or steering vectors to induce specialization, yet they impose a single fixed specialization that fails to adapt to diverse queries. Conversely, MAS achieves dynamic multi-perspective problem solving by orchestrating agents with distinct text-based roles, but fusing these specializations requires multi-turn interactions that inflate context length and inference cost. To address these limitations, we propose Mixture of Roles (MoRe), which adaptively composes multiple specializations into a single steering vector for single-turn inference. Specifically, MoRe learns a diversified codeboox of steering vectors, each of which encodes a latent role. A query-aware router dynamically fuses the codebook into a steering vector that encompasses multiple roles. By steering the backbone LLM with the composed vector, MoRe enables multi-perspective specialization in a single-agent, single-turn inference process. The proposed MoRe can be efficiently trained via a three-stage SFT curriculum and GRPO post-training, while the backbone LLM remains frozen. Experiments across reasoning and personality benchmarks show that MoRe outperforms single-agent baselines by 2.2% on average, and achieves performance on par with MAS while reducing token cost by 20x.

cs.MA↗

arXiv2026

Dissimilar heat transfer enhancement in spatially developing flow between parallel perforated plates by inducing a streamwise travelling-wave disturbance

Travelling-wave-like wall blowing and suction is an effective approach for enhancing heat transfer with a minimal pressure drag penalty. However, achieving such a dissimilar heat transfer enhancement effect in a passive manner remains a challenge. In the present study, we propose introducing parallel perforated plates to induce travelling-wave-like disturbances passively. Pore-resolving simulations of a spatially developing laminar flow between parallel perforated plates are performed across a wide range of Reynolds numbers of $Re = 500-1500$ and pore-to-solid length ratios of $L_\mathrm{p}/L_\mathrm{s} = 0-10$. Dissimilar heat transfer enhancement is confirmed for $9 \leq L_\mathrm{p}/L_\mathrm{s} \leq 10$ at $Re = 1000$ and $4 \leq L_\mathrm{p}/L_\mathrm{s} \leq 7$ at $Re = 1500$. The highest analogy factor, i.e., the ratio of the Stanton number to the friction coefficient is obtained at $Re = 1500$ and $L_\mathrm{p}/L_\mathrm{s} = 6$, yielding an increase of more than $30$\% compared to that of an impermeable solid plate. Analysis of the fluctuating fields shows that, in the travelling-wave flow regime, a pressure-induced wall-normal velocity fluctuation transports temperature fluctuations away from the perforated plate, while breaking the correlation between the streamwise and wall-normal velocity fluctuations. This enhances the turbulent heat flux relative to the Reynolds shear stress near the perforated plate. The present results indicate that introducing a perforated plate with a suitable porosity induces travelling-wave velocity disturbances and also achieves a considerable dissimilar heat transfer effect even at low Reynolds numbers where a standard impermeable flat wall yields a steady laminar flow.

physics.flu-dyn↗

arXiv2026

Building A CSFQ-Inspired Transport for Switched CXL Memory Pooling

Emerging switched CXL memory pooling systems, albeit promising, suffer from significant performance interference due to the shared but performance-uncontrolled data path among concurrent memory streams between a host core and a remote DIMM. We systematically characterize a memory pooling appliance based on XConn's Apollo CXL switch and identify three issues: intra-host contention, in-fabric congestion, and unmanaged host-remote DIMM interaction. This paper presents a new transport layer, MemChannel, which provides the mchannel abstraction to manage end-to-end fabric bandwidth among competing memory flows and enable application-specific traffic for switched CXL memory pooling. Our key idea is to build a sender-driven, fabric-informed transport protocol, inspired by Core-Stateless Fair Queueing (CSFQ), that admits just the right amount of CXL requests to each mchannel based on the estimated core-to-CXL-DIMM bandwidth availability. To address CXL-induced idiosyncrasies, MemChannel introduces time-based rate control, host-side admission control, cross-host bookkeeping, new congestion signals, rate estimation based on the fluid model, and delay-based link-capacity adjustment. We build MemChannel from scratch and support unmodified applications. Evaluations over switched memory pooling demonstrate its effectiveness from performance-isolation, scalability, and multi-tenancy perspectives.

cs.NI↗

arXiv2026

Parameter- and Bandwidth-Efficient Edge--cloud Many-to-Many Speech-to-Text Translation

Multimodal large language models (MLLMs) have demonstrated significant potential for speech-to-text translation (S2TT). However, existing deployment paradigms face critical challenges: pure on-device models suffer from resource constraints, while centralized cloud systems incur bandwidth bottlenecks and privacy risks by transmitting raw voice data. In this paper, we propose Edge--cloud Speech Recognition and Translation (ESRT), a parameter-efficient, bandwidth-efficient, and privacy-aware collaborative Edge--cloud MLLM framework. First, we introduce a multi-task weighted curriculum learning strategy to mitigate catastrophic forgetting, improve multilingual balance, and train parameter-efficient ESRT-1B, ESRT-4B, and ESRT-12B models. Second, we enable bandwidth-efficient Edge--cloud inference by retaining a lightweight speech encoder and adapter on the device and transmitting only a compressed tensor to the cloud. Extensive experiments on FLEURS demonstrate that ESRT models achieve state-of-the-art S2TT performance across 45 languages ($45 \times 44$ directions). Relative to raw audio, ESRT and ESRT-Lite reduce the transmitted tensor size by $5.1\times$ and $10.2\times$, respectively, while keeping raw speech on-device and avoiding its direct exposure to the cloud. The code and models are released to facilitate reproducible, privacy-aware S2TT research.

cs.AI↗

arXiv2026

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration

The state evolution of a complex system arises jointly from object laws, relational propagation, domain conservation, and unmodeled error. Forcing all sources into one black box makes mechanism attribution and constraint preservation unauditable; forcing every mechanism into one equation family discards mature domain solvers. We propose SD-GWM, a Structural Dynamics Graph World Model as an executable structural contract: nodes declare self-dynamics S, edges declare neighbor graph-coupled dynamics N---both fixed-form mechanism assets (rules, ODEs, solvers) calibrating only authorized parameters. An optional bounded residual R concentrates learnability, while a global projection maps states to feasibility, enforcing constraints without guaranteeing accuracy gains. On eight pre-registered research questions, SD-GWM delivers (i) heterogeneous integration: rules and solvers plug in natively; (ii) semantic fidelity: disabling R preserves source semantics bit-for-bit, with four theory properties under explicit proof/empirical boundaries; (iii) auditable governance: stepwise traces enable counterfactual fault localization (top-1 = 1.0) without post-hoc approximations. On a semi-synthetic flood testbed and USGS streamflow, SD-GWM reduces constraint violations to floating-point tolerance in analytical tests and to zero in semi-synthetic and real-data cases. Persistence matches SD-GWM in calm periods, but during a 254-day extreme-flood shift persistence and all neural baselines collapse (90-min RMSE 892-3007 cfs) while SD-GWM holds at 108 cfs (8-28x gain). The bounded residual cuts RMSE ~50% only under backbone bias. We position SD-GWM not as a universally superior forecaster, but as a verifiable substrate for auditable, constraint-safe spatiotemporal mining.

cs.AI↗

arXiv2026

Breaking the Curse of Multilinguality in Many-to-Many Speech-to-Text Translation via a Resource-Aware Mixture of Speech Encoders

Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT). However, when processing multilingual speech inputs, a single speech encoder shared across all languages suffers from the curse of multilinguality: languages at different resource levels compete for limited representation capacity, leading to strong high-resource performance but substantial degradation on low-resource speech. To address this problem and improve multilingual consistency, we propose MSRT, a novel framework built around a resource-aware Mixture of Speech Encoders (MoSE). MoSE uses an explicit language router to assign each utterance to an appropriate expert encoder. A frozen expert preserves high-resource language capabilities, while a trainable expert adapts to and specializes in medium- and low-resource languages. We further introduce a five-stage curriculum learning strategy that substantially reduces data dependence, requiring only 10 hours of paired S2TT data per language for effective alignment. We conduct extensive experiments on 45 languages, systematically evaluating all $45 \times 44$ translation directions. Our 4B-parameter model achieves state-of-the-art performance, outperforming substantially larger baselines. Empirical analyses show that MoSE improves high-, medium-, and low-resource languages simultaneously, with the largest gains on low-resource speech, thereby breaking the curse of multilinguality without compromising high-resource performance. To support future multilingual S2TT research, we release our code and models.

cs.CL↗

arXiv2026

Expert-Choice Routing Enables Adaptive Computation in Diffusion Language Models

Diffusion language models (DLMs) enable parallel, non-autoregressive text generation, yet existing DLM mixture-of-experts (MoE) models inherit token-choice (TC) routing from autoregressive systems, leading to load imbalance and rigid computation allocation. We show that expert-choice (EC) routing is a better fit for DLMs: it provides deterministic load balancing by design, yielding higher throughput and faster convergence than TC. Building on the property that EC capacity is externally controllable, we introduce timestep-dependent expert capacity, which varies expert allocation according to the denoising step. We find that allocating more capacity to low-mask-ratio steps consistently achieves the best performance under matched FLOPs, and provide a mechanistic explanation: tokens in low-mask-ratio contexts exhibit an order-of-magnitude higher learning efficiency, so concentrating compute on these steps yields the largest marginal return. Finally, we show that existing pretrained TC DLMs can be retrofitted to EC by replacing only the router, achieving faster convergence and improved accuracy across diverse downstream tasks. Together, these results establish EC routing as a superior paradigm for DLM MoE models and demonstrate that computation in DLMs can be treated as an adaptive policy rather than a fixed architectural constant. Code is available at https://github.com/zhangshuibai/EC-DLM.

cs.LG↗

arXiv2026

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning

Large language model agents have shown strong potential in complex interactive tasks, yet their reinforcement learning (RL) is often hindered by sparse rewards, as a long multi-turn trajectory may receive only a single outcome-level signal. On-policy self-distillation (OPSD) provides dense token-level supervision from a privileged teacher, but the teacher may not be reliable at every position. Existing methods commonly rely on isolated token-level discrepancies, which can be sensitive to noise, or assign a shared step-level weight that may overlook positional variation. We propose Persistent Consistency Self-Distillation (PCSD), which derives token-level distillation weights from the local persistence of teacher-favoring signals. PCSD combines adaptive windows with exponentially decayed aggregation to capture persistent relative teacher support, applies trend-aware modulation to attenuate locally declining support, and produces continuous weights through sigmoid gating. The resulting objective is jointly optimized with GRPO, combining dense teacher guidance with sparse environmental feedback. Without inference-time skills, PCSD achieves the best ALFWorld Overall results among all baselines on both backbones, exceeding GRPO by 15.6 and 13.3 points and SDAR by 6.2 and 5.5 points, while remaining competitive on WebShop and gaining 15.8 points over GRPO on unseen ALFWorld split.

cs.AI↗