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Ming Liu

Publications and source records attributed to Ming Liu.

At least 19 recordsLinked to original sources
arXiv2026

Uncovering Shortcut Amplification in Video RLVR through the Lens of Perceptual Uncertainty Collapse

Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for enhancing the video reasoning capability of Large Multimodal Models (LMMs). Despite its empirical effectiveness, we show that outcome-level optimization can introduce an undesirable shortcut amplification effect, whereby models increasingly favor text-prior answers even when they are not supported by the visual evidence. To characterize this behavior, we investigate the training dynamics of video RLVR through the lens of perceptual uncertainty, analyzing how predictive uncertainty evolves under video-conditioned and text-only contexts. Our analysis identifies a perceptual uncertainty collapse phenomenon, in which RL training continuously sharpens the model's answer distribution while the uncertainty reduction attributable to visual evidence progressively diminishes. A further gradient-direction analysis reveals that such confidence enhancement becomes increasingly less aligned with visually grounded uncertainty reduction and is instead dominated by text-prior compression. Motivated by these findings, we propose Bifocal Self-distillation Policy Optimization (BSPO), a self-distillation framework that regulates policy updates with two complementary distributional signals. Specifically, BSPO derives empirical reasoning principles from intra-group correct and incorrect rollouts to identify reasoning-sensitive tokens, and introduces shortcut-aware visual perturbation to penalize predictions that remain invariant when task-relevant visual evidence is weakened. By adaptively reweighting the original RL advantage, BSPO preserves the task-solving benefits of RLVR while promoting updates that are both reasoning-sensitive and visually grounded. Extensive experiments validate the effectiveness of BSPO in improving video reasoning performance and strengthening visual faithfulness.

cs.CV↗
arXiv2026

FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation

Layout-to-image (L2I) generation has exhibited promising results in natural domains, but suffers from limited generative fidelity and weak alignment with user-provided layouts when applied to degraded scenes (i.e., low-light, underwater). We primarily attribute these limitations to the "contextual illusion dilemma" in degraded conditions, where foreground instances are overwhelmed by context-dominant frequency distributions. Motivated by this, our paper proposes a new Frequency-Inspired Contextual Disentanglement Generative (FICGen) paradigm, which seeks to transfer frequency knowledge of degraded images into the latent diffusion space, thereby facilitating the rendering of degraded instances and their surroundings via contextual frequency-aware guidance. To be specific, FICGen consists of two major steps. Firstly, we introduce a learnable dual-query mechanism, each paired with a dedicated frequency resampler, to extract contextual frequency prototypes from pre-collected degraded exemplars in the training set. Secondly, a visual-frequency enhanced attention is employed to inject frequency prototypes into the degraded generation process. To alleviate the contextual illusion and attribute leakage, an instance coherence map is developed to regulate latent-space disentanglement between individual instances and their surroundings, coupled with an adaptive spatial-frequency aggregation module to reconstruct spatial-frequency mixed degraded representations. Extensive experiments on 5 benchmarks involving a variety of degraded scenarios-from severe low-light to mild blur-demonstrate that FICGen consistently surpasses existing L2I methods in terms of generative fidelity, alignment and downstream auxiliary trainability.

cs.CV↗
arXiv2026

Alignment-Free Optical Combining with Scalar-Message Expectation Propagation Detection for Distributed Multi-Aperture Coherent Reception

We propose an alignment-free distributed multi-aperture optical receiver with a single coherent front end, in which the differential delays between apertures are large. The branch signals are optically combined without inter-branch delay or phase alignment. Large differential delays produce separated tap clusters, forming a sparse ISI channel with long memory. Scalar-message expectation propagation exploits this sparsity to recover the transmitted symbols. Simulations show lower BER than equal-area single-aperture reception and no clear BER degradation over the tested delay spans. As the maximum differential delay increases from 100.5 to 200.5 symbol periods, the channel span nearly doubles, while the factor-graph edge count increases by only about 1.5\%. The trade-off is higher BER than ideal coherent combining schemes in exchange for fewer coherent front ends than digital combining and no inter-branch delay or phase alignment before optical combining.

physics.optics↗
arXiv2026

LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learning

Byzantine-robust aggregation shields federated learning from malicious updates, but the rules that filter out suspect updates do so by comparing clients' parameter vectors. That comparison requires access to every client's update, which secure aggregation exists to withhold, costs $O(n^2 d)$ in the parameter dimension $d$, and is defined only between clients that share an architecture. We propose LiBRA, which compares clients on what their models compute rather than on the parameters that compute them, using a fingerprint of $C^2$ values for $C$ classes, the class-conditional logit response of each client's model to pseudo-patterns optimised against it. We instantiate LiBRA as variants of Krum, Multi-Krum and Bulyan that change only what the dissimilarity score is computed on. On Fashion-MNIST and CIFAR-10 with three architectures and four backdoor and label-manipulation attacks under i.i.d. partitions, each variant holds every attack its parent holds, and on identical client updates under a backdoor attack the fingerprint ranks malicious clients within 0.006 AUC of the full parameter vector. The variants thus match the robustness of the rules they extend, while their trust decision reads no client parameter, compares up to 49,090 times fewer values, and can by construction compare clients whose architectures differ, at the cost of one pattern optimisation per client per round.

cs.LG↗
arXiv2026

Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification

Knowledge Graphs (KGs) provide a critical foundation for many AI systems, yet their automated construction inevitably introduces noisy triples that undermine data trustworthiness. Reliable KG auditing requires jointly examining internal structural constraints and open-world evidence, whereas existing methods often rely on a single evidence source or static, one-shot inference, limiting adaptive evidence acquisition and auditability. We propose SHARP (Schema-Hybrid Agent for Reliable Prediction), a training-free agent that reformulates triple verification as a dynamic process of strategic planning, active investigation, and evidential reasoning. SHARP uses retrieved historical verification trajectories and entity--relation schema information to construct a task-specific investigation plan. It then iteratively coordinates KG and textual tools to cross-validate structural and semantic evidence, producing a verification verdict with a transparent evidence chain. On independently sampled, balanced test sets constructed from FB15K-237 and Wikidata5M-Ind, SHARP achieves average accuracies of 87.1% and 93.6%, respectively. Controlled comparisons with standard RAG using the same evidence sources, together with ablation studies, further show that the gains arise not merely from external retrieval, but from the combined effects of schema-aware planning, memory augmentation, and iterative hybrid tool use.

cs.AI↗
arXiv2026

SCCL-DT: Semantic-Conflict-Aware Contrastive Learning with Dual-Rule Teacher Distillation for Zero-Shot Chest X-Ray Diagnosis

Contrastive vision-language pre-training supports zero-shot chest X-ray diagnosis, but standard objectives treat every off-diagonal image-report pair in a batch as a negative. In radiology, recurring findings across patients mean that instance mismatch need not imply semantic incompatibility, creating false-negative supervision that we term a semantic conflict. A training-trace audit shows that pair-observations selected by the online mask used during training are enriched for report-derived finding agreement, although this proxy analysis does not establish clinical equivalence. Fixed thresholds can become miscalibrated as similarity distributions evolve, while soft reweighting leaves residual repulsive gradients. We therefore propose Semantic-Conflict-Aware Contrastive Learning (SCCL), which tracks modality-specific similarity distributions with exponential-moving-average (EMA) statistics, derives adaptive thresholds, and hard-masks flagged candidates before softmax normalization. Inclusive (OR) and consensus (AND) rules define two SCCL teachers, whose bidirectional cross-modal rankings are consolidated through standard distillation into a single deployable student, SCCL-DT. Across five public chest X-ray benchmarks, SCCL-DT achieves a mean zero-shot AUC of $0.850$, compared with $0.837$ for the strongest evaluated baseline in our comparison. Code will be made publicly available soon.

cs.CV↗
arXiv2026

LoopRPT: Reinforcement Pre-Training for Looped Language Models

Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-of-thought (CoT) reasoning. However, existing reinforcement learning (RL) paradigms primarily target output tokens, creating a structural mismatch with looped architectures whose reasoning unfolds implicitly. In this work, we propose LoopRPT, a reinforcement pre-training framework tailored for LoopLMs. By reframing next-token prediction as a next-token reasoning task, LoopRPT assigns reinforcement signals directly to latent steps using an EMA teacher reference and noisy latent rollouts. This formulation enables RL to directly shape intermediate representations, compressing effective reasoning into fewer iterations. We instantiate LoopRPT on Ouro across multiple model scales. Results show that LoopRPT improves per-step representation quality and achieves Pareto dominance in accuracy-computation trade-offs. Large gains on hard tokens indicate that LoopRPT enhances early-stage reasoning rather than merely encouraging premature exits. Our findings highlight reinforcement pre-training as a principled paradigm for learning efficient latent reasoning in LoopLMs.

cs.CL↗
arXiv2026

EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds

Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation but not active experience organization, while outcome-based reinforcement learning promotes exploration yet underuses intermediate trajectory information. Therefore, we propose EvolvingBrain, which uses an adaptive skill system as the brain for agent self-evolution, consisting of two following components. Stratified Skill Self-Evolution: extracts Procedural Patterns and Problematic Patterns from model-specific trajectories, summarizes experience by generating skills, and evolves through multiple interactions with the open world, ultimately forming a Stratified Skill Architecture consisting of Domain, Plan, and Action/Trap Skills, which provides the agent with a multi-granularity knowledge repository and a novel evolution approach. State-Aware Adaptive Execution: summarizes long-horizon interactions into Structured Abstractive Memory and uses it to guide the State-Aware Skill Fusion Strategy, enabling the agent to utilize knowledge according to its current state. Experiments on VAB-Minecraft and DiscoveryWorld demonstrate that EvolvingBrain improves success rates on open-world tasks and exhibits generalization capabilities. Our code is provided in the supplementary materials and will be publicly released in the camera-ready version.

cs.AI↗
arXiv2026

Time-JEPA: Grounding Video World Models with Physical Time Series

Video world models learn how visual environments evolve by predicting missing or future content. However, physical-state information can provide useful context for predicting how environments evolve. Prior work incorporates this information through different mechanisms: domain-specific physical laws can complicate multi-domain training, while supervision on individual frames or frame pairs gives limited attention to how physical state evolves over time. We introduce Time-JEPA, which fine-tunes a video world model using low-dimensional, temporally aligned physical time series as auxiliary supervision. Physical time series connect video representations with physical state at corresponding temporal positions, encouraging the model to preserve how physical state evolves. In multiple domains including autonomous driving, robot manipulation, and human motion, Time-JEPA improves current-state decoding, multi-horizon future-state prediction, and kinematic reconstruction over matched video-fine-tuned controls across both condition and state-variable distribution shifts. Multi-domain training provides aggregate gains over matched single-domain training in all three tasks. External evaluations further suggest that time-series supervision improves sensitivity to physical anomalies and the representation of physical interactions, including in unseen scenarios. Aggregate gains on a second video backbone indicate that the method can be applied to more backbones. These results suggest that physical time series help video world models better understand the physical world.

cs.CV↗
arXiv2026

Proactive Physical AI through Continuous Observation and Timely Intervention

Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real-world settings, however, important tasks emerge from continuously changing world states before a person explicitly formulates them. We study this complementary problem as Proactive Physical AI, where an AI system continuously observes the physical world, identifies situations that warrant intervention, and initiates appropriate actions at the right time. We develop ProAgent, an end-to-end system built around two components: a Jev-style proactive timing model (dubbed StreamingJev) that determines whether and when intervention is needed, and a Response Model that generates responses for people or invokes available device capabilities for physical execution. To support learning at scale, we develop an automated data pipeline that converts videos into supervision for proactive timing. Experiments demonstrate strong proactive performance under both explicit objectives and standing roles, combining more accurate intervention timing with fewer false triggers than the source-backbone baseline. Real-robot evaluations further demonstrate ProAgent's ability to identify emerging needs and translate them into appropriate physical actions.

cs.CV↗
arXiv2026

Room-temperature ferroelectric p-wave magnet

P -wave magnets exhibit odd-parity spin polarization in momentum space while retaining zero net magnetization, offering a promising route toward nonrelativistic spintronics. Previously reported p-wave magnets based on combined time-reversal and lattice-translation symmetry, Tt, have thus far exhibited ordering temperatures below the liquid-nitrogen range. Here, we demonstrate a distinct route to a pristine p-wave magnetism that does not require T t symmetry, and instead is based on incommensurate spin-spiral order. Specifically, the cycloidal magnetic structure of the multiferroic BiFeO3 (BFO) intrinsically realizes p-wave magnetism coexisting with ferroelectric polarization at room temperature. Combining symmetry analysis, density-functional-theory calculations, and circular photogalvanic-effect (CPGE) measurements, we find a large nonrelativistic spin splitting of approximately 100 meV associated with the cycloidal order. The resulting p-wave spin polarization gives rise to a characteristic CPGE response whose sign can be reversibly switched by electrically controlling the cycloid chirality. Our results establish ferroelectric BiFeO3 as a room-temperature platform for electrically switchable p-wave magnetism and provide a route toward voltage-controlled spintronic devices based on parity-breaking magnetic order.

cond-mat.mtrl-sci↗
arXiv2026

Right Family, Wrong Skill: Evaluating Risk Exposure in Agent Skill Retrieval

A skill can match a task's topic while conflicting with its resource, procedure, or output requirements. We study this as same-capability risk-exposure retrieval and introduce SameCapRisk-Bench: 890 units and 1,314 query cases across five mechanisms and twelve conflict types. Each unit pairs a skill that meets a query requirement with a same-capability skill that violates it, with evidence for that distinction. The evaluation tests source-task requirements and two kinds of paired queries that reverse which skill fits: changing the requested evidence role or exact output interface while holding the skills fixed. To capture both retrieval success and conflicting exposure, Recall tracks helpful hits, harmful sibling rate (HSR) tracks exposure of the conflicting sibling, and CleanHit requires a helpful hit without that exposure. Four public skill retrievers expose conflicting siblings at HSR@3 of 0.737-0.881 on source-task contracts, compared with 0.099-0.12 on controlled source-role queries. Across the fixed mixture of 1,235 held-out queries, their Recall@3 is 0.903-0.944 and HSR@3 is 0.344-0.393. The conflicting sibling ranks first on 24.8-29.2% of source-task queries for these four retrievers. We also examine what the reranker receives: truncating long skills can remove the passages where the two skills' contracts differ. With the same BGE top-20 candidates, increasing the reranker's input budget from 512 to 4,096 tokens reduces source-task sibling-first errors by 8.27 pp, with an uncertain CleanHit gain.

cs.IR↗
arXiv2026

Strain-engineered long-distance supercurrent in an altermagnetic CrSb based-Josephson junction

Altermagnetic materials, characterized by vanishing net magnetization and momentum-dependent spin splitting, provide a unique platform for realizing unconventional superconducting states without the detrimental stray fields. Here, we demonstrate long-distance Josephson supercurrent transport through epitaxial thin films of the strained altermagnet CrSb. We observe a dissipationless supercurrent across a lateral spacer distance of 150 nm in a CrSb-based Josephson junction, exceeding the conventional spin-singlet coherence length by nearly two orders of magnitude. This supercurrent is further accompanied by a well-defined magnetic-field-induced quantum interference pattern, providing direct evidence for genuine Josephson coupling. Our results reveal altermagnets as a promising route toward generating long-distance supercurrents without macroscopic magnetic stray fields, opening new opportunities for superconducting spintronic applications.

cond-mat.supr-con↗
arXiv2026

SkillContrast: Difference-Guided Text Selection for Agent Skill Reranking

Similar agent skills can share instructions but differ in their conditions of use. Query-based text selection may retain shared instructions and omit these distinctions. We introduce SkillContrast, a training-free selector that compares retrieved skills and retains their differing text with local context for a pretrained reranker. On 1,235 requests from SameCapRisk-Bench, it yields 54-72 more clean hits (requests that retrieve a helpful skill without its marked risky sibling) than TF-IDF query selection at identical per-candidate input lengths, across 2 retrievers and 2 reranker sizes. Length-matched component replacements identify differing text as the main contributor in the primary setting, with smaller, mixed context effects. Relative to full skill bodies, SkillContrast uses 51.1-58.8% fewer model-input tokens, with 10-18 fewer clean hits at 0.6B and matching or higher observed clean-hit counts at 4B. Candidate-relative differences thus complement query relevance in selecting compact reranking inputs.

cs.IR↗
arXiv2026

Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle

Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about systems that do not yet exist, and the pace of both model evolution and hardware cadence means the analysis burden grows every quarter. The evidence behind each decision--hundreds of gigabytes of fresh simulation sweeps over novel design points--is by construction absent from any LLM's pretraining corpus, and there is no external literature to retrieve; naive "chat-with-your-data" approaches hallucinate exactly where correctness matters most. We present Coco (Copilot for Codesign), an agentic platform deployed with TPU architects that accelerates the co-design lifecycle of setting up experiments, sweeping simulators, and deriving insights. Coco is built as four layers: (i) a datastore that automatically registers every simulation sweep into a normalized relational schema, so agents ground every number in a SQL query rather than scraping heterogeneous files; (ii) a library of tools with typed APIs that agents compose without human orchestration; (iii) agents that encode recurring analysis workflows--most notably iso-execution analysis, which compares systems at matched execution configurations, including swept-but-dominated points off the Pareto frontier; and (iv) a platform UX whose navigation state doubles as agent context. We report early deployment experience toward a reduction in time-to-simulation and time-to-insight, and argue that co-design is a distinct agentic domain: its data must be retrieved rather than memorized, its workflows are recurring but context-dependent, and expert adoption hinges on UX that balances IDE-style control with interactive exploration.

cs.PL↗
arXiv2026

DeepJEPA: Scaling World Models from Within

World-model planners typically scale outward by rolling farther, sampling more trajectories, or optimizing longer, while assigning the same computation to every imagined transition. We show that making every transition uniformly deeper wastes computation and can degrade planning because useful refinement is concentrated at a small set of decision-critical events. We introduce DeepJEPA, a weight-tied joint-embedding predictive world model that treats transition depth as an inner test-time scaling axis and learns when another recurrent update is worth computing for each candidate and rollout step. Across five visual-control settings, DeepJEPA improves or matches the strongest fixed-depth planner while averaging only 1.00-1.26 updates per transition. Its additional computation concentrates at contact onset and sustained object interaction, where latent corrections can change which candidates enter the planner's elite set and which action is selected. Representation probes further show that improved planning does not require uniformly better object-state decodability. DeepJEPA therefore reframes world-model scaling as a problem of allocating internal computation where it can change the planner's decision: think deeper at decision-critical transitions instead of making every rollout uniformly deeper or longer.

cs.RO↗
arXiv2026

Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning

Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioning, and extendable partitioning. Fixed partitioning often breaks meaningful temporal boundaries, multi-scale partitioning may introduce redundant representations across scales, and extendable partitioning improves flexibility but still lacks an explicit mechanism for organizing semantic structure and modeling interactions among heterogeneous temporal patterns. To address these limitations, we propose SCPaT, a Transformer based framework built on semantic structured partitioning. SCPaT first decomposes input sequences into semantically consistent units through adaptive semantic unit generation, then constructs a dynamic semantic graph to model directed dependencies among these units and organize them into higher order semantic blocks. Based on these structured representations, an importance aware routing mechanism adaptively dispatches different semantic blocks to different experts for customized modeling. Extensive experiments on 12 real world datasets demonstrate the effectiveness of SCPaT.

cs.AI↗
arXiv2026

Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection

Unsupervised tabular anomaly detection is a critical task with applications spanning offensive language detection, network security, and quality control. Despite extensive research, existing unsupervised anomaly detection methods still face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types. To address these problems, we propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as a monotonic transformation of local density characteristics while maintaining linear time and constant space complexity w.r.t. the dataset size. ISER constructs ensemble representations where hypersphere radii encode local sparsity through a monotonic transformation of density: smaller radii correspond to dense regions while larger radii correspond to sparse regions. We introduce a novel similarity-based scoring method that measures pattern consistency by comparing ensemble representations against a theoretical anomaly reference pattern. Additionally, we enhance the performance of Isolation Forest by using ISER and adapting the scoring function to address axis-parallel bias and local anomaly detection limitations. Comprehensive experiments on 20 real-world datasets demonstrate ISER's competitive performance over 12 SOTA methods.

cs.LG↗