arXiv subjects

Yu Liu

Publications and source records attributed to Yu Liu.

At least 109 records · Page 6Linked to original sources

arXiv2026

ClueWeaver: Reward-Guided Dual-Agent Evidence Reasoning for Compact LLMs on Literary Long Narratives

Humanities and social science research requires close reading of long narrative materials such as novels, scripts, archives, and case reports, yet many users have limited access to costly proprietary long-context models. Compact, locally deployable language models are a practical alternative, but directly feeding them an entire long context remains costly, hard to inspect, and prone to missing sparse evidence. We present ClueWeaver, an evidence-aware dual-agent framework for long-narrative question answering with compact local models. A Finder identifies passages containing answer-critical clues through retrieval-guided segmentation, while an Interpreter derives the answer from the selected evidence, produces rationales with paragraph-ID citations, and applies an internal self-calibration pass for high-risk questions. Both agents are optimized with reward-guided reinforcement learning: Finder rewards emphasize evidence retention and faithful paragraph-ID references, and Interpreter rewards emphasize correctness, grounding, and concise explanations. This decomposition makes evidence selection and reasoning more inspectable than end-to-end prompting. Experiments across multiple long-context narrative question answering and claim verification settings show that ClueWeaver substantially improves local end-to-end language models while providing evidence coverage and paragraph-referenced reasoning traces. Code is available at https://github.com/Ameame1/ClueWeaver.

cs.CL↗

arXiv2026

Exact branch-transfer criterion for common-mode Thomson heat cancellation in thermoelectric couples

Thermoelectric p- and n-type legs are commonly paired by matching their Seebeck magnitudes, although a cooler responds to heat transported through its complete electrical and thermal network. We decompose the leg coefficients into differential thermopower $α=S_p-S_n$ and common thermopower $M=(S_p+S_n)/2$. In a connected steady-state scalar thermoelectric network, a temperature-independent co-shift applied to every electrically active segment is an exact terminal null. A temperature-dependent perturbation of the legs relative to fixed leads is instead physical. At fixed current and shared isothermal endpoints, its first-order cold-port response is the action of $Γ_m=T\,dm/dT$ on the difference between the p- and n-branch oriented collection measures. We prove that every continuous $Γ_m$ cancels if and only if these measures are equal. In the constant-property, linear-common-mode limit, matching $R_i/K_i^{\rm leg}$ is sufficient and does not require identical legs. One- and two-dimensional calculations confirm the analytic reductions within their stated domains. For split thermal pads, the analysis gives the exact array law $ΔQ_{c,Σ}=\sum_j C_jI_jΔT_{c,j}$ and, for series elements with isothermal hot pairs, $IΔV_Σ=-ΔQ_{c,Σ}$. A representative seven-pair model gives corresponding increments of 7.87 mW and $-2.80$ mV. Branch transfer and endpoint topology therefore provide distinct material-pairing and device-test criteria for common-mode Thomson heat.

cond-mat.mtrl-sci↗

arXiv2026

Towards World Models in Biomedical Research

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

cs.AI↗

arXiv2026

Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generation in external knowledge. However, most existing RAG frameworks assume a centralized retrieval corpus, which is often impractical in sensitive domains such as healthcare, where data are inherently distributed and raw content cannot be directly shared across institutions. Recent efforts on decentralized RAG primarily follow prompt-based paradigms that exchange raw, human-readable retrieved content, leading to substantial inference-time computational overhead and direct exposure of retrieved information. To address these limitations, we propose Representation-based Federated RAG (FedRepRAG), a decentralized RAG framework that keeps raw documents at their owning clients and exchanges only compact latent representations during cross-client retrieval. To integrate retrieved knowledge, we introduce a collaboratively trained projector that converts retrieval embeddings into generator-compatible representation tokens for a frozen LLM/VLM backbone. Experiments across decentralized visual question answering (VQA) and question answering (QA) benchmarks show that FedRepRAG consistently outperforms direct inference and local retrieval baselines while substantially reducing retrieval-context length and inference-time computational overhead compared with raw-context transfer. Further analyses confirm the importance of query-relevant retrieved representations and characterize the residual representation-level leakage associated with representation exchange. Overall, FedRepRAG provides an effective and efficient framework for federated RAG without transferring raw retrieved content.

cs.CL↗

arXiv2026

Intertwined Charge Stripes and Majorana Zero Modes in An Iron-Based Superconductor

In type-II superconductors, magnetic fields modulate the amplitude and phase of the superconducting order parameter, forming quantized vortices where superconductivity is locally suppressed and exotic bound states or competing electronic orders emerge. Using spectroscopic-imaging scanning tunneling microscopy on epitaxial Ba(Fe$_{0.94}Co$_{0.06})$_2$As$_2$ films, we discover an incommensurate charge-stripe order aligned with the Fe-Fe bond direction. These charge modulations become strongly enhanced at the vortex core and extend far into the vortex halo. Strikingly, the charge order modulates Andreev bound states of vortices at non-zero energies, producing abelian vortices with half-odd-integer level quantization and non-abelian vortices with integer-quantized core states that host a Majorana zero mode. The distinct vortex types are distinguished by the registry of their centers relative to the charge-stripe pattern and remain robust in ultrathin (2.5-unit-cell) films. Our findings reveal a density-wave-textured vortex matter and provide fresh insights into intertwined phenomena in iron-based superconductors.

cond-mat.supr-con↗

arXiv2026

CoinVE-200K: A Large-Scale High-Quality Dataset for Compositional Instruction-Guided Video Editing

The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supporting compositional instruction-guided video editing. In particular, multiple editing intents must be jointly understood and faithfully executed within the same video. To address this issue, we introduce CoinVE-200K, a large-scale, high-quality dataset for Compositional Instruction-Guided Video Editing. CoinVE-200K contains 1080p video-editing pairs of up to 201 frames, covering diverse compositional scenarios where each sample involves 2 to 5 atomic editing operations. The instructions target humans, objects, and backgrounds, and cover edit types such as addition, removal, modification, and stylization. All samples are built through a carefully designed generation and filtering pipeline to ensure instruction faithfulness, visual quality, temporal consistency, and compositional diversity. We also introduce CoinVE-Bench, a benchmark for compositional-instruction video editing across diverse subjects, operation types, and instruction complexities. Furthermore, we present CoinVE-Edit, a 22B compositional video editing model built upon Wan2.1-T2V-14B and Qwen3-VL-8B-Instruct. CoinVE-Edit disentangles region-aware attention for different editing instructions, enabling precise multi-region editing while preserving irrelevant content and temporal coherence. Experiments on CoinVE-Bench show that CoinVE-Edit achieves strong performance in instruction following, compositional editing accuracy, visual quality, and temporal consistency.

cs.CV↗

arXiv2026

MIRAGE: How Conversation State Shapes Historical Evidence Use in Multimodal Personal Agents

Multimodal large language model (MLLM) agents are increasingly used as personal assistants for long-running tasks. Their utility depends on continuity: agents must retrieve and use earlier evidence across dialogue, files, and workspace state. However, agents can generate plausible answers even when access to that history has degraded, causing outcome-only evaluation to overestimate true evidence use. We present MIRAGE (Multimodal Interaction Retrieval, Attribution, and Grounding Evaluation), a controlled study of historical evidence use under conversation-state variation in multimodal personal agents. MIRAGE holds evidence objects, questions, and scoring fixed while varying only conversation state, and evaluates whether an agent can determine answerability, recover the correct source, and answer from it. Across seven frontier and open-weight multimodal backbones, we find that: 1) pre-compaction depth and post-compaction continuation form distinct, non-monotonic failure regimes rather than a single degradation curve; 2) open-weight models rely heavily on context continuity and are reluctant to spontaneously switch to tool-mediated retrieval when provenance fails; and 3) retrieval pressure improves source attribution in deep pre-compaction states for tool-compliant models, but consistently regresses after compaction, where stored evidence has already degraded. These findings show that historical evidence use should be evaluated under state variation, rather than inferred from outcome-only correctness.

cs.CL↗

arXiv2026

Beyond FAIR Data: Instrument Traces for Active and Autonomous Scientific Experimentation

Artificial intelligence is turning scientific instruments into active systems in which observations can determine what is measured next. We argue that this creates an additional scientific record, the experimental trajectory, complementing sample provenance, acquired data and metadata, and analysis workflows. Instrument Traces should ultimately be synchronized with Sample Traces describing specimen evolution and Decision Traces recording human or algorithmic choices. We reconstruct an Instrument Trace retrospectively from a longitudinal AFM/PFM archive containing 118,000 timestamped events from 2023-2026. Conventional saved files reveal material campaigns, latent probe and calibration states, session-level complexity, experimental decision grammar, and composite tuning actions. They also expose what is missing, including unsaved tuning and failures, explicit sample/probe identities, complete timing, exogenous state, and decision rationale. We therefore propose a prospective trace architecture that records synchronized sample, instrument, and decision histories, enabling reproducible autonomy, predictive maintenance, counterfactual analysis, operator training, and transfer across facilities.

cs.DL↗

arXiv2026

AffineTok: Semantic Affine Consistency for Diffusion-Friendly Visual Tokenizer

Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be organized to facilitate denoising remains underexplored. In this paper, we define the semantic recovery objective: the denoising process should recover the semantic content of the clean image from noisy latent, and a good tokenizer should make it easier. Existing approaches train a projector to predict the semantics directly from the noisy latent. We argue that this predicts the average of clean-image semantics, whereas what really needs to be aligned is the semantics of averaged clean latents. More importantly, we demonstrate that the semantic recovery error orthogonally decomposes into the error of the optimal semantic prediction directly from the noisy latent and the error between these two predictions. We therefore identify their consistency as the missing requirement and call it Semantic Affine Consistency (SAC). To examine whether this overlooked requirement is closely related to downstream generation, we introduce M_SAC, a tokenizer-side proxy for SAC. Across the evaluated tokenizers and diffusion model scales, M_SAC closely tracks generation quality, reaching a Pearson correlation of 0.960 with SiT-XL gFID, thereby motivating SAC-guided tokenizer training. We then introduce AffineTok, which promotes SAC through two complementary, training-only components. Global Semantic Coordination Token (GSCT) coordinates the semantic organization of clean latents, keeping semantic averaging meaningful, while Posterior-Mean Semantic Alignment (PMSA) predicts posterior-mean latents from noisy inputs and supervises their semantics. On ImageNet 256, compared with the baseline, AffineTok reduces gFID by 26% at 20 epochs and, with continued training, achieves a new state-of-the-art gFID of 1.21 without classifier-free guidance and 1.10 with guidance.

cs.CV↗

arXiv2026

Omni-LiveAvatar: Minute-Level Real-Time Streaming Joint Audio-Video Avatar Generation

Joint audio-video generative models serve as foundation for immersive and interactive digital-human generation. Nevertheless, most existing models rely on bidirectional attention and multi-step denoising and can generate only short clips, making them unsuitable for real-time interaction over extended durations. We present Omni-LiveAvatar, the first framework for minute-level, real-time streaming joint audio-video avatar generation. Specifically, we propose (1) a progressive autoregressive distillation pipeline that transfers a large bidirectional joint audio-video diffusion model into a few-step autoregressive generator without auxiliary stabilization mechanisms; (2) a synchronized audio-video long-short-term memory that preserves global consistency under a bounded memory budget; and (3) a hierarchical rolling prompt planning strategy that enables coherent semantic evolution and seamless prompt transitions. Extensive experiments show that Omni-LiveAvatar generates high-quality, synchronized minute-level avatars in real time. In terms of speed, it achieves a 33$\times$ generation speedup over its teacher, LTX-2, on a single NVIDIA H200 GPU; in terms of generation quality, it outperforms accelerated baselines across visual quality, audio quality, cross-modal synchronization, and human fidelity. Our code is available at https://github.com/Aoko955/Omni-LiveAvatar.

cs.MM↗

arXiv2026

Broadband phonon-velocity suppression and a finite anisotropic crossover in twisted bilayer SnSe

Moiré superlattices reshape lattice dynamics without altering chemical composition, yet how crystal anisotropy modifies this control remains unclear. We combine density-functional-theory (DFT)-calibrated lattice-dynamical calculations with angle-matched untwisted controls to study puckered bilayer SnSe across seven commensurate twist angles ($3.18^\circ$--$8.77^\circ$). At 300 K, twisting suppresses the band-path heat-capacity-weighted mean-square group velocity to 2.6--8.4\% of the control values; the suppression spans a broad frequency range rather than a few soft branches. The velocity response crosses over between $4.78^\circ$ and $3.82^\circ$ into a regime where the relaxed stacking textures and frequency-resolved velocity profiles become self-similar, with the normalized mean-square velocity ratio spanning only 11.1\% of its mean across the three smallest angles---a finite anisotropic crossover, not a singular-angle condition. Direct DFT--MACE force-constant agreement ($r=0.996$), uniform $4\times4\times1$ stability scans, and acoustic-sum-rule and path-density tests support the trend. The equilibrium trend is defined by six structures after excluding one relaxation-sensitive case. These results extend phonon twistronics to low-symmetry layered materials and identify crystal anisotropy as a key determinant of finite-angle phonon crossover behavior.

cond-mat.mtrl-sci↗

arXiv2026

P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation

Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.g., CLIP/DINO), which frequently fail to exploit the intrinsic modality characteristics essential for high-fidelity fusion. To address these issues, we propose P2Fusion, a prior-guided distillation-based framework that reformulates IVIF via dual intrinsic prompts. Instead of imposing hard-coded penalties, we distill image-intrinsic priors, thermal saliency and spatial quality, into learnable dynamic regulators. Specifically, a Teach-to-Fuse mechanism provides dual-granularity progressive guidance, coupled with a Gated Dynamic Expert Recalibration (GDER) module for decoupled feature refinement. This design enables the network to adaptively mediate modal competition through expert specialization. Extensive experiments demonstrate that P2Fusion achieves state-of-the-art performance across five mainstream datasets. Notably, our framework demonstrates consistent performance advantages in fusion quality, achieving state-of-the-art results in 14 out of 20 key evaluation metrics across 5 benchmarks. Furthermore, it effectively contributes to the robustness of downstream perception, such as +3.2% mAP on MSRS, +0.5% mAP on M3FD and +0.9% mAP on DroneVehicle for object detection. Our code will be available at https://github.com/YiShi99/P2Fusion

cs.CV↗

arXiv2026

Multitask Scanning Probe Microscopy

Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials. Its increasing use for wafer-scale characterization and combinatorial materials exploration creates a need to distribute measurements efficiently across large spatial domains. This is particularly important when available modalities differ in acquisition time and potential for tip and sample damage, making exhaustive multimodal mapping over spatial grids impractical. Here, we demonstrate multitask scanning probe microscopy, a live, closed-loop workflow in which a multitask Gaussian process learns spatial and cross-modal relationships and autonomously selects both the next measurement location and the next experimental protocol. The approach is implemented on an automated large-sample atomic force microscope and demonstrated on a composition-spread AlScN wafer using tapping-mode and Dual AC Resonance Tracking (DART) measurements. Paired initial measurements establish the relation between the tasks, after which noncoincident measurements are used to update both response landscapes. The resulting workflow extends active learning in scanning probe microscopy from spatial sampling to autonomous allocation of measurement modalities and provides a basis for combining rapid, weakly perturbative imaging with slower contact, electrical, electromechanical, magnetic, or spectroscopic measurements.

cond-mat.mtrl-sci↗

arXiv2026

EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization

Visual query localization (VQL) aims to retrieve and re-localize a queried object in egocentric videos, yet remains challenging when object boundaries are ambiguous and global context cannot effectively guide fine-grained localization. Human vision handles such ambiguity through a hierarchical process: it rapidly screens foreground candidates, selectively attends to the target despite distractors, refines perception via feedback between global context and local detail, and, when a single view is unreliable, integrates evidence across viewpoints according to its credibility. Inspired by these competencies, we propose \textbf{EgoHieraLoc}, a unified framework for VQL-2D and VQL-3D. A Discriminative Parsing Module first extracts foreground-aware query representations using segmentation priors; a Query-Aware Module then performs robust target localization through discriminative correlation filtering with deformable modeling; and a Regional Adaptation Module feeds multi-scale context back into local regions to recover precise object boundaries. To extend this perceptual hierarchy to 3D localization, we introduce Geometric-Semantic Joint Confidence (GSJC), which multiplicatively couples segmentation confidence with local depth consistency, multi-view back-projection consistency, and triangulation-baseline quality, so that a viewpoint contributes to the 3D estimate only when it is credible both semantically and geometrically. Extensive experiments demonstrate state-of-the-art performance on both VQL-2D and -3D benchmarks.

cs.CV↗

arXiv2026

Input convex neural networks as surrogates in mathematical optimisation

Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research. The prevailing approach uses feedforward neural networks (FNNs) with ReLU activations, whose piecewise-linear structure admits an exact but computationally intensive mixed-integer programming (MIP) reformulation as the networks grow. We advocate input convex neural networks (ICNNs) as structurally superior surrogates when the underlying response is approximately convex or concave. The convex architecture offers two computational advantages. First, the ICNN-MIP formulation tends to yield a tighter linear programming (LP) relaxation than its FNN-MIP counterpart, with no integrality gap in favourable instances. Second, ICNNs uniquely admit an LP-based reformulation via epigraph representations of ReLU activations, though this embedding is not always exact. When it is not, we exploit the properties of ICNNs to construct the strongest continuous relaxation over box domains, namely, the convex hull of the ICNN's graph, bounded below by the epigraph and above by the concave envelope; this construction is tractable under input convexity but hard for general ReLU networks. On this basis, we develop a branch-and-bound algorithm that builds this relaxation at each node, branches directly on input variables rather than intermediate variables as in MIP reformulations, and terminates at the root node whenever the epigraph embedding is valid. Case studies on humanitarian food aid, oil well routing, and wine blending show that ICNN surrogates match FNN accuracy and deliver gains in solve time and scalability, supporting ICNN as the default surrogate when the underlying function is convex, concave, or well-approximated as such.

math.OC↗

arXiv2026

Valuation Model of Chinese Convertible Bonds Based on Monte Carlo Simulation

We tackle the problem of pricing Chinese convertible bonds(CCBs) using Monte Carlo simulation and dynamic programming. At each exercise time, we use the state variables of the underlying stock to regress the continuation value, and apply standard backward induction to get the coefficients from the current time to time zero. This process ultimately determines the CCB price. We then apply this pricing method in simulations and evaluate an underpriced strategy: taking long positions in the 10 most undervalued CCBs and rebalancing daily. The results show that this strategy significantly outperforms the benchmark double-low strategy. In practice, CCB issuers often use a downward adjustment clause to prevent financial distress when a put provision is triggered. Therefore, we model the downward adjustment clause as a probabilistic event that triggers the put provision, thereby integrating it with the put provision in a straightforward manner.

q-fin.PR↗

arXiv2026

Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction

Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and supporting decision-making. Existing studies leverage knowledge graphs (KG) to model heterogeneous urban data, and further apply graph representation learning methods for socioeconomic prediction. However, these approaches heavily rely on heuristic ideas and expertise to extract task-relevant knowledge from diverse data, which may not be optimal for specific tasks. Additionally, they tend to overlook the inherent relationships between different indicators, limiting the prediction accuracy. Motivated by the remarkable abilities of large language models (LLMs), in this work, we propose a synergistic framework of LLM agents and KG, which integrates the reasoning and representation learning on KG with LLM agents. We first construct an urban knowledge graph (UrbanKG) to model multi-sourced urban data and finetune an embedding language model to generate embeddings for KG entities with semantic information. Then we leverage the reasoning power of LLM to identify relevant meta-paths in the UrbanKG for each type of socioeconomic prediction task, and design a semantic-guided attention module for knowledge fusion with meta-paths. Moreover, we introduce a cross-task communication mechanism to further enhance performance by enabling knowledge sharing across tasks at both LLM agent and KG levels. On the one hand, the LLM agents for different tasks collaborate to generate more diverse and comprehensive meta-paths. On the other hand, the embeddings from different tasks are adaptively merged. Experiments on two datasets demonstrate the effectiveness of the synergistic design between LLM and KG, providing insights for information sharing across socioeconomic prediction tasks.

cs.CL↗

arXiv2026

Mitigating Gradient Pathology in PINNs through Aligned Constraint

While Physics-Informed Neural Networks (PINNs) are powerful for solving Partial Differential Equations (PDEs), their training is often paralyzed by gradient pathology. The gradients from the PDE residuals and boundary constraints oppose each other, trapping the model in local minima. Current solutions, such as adaptive weighting or hard constraints, either fail to fundamentally resolve this ill-conditioning or are limited to simple geometries. In this study, we systematically analyze the possible causes of this gradient pathology from the perspectives of loss landscapes and optimization dynamics. Based on the obtained conclusion, we propose Constraint-Aligned loss with Manifold Lifting (CAML). By reformulating all zeroth-order terms into aligned constraints, our method effectively mitigates gradient conflicts. In addition, we introduce a delay factor to help the optimizer skip the high-curvature area. Experiments demonstrate that our CAML significantly enhances numerical stability and efficiency in highly complex PINN problems. Our code is open-sourced on https://github.com/YichenLuo-0/CAML.

cs.LG↗