arXiv subjects

Liang Li

Publications and source records attributed to Liang Li.

At least 73 records · Page 4Linked to original sources

arXiv2026

An Intertwined Short and Long GRB with 4-minute Separation

Gamma-ray bursts (GRBs), the most energetic transients in the Universe, are traditionally classified into long-duration ($T_{90}>2$ s) and short-duration ($T_{90}<2$ s) events, associated with the core collapse of massive stars (Type II) and the merger of compact binary systems (Type I), respectively. The two classes exhibit distinct observational properties that serve as key diagnostic criteria for classification. Here we report GRB 160425A, a peculiar event comprising two sub-bursts separated by four minutes: a short-duration burst ($G_1$) and a long-duration burst ($G_2$). Nearly all standard prompt-emission diagnostics, including pulse morphology, duration, hardness ratio, minimum variability timescale, spectral properties, and established empirical correlations, consistently categorize $G_1$ as a short-like (Type I, merger-origin) and $G_2$ as a long-like (Type II, collapsar-origin) GRB. The coexistence of merger and collapsar signatures in a single event challenges existing progenitor frameworks and calls for a re-evaluation of GRB classification schemes and progenitor scenarios.

astro-ph.HE↗

arXiv2026

GeoTikzBridge: Advancing Multimodal Code Generation for Geometric Perception and Reasoning

Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities. However, they struggle to perceive fine-grained geometric structures, constraining their ability of geometric understanding and visual reasoning. To address this, we propose GeoTikzBridge, a framework that enhances local geometric perception and visual reasoning through tikz-based code generation. Within this framework, we build two models supported by two complementary datasets. The GeoTikzBridge-Base model is trained on GeoTikz-Base dataset, the largest image-to-tikz dataset to date with 2.5M pairs (16 $\times$ larger than existing open-sourced datasets). This process is achieved via iterative data expansion and a localized geometric transformation strategy. Subsequently, GeoTikzBridge-Instruct is fine-tuned on GeoTikz-Instruct dataset which is the first instruction-augmented tikz dataset supporting visual reasoning. Extensive experimental results demonstrate that our models achieve state-of-the-art performance among open-sourced MLLMs. Furthermore, GeoTikzBridge models can serve as plug-and-play reasoning modules for any MLLM(LLM), enhancing reasoning performance in geometric problem-solving. Datasets and codes are publicly available at: https://github.com/sjy-1995/GeoTikzBridge.

cs.CV↗

arXiv2026

HAM: A Training-Free Style Transfer Approach via Heterogeneous Attention Modulation for Diffusion Models

Diffusion models have demonstrated remarkable performance in image generation, particularly within the domain of style transfer. Prevailing style transfer approaches typically leverage pre-trained diffusion models' robust feature extraction capabilities alongside external modular control pathways to explicitly impose style guidance signals. However, these methods often fail to capture complex style reference or retain the identity of user-provided content images, thus falling into the trap of style-content balance. Thus, we propose a training-free style transfer approach via $\textbf{h}$eterogeneous $\textbf{a}$ttention $\textbf{m}$odulation ($\textbf{HAM}$) to protect identity information during image/text-guided style reference transfer, thereby addressing the style-content trade-off challenge. Specifically, we first introduces style noise initialization to initialize latent noise for diffusion. Then, during the diffusion process, it innovatively employs HAM for different attention mechanisms, including Global Attention Regulation (GAR) and Local Attention Transplantation (LAT), which better preserving the details of the content image while capturing complex style references. Our approach is validated through a series of qualitative and quantitative experiments, achieving state-of-the-art performance on multiple quantitative metrics.

cs.CV↗

arXiv2026

Few-Shot Generative Model Adaption via Identity Injection and Preservation

Training generative models with limited data presents severe challenges of mode collapse. A common approach is to adapt a large pretrained generative model upon a target domain with very few samples (fewer than 10), known as few-shot generative model adaptation. However, existing methods often suffer from forgetting source domain identity knowledge during adaptation, which degrades the quality of generated images in the target domain. To address this, we propose Identity Injection and Preservation (I$^2$P), which leverages identity injection and consistency alignment to preserve the source identity knowledge. Specifically, we first introduce an identity injection module that integrates source domain identity knowledge into the target domain's latent space, ensuring the generated images retain key identity knowledge of the source domain. Second, we design an identity substitution module, which includes a style-content decoupler and a reconstruction modulator, to further enhance source domain identity preservation. We enforce identity consistency constraints by aligning features from identity substitution, thereby preserving identity knowledge. Both quantitative and qualitative experiments show that our method achieves substantial improvements over state-of-the-art methods on multiple public datasets and 5 metrics.

cs.CV↗

arXiv2026

I/O Optimizations for Graph-Based Disk-Resident Approximate Nearest Neighbor Search: A Design Space Exploration

Approximate nearest neighbor (ANN) search on SSD-backed indexes is increasingly I/O-bound (I/O accounts for 70--90\% of query latency). We present an I/O-first framework for disk-based ANN that organizes techniques along three dimensions: memory layout, disk layout, and search algorithm. We introduce a page-level complexity model that explains how page locality and path length jointly determine page reads, and we validate the model empirically. Using consistent implementations across four public datasets, we quantify both single-factor effects and cross-dimensional synergies. We find that (i) memory-resident navigation and dynamic width provide the strongest standalone gains; (ii) page shuffle and page search are weak alone but complementary together; and (iii) a principled composition, OctopusANN, substantially reduces I/O and achieves 4.1--37.9\% higher throughput than the state-of-the-art system Starling and 87.5--149.5\% higher throughput than DiskANN at matched Recall@10=90\%. Finally, we distill actionable guidelines for selecting storage-centric or hybrid designs across diverse concurrency levels and accuracy constraints, advocating systematic composition rather than isolated tweaks when pushing the performance frontier of disk-based ANN.

cs.DB↗

arXiv2026

Surrogate Modeling with Low-Rank Function Representation for Electromagnetic Simulation

High-fidelity electromagnetic (EM) simulations are indispensable for the design of microwave and wave devices, yet repeated full-wave evaluations over high-dimensional design spaces are often computationally prohibitive. While neural surrogates can amortize this cost, learning high-dimensional EM response mappings remains difficult under limited simulation budgets due to strong and heterogeneous parameter couplings. In this work, we introduce low-rank tensor function representations as a principled surrogate modeling paradigm for EM problems and provide a systematic study of representative low-rank formats, including Tucker-style low-rank tensor function representation (LRTFR) as well as neural functional tensor-train (TT) and tensor-ring (TR) baselines. Building on these insights, we propose a pairwise low-rank tensor network (PLRNet) that uses learnable pairwise interaction factors over compact coordinate-wise embeddings. Experiments on representative EM surrogate tasks demonstrate that the proposed framework achieves a more favorable overall trade-off between accuracy, robustness, and parameter efficiency, with stable optimization in high-dimensional regimes.

cs.CE↗

arXiv2026

Mitigating Overthinking in Large Reasoning Language Models via Reasoning Path Deviation Monitoring

Large Reasoning Language Models (LRLMs) demonstrate impressive capabilities on complex tasks by utilizing long Chain-of-Thought reasoning. However, they are prone to overthinking, which generates redundant reasoning steps that degrade both performance and efficiency. Recently, early-exit strategies are proposed to mitigate overthinking by dynamically and adaptively terminating redundant reasoning. However, current early-exit methods either introduce extra training overhead by relying on proxy models or limit inference throughput due to the frequent content switching between reasoning and generating probing answers. Moreover, most early-exit methods harm LRLMs performance due to over-truncation. Our insight stems from an observation: overthinking often causes LRLMs to deviate from the correct reasoning path, which is frequently accompanied by high-entropy transition tokens. Given this, we propose an early-exit method deeply coupled with the native reasoning process, which leverages the path deviation index as a dedicated monitoring metric for the frequent occurrence of high-entropy transition tokens to dynamically detect and terminate overthinking trajectories. We conduct experiments across multiple benchmarks using LRLMs of different types and scales, and the results indicate that our method delivers the largest performance improvement over vanilla CoT compared to existing early-exit methods.

cs.CL↗

arXiv2026

Microscopic Investigation of Fusion and Quasifission Dynamics

We introduce the application of Time-Dependent Hartree-Fock (TDHF) theory to two key aspects of heavy-ion reaction dynamics for producing superheavy elements: fusion and quasi-fission (QF). For fusion reactions $^{48}$Ca+$^{238}$U, the capture cross sections, fusion probabilities, and evaporation-residue cross sections are calculated using the inputs from TDHF simulations, and the results are found to be in reasonable agreement with available experimental data. For the QF process of $^{48}$Ca+$^{249}$Bk, we show the distribution of the fragments and investigate the impact of the tensor force, significantly enhancing the role of spherical shell effects.

nucl-th↗

arXiv2026

Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models

Recently, unified multimodal models (UMMs) have made remarkable progress in integrating visual understanding and generation, demonstrating strong potential for complex text-to-image (T2I) tasks. Despite their theoretical promise, a persistent capability gap exists: UMMs typically exhibit superior visual understanding but comparatively weaker generative capabilities. This discrepancy arises largely from the intrinsic decoupling between the understanding and generation processes. While a UMM can accurately interpret fine-grained visual details, it often struggles to produce semantically coherent images from complex textual prompts. To address this challenge, we explore UMMs' internal understanding capability to enhance generation quality. We propose a token-level intrinsic text-image alignment reward mechanism, GvU, enabling the UMM to act simultaneously as teacher and student: it evaluates its own outputs using the understanding branch to guide the generations accordingly. Building upon this, we design a self-supervised reinforcement learning framework, allowing UMMs to iteratively improve their generation quality through understanding-based intrinsic reward signals--without reliance on external supervision. Experimental results show that our method substantially boosts UMMs' generation, which in turn strengthens their fine-grained visual understanding, narrowing the capability gap between UMMs' visual understanding and generation.

cs.CV↗

arXiv2026

Pulse-resolved Classification and Characteristics of Long-duration GRBs with \emph{Swift}-BAT Data.II. Main Burst versus Extended Emission

Long gamma-ray bursts (GRBs) frequently exhibit complex prompt emission structures with multiple temporally distinct episodes, such as a main emission (ME) phase followed by a weak extended emission (EE) tail. Whether these subcomponents from a common physical origin with similar classification properties, or instead represent fundamentally different emission mechanisms within a single event, remains an open question. Here, we present a systematic, pulse-resolved analysis of 22 \emph{Swift}/BAT long-duration GRBs, each exhibiting a well-separated, bright ME ($G_1$) followed by a fainter EE ($G_2$) after a background-consistent quiescent gap. For each component, we independently measure standard classification diagnostics, including duration ($T_{90}$), spectral hardness ratio (HR), minimum variability timescale (MVT), and spectral lag. We then compare these properties between the ME and EE within individual bursts. We find that the EE is systematically softer (lower HR in 19 of 22 events), smoother (longer MVT in 17 of 22 events), and more diverse in spectral lag than the ME. However, both components still occupy the long-GRB track in the traditional duration-hardness and duration-MVT planes, indicating a common Type~II (collapsar) origin. These results suggest that the EE in long GRBs represents a physically distinct regime of the central engine, characterized by a lower luminosity, longer emission timescales, and evolved spectral properties, rather than a simple continuation of the main burst. This picture is consistent with late-time fallback accretion onto a black hole or proto-magnetar spin-down.

astro-ph.HE↗

arXiv2026

Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering

Question Answering over Temporal Knowledge Graphs (TKGQA) has attracted growing interest for handling time-sensitive queries. However, existing methods still struggle with: 1) weak incorporation of temporal constraints in question representation, causing biased reasoning; 2) limited ability to perform explicit multi-hop reasoning; and 3) suboptimal fusion of language and graph representations. We propose a novel framework with temporal-aware question encoding, multi-hop graph reasoning, and multi-view heterogeneous information fusion. Specifically, our approach introduces: 1) a constraint-aware question representation that combines semantic cues from language models with temporal entity dynamics; 2) a temporal-aware graph neural network for explicit multi-hop reasoning via time-aware message passing; and 3) a multi-view attention mechanism for more effective fusion of question context and temporal graph knowledge. Experiments on multiple TKGQA benchmarks demonstrate consistent improvements over multiple baselines.

cs.CL↗

arXiv2026

Modeling the Multi-Wavelength Afterglow of Short Gamma-Ray Bursts with a Plateau Phase

Short gamma-ray bursts (GRBs) exhibiting a plateau phase provide valuable insights into the post-merger activity of their central engines. Although the physical origin of the plateau remains uncertain, the magnetar energy injection model offers a compelling explanation that reproduces the observed temporal and luminosity features. However, previous studies relying solely on X-ray data have suffered from strong parameter degeneracies when constraining the magnetar parameters. Here we perform broadband afterglow modeling on seven short GRBs with plateau features by combining X-ray, optical, and radio observations within the framework of the magnetar energy injection model. Key model parameters are derived by using the Markov Chain Monte Carlo method. It is found that the energy injection substantially modifies the afterglow dynamics in most events. Compared with X-ray-only analyses, our broadband modeling yields systematically a lower magnetic field strength and a shorter spin period for the central magnetar, corresponding to a higher injection luminosity. The study clearly shows that incorporating multi-wavelength data effectively alleviates the degeneracy between the magnetar parameters and X-ray radiative efficiency. In addition, the distribution of our short GRBs differs markedly from long GRBs when they are plotted on the initial Lorentz factor versus gamma-ray energy plane. This offset, consistent with the observed harder spectrum of short GRBs, may serve as a useful diagnostic for investigating the progenitor as larger samples are available.

astro-ph.HE↗

arXiv2026

LVK S241125n: Massive Binary Black Hole Merger Produces Gamma Ray Burst in Active Galactic Nucleus Disk

Recently, the gravitational-wave (GW) event S241125n, detected by LIGO/Virgo/KAGRA (LVK), has been reported to coincide with a candidate detected by Swift-BAT/GUANO and an X-ray candidate found by FXT onboard of Einstein Probe (EP) and confirmed by Swift-XRT. We estimate that the joint false alarm rate (FAR) for the three candidates is 1 / 30 yr and that the corresponding false alarm probability (FAP) is $\mathrm{FAP}_{\rm triple} = 0.037$ ($1.8 σ$). The coincidence between the GW and GRB could be an interesting test of their origin and open attractive opportunities for multi-messenger observations, if they are actually associated. Motivated by this, we propose a theoretical model in which a binary black hole (BBH) merger occurs within an active galactic nucleus (AGN) disk. The typically massive and significantly kicked merger remnant accretes disk material at hyper-Eddington rates, and the resulting jet could lead to the GRB associated with the GW event. As the jet interacts with the gas in the AGN disk, the shock breakout produces a Comptonized spectrum, consistent with an unusually soft photon index of the GRB prompt emission observed by Swift-BAT following LVK S241125n. Meanwhile, strong absorption and dust extinction of the afterglow by the high column density typical of AGN disks could explain the unusually hard spectrum observed in the X-ray band by EP, as well as the non-detection of an optical counterpart. Our model is predictive, and we highlight the importance of further constraining the orbital eccentricity of the merger and conducting deep-field observations of the host galaxy to test our explanation.

astro-ph.HE↗

arXiv2026

EXaMCaP: Subset Selection with Entropy Gain Maximization for Probing Capability Gains of Large Chart Understanding Training Sets

Recent works focus on synthesizing Chart Understanding (ChartU) training sets to inject advanced chart knowledge into Multimodal Large Language Models (MLLMs), where the sufficiency of the knowledge is typically verified by quantifying capability gains via the fine-tune-then-evaluate paradigm. However, full-set fine-tuning MLLMs to assess such gains incurs significant time costs, hindering the iterative refinement cycles of the ChartU dataset. Reviewing the ChartU dataset synthesis and data selection domains, we find that subsets can potentially probe the MLLMs' capability gains from full-set fine-tuning. Given that data diversity is vital for boosting MLLMs' performance and entropy reflects this feature, we propose EXaMCaP, which uses entropy gain maximization to select a subset. To obtain a high-diversity subset, EXaMCaP chooses the maximum-entropy subset from the large ChartU dataset. As enumerating all possible subsets is impractical, EXaMCaP iteratively selects samples to maximize the gain in set entropy relative to the current set, approximating the maximum-entropy subset of the full dataset. Experiments show that EXaMCaP outperforms baselines in probing the capability gains of the ChartU training set, along with its strong effectiveness across diverse subset sizes and compatibility with various MLLM architectures.

cs.LG↗

arXiv2026

LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization

We present LongVPO, a novel two-stage Direct Preference Optimization framework that enables short-context vision-language models to robustly understand ultra-long videos without any long-video annotations. In Stage 1, we synthesize preference triples by anchoring questions to individual short clips, interleaving them with distractors, and applying visual-similarity and question-specificity filtering to mitigate positional bias and ensure unambiguous supervision. We also approximate the reference model's scoring over long contexts by evaluating only the anchor clip, reducing computational overhead. In Stage 2, we employ a recursive captioning pipeline on long videos to generate scene-level metadata, then use a large language model to craft multi-segment reasoning queries and dispreferred responses, aligning the model's preferences through multi-segment reasoning tasks. With only 16K synthetic examples and no costly human labels, LongVPO outperforms the state-of-the-art open-source models on multiple long-video benchmarks, while maintaining strong short-video performance (e.g., on MVBench), offering a scalable paradigm for efficient long-form video understanding.

cs.CV↗

arXiv2026

Holographic Entanglement Propagation Through Wormholes

We study how energy and quantum entanglement are transferred when two identical CFTs are entangled locally. This is probed by considering a local operator insertion in one of the CFTs. When the CFTs have holographic duals via the AdS/CFT correspondence, the transfer happens through an AdS wormhole that allows signal propagation even beyond the horizon from one AdS boundary to the other; we demonstrate this in explicit CFT calculations. We argue that this transmission is possible because the insertion of a local operator is not a unitary process but a regularized version of projection measurement, and that this is interpreted as quantum teleportation. We also find that this leads to a phenomenon opposite to scrambling, where mutual information, instead of being suppressed, gets enhanced by the insertion of a local operator excitation.

hep-th↗

arXiv2026

Pulse-resolved Classification and Characteristics of Long-duration GRBs with \emph{Swift}-BAT Data.I. Precursors versus Main Bursts

We present a systematic pulse-by-pulse analysis of 22 long-duration GRBs observed by \emph{Swift}, each exhibiting a well-separated precursor before the main burst. We compare duration, spectral hardness ratio, minimum variability timescale (MVT), and spectral lag between these components. Both precursors and main bursts have durations and hardness broadly consistent with Type II GRBs. However, precursors show longer MVTs (by factors of 3-10) and diverse lags with near-zero median values, while main bursts display variable MVTs and positive lags. These differences suggest precursors may originate from distinct dissipation conditions, possibly due to cocoon shock breakout or early magnetically dominated outflows. Despite temporal differences, both episodes are consistent with a single collapsar origin, providing no evidence for dual-progenitor events. Our findings support pulse-resolved classification and show that precursors offer critical insights into jet formation and pre-burst activity.

astro-ph.HE↗

arXiv2026

DC-VLAQ: Query-Residual Aggregation for Robust Visual Place Recognition

One of the central challenges in visual place recognition (VPR) is learning a robust global representation that remains discriminative under large viewpoint changes, illumination variations, and severe domain shifts. While visual foundation models (VFMs) provide strong local features, most existing methods rely on a single model, overlooking the complementary cues offered by different VFMs. However, exploiting such complementary information inevitably alters token distributions, which challenges the stability of existing query-based global aggregation schemes. To address these challenges, we propose DC-VLAQ, a representation-centric framework that integrates the fusion of complementary VFMs and robust global aggregation. Specifically, we first introduce a lightweight residual-guided complementary fusion that anchors representations in the DINOv2 feature space while injecting complementary semantics from CLIP through a learned residual correction. In addition, we propose the Vector of Local Aggregated Queries (VLAQ), a query--residual global aggregation scheme that encodes local tokens by their residual responses to learnable queries, resulting in improved stability and the preservation of fine-grained discriminative cues. Extensive experiments on standard VPR benchmarks, including Pitts30k, Tokyo24/7, MSLS, Nordland, SPED, and AmsterTime, demonstrate that DC-VLAQ consistently outperforms strong baselines and achieves state-of-the-art performance, particularly under challenging domain shifts and long-term appearance changes.

cs.CV↗