arXiv subjects

Jiacheng Tang

Publications and source records attributed to Jiacheng Tang.

At least 19 recordsLinked to original sources

arXiv2026

Categorical Bass-Serre Theory for Tree Quotients

We develop a categorical framework for Bass-Serre theory, which recovers parts of classical and profinite Bass-Serre theories for tree quotients. More precisely, we show that in what we call a Bass-Serre category, a group object acting on a graph satisfying some assumptions can be recovered as the fundamental group of the associated graph of groups acting on the standard graph. Examples of Bass-Serre categories include the category of sets, the category of profinite spaces, and any Grothendieck topos.

math.CT↗

arXiv2026

CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention

Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads to causal confusion, where models exploit dataset biases as shortcuts, critically harming their reliability and safety in complex scenarios. To address this, we introduce CausalVAD, a de-confounding training framework that leverages causal intervention. At its core, we design the sparse causal intervention scheme (SCIS), a lightweight, plug-and-play module to instantiate the backdoor adjustment theory in neural networks. SCIS constructs a dictionary of prototypes representing latent driving contexts. It then uses this dictionary to intervene on the model's sparse vectorized queries. This step actively eliminates spurious associations induced by confounders, thereby eliminating spurious factors from the representations for downstream tasks. Extensive experiments on benchmarks like nuScenes show CausalVAD achieves state-of-the-art planning accuracy and safety. Furthermore, our method demonstrates superior robustness against both data bias and noisy scenarios configured to induce causal confusion.

cs.CV↗

arXiv2026

DynamicVGGT: Learning Dynamic Point Maps for 4D Scene Reconstruction in Autonomous Driving

Dynamic scene reconstruction in autonomous driving remains a fundamental challenge due to significant temporal variations, moving objects, and complex scene dynamics. Existing feed-forward 3D models have demonstrated strong performance in static reconstruction but still struggle to capture dynamic motion. To address these limitations, we propose DynamicVGGT, a unified feed-forward framework that extends VGGT from static 3D perception to dynamic 4D reconstruction. Our goal is to model point motion within feed-forward 3D models in a dynamic and temporally coherent manner. To this end, we jointly predict the current and future point maps within a shared reference coordinate system, allowing the model to implicitly learn dynamic point representations through temporal correspondence. To efficiently capture temporal dependencies, we introduce a Motion-aware Temporal Attention (MTA) module that learns motion continuity. Furthermore, we design a Dynamic 3D Gaussian Splatting Head that explicitly models point motion by predicting Gaussian velocities using learnable motion tokens under scene flow supervision. It refines dynamic geometry through continuous 3D Gaussian optimization. Extensive experiments on autonomous driving datasets demonstrate that DynamicVGGT significantly outperforms existing methods in reconstruction accuracy, achieving robust feed-forward 4D dynamic scene reconstruction under complex driving scenarios.

cs.CV↗

arXiv2026

Vision-Language Feature Alignment for Road Anomaly Segmentation

Safe autonomous systems in complex environments require robust road anomaly segmentation to identify unknown obstacles. However, existing approaches often rely on pixel-level statistics to determine whether a region appears anomalous. This reliance leads to high false-positive rates on semantically normal background regions such as sky or vegetation, and poor recall of true Out-of-distribution (OOD) instances, thereby posing safety risks for robotic perception and decision-making. To address these challenges, we propose VL-Anomaly, a vision-language anomaly segmentation framework that incorporates semantic priors from pre-trained Vision-Language Models (VLMs). Specifically, we design a prompt learning-driven alignment module that adapts Mask2Forme's visual features to CLIP text embeddings of known categories, effectively suppressing spurious anomaly responses in background regions. At inference time, we further introduce a multi-source inference strategy that integrates text-guided similarity, CLIP-based image-text similarity and detector confidence, enabling more reliable anomaly prediction by leveraging complementary information sources. Extensive experiments demonstrate that VL-Anomaly achieves state-of-the-art performance on benchmark datasets including RoadAnomaly, SMIYC and Fishyscapes.Code is released on https://github.com/NickHezhuolin/VL-aligner-Road-anomaly-segment.

cs.CV↗

arXiv2026

Profinite Cosheaves Valued in Pro-regular Categories

We prove that the category of profinite cosheaves valued in a pro-regular category (satisfying mild assumptions) is itself canonically a pro-completion. As a corollary, we extend Wilkes's cosheaf-bundle equivalence from profinite modules to profinite groups.

math.CT↗

arXiv2025

Decoupling Scene Perception and Ego Status: A Multi-Context Fusion Approach for Enhanced Generalization in End-to-End Autonomous Driving

Modular design of planning-oriented autonomous driving has markedly advanced end-to-end systems. However, existing architectures remain constrained by an over-reliance on ego status, hindering generalization and robust scene understanding. We identify the root cause as an inherent design within these architectures that allows ego status to be easily leveraged as a shortcut. Specifically, the premature fusion of ego status in the upstream BEV encoder allows an information flow from this strong prior to dominate the downstream planning module. To address this challenge, we propose AdaptiveAD, an architectural-level solution based on a multi-context fusion strategy. Its core is a dual-branch structure that explicitly decouples scene perception and ego status. One branch performs scene-driven reasoning based on multi-task learning, but with ego status deliberately omitted from the BEV encoder, while the other conducts ego-driven reasoning based solely on the planning task. A scene-aware fusion module then adaptively integrates the complementary decisions from the two branches to form the final planning trajectory. To ensure this decoupling does not compromise multi-task learning, we introduce a path attention mechanism for ego-BEV interaction and add two targeted auxiliary tasks: BEV unidirectional distillation and autoregressive online mapping. Extensive evaluations on the nuScenes dataset demonstrate that AdaptiveAD achieves state-of-the-art open-loop planning performance. Crucially, it significantly mitigates the over-reliance on ego status and exhibits impressive generalization capabilities across diverse scenarios.

cs.CV↗

arXiv2025

Coproducts Internal to Profinite Spaces

We give a categorical explanation for many properties of profinite coproducts of profinite groups, which were previously proven on a case-by-case basis. All of these properties take the form "certain functors preserve profinite coproducts". We give various examples to show how our framework can be applied. We also point out connections to internal category theory and profinite Bass-Serre theory.

math.CT↗

arXiv2025

Profinite Direct Sums with Applications to Profinite Groups of Type $\Phi_R$

We show that the "profinite direct sum" is a good notion of infinite direct sums for profinite modules having properties similar to direct sums of abstract modules. For example, the profinite direct sum of projective modules is projective, and there is a Mackey's Formula for profinite modules described using these sums. As an application, we prove that the class of profinite groups of type $\Phi_R$ is closed under subgroups.

math.RA↗

arXiv2025

From Metaphor to Mechanism: How LLMs Decode Traditional Chinese Medicine Symbolic Language for Modern Clinical Relevance

Metaphorical expressions are abundant in Traditional Chinese Medicine (TCM), conveying complex disease mechanisms and holistic health concepts through culturally rich and often abstract terminology. Bridging these metaphors to anatomically driven Western medical (WM) concepts poses significant challenges for both automated language processing and real-world clinical practice. To address this gap, we propose a novel multi-agent and chain-of-thought (CoT) framework designed to interpret TCM metaphors accurately and map them to WM pathophysiology. Specifically, our approach combines domain-specialized agents (TCM Expert, WM Expert) with a Coordinator Agent, leveraging stepwise chain-of-thought prompts to ensure transparent reasoning and conflict resolution. We detail a methodology for building a metaphor-rich TCM dataset, discuss strategies for effectively integrating multi-agent collaboration and CoT reasoning, and articulate the theoretical underpinnings that guide metaphor interpretation across distinct medical paradigms. We present a comprehensive system design and highlight both the potential benefits and limitations of our approach, while leaving placeholders for future experimental validation. Our work aims to support clinical decision-making, cross-system educational initiatives, and integrated healthcare research, ultimately offering a robust scaffold for reconciling TCM's symbolic language with the mechanistic focus of Western medicine.

cs.CL↗

arXiv2024

Open Condensed Subgroups and Mackey's Formula

We define what it means for a condensed group action to be open (following Scholze) and show that for open subgroups, many elementary results about abstract modules hold for condensed modules, such as the existence of Mackey's Formula for condensed groups. We also indicate how these results can be "solidified" to obtain their solid versions.

math.CT↗

arXiv2024

Collaborative Participatory Research with LLM Agents in South Asia: An Empirically-Grounded Methodological Initiative and Agenda from Field Evidence in Sri Lanka

The integration of artificial intelligence into development research methodologies presents unprecedented opportunities for addressing persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing from an empirical implementation in Sri Lanka's Sinhala-speaking communities, this paper presents an empirically grounded methodological framework designed to transform participatory development research, situated in the challenging multilingual context of Sri Lanka's flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, this framework deploys a multi-agent system architecture that redefines how data collection, analysis, and community engagement are conducted in linguistically and culturally diverse research settings. This structured agent-based approach enables participatory research that is both scalable and responsive, ensuring that community perspectives remain integral to research outcomes. Field experiences reveal the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, offering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research agenda advocates for AI-driven participatory research tools that maintain ethical considerations, cultural respect, and operational efficiency, highlighting strategic pathways for deploying AI systems that reinforce community agency and equitable knowledge generation, potentially informing broader research agendas across the Global South.

cs.CY↗

arXiv2024

Profinite and Solid Cohomology

Solid abelian groups, as introduced by Dustin Clausen and Peter Scholze, form a subcategory of all condensed abelian groups satisfying some ''completeness'' conditions and having favourable categorical properties. Given a profinite ring $R$, there is an associated condensed ring $\underline{R}$ which is solid. We show that the natural embedding of profinite $R$-modules into solid $\underline{R}$-modules preserves $\mathrm{Ext}$ and tensor products, as well as the fact that profinite rings are analytic.

math.CT↗

arXiv2024

Towards Camera Open-set 3D Object Detection for Autonomous Driving Scenarios

Conventional camera-based 3D object detectors in autonomous driving are limited to recognizing a predefined set of objects, which poses a safety risk when encountering novel or unseen objects in real-world scenarios. To address this limitation, we present OS-Det3D, a two-stage training framework designed for camera-based open-set 3D object detection. In the first stage, our proposed 3D object discovery network (ODN3D) uses geometric cues from LiDAR point clouds to generate class-agnostic 3D object proposals, each of which are assigned a 3D objectness score. This approach allows the network to discover objects beyond known categories, allowing for the detection of unfamiliar objects. However, due to the absence of class constraints, ODN3D-generated proposals may include noisy data, particularly in cluttered or dynamic scenes. To mitigate this issue, we introduce a joint selection (JS) module in the second stage. The JS module uses both camera bird's eye view (BEV) feature responses and 3D objectness scores to filter out low-quality proposals, yielding high-quality pseudo ground truth for unknown objects. OS-Det3D significantly enhances the ability of camera 3D detectors to discover and identify unknown objects while also improving the performance on known objects, as demonstrated through extensive experiments on the nuScenes and KITTI datasets.

cs.CV↗

arXiv2024

The Quadruplon in a Monolayer Semiconductor

Understanding the structure of matter or materials and interaction or correlations among the constituent elementary particles are the central tasks of all branches of science, from physics, chemistry, to biology. In physics, this ultimate goal has spurred a constant search for high-order correlated entities or composite particles for nearly all states and forms of matter, from elementary particles, nuclei, cold atoms, to condensed matter. So far, such composite particles involving two or three constituent particles have been experimentally identified, such as the Cooper pairs, excitons, and trions in condensed matter physics, or diquarks and mesons in quantum chromodynamics. Although the four-body irreducible entities have long been predicted theoretically in a variety of materials systems alternatively as quadruplons, quadrons, or quartets, the closely related experimental observation so far seems to be restricted to the field of elementary particles (e.g. the recent tetraquark at CERN) only. In this article, we present the first experimental evidence for the existence of a four-body irreducible entity, the quadruplon, involving two electrons and two holes in a monolayer of Molybdenum Ditelluride. Using the optical pump-probe technique, we discovered a series of new spectral features that are distinct from those of trions and bi-excitons. By solving the four-body Bethe-Salpeter equation in conjunction with the cluster expansion approach, we are able to explain these spectral features in terms of the four-body irreducible cluster or the quadruplons. In contrast to a bi-exciton which consists of two weakly bound excitons, a quadruplon consists of two electrons and two holes without the presence of an exciton.

cond-mat.mtrl-sci↗

arXiv2024

Spectral Features of the Fourth Order Irreducible Correlations in a Monolayer Semiconductor

Understanding high-order correlations or multi-particle entities in a many-body system is not only of fundamental importance in condensed matter physics, but also critical for many technological applications. So far, higher-order multi-particle irreducible correlations in semiconductors have not been studied beyond the second-order or two-particle case. In this paper, we study the correlation of two electrons and two holes (2e2h) using the four-body Bethe-Salpeter equation (4B-BSE) and applied to the calculation of the helicity-resolved absorption between the two-body and four-body states for a monolayer MoTe2. Surprisingly, we found a rich series of spectral peaks within an energy span of ~40 meV below the exciton that has not been seen before. To understand the origin of the new spectral peaks, the Feynman diagrams of the 4B BSE are recast into the cluster expansion formalism, allowing us to study the individual effects of selected clusters or correlations of various orders. We found that the irreducible clusters of orders up to the 3rd and their factorized combinations cannot explain the spectral features. Importantly, we found that the 4th order irreducible correlation is necessary and sufficient to explain the new features. The 4th order irreducible correlation corresponds to a four-particle irreducible cluster involving two electrons and two holes, alternatively called quadron or quadruplon. The new 4th order correlation or four-particle entity not only enriches our understanding of many-body correlations but also could provide new mechanism for light emission or absorption for possible new optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Many-Body Configurational Spectral Splitting between Trion and Charged Exciton in a Monolayer Semiconductor

Many-body electron-hole complexes in a semiconductor are important both from a fundamental physics point of view and for practical device applications. A three-body system of electrons (e) and holes (h) (2e1h, or 1e2h) in a two-band semiconductor is commonly believed to be associated with two spectral peaks for the exciton and trion (or charged exciton), respectively. But both the validity of this understanding and the physical meaning of a trion or charged exciton have not been thoroughly examined. From the physics point of view, there are two different configurations, (e)(eh) or (eeh), which could be considered charged exciton and trion, respectively. Here (...) represents an irreducible cluster with respect to Coulomb interactions. In this paper, we consider these issues related to the 2e1h three-body problem theoretically and experimentally using monolayer MoTe2 as an example. Our theoretical tools involve the three-body Bethe-Salpeter Equation (BSE) and the cluster expansion technique, especially their correspondence. Experimentally, we measure the photoluminescence spectrum on a gate-controlled monolayer MoTe2. We found two spectral peaks that are 21 and 4 meV, respectively, below the exciton peak, in contrast to the single "trion" peak from the conventional understanding. We show that, while the three-body BSE in a two-band model can reproduce all spectral features, the cluster-expansion technique shows that the two peaks correspond to the charged exciton (e)(eh) and trion (eeh), respectively. In other words, there is a spectral splitting due to the two different many-body configurations. Furthermore, we find that the trion only exists in the intervalley case, while the charged exciton exists both for the intervalley and intravalley cases.

cond-mat.mes-hall↗

arXiv2022

Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel

In this paper, we develop a new change detection algorithm for detecting a change in the Markov kernel over a metric space in which the post-change kernel is unknown. Under the assumption that the pre- and post-change Markov kernel is uniformly ergodic, we derive an upper bound on the mean delay and a lower bound on the mean time between false alarms. A numerical simulation is provided to demonstrate the effectiveness of our method.

eess.SY↗

arXiv2021

Prolonging Valley Polarization Lifetime through Gate-Controlled Exciton-to-Trion Conversion in Monolayer Molybdenum Ditelluride

Monolayer 2D semiconductors provide an attractive option for valleytronics due to the valley-addressability by helicity-specific light beam. But the short valley lifetime for excitons have hindered potential valleytronic applications. In this paper, we demonstrate a strategy for prolonging the valley lifetime by converting excitons to trions through effective gate control and by taking advantage of much longer valley lifetime for trions than for excitons. In continuous-wave experiments, we found the valley polarization increases as gate voltage is tuned away from the charge neutrality, with the degree of valley polarization increased from near zero to 38 % for excitons and to 33 % for trions. This is the first successful observation of valley-polarization in MoTe2 without a magnetic field. In pump-probe experiments, we found that the intervalley scattering process of excitons is significantly suppressed as gate voltage is tuned away from charge neutrality, with scattering time from 0.85 ps to ~ 2.17 ps. In contrast, the intervalley scattering rate for trions increases due to increased availability of partner charges for trion spin flipping, with scattering time from 1.39 ns down to ~100 ps away from charge neutrality. Interestingly, our results show that, despite the accelerated intervalley scattering, the trion polarization degree increases due to polarized trion generation from the exciton-to-trion conversion overtaking the intervalley trion scatterings. Importantly, the efficient exciton-to-trion conversion changed the dominant depolarization mechanisms. As a result, the valley lifetime is dramatically improved by 1000 times from excitons to trions at the charge neutrality. Our results shed new light into the depolarization dynamics and the interplay of various depolarization channels for excitons and trions and provide an effective strategy for prolonging the valley polarization.

cond-mat.mes-hall↗