arXiv subjects

Jie Ma

Publications and source records attributed to Jie Ma.

At least 19 recordsLinked to original sources

arXiv2026

NP-hardness of additive approximation for generalized Turán problems

Given a graph $T$ and a nonempty family $\mathcal{F}$ of graphs, let $\operatorname{ex}(G, T, \mathcal{F})$ denote the maximum number of copies of $T$ in an $\mathcal{F}$-free subgraph of a host graph $G$. Alon and Shikhelman [Algorithmica, 2022] conjectured that if $e(T)> 0$ and no member of $\mathcal{F}$ is a subgraph of a blow-up of $T$, then for every $\varepsilon>0$, approximating $\operatorname{ex}(G, T, \mathcal{F})$ within an additive error of $n^{v(T)-\varepsilon}$ is NP-hard for an input graph $G$ on $n$ vertices. Previous advances toward this conjecture include the resolution of the canonical case $T=K_2$ by Alon, Shapira, and Sudakov [Ann. of Math., 2009], as well as the case of $\operatorname{ex}(G,K_m,K_k)$ by Alon and Shikhelman. In this paper, we confirm this conjecture for every bipartite graph $T$ and every clique $T$. Our proof reductions combine several techniques, including iterative leaf additions, symmetric rooted extensions, and random transversals. Together, they encode the optimal value of the NP-hard Max $r$-Colorable Subgraph problem into the objectives $\operatorname{ex}(G, T, \mathcal{F})$ for a suitably constructed graph $G$.

cs.CC↗

arXiv2026

The colors of protoplanetary disks - Impact of nonspherical dust particles on the polarized and total intensity colors

Dust properties in protoplanetary disks remain poorly understood despite their central role in planet formation. Recent studies based on analyses of scattered-light images and phase functions provide promising insights into dust characterization, but the phase function alone is insufficient to constrain dust properties. Degeneracies between dust remain a major obstacle to linking observations to realistic dust models. We investigate the behavior of the disk color as a function of dust. We used MCFOST to generate synthetic scattered-light images from the visible to the near-infrared, in total and polarized intensity. We considered compact spherical grains using, irregular particles and compact and fractal aggregates using optical properties from the AggScatVIR database. Disk colors were measured from these images. For aggregates larger than the wavelength, monomer size influences color, whereas aggregate size has little effect, especially at low fractal dimensions. Polarized color is more sensitive to dust properties than total intensity color, because it combines the wavelength dependences of scattered intensity and polarization degree. Effective medium theory is reliable only for compact aggregates with small monomers, becoming inaccurate for highly porous aggregates or larger monomers. Effective albedo helps predict disk colors, but radiative transfer is needed to account for multiple scattering. For HD 100546, red optical-to-near-infrared colors exclude compact grains and organic-rich compositions but are consistent with amorphous-carbon aggregates, compact or fractal, containing large monomers. These results show that combining color measurements with polarized scattering phase functions offers a promising way to break degeneracies between dust models. Applying this approach to larger disk samples could better constrain dust properties and evolution.

astro-ph.SR↗

arXiv2026

Vanishing Uniform Turán Densities: Characterization and Jumps

Uniform Turán density problems ask for the minimum density threshold that guarantees a copy of a fixed 3-uniform hypergraph $F$ in every sufficiently large 3-uniform hypergraph whose edges are uniformly distributed. This line of research was initiated by Erdős and Sós in the 1980s, who introduced the notion of \emph{vertex-set Turán density} $π_{vvv}(F)$. Reiher, Rödl and Schacht later defined two related concepts, namely, the \emph{vertex-pair Turán density} $π_{ve}(F)$ and the \emph{cherry Turán density} $π_{ee}(F)$, which satisfy the chain of inequalities $π_{vvv}(F)\geπ_{ve}(F)\geπ_{ee}(F)$. In this paper, we investigate the problem for their vanishing densities. First, we characterize the 3-uniform hypergraphs $F$ for which $π_{ee}(F)=0$, thereby answering a question posed by Schacht. We show that these are precisely the so-called \emph{max-layered} 3-uniform hypergraphs recently introduced by Lamaison. Furthermore, we answer an analogous question of Lamaison for the \emph{minimum-codegree palette density} $π_{ee}^{\rm pal}$. Together, these results imply that the following are equivalent: $π_{ee}(F)=0$, $π_{ee}^{\rm pal}(F)=0$, and $F$ is a max-layered 3-uniform hypergraph. Let $\star \in \{vvv, ve, ee \}$. We say that zero is a \emph{jump} for $π_{\star}$ if there exists $c>0$ such that no $3$-uniform hypergraph $F$ satisfies $π_{\star}(F)\in(0,c)$. A famous result of Reiher, Rödl and Schacht asserts that zero is a jump for $π_{vvv}$. In contrast, we prove that zero is not a jump for either $π_{ve}$ or $π_{ee}$. This also yields a negative answer to Schacht's well-ordering question for $π_{ve}$. Together with existing results, these findings complete the picture for all three notions of uniform Turán density at zero.

math.CO↗

arXiv2026

MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning

Mathematical reasoning is a fundamental capability for educational applications of Large Language Models (LLMs), where reliable problem solving requires not only correct answers but also coherent and verifiable reasoning processes. Multi-agent systems (MASs) have shown growing potential in this domain by leveraging collaboration and verification among multiple LLM agents. However, existing evaluations primarily focus on the correctness of final answers in text-only settings, offering limited insight into multimodal reasoning capabilities, the reliability of intermediate reasoning steps, and robustness against reasoning errors. To address these limitations, we introduce MAMath-Bench, a process-aware benchmark designed to evaluate the multimodal mathematical reasoning capabilities of MASs. MAMath-Bench comprises 1,003 problems spanning 11 mathematical categories, multiple difficulty levels, and several modalities, including text, images, and audio. It features fine-grained milestone annotations to evaluate systems across three dimensions: problem-solving correctness, reasoning-process reliability, and robustness to adversarial reasoning-chain perturbations. Our evaluation yields three key findings. First, multi-agent collaboration does not consistently improve final-answer accuracy over strong single LLMs. Second, collaboration can substantially enhance the coverage of intermediate reasoning, but such gains may come at the cost of increased reasoning redundancy. Third, current systems remain vulnerable to adversarial reasoning errors, particularly those involving subtle structural perturbations. The dataset is available at https://github.com/mira-ai-lab/AgentMath.

cs.AI↗

arXiv2026

Complete tripartite subgraphs of balanced tripartite graphs with large minimum degree

In 1975 Bollobás, Erdős, and Szemerédi asked what minimum degree guarantees an octahedral subgraph $K_3(2)$ in any tripartite graph $G$ with $n$ vertices in each vertex class. We show that $δ(G)\geq n+2n^{\frac{5}{6}}$ suffices thus improving the bound $n+(1+o(1))n^{\frac{11}{12}}$ of Bhalkikar and Zhao obtained by following their approach. Bollobás, Erdős, and Szemerédi conjectured that $n+cn^{\frac{1}{2}}$ suffices and there are many $K_3(2)$-free tripartite graphs $G$ with $δ(G)\geq n+cn^{\frac{1}{2}}$. We confirm this conjecture under the additional assumption that every vertex in $G$ is adjacent to at least $(1/5+\varepsilon)n$ vertices in any other vertex class.

math.CO↗

arXiv2026

JWST Imaging of Edge-on Protoplanetary Disks. V. Flux Ratio Reversal and Disk-edge Structure in the Flying Saucer

We present near- and mid-infrared broadband imaging observations of the edge-on protoplanetary disk 2MASS J16281370-2431391, a.k.a. the 'Flying Saucer', obtained with NIRCam and MIRI on board JWST. These data are combined with archival optical and near-IR broadband imaging from HST, millimeter dust continuum emission from ALMA, and optical lightcurves from the K2 and TESS for a comprehensive multi-wavelength study. The JWST observations reveal a reversal of the flux ratio between the two reflection nebulae at near-infrared wavelengths (2-3 $μm$), which can be explained by a misaligned inner disk. For the first time, JWST reveals the bright lining along the disk edge at 2 and 3 $μm$, arising from forward scattering of background photons by dust grains near the $τ\approx 1$ layer. By combining the dust lane thickness measured with HST and JWST, we find the dust grains up to at least $\sim$3$μ$m are vertically mixed throughout the disk and have undergone significant growth. In addition, comparison of earlier HST/NICMOS J and K band images with those from JWST/NIRCam reveals variability in the scattered-light morphology, as well as measurable proper motion of the system with respect to the background stars, confirming that the Flying Saucer is co-moving with the young star population of $ρ$ Ophiuchi.

astro-ph.EP↗

arXiv2026

FastJEV: Understanding Redundancy for Compact JEV Inference

JEV models make multimodal decisions by directly scoring candidates. Although the common context is encoded once, candidate evaluation can still repeat matching token histories, duplicate inference states, and execute the full backbone. In this paper, we study these sources of redundancy and present FastJEV for compact candidate evaluation. We jointly organize history reuse and state storage, since sharing computation requires preserving states for later branches. We first introduce shared context anchoring to reuse recurrent initial states and omit unused final recurrent caches. We extend this reuse through candidate prefix sharing, retaining the intermediate states needed by subsequent branches. To further reduce the depth of these paths, we apply decision guided pruning based on relative score changes measured on a small unlabeled set. Our method retains full context encoding and all candidates without additional training. We evaluate FastJEV across three OmniJev model sizes on five public benchmarks and reconstructed LIBERO-10 offline questions. At the selected pruning budgets, the complete method reduces candidate depth by 43.75% to 45.83%, while retaining 93.66% to 97.52% of the original task scores on average across the six evaluation sets. Through controlled experiments, we show how candidate overlap and branching structure affect the execution cost of history reuse. In our implementation, candidate prefix sharing can reduce repeated computation while increasing latency. These findings motivate designing sharing granularity and execution schedules together for efficient JEV inference.

cs.CV↗

arXiv2026

Skill-V: Verifiable Self-Evolving Skill Library for Interactive Agents

Interactive agents can turn experience into reusable skills, yet existing self-evolving skill libraries primarily improve by accumulating new knowledge. Failures may lead to new skills, while previously stored skills are less often revisited as new evidence arrives. However, growth alone does not ensure reliability, as a retrieved skill may be inapplicable under the current task conditions, and an existing skill may encode a mis-specified operational boundary. Reliable skill evolution therefore requires not only adding knowledge, but also testing and revising what is already stored. We introduce Skill-V, a verifiable self-evolving skill library. To make stored knowledge testable, we propose representing skills as versioned, falsifiable contracts that link semantic intent to observable behavioral criteria. We use environment outcomes to drive library evolution. Specifically, task failures motivate skill addition, while disagreements between contract evaluations and task outcomes guide revisions to existing skill boundaries. To validate these revisions, we require them to preserve protected semantic constraints and satisfy non-regression criteria for rubric-outcome metrics on historical replay evidence. Finally, we employ an applicability-aware filter to exclude candidates judged confidently inapplicable to the current task. Across ALFWorld and WebShop, Skill-V achieves success rates of 95.3% and 85.9%, respectively, while maintaining a more compact skill library than growth-oriented baselines. Applicability-aware filtering reduces incorrect skill invocations, and outcome-grounded revisions correct mis-specified skill boundaries without degrading performance on previously observed evidence. These results show that reliable skill evolution requires more than accumulating experience: the library must learn which knowledge to retain, when to revise it, and when it should be applied.

cs.CV↗

arXiv2026

Linear circumference in vertex-transitive graphs

We prove that there is an absolute constant $c>0$ such that every connected vertex-transitive graph $G$ on $n \ge 3$ vertices contains a cycle of length at least $cn$. Moreover, every such graph with sufficiently large degree $d$ contains a cycle of length at least $(1-d^{-1/100})n$. This gives the first linear bound towards Lovász's Hamiltonicity conjecture. The proof combines a structural result of DeVos and Mohar on vertex-transitive graphs with a general framework for finding long cycles, which may be of independent interest.

math.CO↗

arXiv2026

Extremal hypergraphs without generalized 4-cycles

In 1977, Erdős posed the problem of determining the maximum number $f_r(n)$ of edges in an $n$-vertex $r$-uniform hypergraph in which all disjoint pairs of edges have distinct unions. Füredi later conjectured that, for every fixed $r\ge 4$ and all sufficiently large $n$, $f_r(n)=\binom{n-1}{r-1}+\lfloor \frac{n-1}{r}\rfloor$. In this paper, we prove this conjecture and determine all extremal configurations. Our proof combines a stability theorem for such dense hypergraphs with a delicate deletion argument applied to an associated bipartite $3$-graph. The stability theorem also resolves a conjecture of Mubayi.

math.CO↗

arXiv2026

Dopant-modulated lattice softening drives drastic thermal conductivity reduction in β-FeSi2 thermoelectrics

Suppressing lattice thermal conductivity (K_lat) is pivotal for thermoelectric efficiency. While traditional strategies rely heavily on phonon scattering from mass- and size-mismatches, we demonstrate a robust K_lat suppression mechanism driven by dopant-induced lattice stiffness modulation. Through a comparative analysis of p-type (Mn) and n-type (Co, Ir) doping in the β-FeSi2 model system, we show that Co and Ir doping significantly reduce K_lat. Notably, Co doping achieves a ~71% reduction at 300 K even without significant mass and size contrast. By correlating transport data with neutron powder diffraction, heat capacity, and Raman spectroscopy, we reveal anomalous lattice expansion, a substantial reduction in Debye temperature, and marked vibrational redshift and broadening. These systematic changes provide strong evidence for atomic-scale lattice softening and a fundamental weakening of interatomic force constants, which synergistically lower phonon group velocities and amplify anharmonic scattering. Our findings establish lattice stiffness manipulation as a powerful strategy for thermal management, offering a distinct design pathway beyond traditional mass- and strain-fluctuation models.

cond-mat.mtrl-sci↗

arXiv2026

QSCP: Beyond Class-Name Prompts for Query-Guided Semantic Change Parsing

Traditional change detection (CD) identifies changes between bi-temporal remote sensing images, while semantic change detection (SCD) assigns predefined land-cover classes. However, mapping all changes may not meet a user's specific needs. Referring change detection (RCD) enables selective retrieval through category prompts. However, existing category-prompted RCD uses the queried category to specify the destination of a change and returns only a binary mask of the corresponding regions. Users may instead request a particular transition and paired semantic maps to understand what changed into what. Such requests require explicit source and target reasoning beyond target-class localization. To address these needs, we propose query-guided semantic change parsing (QSCP), which supports category names, synonyms, and intent-bearing sentences and returns a query-specific mask with paired temporal semantic maps. QSCP parses requests into intents and semantic slots, composes bidirectional visual evidence, and predicts both temporal states with a query-conditioned decoder. On SECOND, QSCP outperforms RCDNet on synonym, sentence, and transition queries and improves end-to-end semantic prediction over evaluated semantic baselines. WHU-CDC experiments further assess cross-dataset transfer and consistency across equivalent expressions without target-domain training. Code is available at https://github.com/qianyuancs/QSCP

cs.CV↗

arXiv2026

Dr. Free: You Don't Need Difficulty Rewards for Self-Evolving Search Agents

A central limitation of current data-free self-evolution methods for training search agents is their reliance on difficulty-based proposer rewards. These methods reward a proposer for generating questions that challenge a co-evolving solver, using solver difficulty as a proxy for question quality. Yet difficulty alone is insufficient to distinguish questions that require cross-passage evidence from those that are answerable via simpler shortcuts. In addition, measuring difficulty demands repeated solver rollouts for every candidate question, leading to substantial computational costs. In this paper, we introduce \methodname, the first self-evolving search framework that eliminates difficulty-based proposer rewards and directly optimizes for evidence necessity relative to shortcut contexts. Dr. Free samples relational chains from a knowledge graph and pairs them with aligned passages, giving question generation an explicit multi-hop structure. A generated question receives a positive information-gain reward only when the likelihood of the target answer under the complete evidence passages exceeds the maximum likelihood under all evaluated shortcut contexts. Because this signal is computed from teacher-forced likelihoods, it removes the need for pass-rate estimation and reduces proposer training time by over $7\times$. Experiments on seven open-domain QA benchmarks show that Dr. Free outperforms prior data-free search agents and the supervised baseline, with large improvements on multi-hop QA benchmarks.

cs.AI↗

arXiv2026

Rydberg-Atom-Mediated Strong Antisymmetric Spin Exchange in Molecular Arrays

Ultracold molecular systems have recently emerged as a versatile platform for quantum computation and simulation. Spin-exchange interactions arising from direct molecular dipolar interactions constitute the key mechanism for generating quantum entanglement and simulating quantum spin models. However, the relatively small electric dipole moments result in weak spin-exchange couplings, fundamentally limiting the speed of quantum information processing and interaction cycle of many-body dynamics. Here, we introduce a framework that employs Rydberg atoms with large electric dipole moments to mediate strong interactions between molecules in optical tweezer arrays that enables individually laser addressing both the Rydberg atom and molecules. Our result reveals that the mediated coupling can realize an effective molecular spin-exchange interaction with an intrinsic Dzyaloshinskii-Moriya character, with the Rydberg atoms dynamically decoupled from the molecular degrees of freedom, and the effective interaction strength enhanced by up to three orders of magnitude. We further demonstrate its versatility through rapid entanglement generation, high-fidelity two-qubit gate operations, and the realization of non-equilibrium symmetry-protected topological phase with long-lived edge coherence. Our work establishes a route toward strong molecular spin interactions and opens opportunities for fast, scalable quantum information processing and quantum simulation of long-time non-equilibrium quantum many-body physics in optical tweezer arrays of ultracold molecules.

cond-mat.quant-gas↗

arXiv2026

From Change Captions to Change Detection: Semantic-Appearance Agreement Framework for Remote Sensing Change Detection

Remote sensing change detection (RSCD) is essential for monitoring land-cover changes and urban development. However, most methods demand pixel-level change masks, which are costly and time-consuming to annotate. Weakly supervised methods reduce this cost by using image-level change labels. Yet these labels indicate only whether a change occurs, leaving models to recover the location of the change and semantic meaning through additional and complex mechanisms. This missing information can be supplied directly by change captions, which describe what changes, what it becomes, and where it occurs. Therefore, we introduce change-caption-guided RSCD, using change captions as the sole task-specific supervision to learn change masks without manually annotated change masks. Our framework has two components: a caption-driven generation pipeline that produces bi-temporal remote sensing image pairs at scale with controlled changes matching each caption, and a change detector guided by the caption's transition semantics. The detector uses our Semantic-Appearance Agreement Framework (SAAF) to combine caption-grounded semantic responses with RGB differences for change localization, while text conditioning guides dense prediction. Experiments on our newly constructed Flair-RSGen dataset and WHU-CDC show that SAAF outperforms the closest reproduced limited-supervision baselines in macro-averaged IoU and F1 under the evaluated protocols. Code is publicly available at https://github.com/qianyuancs/SAAF.

cs.CV↗

arXiv2026

Longest cycles intersect linearly in highly connected graphs

A longstanding conjecture attributed to Smith (1984) asserts that for every $k\ge2$, any two longest cycles in a $k$-connected graph share at least $k$ vertices. In this paper, we prove the first linear lower bound, showing that any two longest cycles in a $k$-connected graph share at least $k/600$ vertices. Departing from previous Turán-type extremal arguments, we develop a novel structural approach that also yields applications to related problems on longest cycles and paths.

math.CO↗

arXiv2026

Integrated Positioning and Communication via LEO Satellites: A Contemporary Overview

Low Earth orbit (LEO) satellites, as a prominent technology in the sixth generation non-terrestrial network, offer both positioning and communication capabilities. While these two applications have each been extensively studied and have achieved substantial progress in recent years, the potential synergistic benefits of integrating them remain an underexplored yet promising avenue. This article provides a contemporary overview of integrated positioning and communication (IPAC) systems enabled by LEO satellites. Leveraging the distinct characteristics of LEO satellites, we examine how communication systems can enhance positioning accuracy and, conversely, how positioning information can be exploited to improve communication efficiency. A representative case study is included to illustrate enhanced LEO positioning signal acquisition with the assistance of communication-side information. Finally, several key open research challenges for LEO-based IPAC systems are discussed.

eess.SP↗

arXiv2026

Orbital Errors in LEO Satellite Positioning: Modeling, Analysis, and Bayesian Calibration

Low-Earth orbit (LEO) satellites offer a promising alternative to global navigation satellite systems for precise positioning. However, their relatively low altitudes make them more susceptible to orbital perturbations, which in turn degrade positioning accuracy. In this work, we study (i) the mechanisms through which orbital errors affect positioning performance and (ii) the fundamental calibration strategies for mitigating low-Earth orbital errors. We first model the satellite orbit and analyze historical two-line element data to characterize the statistical behavior of low-Earth orbital errors. Based on these real-data statistics, we derive the misspecified Cramér-Rao bound to quantify the impact of orbital errors on positioning performance. Subsequently, we develop a Bayesian orbit calibration framework that leverages the learned orbital error statistics as prior information to enhance calibration accuracy. This work sheds light on orbital error modeling, real-data-based evaluation, theoretical impact analysis, and calibration methodologies. Extensive simulations show that stale orbital information can severely degrade positioning performance, whereas learning and incorporating orbital error statistics can effectively enhance orbit calibration and ultimately improve positioning accuracy.

eess.SP↗