The recently reported signal of common red noise between pulsars by several pulsar timing array collaborations has been thought as evidence of the stochastic gravitational wave background (SGWB) due to the Helling-Downs correlation. In this Letter, we search for the non-Gaussianity of SGWB through its non-linear effect on the overlap reduction function in NANOGrav 15-year data set. In particular, we focus on a folded component to SGWB whose amplitude is quantified with a single parameter $\alpha$ in the unpolarized case. The resulting Bayes factor of $1.68 \pm 0.01$ ($1.78 \pm 0.01$ in the case of signals from SMBHBs) indicates that there is no evidence of such a non-Gaussianity of SGWB in the NANOGrav 15-year data yet. If it is detected in future PTA experiments, it will impact our understanding on the origin of the detected SGWB.

We present an approach to deriving positivity bounds on effective field theories by analyzing the thermodynamic behavior of thermal quantum field systems. Focusing on scalar theories with higher-dimensional operators, we compute the finite-temperature entropy using thermal field theory techniques. We argue that consistency with fundamental thermodynamic principles--specifically, the expectation that entropy increases with the introduction of new degrees of freedom--imposes nontrivial constraints on Wilson coefficients. In particular, we show that the coefficient of the leading dimension-8 operator must be strictly positive. This thermodynamic perspective offers an alternative to traditional S-matrix-based derivations of positivity bounds and provides a complementary perspective into the interplay between entropy, unitarity, and causality in quantum field theory.

The discovery of high-performance materials is crucial for technological advancement. Inverse design using multi-agent systems (MAS) shows great potential for new material discovery. However, current MAS for materials research rely on predefined configurations and tools, limiting their adaptability and scalability. To address these limitations, we developed a planner driven multi-agent system (S1-MatAgent) which adopts a Planner-Executor architecture. Planner automatically decomposes complex materials design tasks, dynamically configures various tools to generate dedicated Executor agents for each subtask, significantly reducing reliance on manual workflow construction and specialized configuration. Applied to high-entropy alloy catalysts for hydrogen evolution reactions in alkaline conditions, S1-MatAgent completed full-cycle closed-loop design from literature analysis and composition recommendation to performance optimization and experimental validation. To tackle the deviations between designed materials and target, as well as high experimental verification costs, S1-MatAgent employs a novel composition optimization algorithm based on gradients of machine learning interatomic potential, achieving 27.7 % improvement in material performance. S1-MatAgent designed 13 high-performance catalysts from 20 million candidates, with Ni4Co4Cu1Mo3Ru4 exhibiting an overpotential of 18.6 mV at 10 mA cm-2 and maintaining 97.5 % activity after 500 hours at 500 mA cm-2. The universal MAS framework offers a universal and scalable solution for material discovery, significantly improving design efficiency and adaptability.

We present a fully back-reacted Einstein--Maxwell--Dilaton--flavor model with dynamical light and strange sectors, calibrated to lattice QCD using a machine-learning--assisted spectral method. The model reproduces the 2+1-flavor equation of state and chiral dynamics with quantitative accuracy, and maps the Columbia plot with a tri-critical point at $m_s^{\mathrm{tri}} \simeq 21~\text{MeV}$ and a critical mass $m_c \simeq 0.785~\text{MeV}$, consistent with lattice results. At finite density, it yields a crossover-to-first-order transition and predicts a critical endpoint at $T_C = 75.4~\text{MeV}$ and $\mu_C = 768~\text{MeV}$, within the reach of heavy-ion experiments. These findings establish a unified holographic framework for the QCD phase structure across quark masses and baryon density, providing the first consistent and quantitative description of both deconfinement and chiral transitions within a single holographic model.

Two-dimensional (2D) materials have shown broad application prospects in fields such as energy, environment, and aerospace owing to their unique electrical, mechanical, thermal and other properties. With the development of artificial intelligence (AI), the discovery and design of novel 2D materials have been significantly accelerated. However, due to the lack of basic theories of material synthesis, identifying reliable synthesis processes for theoretically designed materials is a challenge. The emergence of large language model offers new approaches for the reliability prediction of material synthesis processes. However, its development is limited by the lack of publicly available datasets of material synthesis processes. To address this, we present the Material Synthesis 2025 (MatSyn25), a large-scale open dataset of 2D material synthesis processes. MatSyn25 contains 163,240 pieces of synthesis process information extracted from 85,160 high-quality research articles, each including basic material information and detailed synthesis process steps. Based on MatSyn25, we developed MatSyn AI which specializes in material synthesis, and provided an interactive web platform that enables multifaceted exploration of the dataset (this https URL). MatSyn25 is publicly available, allowing the research community to build upon our work and further advance AI-assisted materials science.

The detection of gravitational waves (GWs) has opened a new window to explore the dark Universe. Ultralight dark matter (ULDM), an attractive candidate for dark matter, might induce monochromatic signals in gravitational-wave (GW) laser interferometers. However it is not clear how such signals are disentangled from the GWs emitted by galactic compact binaries. Here we initiate the investigation on the spectral split of monochromatic signals caused by detector's heliocentric motion in space and show the annual modulation can induce distinct structures in the spectral harmonics for GWs and ULDM, which would enable to clearly identify the nature of the signal. We show the physical parameters can be inferred with high precision using the Fisher matrix formalism. Our results provide a practical algorithm for probing ULDM and broaden the scientific objectives of future GW detectors in space, such as LISA and Taiji.

Recently, the rapid development of AIGC has significantly boosted the diversities of fake media spread in the Internet, posing unprecedented threats to social security, politics, law, and etc. To detect the ever-increasingly diverse malicious fake media in the new era of AIGC, recent studies have proposed to exploit Large Vision Language Models (LVLMs) to design robust forgery detectors due to their impressive performance on a wide range of multimodal tasks. However, it still lacks a comprehensive benchmark designed to comprehensively assess LVLMs' discerning capabilities on forgery media. To fill this gap, we present Forensics-Bench, a new forgery detection evaluation benchmark suite to assess LVLMs across massive forgery detection tasks, requiring comprehensive recognition, location and reasoning capabilities on diverse forgeries. Forensics-Bench comprises 63,292 meticulously curated multi-choice visual questions, covering 112 unique forgery detection types from 5 perspectives: forgery semantics, forgery modalities, forgery tasks, forgery types and forgery models. We conduct thorough evaluations on 22 open-sourced LVLMs and 3 proprietary models GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet, highlighting the significant challenges of comprehensive forgery detection posed by Forensics-Bench. We anticipate that Forensics-Bench will motivate the community to advance the frontier of LVLMs, striving for all-around forgery detectors in the era of AIGC. The deliverables will be updated at this https URL

Microscopic charged black holes can provide possibilities to test the consistency of the effective field theory (EFT) corrections to Einstein-Maxwell theory. A particularly interesting result is fixing the sign of a certain combination of EFT couplings from the requirement that all charged black holes should be able to evaporate (Weak Gravity Conjecture). In our work, we analysed the EFT corrections to a set of zero-damping quasinormal modes (QNMs) of the scalar wave probe in a nearly extremal Reissner-Nordström black hole. We review the duality of this setup to the problem of the quantum Seiberg-Witten curve of $N=2$ Super-Yang-Mills theory with three flavors. We provide an analytic result for the EFT corrections to the QNMs obtained from the quantization condition imposed on the Seiberg-Witten cycle. Our main result is that the causality requirement of the gravitational theory formulated for the QNMs translates to the same condition on EFT couplings as the one appearing in the Weak Gravity Conjecture.

We study the holographic dual of the extended thermodynamics of spherically symmetric, charged Gauss-Bonnet AdS black holes in the context of the AdS/CFT correspondence. Compared to Einstein's theory of gravity, Gauss-Bonnet gravity introduces higher-order curvature terms. The coupling constants of these higher-order curvature terms $\alpha$ can serve as new thermodynamic quantities, which will also be dual to thermodynamic quantities on the boundary CFT, a feature not present in the CFT dual to Einstein's gravity previously. Based on the holographic dictionary, we studied the critical behavior and phase transition of the CFT description of the charged Gauss-Bonnet black holes in $d=4$ and $d=5$ in the ensemble at fixed $(C, \mathcal{V}, \tilde{Q}, \tilde{\mathcal{A}})$. The interesting behaviour of free energy stems from the fact that the constraints we introduced to handle the gravitational constant on CFT and the AdS radius differ from conventional approaches. Using the criticality equation, we numerically found the critical points of the zeroth-order and first-order phase transition for $\tilde{\mathcal{A}}$. The relationships between conjugate thermodynamic pairs (equation of state) were also examined. In the case of the $p-\mathcal{V}$, $\tilde{T}-\tilde{S}$ and $\tilde{\Phi}-\tilde{Q}$ conjugate pairs, characteristics that are analogous to the first-order phase transition of van der Waals fluids were found.

Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles. While artificial intelligence (AI) offers promise, its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers. We introduce learning at criticality (LaC), a reinforcement learning (RL) scheme that tunes Large Language Models (LLMs) to a sharp learning transition, addressing this information scarcity. At this transition, LLMs achieve peak generalization from minimal data, exemplified by 7-digit base-7 addition -- a test of nontrivial arithmetic reasoning. To elucidate this peak, we analyze a minimal concept-network model (CoNet) designed to capture the essence of how LLMs might link tokens. Trained on a single exemplar, this model also undergoes a sharp learning transition. This transition exhibits hallmarks of a second-order phase transition, notably power-law distributed solution path lengths. At this critical point, the system maximizes a ``critical thinking pattern" crucial for generalization, enabled by the underlying scale-free exploration. This suggests LLMs reach peak performance by operating at criticality, where such explorative dynamics enable the extraction of underlying operational rules. We demonstrate LaC in quantum field theory: an 8B-parameter LLM, tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums, solves unseen, higher-order problems, significantly outperforming far larger models. LaC thus leverages critical phenomena, a physical principle, to empower AI for complex, data-sparse challenges in fundamental physics.

It is commonly recognized that the primordial scalar spectral index $n_s$ is approximately $0.96-0.975$, depending on the dataset. However, this view is being completely altered by the early dark energy (EDE) resolutions of the Hubble tension, known as the most prominent tension the standard $\Lambda$CDM model is suffering from. In corresponding models with pre-recombination EDE, resolving the Hubble tension (i.e., achieving $H_0\sim 73$km/s/Mpc) must be accompanied by a shift of $n_s$ towards unity to maintain consistency with the cosmological data, which thus implies a scale invariant Harrison-Zel'dovich spectrum with $n_s=1$ $(|n_s-1|\simeq {\cal O}(0.001))$. In this work, we strengthen and reconfirm this result with the latest ground-based CMB data from ACT DR6 and SPT-3G D1, the precise measurements at high multipoles beyond the Planck angular resolution and sensitivity. Our work again highlights the importance of re-examining our understanding on the very early Universe within the broader context of cosmological tensions.

We report a state-of-the-art lattice QCD calculation of the isovector quark transversity distribution of the proton in the continuum and physical mass limit using large-momentum effective theory. The calculation is done at four lattice spacings $a=\{0.098,0.085,0.064,0.049\}$~fm and various pion masses ranging between $220$ and $350$ MeV, with proton momenta up to $2.8$ GeV. The result is non-perturbatively renormalized in the hybrid scheme with self renormalization which treats the infrared physics at large correlation distance properly, and extrapolated to the continuum, physical mass and infinite momentum limit. We also compare with recent global analyses for the nucleon isovector quark transversity distribution.

The $\gamma$-ray from Giant molecular clouds (GMCs) is regarded as the most ideal tool to perform in-situ measurement of cosmic ray (CR) density and spectra in our Galaxy. We report the first detection of $\gamma$-ray emissions in the very-high-energy (VHE) domain from the five nearby GMCs with a stacking analysis based on a 4.5-year $\gamma$-ray observation with the Large High Altitude Air Shower Observatory (LHAASO) experiment. The spectral energy distributions derived from the GMCs are consistent with the expected $\gamma$-ray flux produced via CR interacting with the ISM in the energy interval 1 - 100 $~\rm$ TeV. In addition, we investigate the presence of the CR spectral `knee' by introducing a spectral break in the $\gamma$-ray data. While no significant evidence for the CR knee is found, the current KM2A measurements from GMCs strongly favor a proton CR knee located above 0.9$~\rm$ PeV, which is consistent with the latest measurement of the CR spectrum by ground-based experiments.

We study multi-loop conformal integrals for four-point correlators of planar ${\cal N}=4$ super-Yang-Mills theory, and in particular those contributing to Coulomb branch amplitudes in the ten-dimensional lightlike limit, where linear combinations of such integrals are determined by the large R-charge octagons exactly known from integrability. Exploiting known results for integrands, we review those combinations of dual conformal invariant (DCI) integrals that must evaluate to determinants of ladders, generalizing the simplest cases of Basso-Dixon fishnet integrals; in this way, we summarize all-loop predictions for the integrands (which are extracted from $f$-graphs) contributing to components of Coulomb branch amplitudes, such as next-to-fishnet integrals. Moreover, this exercise produces new ``magic identities", {\it i.e.} certain combinations of DCI integrals equal zero, and we enumerate and simplify such identities up to six loops explicitly. On the other hand, most of these individual integrals have not been computed beyond three loops, and as a first step we consider a bootstrap program for DCI integrals based on their leading singularities and the space of pure functions. We bootstrap the $3$ non-trivial DCI integrals for four-loop Coulomb branch amplitudes (providing an independent verification of the four-loop magic identity), which all take remarkably simple form as weight-$8$ single-valued harmonic polylogarithms. We also compute all leading singularities and a large portion of the pure functions for the $34$ DCI integrals contributing to five-loop amplitudes, where not only some integrals evaluate to functions beyond harmonic polylogarithms but they also contain lower-weight pieces individually.

In this paper, we take a major step towards the construction and applications of an all-loop, all-multiplicity amplituhedron for three-dimensional planar $\mathcal{N}=6$ Chern-Simons matter theory, or the $\textit{ABJM amplituhedron}$. We show that by simply changing the overall sign of the positive region of the original amplituhedron for four-dimensional planar $\mathcal{N}=4$ super-Yang-Mills (sYM) and performing a symplectic reduction, only three-dimensional kinematics in the middle sector of even-multiplicity survive. The resulting form of the geometry, combined with its parity images, gives the full loop integrand. This simple modification geometrically enforces the vanishing of odd-multiplicity cuts, and manifests the correct soft cuts as well as two-particle unitarity cuts. Furthermore, the so-called ``bipartite structures" of four-point all-loop negative geometries also directly generalize to all multiplicities. We introduce a novel approach for triangulating loop amplituhedra based on the kinematics of the tree region, resulting in local integrands tailored to ``prescriptive unitarity". This construction sheds fascinating new light on the interplay between loop and tree amplituhedra for both ABJM and $\mathcal{N}=4$ sYM: the loop geometry demands that the tree region must be dissected into $\textit{chambers}$, defined by the simultaneous positivity of maximal cuts. The loop geometry is then the ``fibration" of the tree region. Using the new construction, we give explicit results of one-loop integrands up to ten points and two-loop integrands up to eight points by computing the canonical form of ABJM loop amplituhedron.

Wide field-of-view (FoV) LiDAR sensors provide dense geometry across large environments, but most existing LiDAR-inertial-visual odometry (LIVO) systems rely on a single camera, leading to limited spatial coverage and degraded robustness. We present Omni-LIVO, the first tightly coupled multi-camera LIVO system that bridges the FoV mismatch between wide-angle LiDAR and conventional cameras. Omni-LIVO introduces a Cross-View direct tracking strategy that maintains photometric consistency across non-overlapping views, and extends the Error-State Iterated Kalman Filter (ESIKF) with multi-view updates and adaptive covariance weighting. The system is evaluated on public benchmarks and our custom dataset, showing improved accuracy and robustness over state-of-the-art LIVO, LIO, and visual-inertial baselines. Code and dataset will be released upon publication.

We construct a holographic QCD model based on the Einstein--dilaton--flavor framework with 2+1 flavors and investigate its phase structure using machine-learning techniques. At zero chemical potential, the model reproduces the equation of state and chiral transition in quantitative agreement with lattice QCD results. By varying the light and strange quark masses, we map out the quark-mass dependence of the transition order and obtain the corresponding phase diagram, which is consistent with phase structures extracted from lattice simulations and other nonperturbative approaches. In particular, the predicted first-order region is found to be small, in line with the most recent lattice QCD analyses. We also examine the critical behavior along the second-order boundaries and the tricritical region, finding that the critical exponents exhibit mean-field scaling characteristic of classical holographic constructions. Integrating machine learning with holographic QCD significantly enhances the efficiency of parameter optimization, providing a robust and practical strategy for improving the predictive power of holographic modeling of QCD thermodynamics.

We define the square amplitudes in planar Aharony-Bergman-Jafferis-Maldacena theory (ABJM), analogous to that in $\mathcal{N}{=}4$ super-Yang-Mills theory (SYM). Surprisingly, the $n$-point $L$-loop integrands with fixed $N{:=}n{+}L$ are unified in a single generating function. Similar to the SYM four-point half-BPS correlator integrand, the generating function enjoys a hidden $S_N$ permutation symmetry in the dual space, allowing us to write it as a linear combination of weight-3 planar $f$-graphs. Remarkably, through Gram identities it can also be represented as a linear combination of bipartite $f$-graphs which manifest the important property that no odd-multiplicity amplitude exists in the theory. The generating function and these properties are explicitly checked against squared amplitudes for all $n$ with $N{=}4,6,8$. By drawing analogies with SYM, we conjecture some graphical rules the generating function satisfy, and exploit them to bootstrap a unique $N{=}10$ result, which provides new results for $n{=}10$ squared tree amplitudes, as well as integrands for $(n,L){=}(4,6),(6,4)$. Our results strongly suggest the existence of a "bipartite correlator" in ABJM theory that unifies all squared amplitudes and satisfies physical constraints underlying these graphical rules.

The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-borne antennas like Taiji and LISA promises deep insights into strong-field gravity and black hole physics. However, the complex, highly degenerate, and non-convex likelihood landscapes characteristic of EMRI parameter spaces pose severe challenges for conventional Markov chain Monte Carlo (MCMC) methods. Under realistic instrumental noise and broad priors, these methods demand impractical computational costs but are prone to becoming trapped in local maxima, leading to biased and unreliable parameter estimates. To address this, we introduce Flow-Matching Markov Chain Monte Carlo (FM-MCMC), a novel Bayesian framework that integrates continuous normalizing flows (CNFs) with parallel tempering MCMC (PTMCMC). By generating high-likelihood regions via CNFs and refining them through PTMCMC, FM-MCMC enables robust exploration of the nontrivial parameter spaces, while achieving orders-of-magnitude improvement in computational efficiency and, more importantly, ensuring statistically reliable and unbiased inference. By enabling real-time, unbiased parameter inference, FM-MCMC could unlock the full scientific potential of EMRI observations, and would serve as a scalable pipeline for precision gravitational-wave astronomy.