arXiv subjects

Tao Liu

Publications and source records attributed to Tao Liu.

At least 109 records · Page 6Linked to original sources

arXiv2025

New Physics Search at the CEPC: a General Perspective

The Circular Electron-Positron Collider (CEPC), a proposed next-generation Higgs factory, provides new opportunities to explore physics beyond the Standard Model (SM). With its clean electron-positron collision environment and the ability to collect large samples of Higgs, W, and Z bosons, the CEPC enables precision measurements and searches for new physics. This white paper outlines the CEPC's discovery potential, including studies of exotic decays of the Higgs, Z, and top quarks, dark matter and dark sector phenomena, long-lived particles, supersymmetry, and neutrino-related signatures. Advanced detector technologies and reconstruction techniques, such as one-to-one correspondence reconstruction and jet origin identification, significantly improve sensitivity to rare and weakly interacting processes. The CEPC is particularly well suited to probe the electroweak phase transition and test models of electroweak baryogenesis and dark sector interactions. In addition, global fit analyses highlight the CEPC's complementary role in constraining a wide range of new physics scenarios. These features position the CEPC as a powerful tool for exploring the next frontier in fundamental particle physics in the post-Higgs discovery era.

hep-ex↗

arXiv2025

ACPO: Adaptive Curriculum Policy Optimization for Aligning Vision-Language Models in Complex Reasoning

Aligning large-scale vision-language models (VLMs) for complex reasoning via reinforcement learning is often hampered by the limitations of existing policy optimization algorithms, such as static training schedules and the rigid, uniform clipping mechanism in Proximal Policy Optimization (PPO). In this work, we introduce Adaptive Curriculum Policy Optimization (ACPO), a novel framework that addresses these challenges through a dual-component adaptive learning strategy. First, ACPO employs a dynamic curriculum that orchestrates a principled transition from a stable, near on-policy exploration phase to an efficient, off-policy exploitation phase by progressively increasing sample reuse. Second, we propose an Advantage-Aware Adaptive Clipping (AAAC) mechanism that replaces the fixed clipping hyperparameter with dynamic, sample-wise bounds modulated by the normalized advantage of each token. This allows for more granular and robust policy updates, enabling larger gradients for high-potential samples while safeguarding against destructive ones. We conduct extensive experiments on a suite of challenging multimodal reasoning benchmarks, including MathVista, LogicVista, and MMMU-Pro. Results demonstrate that ACPO consistently outperforms strong baselines such as DAPO and PAPO, achieving state-of-the-art performance, accelerated convergence, and superior training stability.

cs.AI↗

arXiv2025

Timber: Training-free Instruct Model Refining with Base via Effective Rank

Post-training, which elicits a pretrained Base model into the corresponding Instruct model, is widely considered to be superficial. In this work, we first reinforce this hypothesis by providing novel quantitative evidence from the weight level that the effective rank (eRank) remains negligibly changed. However, this superficiality also suffers a critical trade-off, improving the exploitation capabilities at the cost of limiting its exploration. To tackle this issue, we propose Timber, a simple yet effective training-free method that enhances the exploration capability of the Instruct model while preserving its exploitation. The key insight is to partially revert Instruct towards the paired Base model by subtle yet targeted refinement of the weight deltas. Extensive experiments on Llama and Qwen series demonstrate that Timber consistently improves vanilla Instruct models, particularly on Pass@k performance. Our findings offer new insights into the post-training stage at the weight level and practical strategies to refine the Instruct model without training.

cs.CL↗

arXiv2025

On the inverse transmission eigenvalue problem with a piecewise $W_2^1$ refractive index

In this paper, we consider the inverse spectral problem of determining the spherically symmetric refractive index in a bounded spherical region of radius $b$. Instead of the usual case of the refractive index $ρ\in W^2_2$, by using singular Sturm-Liouville theory, we {first} discuss the case when the refractive index $ρ$ is a piecewise $ W^1_2$ function. We prove that if $\int_0^b \sqrt{ρ(r)} dr<b$, then $ρ$ is uniquely determined by all special transmission eigenvalues; if $\int_0^b \sqrt{ρ(r)} dr=b$, then all special transmission eigenvalues with some additional information can uniquely determine $ρ$. We also consider the mixed spectral problem and obtain that $ρ$ is uniquely determined from partial information of $ρ$ and the ``almost real subspectrum".

math.SP↗

arXiv2025

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation

Large Language Models (LLMs) struggle with complex Text-to-SQL queries that demand both sophisticated mathematical reasoning and intricate schema navigation. Existing methods often tackle these challenges in isolation, creating a fractured reasoning process that compromises logical and structural correctness. To resolve this, we introduce SteinerSQL, a framework that unifies these dual challenges into a single, graph-centric optimization problem. SteinerSQL operates in three stages: mathematical decomposition to identify required tables (terminals), optimal reasoning scaffold construction via a Steiner tree problem, and multi-level validation to ensure correctness. On the challenging LogicCat and Spider2.0-Lite benchmarks, SteinerSQL establishes a new state-of-the-art with 36.10% and 40.04% execution accuracy, respectively, using Gemini-2.5-Pro. Beyond accuracy, SteinerSQL presents a new, unified paradigm for Text-to-SQL, paving the way for more robust and principled solutions to complex reasoning tasks.

cs.AI↗

arXiv2025

Conceptual Design Report of Super Tau-Charm Facility: The Accelerator

Electron-positron colliders operating in the GeV region of center-of-mass energies or the Tau-Charm energy region, have been proven to enable competitive frontier research, due to its several unique features. With the progress of high energy physics in the last two decades, a new-generation Tau-Charm factory, Super Tau Charm Facility (STCF) has been actively promoting by the particle physics community in China. STCF holds great potential to address fundamental questions such as the essence of color confinement and the matter-antimatter asymmetry in the universe in the next decades. The main design goals of STCF are with a center-of-mass energy ranging from 2 to 7 GeV and a peak luminosity surpassing 5*10^34 cm^-2s^-1 that is optimized at a center-of-mass energy of 4 GeV, which is about 50 times that of the currently operating Tau-Charm factory - BEPCII. The STCF accelerator is composed of two main parts: a double-ring collider with the crab-waist collision scheme and an injector that provides top-up injections for both electron and positron beams. As a typical third-generation electron-positron circular collider, the STCF accelerator faces many challenges in both accelerator physics and technology. In this paper, the conceptual design of the STCF accelerator complex is presented, including the ongoing efforts and plans for technological R&D, as well as the required infrastructure. The STCF project aims to secure support from the Chinese central government for its construction during the 15th Five-Year Plan (2026-2030) in China.

physics.acc-ph↗

arXiv2025

Can any model be fabricated? Inverse operation based planning for hybrid additive-subtractive manufacturing

This paper presents a method for computing interleaved additive and subtractive manufacturing operations to fabricate models of arbitrary shapes. We solve the manufacturing planning problem by searching a sequence of inverse operations that progressively transform a target model into a null shape. Each inverse operation corresponds to either an additive or a subtractive step, ensuring both manufacturability and structural stability of intermediate shapes throughout the process. We theoretically prove that any model can be fabricated exactly using a sequence generated by our approach. To demonstrate the effectiveness of this method, we adopt a voxel-based implementation and develop a scalable algorithm that works on models represented by a large number of voxels. Our approach has been tested across a range of digital models and further validated through physical fabrication on a hybrid manufacturing system with automatic tool switching.

cs.GR↗

arXiv2025

Distributed Frequency Control for Multi-Area Power Systems Considering Transient Frequency Safety

High penetration of renewable energy sources intensifies frequency fluctuations in multi-area power systems, challenging both stability and operational safety. This paper proposes a novel distributed frequency control method that ensures transient frequency safety and enforces generation capacity constraints, while achieving steady-state frequency restoration and optimal economic operation. The method integrates a feedback optimization (FO)-based controller and a safety corrector. The FO-based controller generates reference setpoints by solving an optimization problem, driving the system to the steady state corresponding to the optimal solution of this problem. The safety corrector then modifies these references using control barrier functions to maintain frequencies within prescribed safe bounds during transients while respecting capacity constraints. The proposed method combines low computational burden with improved regulation performance and enhanced practical applicability. Theoretical analysis establishes optimality, asymptotic stability, and transient frequency safety for the closed-loop system. Simulation studies show that, compared with conventional FO-based schemes, the method consistently enforces frequency safety and capacity limits, achieves smaller frequency deviations and faster recovery, thereby demonstrating its practical effectiveness and advantages.

eess.SY↗

arXiv2025

Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search

Recent advances in large multimodal models have leveraged image-based tools with reinforcement learning to tackle visual problems. However, existing open-source approaches often exhibit monotonous reasoning patterns and allow only a limited number of interaction turns, making them inadequate for difficult tasks that require trial-and-error exploration. In this work, we address this limitation by scaling up tool-based interactions and introduce Mini-o3, a system that executes deep, multi-turn reasoning -- spanning tens of steps -- and achieves state-of-the-art performance on challenging visual search tasks. Our recipe for reproducing OpenAI o3-style behaviors comprises three key components. First, we construct the Visual Probe Dataset, a collection of thousands of challenging visual search problems designed for exploratory reasoning. Second, we develop an iterative data collection pipeline to obtain cold-start trajectories that exhibit diverse reasoning patterns, including depth-first search, trial-and-error, and goal maintenance. Third, we propose an over-turn masking strategy that prevents penalization of over-turn responses (those that hit the maximum number of turns) during reinforcement learning, thereby balancing training-time efficiency with test-time scalability. Despite training with an upper bound of only six interaction turns, our model generates trajectories that naturally scale to tens of turns at inference time, with accuracy improving as the number of turns increases. Extensive experiments demonstrate that Mini-o3 produces rich reasoning patterns and deep thinking paths, effectively solving challenging visual search problems.

cs.CV↗

arXiv2025

LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning

Text-to-SQL is a critical task in natural language processing that aims to transform natural language questions into accurate and executable SQL queries. In real-world scenarios, these reasoning tasks are often accompanied by complex mathematical computations, domain knowledge, and hypothetical reasoning scenarios. However, existing large-scale Text-to-SQL datasets typically focus on business logic and task logic, neglecting critical factors such as vertical domain knowledge, complex mathematical reasoning, and hypothetical reasoning, which are essential for realistically reflecting the reasoning demands in practical applications and completing data querying and analysis. To bridge this gap, we introduce LogicCat, the first Text-to-SQL benchmark dataset specifically designed for complex reasoning and chain-of-thought parsing, encompassing physics, arithmetic, commonsense, and hypothetical reasoning scenarios. LogicCat comprises 4,038 English questions paired 12,114 detailed chain-of-thought reasoning steps, spanning 45 databases across diverse domains, significantly surpassing existing datasets in complexity. Experimental results demonstrate that LogicCat substantially increases the task difficulty for current state-of-the-art models to at most 33.20% execution accuracy, indicating that this task remains exceptionally challenging. The advancement of LogicCat represents a crucial step toward developing systems suitable for real-world enterprise data analysis and autonomous query generation. We have released our dataset code at https://github.com/Ffunkytao/LogicCat.

cs.CL↗

arXiv2025

Double logarithmic contribution to Higgs pair production in the high-energy limit

We study the leading logarithmic QCD corrections to Higgs pair production in the high-energy limit, which originate from soft quark exchange in the Feynman diagram and are thus suppressed by the quark mass. It is found that triangle Feynman diagrams can be analyzed in the same way as single Higgs production mediated by bottom quarks [45-47], while for diagrams of box type we cannot get an all-order result due to the many more double logarithmic structures they own. In this paper we calculate the abelian corrections at three loops for the first time after obtaining the corresponding one- and two-loop leading logarithms, which are in total agreement with the analytical results in the literature.

hep-ph↗

arXiv2025

Observation of Arbitrarily Configurable Nonlinear Topological Modes

Nonlinear topology is an emerging field that combines the intrinsic reconfigurability of nonlinear systems with the robustness of topological protection, offering fertile ground for unconventional phenomena and novel applications. Recently, arbitrarily configurable nonlinear topological modes (ANTMs) were proposed, enabling wavefunctions to be configured into arbitrary profiles , and offering greatly enhanced capacity for topological modes and high-throughput topological transport. Here we present the first direct experimental demonstration of ANTMs . These nonlinear topological modes are robust against disorder while also being continuously reshaped and reconfigured in real time through external control. These counterintuitive properties highlight the versatility of arbitrarily morphing nonlinear topological modes and pave the way for highly adaptable topological devices capable of operating reliably across diverse application scenarios, including those involving imperfections, signal variability, and dynamic conditions.

physics.optics↗

arXiv2025

From Geostrophic to Magnetically-Damped Turbulence in Liquid Metal Rotating Magnetoconvection

Understanding planetary core convection dynamics requires the study of convective flows in which the Coriolis and Lorentz forces attain a leading-order, so-called magnetostrophic balance. Experimental investigations of rotating magnetoconvection (RMC) in the magnetostrophic regime are therefore essential to broadly characterize the properties of local-scale planetary core flow. Towards this end, we present here the first thermovelocimetric measurements of magnetostrophic, liquid metal convection, which are made using liquid gallium as the working fluid, at moderate rotation rates (Ekman numbers $10^{-4} \leq Ek\leq 10^{-5}$) and in the presence of dynamically strong magnetic fields (Elsasser number $Λ=1$). Complementary rotating convection (RC) experiments are performed at the same rotation rates to serve as reference cases. Our RMC velocity measurements adequately follow a geostrophic turbulent scaling for cases in which local-scale convective inertial forces exceed the Lorentz forces in the fluid bulk. In cases where Lorentz forces exceed local-scale inertia ($N_\ell \gtrsim 3$), the root-mean-square RMC velocities are magnetically damped, yielding values below the geostrophic turbulent RC scaling prediction. An enhancement in heat transfer is observed, which we attribute to the increased coherence of vertically aligned magnetostrophic convective flow. Extrapolating these laboratory results, we predict that convection-scale flows in Earth's core occur in the magnetically damped $N_\ell \gtrsim 3$ regime with Rayleigh number values between $10^{24}$ and $10^{26}$.

physics.flu-dyn↗

arXiv2025

Dark Photon Polarimetry

We propose detecting dark photons (DP), a major candidate for wave dark matter, through polarimetry. The DP can modify Maxwell's equations due to its kinetic mixing with regular photons, inducing an oscillating component in the electromagnetic field. This may leave an imprint on polarimetric light signals, such as Faraday rotation, characterized by a distinctive wave pattern in spacetime. We then apply this method to investigate ultralight DPs produced through the superradiance of supermassive black holes for demonstration. Using the polarimetric measurements of radiation from the M87$^\ast$ and Sgr A$^\ast$ at the Event Horizon Telescope, we show that novel limits on the photon-DP mixing parameter can be set for the rarely-explored DP mass ranges: explicitly $10^{-22} - 10^{-20}\,$eV with the best reach of $\sim 10^{-8}$ achieved at $\sim 10^{-20}$eV, and $10^{-19} - 10^{-17}\,$eV with the best reach of $\sim 10^{-11}$ achieved at $\sim 10^{-17}$eV. Given the universality of its underlying physics, we expect DP polarimetry to be broadly applicable for DP detection in laboratory experiments and astronomical observations.

hep-ph↗

arXiv2025

Versatile Control of Nonlinear Topological States in Non-Hermitian Systems

The non-Hermitian skin effect (NHSE) and nonlinearity can both delocalize topological modes (TMs) from the interface. However, the NHSE requires precise parameter tuning, while nonlinearity in Hermitian systems results in partial delocalization with limited mode capacity. To overcome these limitations, we propose a non-Hermitian nonlinear topological interface model that integrates Hermitian and non-Hermitian lattices with nonreciprocal hopping and nonlinearity. This system enables the complete delocalization of TMs across the entire lattice without fine-tuning, while allowing precise control over the wavefunction profile and spatial distribution through the intrinsic configuration and intensity of the nonlinearity. Using the spectral localizer, we demonstrate the topological protection and robustness of these extended non-Hermitian TMs against disorder. Furthermore, we show that under external pumping, localized excitations evolve into predefined profiles and generate long-range patterns, an effect unattainable in Hermitian systems. These findings reveal how the interplay of nonlinearity and NHSE shapes topological states, paving the way for compact topological devices.

quant-ph↗

arXiv2025

Anderson Delocalization in Strongly Coupled Disordered Non-Hermitian Chains

Disorder and non-Hermitian effects together can upend how waves localize. In a 1D disordered chain, the non-Hermitian skin effect (NHSE) can induce Anderson delocalization, defying the usual rule that disorder in low dimensions always localizes states. While weak disorder leaves the NHSE intact, strong disorder restores Anderson localization. Here, we study a surprising twist: coupling a strongly disordered Hatano-Nelson chain to a disordered Hermitian chain with their disorder anti-symmetrically correlated. Strikingly, once the inter-chain coupling exceeds a threshold, the system undergoes Anderson delocalization irrespective of disorder strength, reinstating the NHSE with no Hermitian counterpart. This transition arises from the interplay of non-reciprocal hopping, inter-chain coupling, and engineered disorder correlations, and is captured by a real-space winding number. To confirm this, we build an electrical-circuit analog and directly observe the re-emergent NHSE via voltage measurements. Our work uncovers unexplored and experimentally accessible physics at the crossroads of non-Hermiticity and disorder.

cond-mat.dis-nn↗

arXiv2025

History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL

With the rapid advancement of large language models (LLMs), reinforcement learning (RL) has emerged as a pivotal methodology for enhancing the reasoning capabilities of LLMs. Unlike traditional pre-training approaches, RL encompasses multiple stages: rollout, reward, and training, which necessitates collaboration among various worker types. However, current RL systems continue to grapple with substantial GPU underutilization, due to two primary factors: (1) The rollout stage dominates the overall RL process due to test-time scaling; (2) Imbalances in rollout lengths (within the same batch) result in GPU bubbles. While prior solutions like asynchronous execution and truncation offer partial relief, they may compromise training accuracy for efficiency. Our key insight stems from a previously overlooked observation: rollout responses exhibit remarkable similarity across adjacent training epochs. Based on the insight, we introduce RhymeRL, an LLM RL system designed to accelerate RL training with two key innovations. First, to enhance rollout generation, we present HistoSpec, a speculative decoding inference engine that utilizes the similarity of historical rollout token sequences to obtain accurate drafts. Second, to tackle rollout bubbles, we introduce HistoPipe, a two-tier scheduling strategy that leverages the similarity of historical rollout distributions to balance workload among rollout workers. We have evaluated RhymeRL within a real production environment, demonstrating scalability from dozens to thousands of GPUs. Experimental results demonstrate that RhymeRL achieves a 2.6x performance improvement over existing methods, without compromising accuracy or modifying the RL paradigm.

cs.LG↗

arXiv2025

Degree of Staleness-Aware Data Updating in Federated Learning

Handling data staleness remains a significant challenge in federated learning with highly time-sensitive tasks, where data is generated continuously and data staleness largely affects model performance. Although recent works attempt to optimize data staleness by determining local data update frequency or client selection strategy, none of them explore taking both data staleness and data volume into consideration. In this paper, we propose DUFL(Data Updating in Federated Learning), an incentive mechanism featuring an innovative local data update scheme manipulated by three knobs: the server's payment, outdated data conservation rate, and clients' fresh data collection volume, to coordinate staleness and volume of local data for best utilities. To this end, we introduce a novel metric called DoS(the Degree of Staleness) to quantify data staleness and conduct a theoretic analysis illustrating the quantitative relationship between DoS and model performance. We model DUFL as a two-stage Stackelberg game with dynamic constraint, deriving the optimal local data update strategy for each client in closed-form and the approximately optimal strategy for the server. Experimental results on real-world datasets demonstrate the significant performance of our approach.

cs.LG↗