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Jun Yang

Publications and source records attributed to Jun Yang.

At least 91 records · Page 5Linked to original sources
arXiv2026

ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation

This paper proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios. Additionally, it introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments. This complex optimization problem is solved by the alternating direction method of multipliers (ADMM), which decomposes the problem into two subproblems tackled by differential dynamic programming (DDP) and quadratic programming (QP), respectively. The effectiveness of this approach is validated in simulation and hardware experiments on a Franka Panda robotic manipulator. Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints.

cs.RO↗
arXiv2026

Towards AI Search Paradigm

In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to evaluate query complexity, decompose problems into executable plans, and orchestrate tool usage, task execution, and content synthesis. We systematically present key methodologies for realizing this paradigm, including task planning and tool integration, execution strategies, aligned and robust retrieval-augmented generation, and efficient LLM inference, spanning both algorithmic techniques and infrastructure-level optimizations. By providing an in-depth guide to these foundational components, this work aims to inform the development of trustworthy, adaptive, and scalable AI search systems.

cs.CL↗
arXiv2026

Orbital-Selective Spin-Orbit Mott Insulator in Fractional Valence Iridate La$_3$Ir$_3$O$_{11}$

The combination of strong spin-orbit coupling and Coulomb interactions makes the $5d$ iridates a unique platform for realizing novel correlated electronic states. Here, utilizing infrared spectroscopy, we demonstrate that a robust Mott insulating state persists in the $1/3$-hole self-doped system La$_3$Ir$_3$O$_{11}$, evidenced by the collapse of the Drude response and the emergence of sharp excitations across the Mott gap. Our theoretical calculations reveal that the insulating behavior arises from the cooperative interplay of structural distortions, spin-orbit coupling, and Coulomb interactions. Specifically, octahedral distortion and Ir-Ir dimerization split the $t_{2g}$ orbitals, driving the $J_{\mathrm{eff}} = 1/2$ bands toward half-filling while keeping the $J_{\mathrm{eff}} = 3/2$ bands away from it. Consequently, electron correlations induce an orbital-selective Mott transition in the $J_{\mathrm{eff}} = 1/2$ bands, whereas a band-insulating gap develops in the $J_{\mathrm{eff}} = 3/2$ bands, thereby stabilizing the unconventional insulating state in La$_3$Ir$_3$O$_{11}$. These findings provide new insights into the design and understanding of the insulating ground state of spin-orbit-coupled iridates.

cond-mat.str-el↗
arXiv2026

Gradient estimates for $p$-Laplacian equation with cubic polynomial nonlinearity on Riemannian manifolds

This paper studies a class of $p$-Laplace equations with cubic polynomial nonlinearity \[ Δ_p v + (v-a_1)(v-a_2)(v-a_3) = 0 \] on complete Riemannian manifolds $M$ with lower Ricci curvature bounds, where $a_1 < a_2 < a_3$ are real constants and $Δ_p v = \operatorname{div}(|\nabla v|^{p-2}\nabla v)$ denotes the $p$-Laplace operator. Depending on whether the solution lies in the intervals $(a_1,a_2), (a_2,a_3)$ or $(a_1,a_3)$, we employ, respectively, a logarithmic transformation or a hyperbolic tangent transformation to convert the original equation to another one for further analysis. Through a detailed analysis of the lower-bound estimate for the linearized operator of the new equation, and by combining Saloff-Coste's Sobolev inequality with a Moser iteration, we establish Cheng-Yau type gradient estimates under an additional assumption on $p$. As applications, the Liouville theorem and a Harnack inequality are further proved.

math.AP↗
arXiv2026

RTLocating: Intent-aware RTL Localization for Hardware Design Iteration

Industrial chip development is inherently iterative, favoring localized, intent-driven updates over rewriting RTL from scratch. Yet most LLM-Aided Hardware Design (LAD) work focuses on one-shot synthesis, leaving this workflow underexplored. To bridge this gap, we for the first time formalize $Δ$Spec-to-RTL localization, a multi-positive problem mapping natural language change requests ($Δ$Spec) to the affected Register Transfer Level (RTL) syntactic blocks. We propose RTLocating, an intent-aware RTL localization framework, featuring a dynamic router that adaptively fuses complementary views from a textual semantic encoder, a local structural encoder, and a global interaction and dependency encoder (GLIDE). To enable scalable supervision, we introduce EvoRTL-Bench, the first industrial-scale benchmark for intent-code alignment derived from OpenTitan's Git history, comprising 1,905 validated requests and 13,583 $Δ$Spec-RTL block pairs. On EvoRTL-Bench, RTLocating achieves 0.568 MRR and 15.08% R@1, outperforming the strongest baseline by +22.9% and +67.0%, respectively, establishing a new state-of-the-art for intent-driven localization in evolving hardware designs.

cs.ET↗
arXiv2026

Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users

Early-stage users in a new scenario intensify cold-start challenges, yet prior works often address only parts of the problem through model architecture. Launching a new user experience to replace an established product involves sparse behavioral signals, low-engagement cohorts, and unstable model performance. We argue that effective recommendations require the synergistic integration of feature engineering, model architecture, and stable model updating. We propose Trinity, a framework embodying this principle. Trinity extracts valuable information from existing scenarios while ensuring predictive effectiveness and accuracy in the new scenario. In this paper, we showcase Trinity applied to a billion-user Microsoft product transition. Both offline and online experiments demonstrate that our framework achieves substantial improvements in addressing the combined challenge of new users in new scenarios.

cs.LG↗
arXiv2026

Perception-Control Coupled Visual Servoing for Textureless Objects Using Keypoint-Based EKF

Visual servoing is fundamental to robotic applications, enabling precise positioning and control. However, applying it to textureless objects remains a challenge due to the absence of reliable visual features. Moreover, adverse visual conditions, such as occlusions, often corrupt visual feedback, leading to reduced accuracy and instability in visual servoing. In this work, we build upon learning-based keypoint detection for textureless objects and propose a method that enhances robustness by tightly integrating perception and control in a closed loop. Specifically, we employ an Extended Kalman Filter (EKF) that integrates per-frame keypoint measurements to estimate 6D object pose, which drives pose-based visual servoing (PBVS) for control. The resulting camera motion, in turn, enhances the tracking of subsequent keypoints, effectively closing the perception-control loop. Additionally, unlike standard PBVS, we propose a probabilistic control law that computes both camera velocity and its associated uncertainty, enabling uncertainty-aware control for safe and reliable operation. We validate our approach on real-world robotic platforms using quantitative metrics and grasping experiments, demonstrating that our method outperforms traditional visual servoing techniques in both accuracy and practical application.

cs.RO↗
arXiv2026

Traffic-aware Hierarchical Integrated Thermal and Energy Management for Connected HEVs

The energy and thermal management systems of hybrid electric vehicles (HEVs) are inherently interdependent. With the ongoing deployment of intelligent transportation systems (ITSs) and increasing vehicle connectivity, the integration of traffic information has become crucial for improving both energy efficiency and thermal comfort in modern vehicles. To enhance fuel economy, this paper proposes a novel traffic-aware hierarchical integrated thermal and energy management (TA-ITEM) strategy for connected HEVs. In the upper layer, global reference trajectories for battery state of charge (SOC) and cabin temperature are planned using traffic flow speed information obtained from ITSs. In the lower layer, a real-time model predictive control (MPC)-based ITEM controller is developed, which incorporates a novel Transformer-based speed predictor with driving condition recognition (TF-DCR) to enable anticipatory tracking of the reference trajectories. Numerical simulations are conducted under various driving cycles and ambient temperature conditions. The results demonstrate that the proposed TA-ITEM approach outperforms conventional rule-based and MPC-SP approaches, with average fuel consumption reductions of 56.36\% and 5.84\%, respectively, while maintaining superior thermal regulation and cabin comfort. These findings confirm the effectiveness and strong generalization capability of TA-ITEM and underscore the advantages of incorporating traffic information.

eess.SY↗
arXiv2026

Solutions with one dimensional concentration for a two dimensional Gross-Pitaevskii model with general potential

We concern standing wave solutions with frequency $λ$ to a two dimensional Gross-Pitaevskii equation with a trap potential under the unit mass constraint, which is used to describe Bose-Einstein condensates with attractive interaction. First, we investigate the necessary conditions for existence of the solutions with concentration phenomena directed along closed smooth curves. Next, not only imposing stationary and non-degeneracy conditions on the curves with respect to an auxiliary weighted length involving the trap potential, but also adding some other technical assumptions, we select a sequence $\{λ_j\}$ of the frequency $λ$ with $-λ_j\rightarrow +\infty$ and construct solutions with concentration directed along the curves. Our result partially answers the conjecture raised in [A. Ambrosetti, A. Malchiodi, W.-M. Ni, Comm. Math. Phys. 2003] about necessary condition for solution concentrating at submanifolds. The solutions constructed in this paper are concentrating on curves whose length are non-uniformly bounded, and hence the situation is quite different from that in [M. del Pino, M. Kowalczyk, J. Wei, Comm. Pure Appl. Math. 2007].

math.AP↗
arXiv2026

Reason to Retrieve: Enhancing Query Understanding through Decomposition and Interpretation

Query understanding (QU) aims to accurately infer user intent to improve document retrieval. It plays a vital role in modern search engines. While large language models (LLMs) have made notable progress in this area, their effectiveness has primarily been studied on short, keyword-based queries. With the rise of AI-driven search, long-form queries with complex intent become increasingly common, but they are underexplored in the context of LLM-based QU. To address this gap, we introduce ReDI, a reasoning-enhanced query understanding method through decomposition and interpretation. ReDI uses the reasoning and understanding capabilities of LLMs within a three-stage pipeline. (i) It decomposes a complex query into a set of targeted sub-queries to capture the user intent. (ii) It enriches each sub-query with detailed semantic interpretations to enhance the retrieval of intent-document matching. And (iii), after independently retrieving documents for each sub-query, ReDI uses a fusion strategy to aggregate the results and obtain the final ranking. We collect a large-scale dataset of real-world complex queries from a commercial search engine and distill the query understanding capabilities of DeepSeek-R1 into small models for practical application. Experiments on public benchmarks, including BRIGHT and BEIR, show that ReDI consistently outperforms strong baselines in both sparse and dense retrieval paradigms, demonstrating its effectiveness. We release our code, generated sub-queries, and interpretations at https://github.com/youngbeauty250/ReDI.

cs.IR↗
arXiv2026

The Global Jacquet-Langlands Correspondence via Tensor Products

We prove that the global Jacquet--Langlands correspondence ${\rm JL}$ for ${\rm GL}(2)$ can be realized via tensor products over Hecke algebras. Let $G$ be a non-split inner form of ${\rm GL}(2)$ over a number field. Using the similitude theta correspondence, the space $L^2(D(\mathbb{A})\times \mathbb{A}^{\times})$ acquires the structure of a $G(\mathbb{A})$-$(G(\mathbb{A})\times {\rm GL}(2,\mathbb{A}))$ bimodule such that $L^2(G(F)\backslash G(\mathbb{A}),χ)\otimes_{\mathcal{H}(G)}L^2(D(\mathbb{A})\times \mathbb{A}^{\times})~\cong~\oplus_{π\in {\mathcal{A}}(G,χ^{-1})}$ $π\otimes{\rm JL}(π).$ This decomposition into irreducible representations of $G(\mathbb{A})\times {\rm GL}(2,\mathbb{A})$ recovers the full global Jacquet-Langlands correspondence.

math.RT↗
arXiv2026

An Archival Optical Counterpart Search for Extragalactic Fast X-Ray Transients Discovered by Einstein Probe

Extragalactic fast X-ray transients (eFXTs) represent a rapidly growing class of high-energy phenomena, whose physical origins remain poorly understood. With its wide-field, sensitive all-sky monitoring, the Einstein Probe (EP) has greatly increased the discovery rate of eFXTs. The search and identification of the optical counterparts of eFXT are vital for understanding their classification and constraining their physical origin. Yet, a considerable fraction of eFXTs still lack secure classifications due to the absence of timely follow-up observations. We carry out a systematic search of publicly available optical survey data and transient databases (including the Zwicky Transient Facility, ZTF, and the Transient Name Server, TNS) for optical counterparts to eFXT candidates detected by EP. In this paper, we describe our ongoing program and report the first results. Specifically, we identified the eFXT EP240506a to be associated with a UV/optical counterpart, AT 2024ofs. Spectroscopy of its host galaxy with VLT yields a redshift of $z = 0.120 \pm 0.002$. By combining archival survey data with early-time multiwavelength observations, we find that the luminosity and light-curve evolution of AT~2024ofs are consistent with a core-collapse supernova origin. From detectability simulations, we estimate a local event rate density $ρ_{0}=8.8^{+21.2}_{-3.9}\ \mathrm{yr^{-1}\, Gpc^{-3}}$ for EP240506a-like events, and completeness-corrected rate of about $36$--$78\ \mathrm{yr^{-1}\ Gpc^{-3}}$ for EP-detected X-ray transients associated with supernovae. Our results demonstrate the potential of EP to uncover prompt high-energy emission from core-collapse supernovae and underscore the critical importance of timely follow-up of future eFXT events.

astro-ph.HE↗
arXiv2026

Ocean Circulation on Tide-locked Lava Worlds: 3D Modeling with a Simple Boundary Iteration Method

Tide-locked lava worlds are surface-melted rocky planets under 1:1 tidally locked orbit (i.e., synchronously rotating) with orbital period being equal to rotation period and with permanent hot dayside and cold nightside. Previous studies on this type of planets employed scaling analyses and two-dimensional (2D) simulations. This work is a continuation of the previous researches but including the effect of the Coriolis force and the simulation domain is extended to a 3D global sphere. We find that under the condition with thermal-only forcing (without surface wind stresses), the area-mean ocean depth is about 50--300 m (depending on vertical diffusivity) and the area-mean effect of horizontal ocean heat transport (in the order of 10$^{3}$ to 10$^{4}$ W m$^{-2}$) is significantly smaller than stellar radiation (in the order of 10$^{6}$ W m$^{-2}$ at the substellar region), being consistent with previous results. Different from 2D results, due to the effect of the Coriolis force, large-scale horizontal gyres form on the dayside, ocean currents near the west boundaries are much stronger than that near the east boundaries (called as ``western intensification''), the deepest ocean is not right at the substellar point but in the middle latitudes as the vertical diffusivity is moderate or large, and meanwhile there exists significant asymmetry between the west and the east of the substellar point. These results establish a first picture for the 3D thermal-driven ocean circulation and confirm that the lava ocean should be shallow on tide-locked lava worlds.

astro-ph.EP↗
arXiv2026

CIM-Tuner: Balancing the Compute and Storage Capacity of SRAM-CIM Accelerator via Hardware-mapping Co-exploration

As an emerging type of AI computing accelerator, SRAM Computing-In-Memory (CIM) accelerators feature high energy efficiency and throughput. However, various CIM designs and under-explored mapping strategies impede the full exploration of compute and storage balancing in SRAM-CIM accelerator, potentially leading to significant performance degradation. To address this issue, we propose CIM-Tuner, an automatic tool for hardware balancing and optimal mapping strategy under area constraint via hardware-mapping co-exploration. It ensures universality across various CIM designs through a matrix abstraction of CIM macros and a generalized accelerator template. For efficient mapping with different hardware configurations, it employs fine-grained two-level strategies comprising accelerator-level scheduling and macro-level tiling. Compared to prior CIM mapping, CIM-Tuner's extended strategy space achieves 1.58$\times$ higher energy efficiency and 2.11$\times$ higher throughput. Applied to SOTA CIM accelerators with identical area budget, CIM-Tuner also delivers comparable improvements. The simulation accuracy is silicon-verified and CIM-Tuner tool is open-sourced at https://github.com/champloo2878/CIM-Tuner.git.

cs.AR↗
arXiv2026

Detection of a Millisecond Periodicity in BATSE Short Gamma-Ray Bursts

Coherent oscillations at kilohertz frequencies have recently been detected in a small number of gamma-ray bursts (GRBs), suggesting quasi-periodic dynamics in their central engines. A prominent example is GRB~230307A, which exhibited a brief, highly coherent, energy-dependent periodic signal interpreted as the possible spin signature of a nascent millisecond magnetar formed after a compact binary merger. Motivated by these developments, we conducted a comprehensive search for similar signals, accounting for both temporal and spectral dependencies, in 532 short GRBs with time-tagged event data recorded by the Burst and Transient Source Experiment (BATSE) onboard the \textit{Compton Gamma-Ray Observatory}. Within this sample, we identify a single statistically significant case: GRB~960616 (BATSE trigger~5502), in which the $\sim$30~ms main emission episode is coherently modulated at 1100~Hz, with the strongest modulation above 320~keV and a fractional amplitude of $\sim$47\%. Assuming the presence of a coherent periodic modulation, we use data-driven Monte Carlo simulations to place an upper limit of $\sim$8\% on the fraction of the total radiated energy that can be modulated by the QPO. This event, exhibiting a periodicity at $\sim$0.91~ms, further supports the possibility that millisecond periodicities can arise during GRBs in merger-driven scenarios.

astro-ph.HE↗
arXiv2026

Sub-Cauchy Sampling: Escaping the Dark Side of the Moon

We introduce a Markov chain Monte Carlo algorithm based on Sub-Cauchy Projection, a geometric transformation that generalizes stereographic projection by mapping Euclidean space into a spherical cap of a hyper-sphere, referred to as the complement of the dark side of the moon. We prove that our proposed method is uniformly ergodic for sub-Cauchy targets, namely targets whose tails are at most as heavy as a multidimensional Cauchy distribution, and show empirically its performance for challenging high-dimensional problems. The simplicity and broad applicability of our approach open new opportunities for Bayesian modeling and computation with heavy-tailed distributions in settings where most existing methods are unreliable.

stat.CO↗
arXiv2026

A reconsideration of quasimonotone variational inequality problems

This paper is based on Tseng's exgradient algorithm for solving variational inequality problems in real Hilbert spaces. Under the assumptions that the cost operator is quasimonotone and Lipschitz continuous, we establish the strong convergence, sublinear convergence, and Q-linear convergence of the algorithm. The results of this paper provide new insights into quasimonotone variational inequality problems, extending and enriching existing results in the literature. Finally, we conduct numerical experiments to illustrate the effectiveness and implementability of our proposed condition and algorithm.

math.OC↗
arXiv2026

ActiShade: Activating Overshadowed Knowledge to Guide Multi-Hop Reasoning in Large Language Models

In multi-hop reasoning, multi-round retrieval-augmented generation (RAG) methods typically rely on LLM-generated content as the retrieval query. However, these approaches are inherently vulnerable to knowledge overshadowing - a phenomenon where critical information is overshadowed during generation. As a result, the LLM-generated content may be incomplete or inaccurate, leading to irrelevant retrieval and causing error accumulation during the iteration process. To address this challenge, we propose ActiShade, which detects and activates overshadowed knowledge to guide large language models (LLMs) in multi-hop reasoning. Specifically, ActiShade iteratively detects the overshadowed keyphrase in the given query, retrieves documents relevant to both the query and the overshadowed keyphrase, and generates a new query based on the retrieved documents to guide the next-round iteration. By supplementing the overshadowed knowledge during the formulation of next-round queries while minimizing the introduction of irrelevant noise, ActiShade reduces the error accumulation caused by knowledge overshadowing. Extensive experiments show that ActiShade outperforms existing methods across multiple datasets and LLMs.

cs.CL↗