arXiv subjects

Bo Ma

Publications and source records attributed to Bo Ma.

At least 55 records · Page 3Linked to original sources

arXiv2025

Planet Across Space and Time (PAST). VII. The origin and tidal evolution of hot Jupiters constrained by a broken age-frequency relation

The discovery of hot Jupiters has challenged the classical planet formation theory. Although various formation mechanisms have been proposed, the dominant channel and relative contributions remain unclear. Furthermore, hot Jupiters offer a unique opportunity to test tidal theory and measure the fundamental tidal quality factor, which is yet to be well-constrained. In this work, based on a hot Jupiter sample around single Sun-like stars with kinematic properties, {we find that the declining trend of their frequency is broken with a ridge at about 2 Gyr, providing direct evidence that hot Jupiters are formed with multiple origins of different timescales. By fitting with the theoretical expectations, we provide a constraint of tidal factor for Sun-like stars, which aligns well with the detected number of hot Jupiters with orbital decay. Moreover, we simultaneously constrain the relative importance of different channels: although the majority of hot Jupiters are formed early, within several tenths of Gyr via 'Early' models (e.g., in-situ formation, disk migration, planet-planet scattering and Kozai-Lidov interaction), a significant portion (about 40%) should be formed late on a relatively long timescale extending up to several Gyr mainly via the secular chaos mechanism, further supported by the obliquity distribution of 'late-arrived' hot Jupiters. Our findings provide a unified framework that reconciles hot Jupiter demographics and long-term evolution with multichannel formation.

astro-ph.EP↗

arXiv2025

The AI Productivity Index (APEX)

We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.

econ.GN↗

arXiv2025

Chat with UAV -- Human-UAV Interaction Based on Large Language Models

The future of UAV interaction systems is evolving from engineer-driven to user-driven, aiming to replace traditional predefined Human-UAV Interaction designs. This shift focuses on enabling more personalized task planning and design, thereby achieving a higher quality of interaction experience and greater flexibility, which can be used in many fileds, such as agriculture, aerial photography, logistics, and environmental monitoring. However, due to the lack of a common language between users and the UAVs, such interactions are often difficult to be achieved. The developments of Large Language Models possess the ability to understand nature languages and Robots' (UAVs') behaviors, marking the possibility of personalized Human-UAV Interaction. Recently, some HUI frameworks based on LLMs have been proposed, but they commonly suffer from difficulties in mixed task planning and execution, leading to low adaptability in complex scenarios. In this paper, we propose a novel dual-agent HUI framework. This framework constructs two independent LLM agents (a task planning agent, and an execution agent) and applies different Prompt Engineering to separately handle the understanding, planning, and execution of tasks. To verify the effectiveness and performance of the framework, we have built a task database covering four typical application scenarios of UAVs and quantified the performance of the HUI framework using three independent metrics. Meanwhile different LLM models are selected to control the UAVs with compared performance. Our user study experimental results demonstrate that the framework improves the smoothness of HUI and the flexibility of task execution in the tasks scenario we set up, effectively meeting users' personalized needs.

cs.RO↗

arXiv2025

Mock Observations for the CSST Mission: CPI-C -- Instrument Simulation

To support the development of the data processing pipeline and the scientific performance assessment for the Cool Planet Imaging Coronagraph (CPI-C) on the Chinese Space Station Survey Telescope (CSST), we have developed the end-to-end instrument simulation program, CPISM. This paper details the core modules of CPISM that simulate the CPI-C instrument, focusing on the simulation of the high-contrast imaging optical system and the visible-band science camera. We modeled key optical components, such as the transmission apodizing filter, the wavefront corrector, and the focal plane mask using the HCIPy package. A $10^{-8}$ contrast dark hole region, consistent with design specifications, was simulated using the Electric Field Conjugation (EFC) optimization method, and broadband observation effects were considered. For the science camera, which is an electron multiplying charge-coupled device (EMCCD), we established a detailed model encompassing photon collection, charge transfer, electron multiplication (EM), and readout processes, based on test data. This model simulates complex instrumental features including dark current, charge transfer efficiency, clock-induced charge, multiplication noise factor, and various readout effects like striping and drift. We also proposed and validated an improved statistical model for the EM process to enhance simulation efficiency. CPISM can generate simulated images containing rich instrumental details, closely similar to the expected real observational data, thus laying the foundation for the development and verification of CPI-C data processing algorithms and preparations for future scientific research.

astro-ph.IM↗

arXiv2025

MLLM-Driven Semantic Identifier Generation for Generative Cross-Modal Retrieval

Generative cross-modal retrieval, which treats retrieval as a generation task, has emerged as a promising direction with the rise of Multimodal Large Language Models (MLLMs). In this setting, the model responds to a text query by generating an identifier corresponding to the target image. However, existing methods typically rely on manually crafted string IDs, clustering-based labels, or atomic identifiers requiring vocabulary expansion, all of which face challenges in semantic alignment or scalability.To address these limitations, we propose a vocabulary-efficient identifier generation framework that prompts MLLMs to generate Structured Semantic Identifiers from image-caption pairs. These identifiers are composed of concept-level tokens such as objects and actions, naturally aligning with the model's generation space without modifying the tokenizer. Additionally, we introduce a Rationale-Guided Supervision Strategy, prompting the model to produce a one-sentence explanation alongside each identifier serves as an auxiliary supervision signal that improves semantic grounding and reduces hallucinations during training.

cs.IR↗

arXiv2025

Irradiated Atmospheres IV: Effect of Mixing Heat Flux on Chemistry

Vertical mixing disrupts the thermochemical equilibrium and introduces additional heat flux that alters exoplanetary atmospheric temperatures. We investigate how this mixing-induced heat flux affects atmospheric chemistry. Temperature increase in the lower atmosphere by the mixing-induced heat flux alters species abundances there and modifies those in the upper atmosphere through vertical transport. In the lower atmosphere, most species follow thermodynamic equilibrium with temperature changes. In the upper layers, species mixing ratios depend on the positions of quenching levels relative to the regions exhibiting significant mixing-induced temperature variations. When the quenching level resides within such region (e.g. CO, $\rm CH_4$, and $\rm H_2O$ with strong mixing), the mixing ratios in the upper atmosphere are modified due to changes in the quenched ratios affected by the temperature variation in the lower atmosphere. This alters the mixing ratio of other species (e.g. NO and $\rm CO_2$) through the chemical reaction network, whose quenching occurs in the region without much temperature change. The mixing ratios of $\rm CH_4$, $\rm H_2O$, and $\rm NH_3$ decrease in the lower atmosphere with increasing mixing heat flux, similarly reducing these ratios in the upper atmosphere. Conversely, the mixing ratios of CO, $\rm CO_2$, and NO rise in the lower atmosphere, with CO and $\rm CO_2$ also increasing in the upper levels, although NO decreases. Weaker host star irradiation lowers the overall temperature of the planet, allowing a smaller mixing to have a similar effect. We conclude that understanding the vertical mixing heat flux is essential for accurate atmospheric chemistry modeling and retrieval.

astro-ph.EP↗

arXiv2025

AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems

Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This paper introduces AgenticRAG, a novel framework that combines tool-augmented foundation models with retrieval-augmented generation for zero-shot explainable recommendations. Our approach integrates external tool invocation, knowledge retrieval, and chain-of-thought reasoning to create autonomous recommendation agents capable of transparent decision-making without task-specific training. Experimental results on three real-world datasets demonstrate that AgenticRAG achieves consistent improvements over state-of-the-art baselines, with NDCG@10 improvements of 0.4\% on Amazon Electronics, 0.8\% on MovieLens-1M, and 1.6\% on Yelp datasets. The framework exhibits superior explainability while maintaining computational efficiency comparable to traditional methods.

cs.IR↗

arXiv2025

AutoMaAS: Self-Evolving Multi-Agent Architecture Search for Large Language Models

Multi-agent systems powered by large language models have demonstrated remarkable capabilities across diverse domains, yet existing automated design approaches seek monolithic solutions that fail to adapt resource allocation based on query complexity and domain requirements. This paper introduces AutoMaAS, a self-evolving multi-agent architecture search framework that leverages neural architecture search principles to automatically discover optimal agent configurations through dynamic operator lifecycle management and automated machine learning techniques. Our approach incorporates four key innovations: (1) automatic operator generation, fusion, and elimination based on performance-cost analysis, (2) dynamic cost-aware optimization with real-time parameter adjustment, (3) online feedback integration for continuous architecture refinement, and (4) enhanced interpretability through decision tracing mechanisms. Extensive experiments across six benchmarks demonstrate that AutoMaAS achieves 1.0-7.1\% performance improvement while reducing inference costs by 3-5\% compared to state-of-the-art methods. The framework shows superior transferability across datasets and LLM backbones, establishing a new paradigm for automated multi-agent system design in the era of large language models.

cs.AI↗

arXiv2025

ExplainRec: Towards Explainable Multi-Modal Zero-Shot Recommendation with Preference Attribution and Large Language Models

Recent advances in Large Language Models (LLMs) have opened new possibilities for recommendation systems, though current approaches such as TALLRec face challenges in explainability and cold-start scenarios. We present ExplainRec, a framework that extends LLM-based recommendation capabilities through preference attribution, multi-modal fusion, and zero-shot transfer learning. The framework incorporates four technical contributions: preference attribution tuning for explainable recommendations, zero-shot preference transfer for cold-start users and items, multi-modal enhancement leveraging visual and textual content, and multi-task collaborative optimization. Experimental evaluation on MovieLens-25M and Amazon datasets shows that ExplainRec outperforms existing methods, achieving AUC improvements of 0.7\% on movie recommendation and 0.9\% on cross-domain tasks, while generating interpretable explanations and handling cold-start scenarios effectively.

cs.IR↗

arXiv2025

Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations

Recent research has explored using Large Language Models for recommendation tasks by transforming user interaction histories and item metadata into text prompts, then having the LLM produce rankings or recommendations. A promising approach involves connecting collaborative filtering knowledge to LLM representations through compact adapter networks, which avoids expensive fine-tuning while preserving the strengths of both components. Yet several challenges persist in practice: collaborative filtering models often use static snapshots that miss rapidly changing user preferences; many real-world items contain rich visual and audio content beyond textual descriptions; and current systems struggle to provide trustworthy explanations backed by concrete evidence. Our work introduces \model{}, a framework that tackles these limitations through three key innovations. We develop an online adaptation mechanism that continuously incorporates new user interactions through lightweight modules, avoiding the need to retrain large models. We create a unified representation that seamlessly combines collaborative signals with visual and audio features, handling cases where some modalities may be unavailable. Finally, we design an explanation system that grounds recommendations in specific collaborative patterns and item attributes, producing natural language rationales users can verify. Our approach maintains the efficiency of frozen base models while adding minimal computational overhead, making it practical for real-world deployment.

cs.IR↗

arXiv2025

AgentRec: Next-Generation LLM-Powered Multi-Agent Collaborative Recommendation with Adaptive Intelligence

Interactive conversational recommender systems have gained significant attention for their ability to capture user preferences through natural language interactions. However, existing approaches face substantial challenges in handling dynamic user preferences, maintaining conversation coherence, and balancing multiple ranking objectives simultaneously. This paper introduces AgentRec, a next-generation LLM-powered multi-agent collaborative recommendation framework that addresses these limitations through hierarchical agent networks with adaptive intelligence. Our approach employs specialized LLM-powered agents for conversation understanding, preference modeling, context awareness, and dynamic ranking, coordinated through an adaptive weighting mechanism that learns from interaction patterns. We propose a three-tier learning strategy combining rapid response for simple queries, intelligent reasoning for complex preferences, and deep collaboration for challenging scenarios. Extensive experiments on three real-world datasets demonstrate that AgentRec achieves consistent improvements over state-of-the-art baselines, with 2.8\% enhancement in conversation success rate, 1.9\% improvement in recommendation accuracy (NDCG@10), and 3.2\% better conversation efficiency while maintaining comparable computational costs through intelligent agent coordination.

cs.AI↗

arXiv2025

LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing

Contemporary generative recommendation systems face significant challenges in handling multimodal data, eliminating algorithmic biases, and providing transparent decision-making processes. This paper introduces an enhanced generative recommendation framework that addresses these limitations through five key innovations: multimodal fusion architecture, retrieval-augmented generation mechanisms, causal inference-based debiasing, explainable recommendation generation, and real-time adaptive learning capabilities. Our framework leverages advanced large language models as the backbone while incorporating specialized modules for cross-modal understanding, contextual knowledge integration, bias mitigation, explanation synthesis, and continuous model adaptation. Extensive experiments on three benchmark datasets (MovieLens-25M, Amazon-Electronics, Yelp-2023) demonstrate consistent improvements in recommendation accuracy, fairness, and diversity compared to existing approaches. The proposed framework achieves up to 2.3% improvement in NDCG@10 and 1.4% enhancement in diversity metrics while maintaining computational efficiency through optimized inference strategies.

cs.IR↗

arXiv2025

TIDE: Achieving Balanced Subject-Driven Image Generation via Target-Instructed Diffusion Enhancement

Subject-driven image generation (SDIG) aims to manipulate specific subjects within images while adhering to textual instructions, a task crucial for advancing text-to-image diffusion models. SDIG requires reconciling the tension between maintaining subject identity and complying with dynamic edit instructions, a challenge inadequately addressed by existing methods. In this paper, we introduce the Target-Instructed Diffusion Enhancing (TIDE) framework, which resolves this tension through target supervision and preference learning without test-time fine-tuning. TIDE pioneers target-supervised triplet alignment, modelling subject adaptation dynamics using a (reference image, instruction, target images) triplet. This approach leverages the Direct Subject Diffusion (DSD) objective, training the model with paired "winning" (balanced preservation-compliance) and "losing" (distorted) targets, systematically generated and evaluated via quantitative metrics. This enables implicit reward modelling for optimal preservation-compliance balance. Experimental results on standard benchmarks demonstrate TIDE's superior performance in generating subject-faithful outputs while maintaining instruction compliance, outperforming baseline methods across multiple quantitative metrics. TIDE's versatility is further evidenced by its successful application to diverse tasks, including structural-conditioned generation, image-to-image generation, and text-image interpolation. Our code is available at https://github.com/KomJay520/TIDE.

cs.CV↗

arXiv2025

A magnetic white dwarf formed through a binary merger within 35 million years

White dwarfs (WDs) represent the final evolutionary stage of most stars, typically originating from progenitor stars with masses below approximately 8 $M_{\odot}$ to 10 $M_{\odot}$. Formation through single-star evolution generally requires at least 25 Myr, with the youngest WDs often near the Chandrasekhar limit of 1.4 $M_{\odot}$. In contrast, WDs formed via binary channels, such as mergers or mass transfer, can develop smaller masses in a shorter timescale and may exhibit unique characteristics, including strong surface magnetic fields and rapid rotation. Accurately determining the ages of these WDs is essential for understanding their formation. A valuable method involves studying WDs in star clusters, where member stars share the same age and chemical composition, allowing for precise constraints on the formation times and metallicities of the WDs' progenitors. Here we report a WD found in the open cluster RSG 5, which is only 35 Myr old. The WD's mass is lower than 1.05 $M_{\odot}$, indicating it may not have formed through single-star evolution. The WD possesses an exceptionally strong surface magnetic field ($\ge 200$ MG), a short rotational period ($\sim 6.5$ min), and, most notably, a co-rotating half-ring of ionized circumstellar debris. This distinctive feature provides evidence for a binary merger origin, a scenario further substantiated by our stellar evolution models.

astro-ph.SR↗

arXiv2025

Breaking the Mass Inclination Degeneracy of Radial Velocity Measurements via Monitoring von Zeipel-Lidov-Kozai Cycles: Implications in the HD 41004 System

We investigate the dynamical stability of the S-type planet in the compact binary HD 41004. Using $N$-body simulations, we find that the planet could be dynamically stable at a mutual angle inclination up to $\sim75^\circ$. The von Zeipel-Lidov-Kozai (vZLK) mechanism becomes active when the mutual inclination is greater than 39.2$^\circ$. High-inclination orbits exhibit coupled oscillations in eccentricity and inclination, along with apsidal precession. Synthetic radial velocity (RV) modeling shows that these secular variations produce measurable signatures across a broad range of timescales, from full vZLK cycles to observationally accessible decades. For instance, a high mutual inclination at 75$^\circ$ can induce RV drifts exceeding 5 m s$^{-1}$ per planetary orbit ($\sim 1.9 \,\text{m s}^{-1}\, \text{yr}^{-1}$) in circular binary configurations. The presence of eccentric vZLK further accelerates these drifts, enhancing the detectability. Long-term RV observations of this system offer a unique pathway to dynamically constrain the orbital inclination, and thus determine the true mass of HD 41004 Ab. The degeneracy of mass-inclination is well known when using RV measurements alone. Our results highlight that HD 41004Ab and potentially other S-type planets in compact binaries are promising targets for breaking such a degeneracy by studying the dynamics induced by the vZLK mechanism through long-term high-precision RV monitoring.

astro-ph.EP↗

arXiv2025

Fingerprinting SDKs for Mobile Apps and Where to Find Them: Understanding the Market for Device Fingerprinting

This paper presents a large-scale analysis of fingerprinting-like behavior in the mobile application ecosystem. We take a market-based approach, focusing on third-party tracking as enabled by applications' common use of third-party SDKs. Our dataset consists of over 228,000 SDKs from popular Maven repositories, 178,000 Android applications collected from the Google Play store, and our static analysis pipeline detects exfiltration of over 500 individual signals. To the best of our knowledge, this represents the largest-scale analysis of SDK behavior undertaken to date. We find that Ads SDKs (the ostensible focus of industry efforts such as Apple's App Tracking Transparency and Google's Privacy Sandbox) appear to be the source of only 30.56% of the fingerprinting behaviors. A surprising 23.92% originate from SDKs whose purpose was unknown or unclear. Furthermore, Security and Authentication SDKs are linked to only 11.7% of likely fingerprinting instances. These results suggest that addressing fingerprinting solely in specific market-segment contexts like advertising may offer incomplete benefit. Enforcing anti-fingerprinting policies is also complex, as we observe a sparse distribution of signals and APIs used by likely fingerprinting SDKs. For instance, only 2% of exfiltrated APIs are used by more than 75% of SDKs, making it difficult to rely on user permissions to control fingerprinting behavior.

cs.CR↗

arXiv2025

Exoplanet Ephemerides Change Observations (ExoEcho). II. Transit timing variation analysis of Brown Dwarfs around Solar-type Stars

Transit timing variation (TTV) is a useful tool for studying the orbital properties of transiting objects. However, few TTV studies have been done on transiting brown dwarfs (BDs) around solar-type stars. Here we study the long-term TTV of a population of close BD companions around solar-type stars using TESS data. We use the measured orbital period change rate to constrain the tidal interaction strength between the host star and the BD companion and put limits on the destruction timescale of these transiting BDs. However, we find no statistically significant evidence of orbital decay or expansion in our sample based on the current data. This may be due to either poor observational data or inherently weak tidal dissipation. We then perform simulations to investigate future observation strategies for detecting orbital decay of transiting BDs, which show NGTS-7A b, TOI-263~b and LP 261-75 b are the most promising targets in the next few years. Our study demonstrates the potential of TTV technique to probe the formation and evolution of close BD companions around solar-type stars.

astro-ph.EP↗

arXiv2025

HST/WFC3 Constraints on the Abundances of OH and FeH in the Atmosphere of the Ultra-Hot Neptune LTT-9779 b

Planets residing within the hot-Neptune Desert are rare, and studying their atmospheres can provide valuable insights into their formation and evolutionary processes. We present the atmospheric characterization of the first known ultra-hot Neptune, LTT-9779 b, using transmission spectroscopic observations obtained with the HST/WFC3 G141 and G102 grisms. Using the Iraclis pipeline and TauREx3 retrieval code, we find that LTT-9779 b likely possesses a H/He-dominated primary atmosphere with an opaque aerosol layer and the pure cloudy, flat-line model is rejected with approximately 2.7-$σ$ confidence. Although we do not find conclusive evidence supporting the presence of any molecular species, we place 95% confidence level upper limits on the volume mixing ratios (VMRs) of hydroxyl radical (OH) and iron hydride (FeH) at $7.18\times10^{-2}$ and $1.52\times10^{-8}$, respectively. Notably, the retrieval results are inconsistent with predictions from equilibrium chemistry models, which favor higher $\rm H_2O$ abundances over OH. This discrepancy suggests that disequilibrium processes, such as photochemistry or vertical mixing, may have altered the atmospheric composition. Comparisons between HST, Spitzer and JWST data reveal no evidence of temporal variations in the atmospheric composition of the terminator region. Our results highlight the need for higher-resolution spectroscopy and secondary eclipse observations to resolve LTT-9779 b's temperature-pressure (T-P) profile and chemical inventory definitively.

astro-ph.EP↗