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Min Gao

Publications and source records attributed to Min Gao.

At least 37 records · Page 2Linked to original sources
arXiv2026

Green-Red Watermarking for Recommender Systems

The widespread open-sourcing of advanced recommendation algorithms and the rising threat of model extraction attacks have made safeguarding the intellectual property of recommender systems an imperative task. While watermarking serves as a potent defense, existing methods primarily rely on forcing models to memorize pre-defined interaction patterns. Such memorization-based approaches often require excessive synthetic data injection and are vulnerable to removal attacks due to their detectable statistical deviations from natural user behavior. To address these limitations, we propose GREW, a novel Green-REd Watermarking framework for recommender systems. GREW leverages a secret key to partition the item space into "green" items for soft promotion and "red" items as anchors, thereby shifting the paradigm from fragile memorization to a stealthy, key-controlled output bias. By integrating watermark signals directly into the intrinsic ranking process, GREW employs three recommendation-tailored modules: (1) Semantic-Consistent Hashing, which utilizes the secret key to cluster green items for performance-aware stealthiness; (2) Decision-Aligned Masking, which confines signal injection to the competitive item subset to preserve ranking logic; and (3) Confidence-Aware Scaling, which dynamically modulates injection intensity based on model uncertainty. Ownership verification is performed via statistical hypothesis testing on aggregated black-box outputs, enabled by the keyed re-partitioning of the item space. Experiments on multiple base models demonstrate that GREW achieves strong ownership verification and robustness against extraction attacks compared to existing baselines while requiring no data injection. Our code is available at https://github.com/Loche2/GREW.

cs.IR↗
arXiv2026

Design and preliminary performance study of the broad-band spectrometer detector for POLAR-2

POLAR-2, the successor of the POLAR experiment aboard China's Tiangong-2 space lab, is set to be deployed on the China Space Station. The POLAR-2 mission aims to conducting high-precision polarization measurements of high-energy transients with a primary focus on Gamma-Ray Bursts (GRBs), following POLAR's pioneering accurate polarization measurements of GRB prompt emission. One of the key advancements in POLAR-2 is the inclusion of a dedicated Broad-band Spectrometer Detector (BSD) instrument, designed to provide precise measurements of GRB location and spectral parameters, which are critical inputs for accurate polarization analysis of POLAR-2's dedicated High-energy Polarimetry Detector (HPD), which is made of plastic scintillator bars array. BSD employs a coded-aperture mask imaging technique and pixelated GAGG scintillation crystals, offering a wide half-coded field of view of ~132° x 125° and an operational energy range of 10-1000 keV. Simulation results indicate that the instrument can achieve a localization accuracy of approximately 1.5° for faint GRBs similar to GRB 170817A, satisfying the core requirements of GRB polarimetry with HPD. BSD also has moderate capability for GRB polarimetry, particularly at several hundred keV energy. This paper outlines the preliminary design of BSD and presents an overall evaluation of its expected scientific performance, based on extensive Monte Carlo simulations and preliminary ground-based calibration tests.

astro-ph.IM↗
arXiv2026

Multi-LLM Token Filtering and Routing for Sequential Recommendation

Large language models (LLMs) have recently shown promise in recommendation by providing rich semantic knowledge. While most existing approaches rely on external textual corpora to align LLMs with recommender systems, we revisit a more fundamental yet underexplored question: Can recommendation benefit from LLM token embeddings alone without textual input? Through a systematic empirical study, we show that directly injecting token embeddings from a single LLM into sequential recommenders leads to unstable or limited gains, due to semantic misalignment, insufficient task adaptation, and the restricted coverage of individual LLMs. To address these challenges, we propose MLTFR, a Multi-LLM Token Filtering and Routing framework for corpus-free sequential recommendation. MLTFR follows an interaction-guided LLM knowledge integration paradigm, where task-relevant token embeddings are selected via user-guided token filtering to suppress noisy and irrelevant vocabulary signals. To overcome the limitations of single-LLM representations, MLTFR integrates multiple LLM token spaces through a Mixture-of-Experts architecture, with a Fisher-weighted semantic consensus expert to balance heterogeneous experts and prevent domination during training. By jointly filtering informative tokens and aggregating complementary semantic knowledge across multiple LLMs, MLTFR enables stable and effective utilization of LLM token embeddings without textual inputs or backbone modification. Extensive experiments demonstrate that MLTFR consistently outperforms state-of-the-art sequential recommendation baselines and existing alignment methods. Our code is available at: https://github.com/ccwwhhh/MLTFR.

cs.IR↗
arXiv2026

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems

Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling iterative preference elicitation and refinement beyond conventional one-shot prediction. However, existing ARS are mainly optimized in a Reflexion-style paradigm, where past interaction trajectories are stored as textual memory and retrieved as prompt context for later reasoning. Although this design allows agents to recall prior feedback and observations, the accumulated experience remains external to model parameters, leaving agents reliant on generic reasoning rather than progressively acquiring recommendation-specific decision-making ability through learning. Reinforcement learning (RL) therefore provides a natural way to internalize such interaction experience into parameters. Yet existing RL methods for ARS still suffer from two key limitations. First, they fail to capture the interactive nature of ARS, in which the recommender agent and the user agent continuously influence each other and can naturally generate endogenous supervision through interaction feedback. Second, they reduce a rich multi-turn interaction process to final outcomes, overlooking the dense supervision embedded throughout the trajectory. To this end, we propose CoARS, a self-distilled reinforcement learning framework for co-evolving agentic recommender systems. CoARS introduces two complementary learning schemes: interaction reward, which derives coupled task-level supervision for the recommender agent and the user agent from the same interaction trajectory, and self-distilled credit assignment, which converts historical trajectories into token-level credit signals under teacher-student conditioning. Experiments on multiple datasets show that CoARS outperforms representative ARS baselines in recommendation performance and user alignment.

cs.IR↗
arXiv2026

Comprehensive Measurement of Spectral Evolution in a GRB Flare: High Time-Resolution Insights into the "Double-Tracking" Phenomenon

The spectral evolution characteristics of the prompt emission in gamma-ray bursts (GRBs) have been extensively studied, but detailed investigations of spectral evolution in a GRB flare remain lacking. In this work, we present the first analysis of spectral parameter evolution in a GRB flare through high time-resolved spectral fitting of the Brightest Flare in GRB 221009A. We find that the $α$-Flux, $E_p$-Flux, and $E_p$-$α$ relationships during both the overall phase and the rise phase of flare can be well described by simple power-law model, showing positive correlations. Therefore, we conclude that Brightest Flare exhibits "Double-tracking" behavior. Since values of $α$ do not exceed the synchrotron "death line" (-2/3), we explain this phenomenon using a magnetic dissipation synchrotron radiation model. In the decay phase of flare, the $E_p$-Flux and $E_p$-$α$ correlations become notably flatter, with their power-law indices decreasing significantly compared to those in the rise phase. This may be due to the fact that the next flare begins to erupt before the Brightest Flare has completely ended, resulting in the combined effects of both two flares. Our study of spectral parameter relations of the Brightest Flare provides new insights into the radiation mechanisms of both GRB prompt emission and flares.

astro-ph.HE↗
arXiv2026

Performance of the Gamma-ray Transient Monitor at the IHEP Electron-Beam Facility

Gamma-Ray Transient Monitor (GTM) is an all-sky monitor onboard the Distant Retrograde Orbit-A (DRO-A) satellite, with the scientific objective of detecting gamma-ray bursts in the energy range of 20 keV to 1 MeV. GTM is equipped with five Gamma-Ray Transient Probes (GTPs), utilizing NaI(Tl) scintillators coupled with silicon photomultiplier (SiPM) arrays for signal readout. To test the performance of the GTP in detecting electrons, we used the IHEP Electron-Beam Facility (a continuous-energy-tunable, low-current, quasi-single-electron accelerator) for ground-based electron tests of the GTP. This paper provides a detailed description of the operating principles of the electron accelerator and presents the process and results of the GTP electron-beam tests. The test results show that the GTP has a dead time of less than 4 $μ$s for normal signals and approximately 70 $μ$s for overflow signals, consistent with the design specifications. The time-recording capability of the GTP was tested and found to be normal, with accurate recording of overflow events. The GTP's response to electrons in the 0.4-1.4 MeV range is also normal. Additionally, we used Geant4 to simulate the GTP's energy response and performed a comparative analysis of the simulation and experimental results. The performance tests and ground-based electron calibration validated the design of the GTP and enhanced the GTP's mass model, laying the foundation for payload development, in-orbit observation strategies, and scientific data analysis.

astro-ph.IM↗
arXiv2026

Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection

With the rapid development of large language models, the generation of fake news has become increasingly effortless, posing a growing societal threat and underscoring the urgent need for reliable detection methods. Early efforts to identify LLM-generated fake news have predominantly focused on the textual content itself; however, because much of that content may appear coherent and factually consistent, the subtle traces of falsification are often difficult to uncover. Through distributional divergence analysis, we uncover prompt-induced linguistic fingerprints: statistically distinct probability shifts between LLM-generated real and fake news when maliciously prompted. Based on this insight, we propose a novel method named Linguistic Fingerprints Extraction (LIFE). By reconstructing word-level probability distributions, LIFE can find discriminative patterns that facilitate the detection of LLM-generated fake news. To further amplify these fingerprint patterns, we also leverage key-fragment techniques that accentuate subtle linguistic differences, thereby improving detection reliability. Our experiments show that LIFE achieves state-of-the-art performance in LLM-generated fake news and maintains high performance in human-written fake news. The code and data are available at https://anonymous.4open.science/r/LIFE-E86A.

cs.CL↗
arXiv2026

DALI: LLM-Agent Enhanced Dual-Stream Adaptive Leadership Identification for Group Recommendations

Group recommendation systems play a pivotal role in supporting collective decisions across various contexts, from leisure activities to organizational team-building. Existing group recommendation approaches typically use either handcrafted aggregation rules (e.g. mean, least misery, weighted sum) or neural aggregation models (e.g. attention-based deep learning frameworks), yet both fall short in distinguishing leader-dominated from collaborative groups and often misrepresent true group preferences, especially when a single member disproportionately influences group choices. To address these limitations, we propose the Dual-stream Adaptive Leadership Identification (DALI) framework, which uniquely combines the symbolic reasoning capabilities of Large Language Models (LLMs) with neural network-based representation learning. Specifically, DALI introduces two key innovations: a dynamic rule generation module that autonomously formulates and evolves identification rules through iterative performance feedback, and a neuro-symbolic aggregation mechanism that concurrently employs symbolic reasoning to robustly recognize leadership groups and attention-based neural aggregation to accurately model collaborative group dynamics. Experiments conducted on the Mafengwo travel dataset confirm that DALI significantly improves recommendation accuracy compared to existing frameworks, highlighting its capability to dynamically adapt to complex, real-world group decision environments.

cs.IR↗
arXiv2026

A New Method for Identifying Contaminating Sources and Locating Target Sources through the Cross-Arm Features of Micro Pore Optics

The Pathfinder of the Type-A satellites in the Chasing All Transients Constellation Hunters (CATCH) space mission is equipped with Micro-Pore Optics (MPOs) and four single-pixel Silicon Drift Detectors (SDDs). Due to the lack of position resolution in an individual SDD, we propose a new method based on the cross-arms in the point spread function (PSF) of MPOs to enhance the satellite's capability in identifying contaminating sources and locating target sources. By placing one detector on each of the horizontal and vertical cross-arms on the focal plane, we can use the changes in the relative counts on the cross-arms detectors to deduce the location of the source. Simulated observations demonstrate that, for a target source with a flux of 1 Crab and an exposure time of 200 s, the cross-arms detectors can identify contaminating source with the same flux level at an off-axis angle larger than 8', and improve positioning accuracy to 6'. Furthermore, we extend the simulation study to CATCH Type-A, which plans to use an SDD array. In situations where sources exhibit the same flux of 1 Crab and the exposure time is merely 1 s, a 16x16 SDD array is capable of identifying contaminating source with an off-axis angle greater than 2.4' and can achieve a positioning precision of 1.8'.

astro-ph.IM↗
arXiv2026

DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation

Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one account. Existing shared-account sequential recommendation (SSR) methods often assume a fixed number of latent users per account, limiting their ability to adapt to diverse sharing patterns and reducing recommendation accuracy. Recent latent reasoning technique applied in sequential recommendation (SR) generate intermediate embeddings from the user embedding (e.g, last item embedding) to uncover users' potential interests, which inspires us to treat the problem of inferring the number of latent users as generating a series of intermediate embeddings, shifting from inferring preferences behind user to inferring the users behind account. However, the last item cannot be directly used for reasoning in SSR, as it can only represent the behavior of the most recent latent user, rather than the collective behavior of the entire account. To address this, we propose DisenReason, a two-stage reasoning method tailored to SSR. DisenReason combines behavior disentanglement stage from frequency-domain perspective to create a collective and unified account behavior representation, which serves as a pivot for latent user reasoning stage to infer the number of users behind the account. Experiments on four benchmark datasets show that DisenReason consistently outperforms all state-of-the-art baselines across four benchmark datasets, achieving relative improvements of up to 12.56\% in MRR@5 and 6.06\% in Recall@20.

cs.IR↗
arXiv2026

GECAM discovery of the second FRB-associated Magnetar X-ray Burst from SGR J1935+2154

Fast radio burst (FRB) is mysterious phenomenon with millisecond-duration radio pulses observed mostly from cosmological distance. The association between FRB 200428 and a magnetar X-ray burst (MXB) from SGR J1935+2154 has significantly advanced the understanding of FRB and magnetar bursts. However, it is uncertain whether this association between MXB and FRB (i.e. MXB/FRB 200428) is genuine or just coincidental only based on this single event. Here we report the discovery of a bright ($\rm\sim7.6\times10^{-7}\,erg \cdot cm^{-2}$ in 1-250 keV) magnetar X-ray burst detected by GECAM on October 14th, 2022 (dubbed as MXB 221014) from SGR J1935+2154, which is associated with a FRB detected by CHIME and GBT. We conducted a detailed temporal and spectral analysis of MXB 221014 with GECAM data and find that it is a bright and typical ($T_{90}\sim$250 ms) X-ray burst from this magnetar. Interestingly, we find two narrow X-ray pulses in the MXB, one of which temporally aligns with the main pulse of the FRB 221014 $\sim5.70$ ms latter than the peak time of FRB 221014), resembling the feature found in MXB/FRB 200428. Furthermore, we did comprehensive comparison between MXB/FRB 221014 and MXB/FRB 200428, and find that while the two events share several common features, they also exhibit distinct differences, highlighting the variety of the MXB-FRB association morphology. This finding not only confirms the association between MXB and FRB but also provides new insights into the mechanism of and the relationship between FRB and MXB.

astro-ph.HE↗
arXiv2026

Systematic Study of the Simultaneous Events Detected by GECAM

GECAM is a constellation of all-sky monitors in hard X-ray and gamma-ray band primarily aimed at high energy transients such as gamma-ray bursts, soft gamma-ray repeaters, solar flares and terrestrial gamma-ray flashes. As GECAM has the highest temporal resolution (0.1~$μ$s) among instruments of its kind, it can identify the so-called simultaneous events (STE) that deposit signals in multiple detectors nearly at the same time (with a 0.3~$μ$s window). However, the properties and origin of STE have not yet been explored. In this work, we implemented, for the first time, a comprehensive analysis of the STE detected by GECAM, including their morphology, energy deposition, and the dependence on the geomagnetic coordinates. We find that these STE probably result from direct interactions between high-energy charged cosmic rays and satellite. These results demonstrate that GECAM can detect, identify, and characterize high-energy cosmic rays, making it a Micro Cosmic-Ray Observatory (MICRO) in low Earth orbit.

astro-ph.HE↗
arXiv2026

PAMAS: Self-Adaptive Multi-Agent System with Perspective Aggregation for Misinformation Detection

Misinformation on social media poses a critical threat to information credibility, as its diverse and context-dependent nature complicates detection. Large language model-empowered multi-agent systems (MAS) present a promising paradigm that enables cooperative reasoning and collective intelligence to combat this threat. However, conventional MAS suffer from an information-drowning problem, where abundant truthful content overwhelms sparse and weak deceptive cues. With full input access, agents tend to focus on dominant patterns, and inter-agent communication further amplifies this bias. To tackle this issue, we propose PAMAS, a multi-agent framework with perspective aggregation, which employs hierarchical, perspective-aware aggregation to highlight anomaly cues and alleviate information drowning. PAMAS organizes agents into three roles: Auditors, Coordinators, and a Decision-Maker. Auditors capture anomaly cues from specialized feature subsets; Coordinators aggregate their perspectives to enhance coverage while maintaining diversity; and the Decision-Maker, equipped with evolving memory and full contextual access, synthesizes all subordinate insights to produce the final judgment. Furthermore, to improve efficiency in multi-agent collaboration, PAMAS incorporates self-adaptive mechanisms for dynamic topology optimization and routing-based inference, enhancing both efficiency and scalability. Extensive experiments on multiple benchmark datasets demonstrate that PAMAS achieves superior accuracy and efficiency, offering a scalable and trustworthy way for misinformation detection.

cs.IR↗
arXiv2026

SWGCN: Synergy Weighted Graph Convolutional Network for Multi-Behavior Recommendation

Multi-behavior recommendation paradigms have emerged to capture diverse user activities, forecasting primary conversions (e.g., purchases) by leveraging secondary signals like browsing history. However, current graph-based methods often overlook cross-behavioral synergistic signals and fine-grained intensity of individual actions. Motivated by the need to overcome these shortcomings, we introduce Synergy Weighted Graph Convolutional Network (SWGCN). SWGCN introduces two novel components: a Target Preference Weigher, which adaptively assigns weights to user-item interactions within each behavior, and a Synergy Alignment Task, which guides its training by leveraging an Auxiliary Preference Valuator. This task prioritizes interactions from synergistic signals that more accurately reflect user preferences. The performance of our model is rigorously evaluated through comprehensive tests on three open-source datasets, specifically Taobao, IJCAI, and Beibei. On the Taobao dataset, SWGCN yields relative gains of 112.49% and 156.36% in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG), respectively. It also yields consistent gains on IJCAI and Beibei, confirming its robustness and generalizability across various datasets. Our implementation is open-sourced and can be accessed via https://github.com/FangdChen/SWGCN.

cs.IR↗
arXiv2026

Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing

Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individual Treatment Effect (ITE). In an e-commerce setting, user behavior follows a defined sequential chain, including impression, click, and conversion. Marketing strategies exert varied uplift effects at each stage within this chain, impacting metrics like click-through and conversion rate. Despite its utility, existing research has neglected to consider the inter-task across all stages impacts within a specific treatment and has insufficiently utilized the treatment information, potentially introducing substantial bias into subsequent marketing decisions. We identify these two issues as the chain-bias problem and the treatment-unadaptive problem. This paper introduces the Entire Chain UPlift method with context-enhanced learning (ECUP), devised to tackle these issues. ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment. Extensive experiments on public and industrial datasets validate ECUPs effectiveness. Moreover, ECUP has been deployed on the Meituan food delivery platform, serving millions of daily active users, with the related dataset released for future research.

cs.IR↗
arXiv2026

RCDN: Real-Centered Detection Network for Robust Face Forgery Identification

Image forgery has become a critical threat with the rapid proliferation of AI-based generation tools, which make it increasingly easy to synthesize realistic but fraudulent facial content. Existing detection methods achieve near-perfect performance when training and testing are conducted within the same domain, yet their effectiveness deteriorates substantially in crossdomain scenarios. This limitation is problematic, as new forgery techniques continuously emerge and detectors must remain reliable against unseen manipulations. To address this challenge, we propose the Real-Centered Detection Network (RCDN), a frequency spatial convolutional neural networks(CNN) framework with an Xception backbone that anchors its representation space around authentic facial images. Instead of modeling the diverse and evolving patterns of forgeries, RCDN emphasizes the consistency of real images, leveraging a dual-branch architecture and a real centered loss design to enhance robustness under distribution shifts. Extensive experiments on the DiFF dataset, focusing on three representative forgery types (FE, I2I, T2I), demonstrate that RCDN achieves both state-of-the-art in-domain accuracy and significantly stronger cross-domain generalization. Notably, RCDN reduces the generalization gap compared to leading baselines and achieves the highest cross/in-domain stability ratio, highlighting its potential as a practical solution for defending against evolving and unseen image forgery techniques.

cs.CV↗
arXiv2026

Brightest GRB flare observed in GRB 221009A: bridge the last gap between flare and prompt emission in GRB

Flares are usually observed during the afterglow phase of Gamma-Ray Bursts (GRBs) in soft X-ray, optical and radio bands, but rarely in gamma-ray band. Despite the extraordinary brightness, GECAM-C has accurately measured both the bright prompt emission and flare emission of GRB 221009A without instrumental effects, offering a good opportunity to study the relation between them. In this work, we present a comprehensive analysis of flare emission of GRB 221009A, which is composed of a series of flares. Among them, we identify an exceptionally bright flare with a record-breaking isotropic energy $E_{\rm iso} = 1.82 \times 10^{53}$ erg of GRB flares. It exhibits the highest peak energy ever detected in GRB flares, $E_{\rm peak} \sim 300$ keV, making it a genuine gamma-ray flare. It also shows rapid rise and decay timescales, significantly shorter than those of typical X-ray flares observed in soft X-ray or optical band, but comparable to those observed in prompt emissions. Despite these exceptional properties, the flare shares several common properties with typical GRB flares. We note that this is the first observation of a GRB flare in the keV-MeV band with sufficiently high temporal resolution and high statistics, which bridges the last gap between prompt emission and flare.

astro-ph.HE↗
arXiv2026

When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents

This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues. When such group boundaries align with the agent-human divide, a new bias risk emerges: agents may treat other AI agents as the ingroup and humans as the outgroup. To examine this risk, we conduct a controlled multi-agent social simulation and find that agents display consistent intergroup bias in an all-agent setting. More critically, this bias persists even in human-facing interactions when agents are uncertain about whether the counterpart is truly human, revealing a belief-dependent fragility in bias suppression toward humans. Motivated by this observation, we identify a new attack surface rooted in identity beliefs and formalize a Belief Poisoning Attack (BPA) that can manipulate agent identity beliefs and induce outgroup bias toward humans. Extensive experiments demonstrate both the prevalence of agent intergroup bias and the severity of BPA across settings, while also showing that our proposed defenses can mitigate the risk. These findings are expected to inform safer agent design and motivate more robust safeguards for human-facing agents.

cs.AI↗