arXiv subjects

Peng Jiang

Publications and source records attributed to Peng Jiang.

At least 109 records · Page 6Linked to original sources

arXiv2025

Starspots as the origin of ultrafast drifting radio bursts from an active M dwarf

Detecting coherent radio bursts from nearby M dwarfs provides opportunities for exploring their magnetic activity and interaction with orbiting exoplanets. However, it remains uncertain if the emission is related to flare-like activity similar to the Sun or magnetospheric process akin to magnetized planets. Using observations (1.0 - 1.5 GHz) taken by the Five-hundred-meter Aperture Spherical radio Telescope, we found a type of millisecond-scale radio bursts with exceptionally high frequency drift rates ($\sim 8\;\rm{GHz\;s^{-1}}$) from an active M dwarf, AD Leo. The ultrafast drift rates point to a source region with a notably low magnetic scale height ($<0.15\; r_\star$, $r_\star$ as the stellar radius), a feature not expected in a commonly assumed dipole-like global field but highly possible in localized strong-field structures, i.e. starspots. Our findings suggest that a concentrated magnetic field above starspots could be responsible for some of the most intense radio bursts from M dwarfs, supporting a solar-like electron acceleration mechanism.

astro-ph.SR↗

arXiv2025

Scaling Laws for Online Advertisement Retrieval

The scaling law is a notable property of neural network models and has significantly propelled the development of large language models. Scaling laws hold great promise in guiding model design and resource allocation. Recent research increasingly shows that scaling laws are not limited to NLP tasks or Transformer architectures; they also apply to domains such as recommendation. However, there is still a lack of literature on scaling law research in online advertisement retrieval systems. This may be because 1) identifying the scaling law for resource cost and online revenue is often expensive in both time and training resources for industrial applications, and 2) varying settings for different systems prevent the scaling law from being applied across various scenarios. To address these issues, we propose a lightweight paradigm to identify online scaling laws of retrieval models, incorporating a novel offline metric and an offline simulation algorithm. We prove that under mild assumptions, the correlation between the novel metric and online revenue asymptotically approaches 1 and empirically validates its effectiveness. The simulation algorithm can estimate the machine cost offline. Based on the lightweight paradigm, we can identify online scaling laws for retrieval models almost exclusively through offline experiments, and quickly estimate machine costs and revenues for given model configurations. We further validate the existence of scaling laws across mainstream model architectures (e.g., Transformer, MLP, and DSSM) in our real-world advertising system. With the identified scaling laws, we demonstrate practical applications for ROI-constrained model designing and multi-scenario resource allocation in the online advertising system. To the best of our knowledge, this is the first work to study identification and application of online scaling laws for online advertisement retrieval.

cs.IR↗

arXiv2025

Robust cross-chain surface interstitial electronic states and doping-enhanced superconductivity in monolayer $M_2$N ($M$= Ti, Zr, Hf) electrides

The exploration of electrides holds great promise for advancing both fundamental physics and chemistry, owing to their unique characteristics arising from loosely bound interstitial anionic electrons. Here we report a class of cross-chain electrides, distinguished by two distinct anionic electron subchannels forming alternating chains in real space. Through structural symmetry analysis and first-principles calculations, we identify two-dimensional $M_2$N ($M$ = Ti, Zr, Hf) materials as prototypical systems exhibiting these unique features. The anionic electron channels on the upper and lower surfaces of these materials display a vertically alternating pattern, with their projected bands revealing momentum-dependent splitting behavior in the reciprocal space, protected by a crystal symmetry operation $\mathcal{O}$. Notably, the cross-chain electride characteristic in the $M_2$N monoalyers is independent of the layer number and remains robust on the upper and lower surfaces of layered structures, presenting a pronounced and robust surface interstitial electronic state. Additionally, we have explored the superconductivity of these systems, and found that both Ti$_2$N and Zr$_2$N are intrinsic superconductors with superconducting transition temperatures below 1.0 K. Further results show that appropriate hole doping can significantly enhance their superconducting transition temperatures and can induce the Hf$_2$N monolayer to exhibit superconductivity. Our findings provide valuable insights into the design and tuning of novel electrides with enhanced superconducting properties, offering another pathway for deeply understanding the interplay between electride behavior and superconductivity in novel materials.

cond-mat.supr-con↗

arXiv2025

KnowHow: Automatically Applying High-Level CTI Knowledge for Interpretable and Accurate Provenance Analysis

High-level natural language knowledge in CTI reports, such as the ATT&CK framework, is beneficial to counter APT attacks. However, how to automatically apply the high-level knowledge in CTI reports in realistic attack detection systems, such as provenance analysis systems, is still an open problem. The challenge stems from the semantic gap between the knowledge and the low-level security logs: while the knowledge in CTI reports is written in natural language, attack detection systems can only process low-level system events like file accesses or network IP manipulations. Manual approaches can be labor-intensive and error-prone. In this paper, we propose KnowHow, a CTI-knowledge-driven online provenance analysis approach that can automatically apply high-level attack knowledge from CTI reports written in natural languages to detect low-level system events. The core of KnowHow is a novel attack knowledge representation, gIoC, that represents the subject, object, and actions of attacks. By lifting system identifiers, such as file paths, in system events to natural language terms, KnowHow can match system events to gIoC and further match them to techniques described in natural languages. Finally, based on the techniques matched to system events, KnowHow reasons about the temporal logic of attack steps and detects potential APT attacks in system events. Our evaluation shows that KnowHow can accurately detect all 16 APT campaigns in the open-source and industrial datasets, while existing approaches all introduce large numbers of false positives. Meanwhile, our evaluation also shows that KnowHow reduces at most 90% of node-level false positives while having a higher node-level recall and is robust against several unknown attacks and mimicry attacks.

cs.CR↗

arXiv2025

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples

We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output and numerical features which is critical for interpretability and effectiveness of recommender systems. CCSS models the monotonicity via a two-stage process: synthesizing counterfactual samples and contrasting the counterfactual samples. The two techniques are naturally integrated into a model-agnostic framework, forming an end-to-end training process. Abundant empirical tests are conducted on a publicly available dataset and a real industrial dataset, and the results well demonstrate the effectiveness of our proposed CCSS. Besides, CCSS has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.

cs.IR↗

arXiv2025

Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner

Auto-bidding is central to computational advertising, achieving notable commercial success by optimizing advertisers' bids within economic constraints. Recently, large generative models show potential to revolutionize auto-bidding by generating bids that could flexibly adapt to complex, competitive environments. Among them, diffusers stand out for their ability to address sparse-reward challenges by focusing on trajectory-level accumulated rewards, as well as their explainable capability, i.e., planning a future trajectory of states and executing bids accordingly. However, diffusers struggle with generation uncertainty, particularly regarding dynamic legitimacy between adjacent states, which can lead to poor bids and further cause significant loss of ad impression opportunities when competing with other advertisers in a highly competitive auction environment. To address it, we propose a Causal auto-Bidding method based on a Diffusion completer-aligner framework, termed CBD. Firstly, we augment the diffusion training process with an extra random variable t, where the model observes t-length historical sequences with the goal of completing the remaining sequence, thereby enhancing the generated sequences' dynamic legitimacy. Then, we employ a trajectory-level return model to refine the generated trajectories, aligning more closely with advertisers' objectives. Experimental results across diverse settings demonstrate that our approach not only achieves superior performance on large-scale auto-bidding benchmarks, such as a 29.9% improvement in conversion value in the challenging sparse-reward auction setting, but also delivers significant improvements on the Kuaishou online advertising platform, including a 2.0% increase in target cost.

cs.GT↗

arXiv2025

Personalized Tree-Based Progressive Regression Model for Watch-Time Prediction in Short Video Recommendation

In online video platforms, accurate watch time prediction has become a fundamental and challenging problem in video recommendation. Previous research has revealed that the accuracy of watch time prediction highly depends on both the transformation of watch-time labels and the decomposition of the estimation process. TPM (Tree based Progressive Regression Model) achieves State-of-the-Art performance with a carefully designed and effective decomposition paradigm. TPM discretizes the watch time into several ordinal intervals and organizes them into a binary decision tree, where each node corresponds to a specific interval. At each non-leaf node, a binary classifier is used to determine the specific interval in which the watch time variable most likely falls, based on the prediction outcome at its parent node. The tree structure is central to TPM, as it defines the decomposition of watch time estimation and how ordinal intervals are discretized. However, TPM uses a predefined full binary tree, which may be sub-optimal for two reasons. First, full binary trees imply equal partitioning of the watch time space, which may fail to capture the complexity of real-world distributions. Second, rather than relying on a fixed global structure, we advocate for a personalized, data-driven tree that can be learned end-to-end. Thus, we propose PTPM to enable highly personalized decomposition of watch estimation with better efficacy and efficiency. Moreover, we show that TPM suffers from selection bias due to conditional modeling and propose a simple solution. We conduct extensive experiments on offline datasets and online environments. Offline results show improved watch time accuracy, and online A/B tests further validate the effectiveness of our framework. PTPM has been fully deployed in core traffic scenarios and now serves over 400 million users daily.

cs.IR↗

arXiv2025

Expert-Guided Diffusion Planner for Auto-Bidding

Auto-bidding is widely used in advertising systems, serving a diverse range of advertisers. Generative bidding is increasingly gaining traction due to its strong planning capabilities and generalizability. Unlike traditional reinforcement learning-based bidding, generative bidding does not depend on the Markov Decision Process (MDP), thereby exhibiting superior planning performance in long-horizon scenarios. Conditional diffusion modeling approaches have shown significant promise in the field of auto-bidding. However, relying solely on return as the optimality criterion is insufficient to guarantee the generation of truly optimal decision sequences, as it lacks personalized structural information. Moreover, the auto-regressive generation mechanism of diffusion models inherently introduces timeliness risks. To address these challenges, we introduce a novel conditional diffusion modeling approach that integrates expert trajectory guidance with a skip-step sampling strategy to improve generation efficiency. The efficacy of this method has been demonstrated through comprehensive offline experiments and further substantiated by statistically significant outcomes in online A/B testing, yielding an 11.29% increase in conversions and a 12.36% growth in revenue relative to the baseline.

cs.LG↗

arXiv2025

VQL: An End-to-End Context-Aware Vector Quantization Attention for Ultra-Long User Behavior Modeling

In large-scale recommender systems, ultra-long user behavior sequences encode rich signals of evolving interests. Extending sequence length generally improves accuracy, but directly modeling such sequences in production is infeasible due to latency and memory constraints. Existing solutions fall into two categories: (1) top-k retrieval, which truncates the sequence and may discard most attention mass when L >> k; and (2) encoder-based compression, which preserves coverage but often over-compresses and fails to incorporate key context such as temporal gaps or target-aware signals. Neither class achieves a good balance of low-loss compression, context awareness, and efficiency. We propose VQL, a context-aware Vector Quantization Attention framework for ultra-long behavior modeling, with three innovations. (1) Key-only quantization: only attention keys are quantized, while values remain intact; we prove that softmax normalization yields an error bound independent of sequence length, and a codebook loss directly supervises quantization quality. This also enables L-free inference via offline caches. (2) Multi-scale quantization: attention heads are partitioned into groups, each with its own small codebook, which reduces quantization error while keeping cache size fixed. (3) Efficient context injection: static features (e.g., item category, modality) are directly integrated, and relative position is modeled via a separable temporal kernel. All context is injected without enlarging the codebook, so cached representations remain query-independent. Experiments on three large-scale datasets (KuaiRand-1K, KuaiRec, TMALL) show that VQL consistently outperforms strong baselines, achieving higher accuracy while reducing inference latency, establishing a new state of the art in balancing accuracy and efficiency for ultra-long sequence recommendation.

cs.IR↗

arXiv2025

TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation

The extensive world knowledge and powerful reasoning capabilities of large language models (LLMs) have attracted significant attention in recommendation systems (RS). Specifically, The chain of thought (CoT) has been shown to improve the performance of LLMs on complex reasoning tasks for RS. However, due to the fact that LLMs often suffer from hallucination issues, there is no guarantee that their reasoning CoT is effective. A key challenge is to further enhance the recommendation capabilities of LLMs through effective CoT reasonings. Therefore, we propose \textbf{TrackRec}, a framework designed to enhance reasoning capabilities of LLMs for RS. TrackRec specifically focuses on accurately inferring recommendation CoT \textbf{(RecCoT)} for user preference using the knowledge from LLMs. This RecCoT can serve both as an explanation for the LLM's completion of recommendation tasks and as auxiliary features to assist recommendation models in accomplishing recommendation tasks. TrackRec consists of a RecCoT generator $(G)$ and a RecCoT validator $(V)$. Furthermore, we design alternating feedback learning mechanism that $G$ undergoes direct preference optimization via feedback from $V$ to produce increasingly accurate RecCoT aligned with $V$'s standards. Meanwhile, $V$ is fine-tuned using the inference feedback from $G$ to enhance its validation capabilities in alignment with recommendation tasks. Through iterative alternating feedback learning between $G$ and $V$, TrackRec continuously improves the user preference analysis capability of $G$ and the validation capacity of $V$. Extensive experiments demonstrate the effectiveness of our approach, showing that it surpasses state-of-the-art methods. Moreover, TrackRec has been deployed on a lagre advertising platform with hundreds of millions of users, achieving substantial gains.

cs.IR↗

arXiv2025

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems

Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest. However, existing approaches primarily involve basic prompt techniques for knowledge acquisition, which resemble System-1 thinking. This makes these methods highly sensitive to errors in the reasoning path, where even a small mistake can lead to an incorrect inference. To this end, in this paper, we propose $R^{4}$ec, a reasoning, reflection and refinement framework that evolves the recommendation system into a weak System-2 model. Specifically, we introduce two models: an actor model that engages in reasoning, and a reflection model that judges these responses and provides valuable feedback. Then the actor model will refine its response based on the feedback, ultimately leading to improved responses. We employ an iterative reflection and refinement process, enabling LLMs to facilitate slow and deliberate System-2-like thinking. Ultimately, the final refined knowledge will be incorporated into a recommendation backbone for prediction. We conduct extensive experiments on Amazon-Book and MovieLens-1M datasets to demonstrate the superiority of $R^{4}$ec. We also deploy $R^{4}$ec on a large scale online advertising platform, showing 2.2\% increase of revenue. Furthermore, we investigate the scaling properties of the actor model and reflection model.

cs.IR↗

arXiv2025

H I absorption line and anomalous dispersion in the radio pulses of PSR B1937+21

We use the Five-hundred-meter Aperture Spherical radio Telescope to observe the bright millisecond pulsar PSR B1937+21 (J1939+2134) and record the data in the band from 1.0 to 1.5 GHz. We measure the neutral hydrogen (HI) emission and absorption lines near 1420 MHz ($λ\simeq 21$ cm). We derive the kinematic distance of the pulsar with the HI observation. By comparing this with the archival absorption spectra observed decades ago, we notice possible variations in the absorption spectra toward this pulsar, which correspond to a possible tiny-scale atomic structure of a few astronomical units in size. We also verify the apparent faster-than-light anomalous dispersion at the HI absorption line of this pulsar previously reported.

astro-ph.HE↗

arXiv2025

EBaReT: Expert-guided Bag Reward Transformer for Auto Bidding

Reinforcement learning has been widely applied in automated bidding. Traditional approaches model bidding as a Markov Decision Process (MDP). Recently, some studies have explored using generative reinforcement learning methods to address long-term dependency issues in bidding environments. Although effective, these methods typically rely on supervised learning approaches, which are vulnerable to low data quality due to the amount of sub-optimal bids and low probability rewards resulting from the low click and conversion rates. Unfortunately, few studies have addressed these challenges. In this paper, we formalize the automated bidding as a sequence decision-making problem and propose a novel Expert-guided Bag Reward Transformer (EBaReT) to address concerns related to data quality and uncertainty rewards. Specifically, to tackle data quality issues, we generate a set of expert trajectories to serve as supplementary data in the training process and employ a Positive-Unlabeled (PU) learning-based discriminator to identify expert transitions. To ensure the decision also meets the expert level, we further design a novel expert-guided inference strategy. Moreover, to mitigate the uncertainty of rewards, we consider the transitions within a certain period as a "bag" and carefully design a reward function that leads to a smoother acquisition of rewards. Extensive experiments demonstrate that our model achieves superior performance compared to state-of-the-art bidding methods.

cs.LG↗

arXiv2025

Music Grounding by Short Video

Adding proper background music helps complete a short video to be shared. Previous work tackles the task by video-to-music retrieval (V2MR), aiming to find the most suitable music track from a collection to match the content of a given query video. In practice, however, music tracks are typically much longer than the query video, necessitating (manual) trimming of the retrieved music to a shorter segment that matches the video duration. In order to bridge the gap between the practical need for music moment localization and V2MR, we propose a new task termed Music Grounding by Short Video (MGSV). To tackle the new task, we introduce a new benchmark, MGSV-EC, which comprises a diverse set of 53k short videos associated with 35k different music moments from 4k unique music tracks. Furthermore, we develop a new baseline method, MaDe, which performs both video-to-music matching and music moment detection within a unified end-to-end deep network. Extensive experiments on MGSV-EC not only highlight the challenging nature of MGSV but also set MaDe as a strong baseline.

cs.MM↗

arXiv2025

Deep neutral hydrogen observations of the early-type galaxy NGC 2768: collided by a newly discovered satellite galaxy?

We present the results of a deep neutral hydrogen (Hi) observation of the early-type galaxy NGC 2768 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Leveraging the high sensitivity of FAST, we discover an extended gas envelope around NGC 2768. The total Hi mass is measured to be 8.1 x 10^8 M_sun , representing a magnitude increase compared to previous Westerbork Synthesis Radio Telescope (WSRT) studies. Position-velocity (PV) diagram indicates the envelope mainly involves two components: an Hi disk of NGC 2768 and a newly discovered satellite galaxy without detectable counterparts in currently deep optical surveys. The center of the gas disk is mis-aligned with the optical disk of NGC 2768, with more gas redshifted, indicating it has been disturbed. Our study indicates NGC 2768 is currently undergoing a transition from a spiral galaxy to an S0. Previous deep WSRT observations reveal two dense clumps (named as Clumps A and Clump B throughout this paper) in the center of the envelope. We find Clump A corresponds to the densest part of the disk, while Clump B might be a newly discovered satellite galaxy which probably collided NGC 2768 about 0.38 Gyr ago. We also find tidal interactions between Clump B and PGC 2599651, NGC 2768 and UGC 4808. Based on these new findings, we finally analyze hierarchical accretion history of NGC 2768.

astro-ph.GA↗

arXiv2025

Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systems

Offline reinforcement learning (RL) has emerged as a prevalent and effective methodology for real-world recommender systems, enabling learning policies from historical data and capturing user preferences. In offline RL, reward shaping encounters significant challenges, with past efforts to incorporate prior strategies for uncertainty to improve world models or penalize underexplored state-action pairs. Despite these efforts, a critical gap remains: the simultaneous balancing of intrinsic biases in world models and the diversity of policy recommendations. To address this limitation, we present an innovative offline RL framework termed Reallocated Reward for Recommender Systems (R3S). By integrating inherent model uncertainty to tackle the intrinsic fluctuations in reward predictions, we boost diversity for decision-making to align with a more interactive paradigm, incorporating extra penalizers with decay that deter actions leading to diminished state variety at both local and global scales. The experimental results demonstrate that R3S improves the accuracy of world models and efficiently harmonizes the heterogeneous preferences of the users.

cs.IR↗

arXiv2025

Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement

In the realm of online advertising, advertisers partake in ad auctions to obtain advertising slots, frequently taking advantage of auto-bidding tools provided by demand-side platforms. To improve the automation of these bidding systems, we adopt generative models, namely the Decision Transformer (DT), to tackle the difficulties inherent in automated bidding. Applying the Decision Transformer to the auto-bidding task enables a unified approach to sequential modeling, which efficiently overcomes short-sightedness by capturing long-term dependencies between past bidding actions and user behavior. Nevertheless, conventional DT has certain drawbacks: (1) DT necessitates a preset return-to-go (RTG) value before generating actions, which is not inherently produced; (2) The policy learned by DT is restricted by its training data, which is consists of mixed-quality trajectories. To address these challenges, we introduce the R* Decision Transformer (R* DT), developed in a three-step process: (1) R DT: Similar to traditional DT, R DT stores actions based on state and RTG value, as well as memorizing the RTG for a given state using the training set; (2) R^ DT: We forecast the highest value (within the training set) of RTG for a given state, deriving a suboptimal policy based on the current state and the forecasted supreme RTG value; (3) R* DT: Based on R^ DT, we generate trajectories and select those with high rewards (using a simulator) to augment our training dataset. This data enhancement has been shown to improve the RTG of trajectories in the training data and gradually leads the suboptimal policy towards optimality. Comprehensive tests on a publicly available bidding dataset validate the R* DT's efficacy and highlight its superiority when dealing with mixed-quality trajectories.

cs.LG↗

arXiv2025

ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval

E-commerce is increasingly multimedia-enriched, with products exhibited in a broad-domain manner as images, short videos, or live stream promotions. A unified and vectorized cross-domain production representation is essential. Due to large intra-product variance and high inter-product similarity in the broad-domain scenario, a visual-only representation is inadequate. While Automatic Speech Recognition (ASR) text derived from the short or live-stream videos is readily accessible, how to de-noise the excessively noisy text for multimodal representation learning is mostly untouched. We propose ASR-enhanced Multimodal Product Representation Learning (AMPere). In order to extract product-specific information from the raw ASR text, AMPere uses an easy-to-implement LLM-based ASR text summarizer. The LLM-summarized text, together with visual data, is then fed into a multi-branch network to generate compact multimodal embeddings. Extensive experiments on a large-scale tri-domain dataset verify the effectiveness of AMPere in obtaining a unified multimodal product representation that clearly improves cross-domain product retrieval.

cs.MM↗