pwc-archive/papers-with-abstracts huggingface dataset:
Harnessing Large Language Models for Scientific Novelty Detection
Authors: Yan Liu, Zonglin Yang, Soujanya Poria, Thanh-Son Nguyen, Erik Cambria
Date: 2025-05-30 00:00:00
Tasks: Marketing, Novelty Detection, Retrieval

In an era of exponential scientific growth, identifying novel research ideas is crucial and challenging in academia. Despite potential, the lack of an appropriate benchmark dataset hinders the research of novelty detection. More importantly, simply adopting existing NLP technologies, e.g., retrieving and then cross-checking, is not a one-size-fits-all solution due to the gap between textual similarity and idea conception. In this paper, we propose to harness large language models (LLMs) for scientific novelty detection (ND), associated with two new datasets in marketing and NLP domains. To construct the considerate datasets for ND, we propose to extract closure sets of papers based on their relationship, and then summarize their main ideas based on LLMs. To capture idea conception, we propose to train a lightweight retriever by distilling the idea-level knowledge from LLMs to align ideas with similar conception, enabling efficient and accurate idea retrieval for LLM novelty detection. Experiments show our method consistently outperforms others on the proposed benchmark datasets for idea retrieval and ND tasks. Codes and data are available at https://anonymous.4open.science/r/NoveltyDetection-10FB/.
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder
Authors: Yiqun Sun, Qiang Huang, Anthony K. H. Tung, Jun Yu
Date: 2025-05-30 00:00:00
Tasks: Articles, Semantic Similarity, Semantic Textual Similarity

Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. While existing embedding models excel at capturing general meaning, they often overlook ideological nuances, limiting their effectiveness in tasks that require an understanding of political bias. To address this gap, we introduce PRISM, the first framework designed to Produce inteRpretable polItical biaS eMbeddings. PRISM operates in two key stages: (1) Controversial Topic Bias Indicator Mining, which systematically extracts fine-grained political topics and their corresponding bias indicators from weakly labeled news data, and (2) Cross-Encoder Political Bias Embedding, which assigns structured bias scores to news articles based on their alignment with these indicators. This approach ensures that embeddings are explicitly tied to bias-revealing dimensions, enhancing both interpretability and predictive power. Through extensive experiments on two large-scale datasets, we demonstrate that PRISM outperforms state-of-the-art text embedding models in political bias classification while offering highly interpretable representations that facilitate diversified retrieval and ideological analysis. The source code is available at https://github.com/dukesun99/ACL-PRISM.
Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)
Authors: Yarin Benyamin, Argaman Mordoch, Shahaf Shperberg, Wiktor Piotrowski, Roni Stern
Date: 2024-06-01 00:00:00
Proceeding: International Symposium on Combinatorial Search 2024 6
Tasks: Heuristic Search, Minecraft, Reinforcement Learning (RL)

Minecraft is a widely popular video game renowned for its intricate environment. The game's open-ended design allows the creation of unique tasks and challenges for the agents, providing a broad spectrum for researchers to experiment with different AI techniques and applications. Indeed, various Minecraft tasks have been posed as an AI challenge. Most AI research on Minecraft focused on either applying Reinforcement Learning (RL) to solve the problem, learning an action model for planning, or modeling the problem for a domain-independent planner. In this work, we focus on the combinatorial search aspect of solving the Craft Wooden Pogo task within the Polycraft World AI Lab (PAL) Minecraft environment. PAL is an interface to Minecraft that provides an API for AI agents to interact with Minecraft's environment and send commands to the main character. PAL supports symbolic observations of the current state, making it ideal for planning algorithms, which require a symbolic model of the environment for problem-solving. Other Minecraft research frameworks such as MineRL, provide a visual, pixel-based representation of the game.
Gibbs randomness-compression proposition: An efficient deep learning
Authors: M. Süzen
Date: 2025-05-29 00:00:00
Tasks: Deep Learning, Neural Architecture Search

A proposition that connects randomness and compression put forward via Gibbs entropy over set of measurement vectors associated with a compression process. The proposition states that a lossy compression process is equivalent to {\it directed randomness} that preserves information content. The proposition originated from the observed behaviour in newly proposed {\it Dual Tomographic Compression} (DTC) compress-train framework. This is akin to tomographic reconstruction of layer weight matrices via building compressed sensed projections, so called {\it weight rays}. This tomographic approach is applied to previous and next layers in a dual fashion, that triggers neuronal-level pruning. This novel model compress-train scheme appear in iterative fashion and act as smart neural architecture search, Experiments demonstrated utility of this dual-tomography producing state-of-the-art performance with efficient compression during training, accelerating and supporting lottery ticket hypothesis. However, random compress-train iterations having similar performance demonstrated the connection between randomness and compression from statistical physics perspective, we formulated so called {\it Gibbs randomness-compression proposition}, signifying randomness-compression relationship via Gibbs entropy. Practically, DTC framework provides a promising approach for massively energy and resource efficient deep learning training approach.
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization
Authors: Hyuntak Kim, Byung-Hak Kim
Date: 2025-05-30 00:00:00
Tasks: Descriptive, Form, Long-Form Narrative Summarization

Summarizing long-form narratives--such as books, movies, and TV scripts--requires capturing intricate plotlines, character interactions, and thematic coherence, a task that remains challenging for existing LLMs. We introduce NexusSum, a multi-agent LLM framework for narrative summarization that processes long-form text through a structured, sequential pipeline--without requiring fine-tuning. Our approach introduces two key innovations: (1) Dialogue-to-Description Transformation: A narrative-specific preprocessing method that standardizes character dialogue and descriptive text into a unified format, improving coherence. (2) Hierarchical Multi-LLM Summarization: A structured summarization pipeline that optimizes chunk processing and controls output length for accurate, high-quality summaries. Our method establishes a new state-of-the-art in narrative summarization, achieving up to a 30.0% improvement in BERTScore (F1) across books, movies, and TV scripts. These results demonstrate the effectiveness of multi-agent LLMs in handling long-form content, offering a scalable approach for structured summarization in diverse storytelling domains.
ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark
Authors: Michael Shalyt, Rotem Elimelech, Ido Kaminer
Date: 2025-05-28 00:00:00
Tasks: Math

Large language models (LLMs) are rapidly approaching the level of proficiency in university-level symbolic mathematics required for applications in advanced science and technology. However, existing benchmarks fall short in assessing the core skills of LLMs in symbolic mathematics-such as integration, differential equations, and algebraic simplification. To address this gap, we introduce ASyMOB, a novel assessment framework focused exclusively on symbolic manipulation, featuring 17,092 unique math challenges, organized by similarity and complexity. ASyMOB enables analysis of LLM generalization capabilities by comparing performance in problems that differ by simple numerical or symbolic `perturbations'. Evaluated LLMs exhibit substantial degradation in performance for all perturbation types (up to -70.3%), suggesting reliance on memorized patterns rather than deeper understanding of symbolic math, even among models achieving high baseline accuracy. Comparing LLM performance to computer algebra systems, we identify examples where they fail while LLMs succeed, as well as problems solved only by combining both approaches. Models capable of integrated code execution yielded higher accuracy compared to their performance without code, particularly stabilizing weaker models (up to +33.1% for certain perturbation types). Notably, the most advanced models (o4-mini, Gemini 2.5 Flash) demonstrate not only high symbolic math proficiency (scoring 96.8% and 97.6% on the unperturbed set), but also remarkable robustness against perturbations, (-21.7% and -21.2% vs. average -50.4% for the other models). This may indicate a recent "phase transition" in the generalization capabilities of frontier LLMs. It remains to be seen whether the path forward lies in deeper integration with sophisticated external tools, or in developing models so capable that symbolic math systems like CAS become unnecessary.
BiasFilter: An Inference-Time Debiasing Framework for Large Language Models
Authors: Xiaoqing Cheng, Ruizhe Chen, Hongying Zan, Yuxiang Jia, Min Peng
Date: 2025-05-28 00:00:00
Tasks: Fairness

Mitigating social bias in large language models (LLMs) has become an increasingly important research objective. However, existing debiasing methods often incur high human and computational costs, exhibit limited effectiveness, and struggle to scale to larger models and open-ended generation tasks. To address these limitations, this paper proposes BiasFilter, a model-agnostic, inference-time debiasing framework that integrates seamlessly with both open-source and API-based LLMs. Instead of relying on retraining with balanced data or modifying model parameters, BiasFilter enforces fairness by filtering generation outputs in real time. Specifically, it periodically evaluates intermediate outputs every few tokens, maintains an active set of candidate continuations, and incrementally completes generation by discarding low-reward segments based on a fairness reward signal. To support this process, we construct a fairness preference dataset and train an implicit reward model to assess token-level fairness in generated responses. Extensive experiments demonstrate that BiasFilter effectively mitigates social bias across a range of LLMs while preserving overall generation quality.
4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification
Authors: Andrew P. Berg, Qian Zhang, Mia Y. Wang
Date: 2025-05-21 00:00:00
Tasks: Audio Classification, parameter-efficient fine-tuning

Unmanned aerial vehicle (UAV) usage is expected to surge in the coming decade, raising the need for heightened security measures to prevent airspace violations and security threats. This study investigates deep learning approaches to UAV classification focusing on the key issue of data scarcity. To investigate this we opted to train the models using a total of 4,500 seconds of audio samples, evenly distributed across a 9-class dataset. We leveraged parameter efficient fine-tuning (PEFT) and data augmentations to mitigate the data scarcity. This paper implements and compares the use of convolutional neural networks (CNNs) and attention-based transformers. Our results show that, CNNs outperform transformers by 1-2\% accuracy, while still being more computationally efficient. These early findings, however, point to potential in using transformers models; suggesting that with more data and further optimizations they could outperform CNNs. Future works aims to upscale the dataset to better understand the trade-offs between these approaches.
A general method for extracting the amplitude spectrum of the seismic wavelet from the seismic traces
Authors: Haoqi Zhao, Jinghuai Gao and Junxiong Jia
Date: 2023-05-12 00:00:00
Proceeding: Inverse Problems 2023 5
Tasks: Geophysics

When solving geophysical problems using reflection seismology, extracting the amplitude spectrum of the seismic wavelet (ASSW) from a seismic trace is the basis for many downstream works, such as deconvolution and sparse reflection coefficient inversion. This paper pertains to statistical methods for estimating ASSW, that is, separating the scattering effect of the formation from the amplitude spectrum of a seismic trace to obtain a band-limited ASSW. Common methods assume the reflection coefficient sequence (RCS) is white, making them unsatisfactory in many cases. Gao et al (2017 Inverse Problems33 085005) proposed an ASSW extraction method based on contraction operator mapping (COM method) and experimentally proved it effective for non-white RCSs. However, this method is only valid for unimodal ASSW, which makes it unsuitable for most sources. We propose a general method for estimating ASSW which does not require the whiteness assumption of the RCS and does not restrict the specific form of ASSW. We show that the COM method has function representation limitations and redefines a new operator, proving that it is also a contractive mapping. Our method can be regarded as a general form of the COM method, filling the theoretical gap that existing methods cannot be applied to non-white RCS and non-unimodal ASSW. It provides a general method for the estimation of ASSW. We thoroughly verify this generality by simulating different source types while considering wavelet attenuation. Compared with the COM method, the proposed method has absolute advantages.
An IMAP method for inversion of medium Q factor using zero-offset VSP data
Authors: Haoqi Zhao, Jinghuai Gao
Date: 2022-04-08 00:00:00
Proceeding: IEEE Geoscience and Remote Sensing Letters 2022 4
Tasks: Geophysics

In the zero-offset vertical seismic profile (VSP), if the data noise is weak, the spectral ratio method (SRM) is considered to be the most reliable and commonly used method for estimating the medium Q factor. However, due to the possible picking error in the first-arrival time of the downgoing waveform, the estimated Q value fluctuates, which affects the subsequent attenuation compensation and lithology analysis. In order to overcome this fluctuation, we present an iterative maximum posterior probability (IMAP) method that uses zero-offset VSP data to invert the medium Q factor. Based on SRM, this method gives the maximum posterior probability (MAP) method for inverting the quality factor Q , establishes the update formulas of quality factor Q and travel time difference Δt , and performs alternate iterations on them. Since the prior information of the formation is introduced and Δt is corrected in each iteration, the inversion result can eliminate the fluctuations in the Q value and velocity caused by the Δt error. Two synthetic experimental examples and one field experiment verify the effectiveness and superiority of the proposed method.
Absorption-Constrained Wavelet Power Spectrum Inversion for Robust Q Extraction From VSP Data
Authors: Haoqi Zhao, Jinghuai Gao, Zhen Li
Date: 2023-12-13 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 12
Tasks: Geophysics, Seismic Imaging

Extracting a reliable model of quality factor Q from the spectral information of seismic signals is a significantly important step for seismic imaging and reservoir characterization. However, the conventional Q estimation approach based on the frequency domain is susceptible to the high oscillative spectrum created by the unavoidable random and coherent noise in the observed signal. To address this issue, we introduce an absorption-constrained wavelet power spectrum inversion (AWPSI) method. The inversion involves absorption constraint (AC) and spectrum constraint (SC), where the AC term is a novel constraint for estimating the wavelet’s amplitude spectrum or power spectrum. By treating medium absorption as a physical prior and utilizing information from all waveforms rather than individual ones, AWPSI can yield high-quality wavelet power spectra and ensure stable Q estimation results. In addition, the proposed method has no limitations on the type of wavelet. Since its inversion target is wavelet power spectra, AWPSI is broadly applicable to various frequency-based Q estimation approaches. We validate the effectiveness of the proposed method using both synthetic and actual vertical seismic profile (VSP) data.
Absorption-Constrained Wavelet Power Spectrum Inversion for Enhancing Resolution of Nonstationary Seismic Data
Authors: Haoqi Zhao, Jinghuai Gao, Yajun Tian, Liang Zhao, Chuangji Meng
Date: 2023-12-26 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 12
Tasks: Geophysics

Improving the resolution of nonstationary reflection seismic data without well data is challenging, as it requires prior estimation of the Q -value. The coupling of reflectivity sequence and wavelet introduces spectral fluctuations, rendering the extraction of Q highly unstable. While some statistical wavelet amplitude estimation methods, such as spectral shaping (SS) and contraction operator mapping (COM), can yield smooth wavelet amplitude spectra, they do not contribute to Q analysis as they lack a physical mechanism for absorption attenuation. Therefore, we propose an absorption-constrained wavelet power spectrum inversion (AWPSI) method based on the segmented stationary convolution model (SSCM). The absorption constraint (AC) term incorporates prior knowledge of the medium’s Q -effect and enables simultaneous inversion of wavelet amplitude spectra for all windows. Incorporating AWPSI into COM and SS methods leads to AWPSI-COM and AWPSI-SS methods, which yield higher precision wavelet power spectra and remove the spectral fluctuations caused by reflectivity sequences. We demonstrate that AWPSI enables COM to extract wavelet amplitude spectra more accurately while preserving the attenuation characteristics caused by Q -effect. Using the equivalent Q field obtained by the AWPSI-COM method, an inverse Q -filter can be performed to generate accurate high-resolution seismic profiles. Synthetic and actual stacked seismic data verify the effectiveness of the proposed method.
Two-Dimensional Nonstationary Filtering by Operator Scaling
Authors: Haoqi Zhao, Jinghuai Gao
Date: 2024-04-01 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 4
Tasks: Geophysics

f–k filtering and Radon transform (RT) are classical methods for processing 2-D seismic signals. They assume that target signal events exhibit specific trajectories in the time–offset domain (linear, parabolic, and hyperbolic) and process them accordingly. In fact, seismic signals are nonstationary, with geometric and dynamic characteristics changing pointwise. This makes that these methods suboptimal for processing such signals. The 2-D nonstationary convolution filtering conducted in the time–space domain allows filter variations point-by-point, adapting to the nonstationarity of seismic signals. However, obtaining the filter factors involves complex calculations, making it not widely applicable. This article proposes an efficient method based on a two-dimensional nonstationary convolution model (TNCM) and termed two-dimensional nonstationary filtering by operator scaling (TNFOS). Leveraging the resemblance among filters, specific filter factors can be easily acquired by extracting and scaling the reference filter factors. Through the cascading of filters, TNFOS effectively fulfills nearly all design requirements for 2-D velocity or dip filtering, enabling the broad implementation of 2-D nonstationary filtering. Using synthetic and field experiments, we tested the efficiency of TNFOS and provided four successful applications in suppressing seismic interference.
Nonstationary blind deconvolution using spectral constraints
Authors: Haoqi Zhao, Jinghuai Gao, and Hongling Chen
Date: 2025-05-28 00:00:00
Proceeding: Geophysics 2025 5
Tasks: Geophysics

This study develops a nonstationary blind deconvolution using spectral constraints (NBDSC), a novel method to address the ill-posed nature of wavelet inversion in nonstationary seismic data. NBDSC does not require prior knowledge about the 𝑄
-factor of the medium and source wavelet, which are typically unknown. Our power spectrum-guided waveform inversion method corrects a wavelet to match any desired spectral shape. This method can be introduced as a wavelet-smoothing constraint, yielding blind deconvolution with spectral constraints (BDSC). First, based on a segmented stationary convolution model (SSCM), the original nonstationary convolution problem is decomposed into multiple stationary subproblems. Next, an absorption-constrained wavelet power spectrum inversion method is used on the SSCM decomposition results to extract the time-varying wavelet power spectrum. Finally, the estimated time-varying wavelet power spectrum is used as a spectral constraint to perform BDSC on each subseismic trace. We use synthetic and field data examples to demonstrate how the NBDSC enhances the resolution of nonstationary seismic data. Compared with conventional blind deconvolution methods, NBDSC incorporates prior knowledge of the wavelet power spectrum, thereby reducing inversion ambiguity and improving noise robustness.
Transformers over Directed Acyclic Graphs
Authors: Yuankai Luo, Veronika Thost, Lei Shi
Date: 2022-10-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Property Prediction, Graph Representation Learning, Representation Learning

Transformer models have recently gained popularity in graph representation learning as they have the potential to learn complex relationships beyond the ones captured by regular graph neural networks. The main research question is how to inject the structural bias of graphs into the transformer architecture, and several proposals have been made for undirected molecular graphs and, recently, also for larger network graphs. In this paper, we study transformers over directed acyclic graphs (DAGs) and propose architecture adaptations tailored to DAGs: (1) An attention mechanism that is considerably more efficient than the regular quadratic complexity of transformers and at the same time faithfully captures the DAG structure, and (2) a positional encoding of the DAG's partial order, complementing the former. We rigorously evaluate our approach over various types of tasks, ranging from classifying source code graphs to nodes in citation networks, and show that it is effective in two important aspects: in making graph transformers generally outperform graph neural networks tailored to DAGs and in improving SOTA graph transformer performance in terms of both quality and efficiency.