We present the AM$^3$ ("Astrophysical Multi-Messenger Modeling") software. AM$^3$ is a documented open source software that efficiently solves the coupled integro-differential equations describing the temporal evolution of the spectral densities of particles interacting in astrophysical environments, in-cluding photons, electrons, positrons, protons, neutrons, pions, muons, and neutrinos. The software has been extensively used to simulate the multi-wavelength and neutrino emission from active galactic nuclei (including blazars), gamma-ray bursts, and tidal disruption events. The simulations include all relevant non-thermal processes, namely synchrotron emission, inverse Compton scattering, photon-photon annihilation, proton-proton and proton-photon pion production, and photo-pair production. The software self-consistently calculates the full cascade of primary and secondary particles, including non-linear feedback processes and predictions in the time domain. It also allows to track separately the particle densities produced by means of each distinct interaction processes, including the different hadronic channels. With its efficient hybrid solver combining analytical and numerical techniques, AM$^3$ combines efficiency and accuracy at a user-adjustable level. We describe the technical details of the numerical framework and present three examples of applications to different astrophysical environments.

Motivated by the fermionic Berry's phase in momentum space, we study a local Abelian phase in momentum space coupled to electromagnetism, for complex scalars in the phase-space worldline formalism. The interaction of both Abelian fields is shown to give rise to a momentum gauge dependent emergent spacetime. As a concrete example, we further study classical solutions of the Berry-inspired gauge field that lead to an emergent Newtonian gravity with gravitational potential predicted by coupled Coulomb fields both in configuration and momentum spaces. Noncommutative aspects of the theory are also provided.

This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical scoring methods. Natural language feedback provides instructive recommendations and detailed evaluations, yet existing datasets lack the comprehensive annotations needed for this approach. To bridge this gap, we introduce QualiSpeech, a comprehensive low-level speech quality assessment dataset encompassing 11 key aspects and detailed natural language comments that include reasoning and contextual insights. Additionally, we propose the QualiSpeech Benchmark to evaluate the low-level speech understanding capabilities of auditory large language models (LLMs). Experimental results demonstrate that finetuned auditory LLMs can reliably generate detailed descriptions of noise and distortion, effectively identifying their types and temporal characteristics. The results further highlight the potential for incorporating reasoning to enhance the accuracy and reliability of quality assessments. The dataset will be released at this https URL.

While there are many music datasets with emotion labels in the literature, they cannot be used for research on symbolic-domain music analysis or generation, as there are usually audio files only. In this paper, we present the EMOPIA (pronounced `yee-mò-pi-uh') dataset, a shared multi-modal (audio and MIDI) database focusing on perceived emotion in pop piano music, to facilitate research on various tasks related to music emotion. The dataset contains 1,087 music clips from 387 songs and clip-level emotion labels annotated by four dedicated annotators. Since the clips are not restricted to one clip per song, they can also be used for song-level analysis. We present the methodology for building the dataset, covering the song list curation, clip selection, and emotion annotation processes. Moreover, we prototype use cases on clip-level music emotion classification and emotion-based symbolic music generation by training and evaluating corresponding models using the dataset. The result demonstrates the potential of EMOPIA for being used in future exploration on piano emotion-related MIR tasks.

Document expansion (DE) via query generation tackles vocabulary mismatch in sparse retrieval, yet faces limitations: uncontrolled generation producing hallucinated or redundant queries with low diversity; poor generalization from in-domain training (e.g., MS MARCO) to out-of-domain data like BEIR; and noise from concatenation harming dense retrieval. While Large Language Models (LLMs) enable cross-domain query generation, basic prompting lacks control, and taxonomy-based methods rely on domain-specific structures, limiting applicability. To address these challenges, we introduce Doc2Query++, a DE framework that structures query generation by first inferring a document's latent topics via unsupervised topic modeling for cross-domain applicability, then using hybrid keyword selection to create a diverse and relevant keyword set per document. This guides LLM not only to leverage keywords, which ensure comprehensive topic representation, but also to reduce redundancy through diverse, relevant terms. To prevent noise from query appending in dense retrieval, we propose Dual-Index Fusion strategy that isolates text and query signals, boosting performance in dense settings. Extensive experiments show Doc2Query++ significantly outperforms state-of-the-art baselines, achieving substantial gains in MAP, nDCG@10 and Recall@100 across diverse datasets on both sparse and dense retrieval.

One of the most remarkable discoveries of JWST is a population of compact, red sources at z > 4, commonly referred to as Little Red Dots (LRDs). Spectroscopic identifications reported that most LRDs are active galactic nuclei (AGNs), which are preferentially found around z~6 and could imply a key phase in the formation and growth of black holes (BHs) in the early universe. Photometric surveys at lower redshift have recently been carried out to trace their evolution across cosmic time, and a small number of LRDs have been spectroscopically identified at both Cosmic Noon and in the local universe. Here we report the discovery of one of the lowest-z analogs of LRDs, J204837.26-002437.2 (hereafter J2048) at z = 0.4332, using new Gemini-N/GMOS IFU observations combined with archival multi-band photometric SED data. The GMOS data reveal extended blue emission from starburst with a star formation rate of 400 Msun yr-1, together with an extended, highly fast ionized outflow. This is the first spectroscopic confirmation of extended host emission and outflow in an LRD-like galaxy, providing a unique laboratory for understanding the nature of their high-redshift counterparts. Moreover, J2048 would host an extremely overmassive BH with a BH-to-stellar mass ratio of 0.6, with the BH mass and host stellar mass estimated to be 10^10.2 and 10^10.4 Msun, respectively. We discuss the origin and evolutionary fate of J2048, and the implications that such low-z analogs have for interpreting the properties of high-z LRDs.

Kramers-Wannier duality, a hallmark of the Ising model, has recently gained renewed interest through its reinterpretation as a non-invertible symmetry with a state-level action. Using sequential quantum circuits (SQC), we argue that this duality governs the stability of quantum many-body scar (QMBS) states in a nonintegrable model, depending on whether the dual preserves the embedding conditions for scarring. This is supported by good agreement between first-order perturbation theory and numerics, which capture scar dynamics despite chaotic spectra. Our results establish non-invertible dualities as both a generative mechanism and a diagnostic tool for quantum many- body scarring, offering a generalized symmetry-based route to weak ergodicity breaking.

The Interspeech 2025 URGENT Challenge aimed to advance universal, robust, and generalizable speech enhancement by unifying speech enhancement tasks across a wide variety of conditions, including seven different distortion types and five languages. We present Universal Speech Enhancement Mamba (USEMamba), a state-space speech enhancement model designed to handle long-range sequence modeling, time-frequency structured processing, and sampling frequency-independent feature extraction. Our approach primarily relies on regression-based modeling, which performs well across most distortions. However, for packet loss and bandwidth extension, where missing content must be inferred, a generative variant of the proposed USEMamba proves more effective. Despite being trained on only a subset of the full training data, USEMamba achieved 2nd place in Track 1 during the blind test phase, demonstrating strong generalization across diverse conditions.

This work aims to investigate the use of a recently proposed, attention-free, scalable state-space model (SSM), Mamba, for the speech enhancement (SE) task. In particular, we employ Mamba to deploy different regression-based SE models (SEMamba) with different configurations, namely basic, advanced, causal, and non-causal. Furthermore, loss functions either based on signal-level distances or metric-oriented are considered. Experimental evidence shows that SEMamba attains a competitive PESQ of 3.55 on the VoiceBank-DEMAND dataset with the advanced, non-causal configuration. A new state-of-the-art PESQ of 3.69 is also reported when SEMamba is combined with Perceptual Contrast Stretching (PCS). Compared against Transformed-based equivalent SE solutions, a noticeable FLOPs reduction up to ~12% is observed with the advanced non-causal configurations. Finally, SEMamba can be used as a pre-processing step before automatic speech recognition (ASR), showing competitive performance against recent SE solutions.

Music Information Retrieval (MIR) encompasses a broad range of computational techniques for analyzing and understanding musical content, with recent deep learning advances driving substantial improvements. Building upon these advances, this paper explores how large language models (LLMs) can serve as an integrative bridge to connect and integrate information from multiple MIR tools, with a focus on enhancing automatic chord recognition performance. We present a novel approach that positions text-based LLMs as intelligent coordinators that process and integrate outputs from diverse state-of-the-art MIR tools-including music source separation, key detection, chord recognition, and beat tracking. Our method converts audio-derived musical information into textual representations, enabling LLMs to perform reasoning and correction specifically for chord recognition tasks. We design a 5-stage chain-of-thought framework that allows GPT-4o to systematically analyze, compare, and refine chord recognition results by leveraging music-theoretical knowledge to integrate information across different MIR components. Experimental evaluation on three datasets demonstrates consistent improvements across multiple evaluation metrics, with overall accuracy gains of 1-2.77% on the MIREX metric. Our findings demonstrate that LLMs can effectively function as integrative bridges in MIR pipelines, opening new directions for multi-tool coordination in music information retrieval tasks.