OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models

Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we introduce OmniACBench, a benchmark for evaluating context-grounded acoustic control in omni-modal models. Given a spoken instruction, a text script, and an image, a model must read the script aloud with an appropriate tone and manner. OmniACBench comprises 3,559 verified instances covering six acoustic features: speech rate, phonation, pronunciation, emotion, global accent, and timbre. Extensive experiments on eight models reveal their limitations in the proposed setting, despite their strong performance on prior textual-output evaluations. Our analyses show that the main bottleneck lies not in processing individual modalities, but in integrating multimodal context for faithful speech generation. Moreover, we identify three common failure modes-weak direct control, failed implicit inference, and failed multimodal grounding-providing insights for developing models that can verbalize responses effectively.

SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection

Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the efficacy of self-supervised learning-based speech encoders for deepfake detection, these models struggle to generalize across unseen speakers. Our quantitative analysis suggests these encoder representations are substantially influenced by speaker information, causing detectors to exploit speaker-specific correlations rather than artifact-related cues. We call this phenomenon speaker entanglement. To mitigate this reliance, we introduce SNAP, a speaker-nulling framework. We estimate a speaker subspace and apply orthogonal projection to suppress speaker-dependent components, isolating synthesis artifacts within the residual features. By reducing speaker entanglement, SNAP encourages detectors to focus on artifact-related patterns, leading to state-of-the-art performance.

Talk to Your Slides: Efficient Slide Editing Agent with Large Language Models

Existing research on large language models (LLMs) for PowerPoint predominantly focuses on slide generation, overlooking the common yet tedious task of editing existing slides. We introduce Talk-to-Your-Slides, an LLM-powered agent that directly edits slides within active PowerPoint sessions through COM communication. Our system employs a two-level approach: (1) high-level processing where an LLM agent interprets instructions and formulates editing plans, and (2) low-level execution where Python scripts directly manipulate PowerPoint objects. Unlike previous methods relying on predefined operations, our approach enables more flexible and contextually-aware editing. To facilitate evaluation, we present TSBench, a human-annotated dataset of 379 diverse editing instructions with corresponding slide variations. Experimental results demonstrate that Talk-to-Your-Slides significantly outperforms baseline methods in execution success rate, instruction fidelity, and editing efficiency. Our code and benchmark are available at https://anonymous.4open.science/r/talk-to-your-slides/

Spectral and Temporal Denoising for Differentially Private Optimization

This paper introduces the FFT-Enhanced Kalman Filter (FFTKF), a differentially private optimization method that addresses the challenge of preserving performance in DP-SGD, where added noise typically degrades model utility. FFTKF integrates frequency-domain noise shaping with Kalman filtering to enhance gradient quality while preserving $(\varepsilon, \delta)$-DP guarantees. It employs a high-frequency shaping mask in the Fourier domain to concentrate differential privacy noise in less informative spectral components, preserving low-frequency gradient signals. A scalar-gain Kalman filter with finite-difference Hessian approximation further refines the denoised gradients. With a per-iteration complexity of $\mathcal{O}(d \log d)$, FFTKF demonstrates improved test accuracy over DP-SGD and DiSK across MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets using CNNs, Wide ResNets, and Vision Transformers. Theoretical analysis confirms that FFTKF maintains equivalent privacy guarantees while achieving a tighter privacy-utility trade-off through reduced noise and controlled bias.

MathReader : Text-to-Speech for Mathematical Documents

TTS (Text-to-Speech) document reader from Microsoft, Adobe, Apple, and OpenAI have been serviced worldwide. They provide relatively good TTS results for general plain text, but sometimes skip contents or provide unsatisfactory results for mathematical expressions. This is because most modern academic papers are written in LaTeX, and when LaTeX formulas are compiled, they are rendered as distinctive text forms within the document. However, traditional TTS document readers output only the text as it is recognized, without considering the mathematical meaning of the formulas. To address this issue, we propose MathReader, which effectively integrates OCR, a fine-tuned T5 model, and TTS. MathReader demonstrated a lower Word Error Rate (WER) than existing TTS document readers, such as Microsoft Edge and Adobe Acrobat, when processing documents containing mathematical formulas. MathReader reduced the WER from 0.510 to 0.281 compared to Microsoft Edge, and from 0.617 to 0.281 compared to Adobe Acrobat. This will significantly contribute to alleviating the inconvenience faced by users who want to listen to documents, especially those who are visually impaired. The code is available at https://github.com/hyeonsieun/MathReader.

MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula

In various academic and professional settings, such as mathematics lectures or research presentations, it is often necessary to convey mathematical expressions orally. However, reading mathematical expressions aloud without accompanying visuals can significantly hinder comprehension, especially for those who are hearing-impaired or rely on subtitles due to language barriers. For instance, when a presenter reads Euler's Formula, current Automatic Speech Recognition (ASR) models often produce a verbose and error-prone textual description (e.g., e to the power of i x equals cosine of x plus i $\textit{side}$ of x), instead of the concise $\LaTeX{}$ format (i.e., $ e^{ix} = \cos(x) + i\sin(x) $), which hampers clear understanding and communication. To address this issue, we introduce MathSpeech, a novel pipeline that integrates ASR models with small Language Models (sLMs) to correct errors in mathematical expressions and accurately convert spoken expressions into structured $\LaTeX{}$ representations. Evaluated on a new dataset derived from lecture recordings, MathSpeech demonstrates $\LaTeX{}$ generation capabilities comparable to leading commercial Large Language Models (LLMs), while leveraging fine-tuned small language models of only 120M parameters. Specifically, in terms of CER, BLEU, and ROUGE scores for $\LaTeX{}$ translation, MathSpeech demonstrated significantly superior capabilities compared to GPT-4o. We observed a decrease in CER from 0.390 to 0.298, and higher ROUGE/BLEU scores compared to GPT-4o.

TeXBLEU: Automatic Metric for Evaluate LaTeX Format

LaTeX is highly suited to creating documents with special formatting, particularly in the fields of science, technology, mathematics, and computer science. Despite the increasing use of mathematical expressions in LaTeX format with language models, there are no evaluation metrics for evaluating them. In this study, we propose TeXBLEU, an evaluation metric tailored for mathematical expressions in LaTeX format, based on the n-gram-based BLEU metric that is widely used for translation tasks. The proposed TeXBLEU includes a predefined tokenizer trained on the arXiv paper dataset and a finetuned embedding model. It also considers the positional embedding of tokens. Simultaneously, TeXBLEU compares tokens based on n-grams and computes the score using exponentiation of a logarithmic sum, similar to the original BLEU. Experimental results show that TeXBLEU outperformed traditional evaluation metrics such as BLEU, Rouge, CER, and WER when compared to human evaluation data on the test dataset of the MathBridge dataset, which contains 1,000 data points. The average correlation coefficient with human evaluation was 0.71, which is an improvement of 87% compared with BLEU, which had the highest correlation with human evaluation data among the existing metrics. The code is available at https://github.com/KyuDan1/TeXBLEU.

MathBridge: A Large Corpus Dataset for Translating Spoken Mathematical Expressions into $LaTeX$ Formulas for Improved Readability

Understanding sentences that contain mathematical expressions in text form poses significant challenges. To address this, the importance of converting these expressions into a compiled formula is highlighted. For instance, the expression ``x equals minus b plus or minus the square root of b squared minus four a c, all over two a'' from automatic speech recognition (ASR) is more readily comprehensible when displayed as a compiled formula $x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}$. To develop a text-to-formula conversion system, we can break down the process into text-to-LaTeX and LaTeX-to-formula conversions, with the latter managed by various existing LaTeX engines. However, the former approach has been notably hindered by the severe scarcity of text-to-LaTeX paired data, which presents a significant challenge in this field. In this context, we introduce MathBridge, the first extensive dataset for translating mathematical spoken expressions into LaTeX, to establish a robust baseline for future research on text-to-LaTeX translation. MathBridge comprises approximately 23 million LaTeX formulas paired with the corresponding spoken English expressions. Through comprehensive evaluations, including fine-tuning and testing with data, we discovered that MathBridge significantly enhances the capabilities of pretrained language models for text-to-LaTeX translation. Specifically, for the T5-large model, the sacreBLEU score increased from 4.77 to 46.8, demonstrating substantial enhancement. Our findings indicate the need for a new metric, specifically for text-to-LaTeX conversion evaluations.