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Canada-based Clio, which makes cloud tools for law firms, agrees to acquire vLex, which offers a legal and regulatory database and an AI assistant, for $1B (Alex Riehl/BetaKit)
https://betakit.com/clio-to-acquire-american-legaltech-vlex-for-1-billion…

Clio to acquire American legaltech vLex for $1 billion USD
vLex offers an extensive legal and regulatory database alongside an AI-powered assistant.

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
Shixiao Wang, Yifan Zhuang, Runsheng Zhang, Zhijun Song
https://arxiv.org/abs/2507.00540

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
This paper proposes a user semantic intent modeling algorithm based on Capsule Networks to address the problem of insufficient accuracy in intent recognition for human-computer interaction. The method represents semantic features in input text through a vectorized capsule structure. It uses a dynamic routing mechanism to transfer information across multiple capsule layers. This helps capture hierarchical relationships and part-whole structures between semantic entities more effectively. The mod…

Hybrid SLC-MLC RRAM Mixed-Signal Processing-in-Memory Architecture for Transformer Acceleration via Gradient Redistribution
Chang Eun Song, Priyansh Bhatnagar, Zihan Xia, Nam Sung Kim, Tajana Rosing, Mingu Kang
https://arxiv.org/abs/2506.00020

Hybrid SLC-MLC RRAM Mixed-Signal Processing-in-Memory Architecture for Transformer Acceleration via Gradient Redistribution
Transformers, while revolutionary, face challenges due to their demanding computational cost and large data movement. To address this, we propose HyFlexPIM, a novel mixed-signal processing-in-memory (PIM) accelerator for inference that flexibly utilizes both single-level cell (SLC) and multi-level cell (MLC) RRAM technologies to trade-off accuracy and efficiency. HyFlexPIM achieves efficient dual-mode operation by utilizing digital PIM for high-precision and write-intensive operations while ana…

EDGChain-E: A Decentralized Git-Based Framework for Versioning Encrypted Energy Data
Alper Alimoglu, Kamil Erdayandi, Mustafa A. Mustafa, \"Umit Cali
https://arxiv.org/abs/2507.01615

EDGChain-E: A Decentralized Git-Based Framework for Versioning Encrypted Energy Data
This paper proposes a new decentralized framework, named EDGChain-E (Encrypted-Data-Git Chain for Energy), designed to manage version-controlled, encrypted energy data using blockchain and the InterPlanetary File System. The framework incorporates a Decentralized Autonomous Organization (DAO) to orchestrate collaborative data governance across the lifecycle of energy research and operations, such as smart grid monitoring, demand forecasting, and peer-to-peer energy trading. In EDGChain-E, initi…

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data
Tahir Javed, Kaushal Bhogale, Mitesh M. Khapra
https://arxiv.org/abs/2507.00534

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data
We introduce Nirantar, a comprehensive framework for evaluating continual learning (CL) in multilingual and multi-domain ASR. Designed to reflect real-world CL challenges, Nirantar leverages data collected incrementally across 22 languages and 208 districts in India through natural episodes. This enables evaluation across Language-Incremental (LIL), Domain-Incremental (DIL), and the novel Language-Incremental Domain-Incremental Learning (LIDIL) scenarios. Unlike prior work that relies on simula…

Parallelizing Drug Discovery: HPC Pipelines for Alzheimer's Molecular Docking and Simulation
Paul Ruiz Alliata, Diana Rubaga, Daniel Kumlin, Alberto Puliga
https://arxiv.org/abs/2509.00937

Parallelizing Drug Discovery: HPC Pipelines for Alzheimer's Molecular Docking and Simulation
High-performance computing (HPC) is reshaping computational drug discovery by enabling large-scale, time-efficient molecular simulations. In this work, we explore HPC-driven pipelines for Alzheimer's disease drug discovery, focusing on virtual screening, molecular docking, and molecular dynamics simulations. We implemented a parallelised workflow using GROMACS with hybrid MPI-OpenMP strategies, benchmarking scaling performance across energy minimisation, equilibration, and production stages. Ad…

Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach
Aditya Tomar, Rudra Murthy, Pushpak Bhattacharyya
https://arxiv.org/abs/2507.01715

Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach
Bias and stereotypes in language models can cause harm, especially in sensitive areas like content moderation and decision-making. This paper addresses bias and stereotype detection by exploring how jointly learning these tasks enhances model performance. We introduce StereoBias, a unique dataset labeled for bias and stereotype detection across five categories: religion, gender, socio-economic status, race, profession, and others, enabling a deeper study of their relationship. Our experiments c…

EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Sanchit Ahuja, Praneetha Vaddamanu, Barun Patra
https://arxiv.org/abs/2507.00246

EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Despite recent advances in Language Reasoning Models (LRMs), most research focuses solely on English, even though many models are pretrained on multilingual data. In this work, we investigate: Is English the most token-efficient language for reasoning? We evaluate three open-source RLMs: DeepSeek R1, Qwen 2.5 and Qwen 3, across four math datasets and seven typologically diverse languages. We find that reasoning in non-English languages not only reduces token usage, but also preserves accuracy. …

From Guidelines to Practice: A New Paradigm for Arabic Language Model Evaluation
Serry Sibaee, Omer Nacar, Adel Ammar, Yasser Al-Habashi, Abdulrahman Al-Batati, Wadii Boulila
https://arxiv.org/abs/2506.01920

From Guidelines to Practice: A New Paradigm for Arabic Language Model Evaluation
This paper addresses critical gaps in Arabic language model evaluation by establishing comprehensive theoretical guidelines and introducing a novel evaluation framework. We first analyze existing Arabic evaluation datasets, identifying significant issues in linguistic accuracy, cultural alignment, and methodological rigor. To address these limitations in LLMs, we present the Arabic Depth Mini Dataset (ADMD), a carefully curated collection of 490 challenging questions spanning ten major domains …