TJS Articles & News

Parallel Test-Time Scaling for Latent Reasoning Modelscs.AI updates on arXiv.org arXiv:2510.07745v3 Announce Type: replace-cross
Abstract: Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggregating outcomes through voting or search. Recent advances in latent reasoning, where intermediate reasoning unfolds in continuous vector spaces, offer a more efficient alternative to explicit Chain-of-Thought, yet whether such latent models can similarly benefit from parallel TTS remains open, mainly due to the absence of sampling mechanisms in continuous space, and the lack of probabilistic signals for advanced trajectory aggregation. This work enables parallel TTS for latent reasoning models by addressing the above issues. For sampling, we introduce two uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive Gaussian Noise. For aggregation, we design a Latent Reward Model (LatentRM) trained with step-wise contrastive objective to score and guide latent reasoning. Extensive experiments and visualization analyses show that both sampling strategies scale effectively with compute and exhibit distinct exploration dynamics, while LatentRM enables effective trajectory selection. Together, our explorations open a new direction for scalable inference in continuous spaces. Code and checkpoints released at https://github.com/ModalityDance/LatentTTS

 arXiv:2510.07745v3 Announce Type: replace-cross
Abstract: Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggregating outcomes through voting or search. Recent advances in latent reasoning, where intermediate reasoning unfolds in continuous vector spaces, offer a more efficient alternative to explicit Chain-of-Thought, yet whether such latent models can similarly benefit from parallel TTS remains open, mainly due to the absence of sampling mechanisms in continuous space, and the lack of probabilistic signals for advanced trajectory aggregation. This work enables parallel TTS for latent reasoning models by addressing the above issues. For sampling, we introduce two uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive Gaussian Noise. For aggregation, we design a Latent Reward Model (LatentRM) trained with step-wise contrastive objective to score and guide latent reasoning. Extensive experiments and visualization analyses show that both sampling strategies scale effectively with compute and exhibit distinct exploration dynamics, while LatentRM enables effective trajectory selection. Together, our explorations open a new direction for scalable inference in continuous spaces. Code and checkpoints released at https://github.com/ModalityDance/LatentTTS Read More  

Banks operationalise as Plumery AI launches standardised integrationAI News A new technology from digital banking platform Plumery AI aims to address a dilemma for financial institutions: how to move beyond proofs of concept and embed artificial intelligence into everyday banking operations without compromising governance, security, or regulatory compliance. Plumery’s “AI Fabric” has been positioned by the company as a standardised framework for connecting generative
The post Banks operationalise as Plumery AI launches standardised integration appeared first on AI News.

 A new technology from digital banking platform Plumery AI aims to address a dilemma for financial institutions: how to move beyond proofs of concept and embed artificial intelligence into everyday banking operations without compromising governance, security, or regulatory compliance. Plumery’s “AI Fabric” has been positioned by the company as a standardised framework for connecting generative
The post Banks operationalise as Plumery AI launches standardised integration appeared first on AI News. Read More  

Top 5 Open-Source AI Model API ProvidersKDnuggets Large open-source language models are now widely accessible, and this article compares leading AI API providers on performance, pricing, latency, and real-world reliability to help you choose the right option.

 Large open-source language models are now widely accessible, and this article compares leading AI API providers on performance, pricing, latency, and real-world reliability to help you choose the right option. Read More  

Retailers bring conversational AI and analytics closer to the userAI News After years of experimentation with artificial intelligence, retailers are striving to embed consumer insight directly into everyday commercial decisions. First Insight, a US-based analytics company specialising in predictive consumer feedback, argues that the next phase of retail AI should be epitomised by dialogue, not dashboards. Following a three-month beta programme, First Insight has made its
The post Retailers bring conversational AI and analytics closer to the user appeared first on AI News.

 After years of experimentation with artificial intelligence, retailers are striving to embed consumer insight directly into everyday commercial decisions. First Insight, a US-based analytics company specialising in predictive consumer feedback, argues that the next phase of retail AI should be epitomised by dialogue, not dashboards. Following a three-month beta programme, First Insight has made its
The post Retailers bring conversational AI and analytics closer to the user appeared first on AI News. Read More  

From RGB to Lab: Addressing Color Artifacts in AI Image CompositingTowards Data Science A multi-tier approach to segmentation, color correction, and domain-specific enhancement
The post From RGB to Lab: Addressing Color Artifacts in AI Image Compositing appeared first on Towards Data Science.

 A multi-tier approach to segmentation, color correction, and domain-specific enhancement
The post From RGB to Lab: Addressing Color Artifacts in AI Image Compositing appeared first on Towards Data Science. Read More