Pace
PulseAugur coverage of Pace — every cluster mentioning Pace across labs, papers, and developer communities, ranked by signal.
- 2026-05-27 funding NYC-based Pace, an AI company automating insurance operations, raised $46 million in Series B funding led by Thrive and Sequoia at a $375 million valuation. source
- 2026-05-27 funding Pace raised $46 million in Series B funding co-led by Thrive Capital and Sequoia Capital, valuing the company at $375 million. source
- 2026-05-25 research_milestone A new research paper introduces the PACE framework for self-evolving small language model agents. source
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SkillAudit framework evolves LLM agent skills without ground-truth feedback
Researchers have developed SkillAudit, a novel framework designed to evolve agent skills for LLMs without requiring ground-truth feedback. This method utilizes paired trajectory auditing, where a task is executed with a…
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Open-source PACE tool automates content analysis with parallel LLM batching
An open-source Streamlit application called PACE has been developed to automate the analysis of various content types, including research papers, videos, and articles. The pipeline ingests content from five sources, cle…
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New ML methods model cell trajectories using interaction and geometry
Two new research papers introduce advanced machine learning techniques for inferring cellular development trajectories from single-cell RNA sequencing data. CellBRIDGE uses interaction-aware alignment to model ligand-re…
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AI firm Pace raises $46M Series B at $375M valuation
Pace, an AI company focused on automating insurance operations, has secured $46 million in Series B funding led by Thrive and Sequoia, reaching a $375 million valuation. The funding round highlights significant activity…
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AI insurance automation firm Pace raises $46M at $375M valuation
Pace, an AI startup focused on automating insurance back-office tasks, has secured $46 million in Series B funding. The company utilizes AI agents capable of interacting with legacy systems to process insurance claims w…
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PACE framework enables small language models to self-evolve
Researchers have developed PACE, a novel framework for enabling small language model (SLM) agents to self-evolve without requiring model weight updates or access to frontier models. This two-timescale approach separates…
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PACE framework enhances single-cell trajectory inference with geometry-aware transport
Researchers have developed PACE, a new framework for single-cell trajectory inference that addresses the inherent ill-posed nature of reconstructing cellular dynamics from time-course snapshots. PACE utilizes a geometry…
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SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models
Researchers have developed SpecTM, a novel physics-informed masking technique for foundation models used in Earth observation. This method enhances trustworthiness by enforcing physical constraints, particularly for pre…
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PiCSRL method enhances adaptive sensing for high-dimensional data
Researchers have developed PiCSRL, a novel Physics-Informed Contextual Spectral Reinforcement Learning method designed to improve adaptive sensing in high-dimensional, low-sample-size environments. This approach integra…
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New research explores ensemble models for improved AI performance and robustness
Two new research papers introduce novel methods for improving ensemble models in machine learning. The first, PACE, combines pruning and compression techniques to create more efficient and interpretable ensembles, outpe…
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PACE method improves reinforcement learning generalization via parameter change evaluation
Researchers have introduced PACE, a novel approach to Unsupervised Environment Design (UED) for enhancing reinforcement learning generalization. PACE directly measures an environment's value by assessing the policy para…
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PACE model slashes LiDAR point cloud compression latency by 90%
Researchers have introduced PACE, a novel framework designed to significantly improve the efficiency and reduce the latency of LiDAR point cloud compression for autonomous systems. PACE addresses existing bottlenecks by…
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AI model ViTCG enhances satellite AOD estimation with 62% error reduction
Researchers have developed ViTCG, a novel Vision Transformer model for estimating Aerosol Optical Depth (AOD) from satellite data. This new framework leverages spatial and spectral information from hyperspectral imagery…
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Pace connects wearables to Claude AI for personalized health insights
Pace is a new application that connects users' wearable device data to Anthropic's Claude AI. This integration allows users to ask natural language questions about their health and fitness metrics, receiving personalize…
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Partnering with Pace: Making Work Weightless
Sequoia Capital is leading a Series A investment in Pace, a company developing AI agents designed to automate complex knowledge work in regulated industries. Pace's agents can read standard operating procedures, process…
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AI agents advance with new RAG, simulation, and compliance tools
Researchers are developing advanced agent frameworks to improve AI reliability and efficiency across various domains. Google introduced an agentic RAG system that enhances enterprise query handling by iteratively search…