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An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response1h◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training1h◆Large language model and reinforcement learning based autonomous construction and progress updates1h◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring1h◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution1h◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing1h◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning1h◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System1h◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability1h◆Physical AI: Concepts, Techniques, and Challenges1h◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems1h◆A general-purpose System One decision model for clinical records1h◆EDMA: Entropy-Driven Multimodal Answering1h◆Learned Control Variates for Rare Grid-Outage Estimation at Electric Vehicle Charging Stations1h◆TEX: Test-Time Scaling Testing Agents via Execution-based Cross-Validation1h◆When PTQ Calibration Helps or Hurts: Dose-Driven Collapse and Validation-Gated Abstention in Quantized Text Embedders1h◆Unified Sharp Bounds on Probabilities of Causation1h◆ProMemAgent: Long-Horizon Proactive Assistance with Context-Aware Prospective Memory from Sensory Perceptions1h◆Harness Self-Evolution for Long-Horizon Terminal Agents1h◆Ask to Distill: Learning to Synthesize Data for On-Policy Distillation1h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response1h◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training1h◆Large language model and reinforcement learning based autonomous construction and progress updates1h◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring1h◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution1h◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing1h◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning1h◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System1h◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability1h◆Physical AI: Concepts, Techniques, and Challenges1h◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems1h◆A general-purpose System One decision model for clinical records1h◆EDMA: Entropy-Driven Multimodal Answering1h◆Learned Control Variates for Rare Grid-Outage Estimation at Electric Vehicle Charging Stations1h◆TEX: Test-Time Scaling Testing Agents via Execution-based Cross-Validation1h◆When PTQ Calibration Helps or Hurts: Dose-Driven Collapse and Validation-Gated Abstention in Quantized Text Embedders1h◆Unified Sharp Bounds on Probabilities of Causation1h◆ProMemAgent: Long-Horizon Proactive Assistance with Context-Aware Prospective Memory from Sensory Perceptions1h◆Harness Self-Evolution for Long-Horizon Terminal Agents1h◆Ask to Distill: Learning to Synthesize Data for On-Policy Distillation1h◆
News/Meta enters the crowded AI coding battle with Muse Spark 1.1
techcrunch
PublishedJuly 9, 2026 at 7:40 PM
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Meta enters the crowded AI coding battle with Muse Spark 1.1

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Meta's pitch to users is Spark's ability to handle large agentic workloads, fix bugs, and help with large code migrations — the kind of automation that enterprises are increasingly turning to AI companies to provide.

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