Verses on a Topic – God’s Faithfulness Deu 7:9 Know therefore that the LORD thy God, he is God, the faithful God, which keepeth covenant and mercy with them that love him and keep his commandments to a thousand generations; Psa 105:8 He hath remembered his covenant for ever, the word which he commanded to a thousand generations. Heb 10:23 Let us hold fast the profession of our faith without wavering; (for he is faithful that promised;) 2Ti 2:13 If we believe not, yet he abideth faithful: he cannot deny himself. Psa 9:10 And they that know thy name will put their trust in thee: for thou, LORD, hast not forsaken them that seek thee. Psa 89:34 My covenant will I not break, nor alter the thing that is gone out of my lips. Isa 46:11 Calling a ravenous bird from the east, the man that executeth my counsel from a far country: yea, I have spoken it, I will also bring it to pass; I have purposed it, I will also do it.

TL;DR: AgentGarten is a virtual-world framework that keeps simulator-grade faithfulness while matching video-model realism, and it taught agents hide-and-seek tool-use strategies in a handful of rounds that took conventional RL millions of episodes. Title: AgentGarten: Code Worlds for Evolving Agents URL: Points 🎮 Couples a scene program (state, rules) with a neural renderer (visuals) through structured conditions like depth and normal maps, decoupling physics from appearance 🔬 A new technique, "Adversarial Forcing," makes history prefilling differentiable via bitwise-exact replay with block-by-block SDPA attention, where standard approaches weren't differentiable at all ⚡ Achieves real-time rendering at 480×832 and 36.5fps on a single H100 🤖 A "playbook" mechanism lets agents carry round-by-round reflections forward, accumulating knowledge with no gradient updates 🏃 In hide-and-seek, shelter-building emerged by round 4 (vs. ~25M episodes for self-play RL) and ramp use by round 10 (vs. ~100M episodes) 📈 Other environments — herding, a quarry loader — also show clear gains within just 4 rounds What's striking is how much learning efficiency you gain just by architecturally separating state dynamics from visual generation. #WorldModels# #AgentLearning#

"In a time of daily uncertainty, we hold fast to the faithfulness of our Father. We trust him. We know God is still on the throne and has promised to unleash his unlimited resources for the fulfillment of his kingdom purposes." Read more of "7 WORDS D...

Faith is trusting God’s plan, even when we can’t see what lies ahead. His timing is perfect, His promises are true, and His faithfulness never fails. Happy Sunday! 🙏🇺🇸

How @OrangeBusiness evaluate agents in their enterprise AI platform: ✅ RAG quality: golden datasets, retrieval recall, answer faithfulness ✅ Agent behavior: tool selection accuracy, prompt injection robustness ✅ Production traces: latency, cost, failed runs, human feedback All in LangSmith, with regression evals before changes reach production, and LangChain for model neutrality & portability

TL;DR: ATAS is a world-state-centric framework that helps AI video generators keep "the same place" and "the same event" consistent across multiple separate shots. Title: Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation URL: Key points 🗺️ Keeps scene memory (static spatial structure) and event memory (dynamic happenings) as two separate, persistent stores 🎬 Recursively compiles a screenplay into shot requests tagged with location, camera, and time ✅ Only validated observations update the world state, so a failed shot's errors don't propagate into later ones 📈 Achieves the top aggregate score across four different video-generation backbones 🧪 Hits a PSNR-C of 15.968 on spatial revisits and an event-identity score of 0.750 on recapture tasks 🙋 Wins highest human ratings on all six criteria, including 4.40/5.0 for script faithfulness and 4.22/5.0 for character consistency I appreciate that the paper is upfront about the trade-off: per-story runtime grows roughly 1.25x in exchange for that consistency. #VideoGeneration# #AIAgents#

TL;DR: ATAS is a world-state-centric framework that helps AI video generators keep "the same place" and "the same event" consistent across multiple separate shots. Title: Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation URL: Key points 🗺️ Keeps scene memory (static spatial structure) and event memory (dynamic happenings) as two separate, persistent stores 🎬 Recursively compiles a screenplay into shot requests tagged with location, camera, and time ✅ Only validated observations update the world state, so a failed shot's errors don't propagate into later ones 📈 Achieves the top aggregate score across four different video-generation backbones 🧪 Hits a PSNR-C of 15.968 on spatial revisits and an event-identity score of 0.750 on recapture tasks 🙋 Wins highest human ratings on all six criteria, including 4.40/5.0 for script faithfulness and 4.22/5.0 for character consistency I appreciate that the paper is upfront about the trade-off: per-story runtime grows roughly 1.25x in exchange for that consistency. #VideoGeneration# #AIAgents#