I finished doing the outdoors ^^
#FF14 #FFXIV #FFXIVhousing #VirtualHome

alternate universe where instead of deltarune dotcom/chapter5, it's virtualhome dotcom/myfinalhome

SR improvement in VirtualHome and a +3.30% GCR and +2.11% SR improvement in Habitat over the best-performing baselines. [8/8 of https://arxiv.org/abs/2502.20742v1]

benchmark covering 1,509 tasks across VirtualHome and Habitat 2.0, categorized into ultra-short, short, medium, and long tasks. Experimental results demonstrate that SPO significantly improves reasoning quality and final [6/8 of https://arxiv.org/abs/2502.20742v1]

#WarcraftCommunity #WoWMemories
#BlizzardNostalgia #MMORPGLegacy
#GamingCulture #DigitalFamily
#WorldOfWarcraft #WoWClassicFeels
#GamerLife #TogetherMeantSomething
#MetzenEra #MMORPGHistory
#OnlineFriendships #VirtualHome
#OldSchoolWoW #GamersRemember

1288 – Brewing Up a Storm
thefoxclawpost.wordpress.com/2026/05/05/1...
@secondlife.bsky.social @fantasyfairesl.bsky.social @ropedpassions.bsky.social #Veilspire #QEHome #Raindale #Multifarious #Ichor #FallenGods #anxiousangel #cauldron #virtualhome #virtualgarden #RelayforLife #FF2026

Jiaqi Xu, Tao Huang, Kai Zhang: Structured Self-Consistency:A Multi-Task Evaluation of LLMs on VirtualHome https://arxiv.org/abs/2602.00611 https://arxiv.org/pdf/2602.00611 https://arxiv.org/html/2602.00611

Jiaqi Xu, Tao Huang, Kai Zhang: Structured Self-Consistency:A Multi-Task Evaluation of LLMs on VirtualHome https://arxiv.org/abs/2602.00611 https://arxiv.org/pdf/2602.00611 https://arxiv.org/html/2602.00611

triggers the knowledge refinement process across domains. Experiments conducted on diverse embodied task benchmarks-including ALFWorld, VirtualHome, Minecraft, RLBench, and a real-world robotic scenario-demonstrate that [6/7 of https://arxiv.org/abs/2503.00870v1]

of up to $3\times$. Finally, we expanded this pipeline by applying it to simulate complex household tasks in real-world scenarios, specifically in VirtualHome, enhancing the handling of failure cases. We release our code and [7/8 of https://arxiv.org/abs/2502.13170v1]

some of the real use cases available in the domestic environments of VirtualHome. Additionally, since experiments with VirtualHome have shown the need to reduce the response time (which increases as the agent's decision space grows), we have proposed [5/6 of https://arxiv.org/abs/2505.02144v1]

mid-level instructions while justifying the selection of these instructions. To validate its use in real applications we present a framework that integrates the reasoner into the VirtualHome simulator and compares its accuracy with GPT-4o, running [4/6 of https://arxiv.org/abs/2505.02144v1]

fine-grained, intermediate reward for effective agent training. Extensive experiments on common agent benchmarks (including Webshop, ALFWorld, and VirtualHome) demonstrate that SPA consistently outperforms the state-of-the-art method in both success [6/8 of https://arxiv.org/abs/2505.20732v1]