
esologic.com5d ago

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To achieve their goals, agents, whether biological or artificial, build approximate models of their environment to anticipate the consequences of their actions. In research, these world models are compressed spatial and temporal representations [1] of an
reka.ai·Twitter AI·16d ago·7 min readZhipu AI's GLM-5.2 nearly matches Claude Opus 4.7 in a Snowflake benchmark with 103 coding tasks at one-fifth the cost per output token. But the Chinese model burns through nearly twice as many tokens per task. Still, that pricing gap is putting real pres

Real-world LLM applications are moving beyond single-agent workflows toward orchestrated multi-agent systems, yet current models still struggle to determine what each sub-agent needs to know. To measure this, we introduce PerspectiveGap, a benchmark for e



Are LLM-based search agents genuinely searching, or using the web to verify what they already know? We study this question on BrowseComp with three diagnostics. Our analysis reveals Intrinsic Knowledge Dependence (IKD): even with tool access, agents often





Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark of 86 tasks across 11 domai
