LabyrinthBench measures context recall under interference for multi-step agentic tasks on local hardware with a judge-free benchmark
Read the original at old.reddit.com→LabyrinthBench measures the thing that actually kills long agent runs — whether a model can still use what it learned twenty turns ago — deterministically, with no LLM judge, on your own hardware, with a swappable...
Original headline: "LabyrinthBench: a local-focused, judge-free LLM benchmark that measures context recall under interference for multi-step agentic tasks."
Coverage timeline
- Aug 7, 12:19 UTC r/LocalLLaMA lead source LabyrinthBench: a local-focused, judge-free LLM benchmark that measures context recall under interference for multi-step agentic tasks.