Unverified50% confidenceFactExact time
The deterministic simulator simplifies the semantic richness and failure modes of real tools, so the paper specifically states that the results cannot be directly extrapolated to production systems.
1
Sources
50%
Confidence
Long-term
Relevance
11/5/2026
First Seen
Sources
SLATE Benchmark Exposes AI Agent Weakness: 74% Single-Step Accuracy but Only 5% Task Success Rate
redditr/LangChain11/5/2026
Related Entities
Related Claims
UnverifiedPure prompt-based disambiguation has no hard constraints and relies only on instructions, worsening as the number and overlap of tools increase; it is suitable for demos but not for real automation.76% similarUnverifiedTraditional automated provers adopt a decomposition strategy that splits complex proof obligations into smaller lemmas, but they cannot diagnose the root causes of sub-lemma failures.74% similarUnverifiedMuch semantics cannot be derived from syntax and must be measured empirically; the model initially did no optimization at all in query optimization and only improved once performance benchmarks and real datasets were provided.74% similarUnverifiedOutput quality alone cannot be used to reverse-engineer which model version is being used on the backend, as prompts, toolchains, or the task itself may all affect results.74% similarUnverifiedUnder a sufficiently capable external harness, the structured products of knowledge compilation show no detectable accuracy gain on the FRAMES benchmark.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1138688API
curl https://kongchang.com/api/v1/knowledge/claims/1138688MCP
get_claim(id=1138688)