arXiv (All)
open access
SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals
Evaluating statistical discovery by large language model (LLM) agents requires verifiable analytical ground truth. Establishing such ground truth for real-world datasets is costly, and prior knowledge of public datasets can influence agent responses. We introduce SLEUTHBENCH, a benchmark that addres
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Measuring the Depth of LLM Unlearning via Activation Patching#793684
- Measuring the Depth of LLM Unlearning via Activation Patching#789680
- Single-Query Black-Box Calibration Auditing via Logit Bias#972074
- From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models#1050270
- From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models#1028600
Record · ID 1119301
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive