LLM-as-a-Verifier: A General-Purpose Verification Framework
Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis. To unlock this and demonstrate its effectiveness, we introduce LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback for agentic tasks without requiring additional training. Unlike standard LM judges that prompt LLMs to produce discrete scores for candidate solutions, LLM-as-a-Verifier computes the expect
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 59%OpenDCAI/One-Eval →
- PossiblePossibly related (embedding) · 58%Giskard-AI/giskard-oss →
- PossiblePossibly related (embedding) · 58%langwatch/langwatch →
- PossiblePossibly related (embedding) · 56%Evaluate a model properly →
- PossiblePossibly related (embedding) · 55%agent-tools →
- LinkedLinked via arxiv author · 85%Jacky Kwok →
“LLM-as-a-Verifier: A General-Purpose Verification Framework”
- LinkedLinked via arxiv author · 85%Shulu Li →
“LLM-as-a-Verifier: A General-Purpose Verification Framework”
- LinkedLinked via arxiv author · 85%Pranav Atreya →
“LLM-as-a-Verifier: A General-Purpose Verification Framework”