@omarsar0: Verifiers are a big deal. Without good verifiers, /goal & /loop breaks a lot. Anything out of distribution for an LLM, …
Summary
Emphasizes the importance of verifiers for LLM-based agents, noting that out-of-distribution tasks cause failures, and suggests tuning custom verifiers.
View Cached Full Text
Cached at: 06/15/26, 11:08 PM
Verifiers are a big deal.
Without good verifiers, /goal & /loop breaks a lot.
Anything out of distribution for an LLM, the agent will struggle to verify work correctly.
I think it’s worth tuning your own verifiers and figuring out how to hook them up with your current agents.
Similar Articles
Look Before You Leap: Pre-Action Verification for LLM Agents
This paper introduces a deterministic verification framework for LLM agents to prevent silent failures in shell commands and code edits, demonstrating high catch rates and releasing benchmarks and verifiers.
LLM-as-a-Verifier: A General-Purpose Verification Framework
LLM-as-a-Verifier introduces a probabilistic verification framework that computes continuous scores from LLM logits, scaling across granularity, repeated evaluation, and criteria decomposition. It achieves state-of-the-art results on multiple agentic benchmarks and provides dense feedback for RL.
VerifierBench: Benchmarking the Generation of Executable Verifiers for LLM Agents
VerifierBench introduces a benchmark of 222 tasks and 4,993 hidden test cases to evaluate how well LLM agents generate executable verifiers, finding that verifiers tend to over-reject valid solutions due to hidden assumptions that survive self-testing.
@Azaliamirh: Check out LLM-as-a-Verifier: a simple, cheap, & general-purpose self-improvement technique that boosts performance on "…
LLM-as-a-Verifier is a simple, cheap, general-purpose self-improvement technique for agentic tasks, using fine-grained scoring and logprob-based ranking to achieve SOTA on multiple benchmarks like SWE-Bench Verified and Terminal-Bench V2.
LLM-as-a-Verifier (GitHub Repo)
LLM-as-a-Verifier is a general-purpose verification framework that provides fine-grained feedback for AI agents, achieving state-of-the-art performance on benchmarks like Terminal-Bench and SWE-Bench.