Agentic SEER Reproducibility at Scale
An AI agent reproduces thousands of SEER-based claims and stress-tests their analytical choices, flagging fragile results for human review.
Evaluation, benchmarks, observability, guardrails, reliability of agentic systems
An AI agent reproduces thousands of SEER-based claims and stress-tests their analytical choices, flagging fragile results for human review.
SolBehave shows that compilation is a weak signal for LLM-generated Solidity and that you need behavioral test oracles wired into your agent stack.
Using numerical and vacuity checks to keep Lean formalizations honest, beyond “LLM judge” pipelines.
What a controlled Minecraft benchmark exposes about “learning from experience” in embodied agents.
How Open-MMUnlearning standardizes methods and metrics for multimodal model unlearning—and what you can actually do with it.
What FastBench reveals about streaming VLM limits and how to design frame policies that don’t miss fast events
A new benchmark tests whether AI agents can build valid climate models without ever seeing the grading code.
How ScienceClaw turns task runs into persistent skills for AI-for-science agents.
Gracr replaces trained GNNs with compiled logic to make explainer benchmarks actually sound.
How NeutronGym turns neutron instrument design into an executable, physics-grounded gym for LLM agents.
What HumanoidToolBench actually measures and how to architect agents that don’t just pick the right tool but finish the job.
Inside a 7.5B-row simulated ERP that finally tests agents on end-to-end enterprise workflows.
Using expert routing signals to catch hallucinated objects before your VLM answers.
How GameHorizon’s multi-horizon gameplay data and evals change how you train and debug agentic systems.
A new benchmark hides the task inside a story, exposing how brittle our current evaluation and agent designs are.