Counterfactual evaluation for World Action Models
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Updated
Sep 1, 2026 - Python
Counterfactual evaluation for World Action Models
Offline recommender metrics vs true online lift, graded against a known answer (Open Bandit Dataset). Ends in an evaluation gate that refuses to report a number when the diagnostics say it would be meaningless.
Branching rollouts show that replay-based evaluation of per-step model routing in LLM agents scores states that never occur. Efficient Reasoning Workshop @ COLM 2026.
Counterfactual dependability framework for testing whether language-model safety mechanisms are behaviorally load-bearing under removal, misrouting, bypass, and family-ablation controls.
Position-bias-aware ranking: estimating and correcting position bias to optimize Earnings Per Visitor (EPV) · simulation study · IPW & propensity modeling · Python
Off policy evaluation audit for logged bandits: measures how much of a target policy's probability mass sits outside what the log could have produced, shows that a 95 percent interval around the standard estimator covers 0.26 of the time, and returns exit 2 rather than a number when the estimate would be about a different quantity.
SAST-IR: evaluating LLM factual robustness with a stateful attacker and stateless target
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