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Found 5 papers, 4 papers with code
Measuring AI Ability to Complete Long Tasks
Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear.
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
We confirm that our experts make progress in the environments given 8 hours, with 82% of expert attempts achieving a non-zero score and 24% matching or exceeding our strong reference solutions.
Evaluating Language-Model Agents on Realistic Autonomous Tasks
We find that these language model agents can only complete the easiest tasks from this list, although they make some progress on the more challenging tasks.
Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits
In many domains, worst-case guarantees on the performance (e. g., prediction accuracy) of a decision function subject to distributional shifts and uncertainty about the environment are crucial.
Shielding Atari Games with Bounded Prescience
We present the first exact method for analysing and ensuring the safety of DRL agents for Atari games.
