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This paper studies rational closure for the DL-Lite family of description logics, providing a plug-in architecture for efficient non-monotonic reasoning and conjunctive query answering with minimal computational overhead.
This paper empirically tests the common assumption that more structured harnesses universally improve LLM agent reliability, finding a non-monotone relationship across model tiers. It introduces the HEAT-24 benchmark and reveals that strict harnesses can harm frontier chat models while benefiting reasoning models.