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Direct-decision models turn text into low-latency structured labels and scores, making them attractive for classification and automatic evaluation. Yet reliability requires more than accuracy: a model must also use the ordinal decision scale supplied by the user faithfully. We analyze JEV~1.13 and three open KEV models...
Multi-turn interaction creates a feedback process in which an LLM's previous responses become context for later behavior. Prior work shows substantial multi-turn degradation and that assistant-generated history can affect later behavior. However, it remains unclear how these effects manifest across models, tasks, turns...
This work proposes MMPCBench, a comprehensive framework for evaluating MLLMs' proactive critique competence, which adopts a hierarchical evaluation protocol to measure models' error detection, diagnosis and resolution performance, and applies alignment-aware metrics to assess the coherence between internal reasoning an...
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