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The Profiling, Investigation, and Judgment (PIJ), comprising 2,500 real homicide cases from five countries, is introduced and it is found that performance degrades systematically as tasks shift from explicit fact extraction to implicit reasoning over unknown suspect profiles.
Experimental results across three models across both domain-specific and general benchmarks demonstrate that NGFT significantly outperforms existing mainstream fine-tuning methods in both efficiency and performance, while effectively mitigating catastrophic forgetting.
A unified reasoning reward model supporting multiple languages and evaluation paradigms, UniRRM uses a staged reasoning chain to dynamically generate task-generic and instruction-specific criteria, enabling fine-grained, input-adaptive judgments while maintaining consistency across languages.
SelfMem provides an environment with memory tools and feedback signals that allow the agent to explore, evaluate, and refine its own memory strategy, following the principle of teaching an agent to fish rather than giving it a fish.
IndicDetect provides standard data splits, an evaluation protocol, and baselines to establish a robust, language-aware foundation for AI-generated text detection in Indic scripts, and finds substantial robustness failures.
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