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This article presents LittleLearner, a controlled sandbox for studying LLM knowledge acquisition using a K-5 curriculum-filtered dataset, finding that interventions like scaling and post-training enhance in-scope performance but do not improve out-of-scope capabilities.
This paper addresses the knowledge boundary problem in visual generation by introducing the SearchGen-20K benchmark and SearchGen-Corpus-1M, and proposes a teach-then-search co-training framework to handle evolving, long-tailed user requests beyond a generator's training data.
This paper introduces Know2Guess, a contamination-aware multi-zone benchmark designed to evaluate the transition from answerable knowledge to expected abstention in large language models, addressing data contamination, prompt sensitivity, and refusal behavior. The authors assess FLAN-T5, Qwen2.5-Instruct, and Llama-3-Instruct models, finding that stronger models show selective but incomplete abstention. The benchmark and dataset are publicly released.
This paper proposes ReverieMem, a three-layer memory architecture for book-based LLM role-playing agents that prevents factual overreach and stylistic monotony. It also introduces the KBF-QA benchmark and achieves significant improvements in knowledge boundary fidelity and narrative quality.
This paper proposes AKBE, an on-policy method for LLM agent reinforcement learning that dynamically identifies when tool use is needed versus when internal knowledge suffices, improving accuracy by +1.85 on average and reducing tool calls by 18% over standard agentic RL.