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SelfSearch is introduced, a reward-free search procedure in which agents modify themselves using records of previous self-improvement episodes, providing concrete experience for improving both task solving and self-modification.
Overall, a systematic understanding of how RL reshapes VLA policies is provided by studying how learned signals are encoded in parameter space, offering insights into more efficient and interpretable VLA post-training.
The results suggest that the representation space in which diffusion operates should itself be treated as a first-class design variable for continuous diffusion in language models.
Masked diffusion language models (MDMs) admit flexible generation orders, making the unmasking strategy an inference decision. Existing methods vary in how they prioritize positions, control parallelism, restrict selection regions, revise predictions, or plan future denoising, yet it remains unclear when these choices...
Large language model (LLM) agents assist users with everyday tasks that can be completed in many reasonable ways. Even when their answers are useful, how agents carry out these tasks may not match users'preferences and needs. For example, agents differ in whether they ask clarifying questions or search the web. We intr...
ProTTT is introduced, a profile-supervised meta-learning framework for learning semantic user memory that consistently outperforms both full history ICL and all parametric baselines across diverse benchmarks, while substantially reducing inference cost by compressing user history into a lightweight parameterized memory...
LLM agents use tools to access information and perform computations beyond their parametric knowledge. Existing tool-use benchmarks evaluate whether agents select and call the right tools, assuming that tool returns are reliable. However, tools can return plausible but incorrect outputs. We evaluate 14 models with thre...
Six models under six ways of providing narrative context are evaluated, suggesting that faithful role-play requires evolving character states and tracking their trajectory, rather than merely retrieving relevant episodic evidence.
This study indicates that questionnaire scores alone should not be treated as evidence of LLMs'response tendencies in realistic user interactions, and supports generation-probability profiling with ecologically valid items as a complementary behavioral measure.
SpokenUS is presented, a spoken user simulator grounded in TOD that decides when to speak through a dedicated turn-taking head that achieves comparable goal coverage to much larger models while substantially outperforming all baselines in human MOS.
A conversational diagnosis system that explores a diagnostic knowledge graph to reason in two steps, generating diagnostic hypotheses from the dialogue context and verifying hypotheses through clarifying questions, which are repeated until a final diagnosis is reached.
Large language models (LLMs) achieve strong performance on mathematical reasoning benchmarks, yet the mathematically meaningful skills underlying their reasoning remain underexplored. We introduce \texttt{SHAPE}, a framework that analyzes Chain-of-Thought (CoT) trajectories through two lenses developed in mathematics e...
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