DialToM is a Theory of Mind benchmark created from naturalistic human-human dialogues, using a multiple-choice format. It introduces a State-Driven Diagnostic Probe that requires models to predict dialogue trajectories based solely on isolated mental-state profiles, without dialogue context. The evaluation shows that large language models are good at inferring mental states (Literal ToM) but struggle to use them for social forecasting (Functional ToM), while a domain expert scores 100% accuracy, highlighting a clear human‑AI gap.
By Neemesh Yadav, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim
SCOPE compiles natural‑language policies into neurosymbolic predicate programs that combine small neural models, deterministic extractors, and logical composition. The system is jointly optimized with adversarially generated counterexamples, achieving higher accuracy than prompt‑optimization, task‑decomposition, and workflow‑generation baselines on real‑world policy benchmarks. Programs trained with the weakest executor transfer to other models, enabling deployment on inexpensive models with stable, interpretable predictions.
By Arnon Mazza, Elad Levi
arXiv:2601. 20379v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption.
By Zhengbo Jiao, Hongyu Xian, Qinglong Wang, Yunpu Ma, Zhebo Wang, Zifan Zhang, Dezhang Kong, Meng Han
The paper introduces recirculation, an inference‑time architectural enhancement for foundation models that reduces perplexity and improves accuracy on generation and reasoning tasks without adding significant latency. Recirculation adds a specific form of recurrence, enabling the model to function as a dynamical system that tracks belief states, and is distinct from chain‑of‑thought or depth‑recurrence methods. An adaptive variant requires minimal hyperparameter tuning and achieves notable gains on the Gemma3 family, including a 23% perplexity drop and a 21% accuracy increase on GSM8k.
By Michael C. Mozer, Shoaib Ahmed Siddiqui, Danny Sawyer, Sunny Sanyal, Rosanne Liu
arXiv:2508.01300v2 Announce Type: replace
Abstract: AI planning provides agents the ability to act autonomously towards achieving a goal in a given environment. Recent advances in Large Language Mode...
By Ma'ayan Armony, Albert Mero\~no-Pe\~nuela, Gerard Canal
arXiv:2609.22878v1 Announce Type: new
Abstract: Large language model code generation benchmarks primarily evaluate well-resourced languages like Python and Java, where models benefit from abundant tr...
By Aditya Pola, Arkaprava Majumdar, Vineeth N. Balasubramanian