The paper investigates whether the tokens highlighted by large language models (LLMs) as key to their predictions are behaviorally consistent when those tokens are manipulated. Using sufficiency, comprehensiveness, and counterfactuality metrics, the authors evaluate closed‑source GPT‑4o‑mini and open‑source models Gemma3, Granite8B, and DeepSeek on IMDB and Steam review datasets. Results show that GPT‑4o‑mini consistently follows expected patterns, Gemma3 and Granite8B maintain sufficiency but falter under stricter interventions, and DeepSeek variants exhibit structural inconsistencies, indicating that explanation reliability varies across model families and sizes.
By Alisson Rosa Pereira, Bruno Iochins Grisci
FOLLIE is a new French natural language inference resource that links sentences to compositional first‑order logic representations, comprising 19,983 inference records and 36,987 sentence‑slot‑formula mappings derived from six heterogeneous NLI sources. The dataset’s silver annotations are generated by a neural‑symbolic pipeline, including 111 LLM‑assisted additions, and the construction integrates morphosyntactic analysis, probabilistic tagging, grammatical proof search, discourse representation, and first‑order semantic construction. An automated inference engine built on Prover9 and Mace4 achieves up to 71.35% accuracy on SICK‑FR, with high agreement rates on committed decisions, illustrating the resource’s utility for studying deductive inference in French.
By Maximos Skandalis
arXiv:2606. 08532v5 Announce Type: replace Abstract: Modern artificial intelligence excels at prediction but cannot explain.
By Lei Lin, Xinlong Pan, Ronghao Wang, Chunbao Zhou, Jue Wang, Yangang Wang, Ivana Rasovska
arXiv:2606. 16603v1 Announce Type: cross Abstract: LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their reasoning difficult to audit.
By Jiajie Jin, Zhao Yang, Wenle Liao, Yuyang Hu, Guanting Dong, Xiaoxi Li, Yutao Zhu, Zhicheng Dou
NeSySpec is a neuro‑symbolic audit framework that uses Logic Tensor Networks to evaluate engineering specifications, detect violations, identify altered fields, propose minimal corrections, and link image evidence to parts. It learns tolerances from real records and integrates all audit outputs into a single verification report. The system was validated on 107 aircraft types, achieving superior macro‑F1 scores and higher accuracy in diagnosing constraints and locating altered fields compared to existing methods.
By Pier Mattia Basciano, Gianvito Antenori, Cristiano Andolfi, Giovanni Farina, Salvatore Monteleone
arXiv:2609.39243v1 Announce Type: new
Abstract: Causal interventions such as activation patching and distributed alignment search (DAS) are the main tool for making mechanistic claims about neural ne...
By Beiming Liu, Minjie Chen