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.
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.
The study evaluates hybrid retrieval, NLI, and open‑source LLMs for scientific claim verification. Using a benchmark of 2,035 claims, the authors compare a DeBERTa‑based NLI baseline with Llama 3.1, Qwen3, and Mistral LLMs, finding that Qwen3 achieves the highest Macro‑F1 score. A conservative fusion rule that labels a claim as Support or Contradict only when both models agree actually degrades performance, and error analysis reveals that disagreements often overturn correct LLM predictions.
arXiv:2405.01259v2 Announce Type: replace Abstract: Argumentation is an important cognitive activity that involves the scrutiny of arguments and counterarguments. When given an argument, it is desira...
The paper introduces a neuro‑symbolic framework for scientific reasoning that separates symbolic validity and semantic groundedness. A deterministic symbolic verifier acts as a hard filter to guarantee syntactic and arithmetic correctness, while a Process Reward Model (PRM) is trained on verifier‑accepted steps to assess contextual grounding. The authors propose Counterfactual Symbolic Perturbation (CSP) to generate hard negative examples that pass the verifier but are logically flawed, enabling efficient PRM training and a verifier‑first constrained search at inference.
arXiv:2603. 06114v2 Announce Type: replace-cross Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.
arXiv:2607. 20500v1 Announce Type: new Abstract: Large Language Models (LLMs) perform strongly on well-specified reasoning tasks with a feasible answer.
The paper introduces Bounded-RS, a protocol for testing whether neural networks are representationally systematic. It evaluates explanations by checking if the claimed representation is recoverable and causally used, then classifies the explanation into one of four states: Unsupported, Present-only, Causal-use, or Undetermined. Applied to a semantic‑parsing benchmark, the protocol finds that T5’s role‑filler Binding explanation is Causal‑use while its Geometry explanation is Unsupported, and it demonstrates high reliability across different configurations.
arXiv:2606. 02837v1 Announce Type: cross Abstract: Accurate translation from Natural Language to First-Order Logic (NL-to-FOL) underpins neurosymbolic AI systems and Natural Language Inference (NLI), making the quality of NL-to-FOL benchmarks essential -- yet these datasets have never been rigorously audited.
arXiv:2507. 09751v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but exhibit problems with logical consistency in their output.
The paper introduces GUARD, a neuro‑symbolic system that autoformalizes argumentative material by completing missing premises (guards) before formal verification. It uses large language models to generate candidate guards, Isabelle/HOL to verify them, and a contrastive test to ensure the proof depends on the original premises and does not over‑generalize. Experiments on Debatepedia and ARCT show that GUARD improves verified‑faithful scores by over 30 points and reduces leakage by about 20 points compared to prior LLM‑driven theorem proving methods.
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis....