LogicSkills is a benchmark designed to isolate three core logical abilities in large language models: formal symbolization, countermodel construction, and validity assessment. The dataset draws items from the two-variable fragment of first‑order logic without identity, presented in both English and a Carrollian nonce‑word language, and all instances are solver‑verified with Z3. Results show that conventional instruction‑tuned LLMs excel at validity assessment but struggle with symbolization and countermodel construction, whereas recent reasoning‑tuned models perform well across all tasks, indicating a more systematic logical skill profile.
arXiv:2509. 14704v3 Announce Type: replace Abstract: Benchmark saturation and training-data contamination increasingly obscure whether reported gains in large language models (LLMs) reflect genuine advances in reasoning or familiarity with recurring patterns in benchmark problems.
arXiv:2606. 20227v1 Announce Type: new Abstract: Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making.
arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.
arXiv:2606. 08471v1 Announce Type: cross Abstract: Recently, language models have made rapid progress across various domains and applications.
arXiv:2608. 19009v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly paired with verifiers (step checkers, self-consistency filters, tool-based fact checkers, formal proof assistants) that claim to detect the model's errors.
arXiv:2604.03754v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been shown to encode truth of statements in their activation space along a linear truth direction. Previous...
arXiv:2608. 08447v1 Announce Type: cross Abstract: Multilingual reasoning models are commonly evaluated by whether they arrive at the correct answer, but not by whether they preserve the intended language while reasoning and responding.
arXiv:2608. 08514v1 Announce Type: new Abstract: We independently reproduce two recent methods for making large language model (LLM) reasoning more reliable, and stress-test them across domains and models (RPC across four new task domains with Qwen3-8B, LCF across four 7-8B models).
arXiv:2605.31378v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) still struggle with fine-grained translation quality estimation (QE), even with long reasoning chains. We argue that...
arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?
arXiv:2607. 14528v1 Announce Type: cross Abstract: Large language models (LLMs) frequently contradict themselves when the surface form of a logically equivalent question changes.
arXiv:2607. 09999v1 Announce Type: cross Abstract: We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved.