Chain-of-thought (CoT) prompting improves average-case reasoning, but a non-trivial fraction of CoT traces contain internal contradictions that the model nevertheless ignores when producing its final answer. We propose SV-CoT, a self-verifying variant in which the model is asked, between reasoning and answer, to enumerate a small number of consistency claims and check them against the trace.
Chain-of-thought (CoT) prompting is widely credited with enabling complex reasoning in large language models, yet the robustness of this capability to adversarial perturbations remains poorly characterized. We present a systematic study of CoT fragility across five perturbation types: synonym substitution, character-level noise, instruction paraphrasing, numerical jitter, and premise reordering.
We present Spectrography, a framework establishing geometric invariants on S^23. Two core findings: (1) geometric tension tau measures semantic structure, not truth value (p=0.
We present Spectrography, a framework detecting logical contradictions in AI reasoning via geometric analysis on S^23. Geometric tension tau = ||z_i - z_i+1||_2 measures semantic distance.
We present Spectrography, a metrological framework establishing geometric invariants of the 24-dimensional unit hypersphere S^23 across 28 experimental sessions. Post-publication tests clarify that r = 24 is an architectural constraint (not an emergent Leech lattice property), and Δτ does not generalise without recalibration (0/3 unseen domains reach d > 1.
Chain-of-thought (CoT) prompting has demonstrated remarkable effectiveness in eliciting complex reasoning capabilities from large language models (LLMs). In this work, we systematically investigate the emergent reasoning patterns that arise when LLMs are prompted to generate intermediate reasoning steps.