Follow the Heads, Not the Hype: How LLMs Route Deductive Reasoning
A mechanism-first reading of how attention-head circuits route premise selection, rule matching, and traversal strategy in symbolic deductive reasoning.
A mechanism-first reading of how attention-head circuits route premise selection, rule matching, and traversal strategy in symbolic deductive reasoning.
Entropy-Gradient Inversion reframes LLM reasoning as an internal training signal, not just a benchmark score.
A mechanism-first reading of why LLMs can follow conditional logic yet still fail at the pragmatic reasoning businesses actually need.
A three-paper synthesis showing why dependable LLM reasoning needs mechanistic caution, multidimensional evaluation, and adaptive scaffold design rather than leaderboard confidence.
A mechanism-first reading of M2A, a training-free method for injecting mathematical reasoning into coding agents without breaking their think-act-observe loop.
A mechanism-first reading of why LLM mathematical reasoning fails when fluent explanations are mistaken for verified symbolic work.
A mechanism-first reading of Spectral Retrieval: why dense retrieval can bury localized evidence, how multi-scale sinc convolution tries to recover it, and where the business value actually begins.
A practical synthesis of three jailbreak-defense papers showing why AI safety should test the path from prompt to response, not just the prompt itself.
A business-oriented framework for evaluating LLM jailbreak risk across prompt quality, reasoning traces, and time-to-failure under repeated attacks.
A mechanism-first reading of DualGraph, SpecsQA, and why semi-structured business QA needs symbolic querying alongside semantic retrieval.