Vibe Coding a Theorem Prover: When LLMs Prove (and Break) Themselves
Why Isabellm’s real lesson is not autonomous AI reasoning, but verifier-gated system design for domains where being plausibly right is still wrong.
Why Isabellm’s real lesson is not autonomous AI reasoning, but verifier-gated system design for domains where being plausibly right is still wrong.
A mechanism-first reading of how LLM semantic understanding, knowledge graphs, and reinforcement learning can turn enterprise text into operational decisions.
AquaForte shows how LLMs can guide quantified SMT solving by proposing mathematical function instantiations while traditional solvers keep the formal guarantees.
A paper on evaluative fingerprints shows why LLM judges are not interchangeable scoring machines but stable measurement devices with their own theories of quality.
A mechanism-first reading of MineNPC-Task, a Minecraft benchmark that shows how memory-aware agents should be tested before anyone trusts them in real workflows.
ReasonMark shows why watermarking reasoning models may depend less on stronger token bias and more on putting the watermark in the right phase of generation.
A mechanism-first reading of SimuAgent, a Simulink modeling assistant that shows why representation, validation, curriculum, and reflection matter more than merely attaching a larger model to an engineering tool.
A mechanism-first reading of how self-generated training data and user feedback can turn ordinary LLM fine-tuning pipelines into bias amplifiers.
A close reading of NP-DNN shows why impressive stock-prediction accuracy needs a harder audit before anyone calls it investment intelligence.
A mechanism-first reading of how internal model states can become a real-time safety gate for LLM tool calls.