FAME or Fortune? How Formal Explanations Finally Scale to Real Neural Networks
FAME shows how formal neural-network explanations can scale by using abstract verification to prune the search space before exact refinement.
Cognaptus Insights
Industry thoughts, trends, and commentary.
From Hallucination to Verification: Why AI Needs a Pharmacist’s Mindset
A prescription-auditing paper shows why safe AI needs hybrid knowledge stores, deterministic checks, and evidence-grounded reasoning—not just bigger models.
Many Roads? Not Quite: Why LLM Alignment May Prefer a Single Moral Lane
A close reading of arXiv 2603.10588 shows why moral-reasoning alignment may not benefit from diversity-seeking RL as much as intuition suggests.
Agents That Learn From Their Own Mistakes: The Rise of Retroactive AI
A mechanism-first reading of RetroAgent, a reinforcement learning framework that teaches LLM agents to improve from partial progress, reflected lessons, and controlled memory retrieval.
Conviction Capital: Why Trust in AI May Depend on Being Proven Right
A mechanism-first reading of why AI trust may require claim-level verification, not just benchmark scores or better guardrails.
Green Algorithms, Greener Economies: Optimizing AI for Sustainable Entrepreneurship
A mechanism-first reading of EcoAI-Resilience, a framework that treats sustainable AI deployment as a three-way optimization problem across impact, resilience, and environmental cost.
Mirror, Mirror on the Agent: Teaching LLMs to Judge Their Own Actions
A mechanism-first reading of Agentic Critical Training and why teaching agents to compare actions may matter more than teaching them to explain themselves.
Paperwork Intelligence: Why AI Still Struggles With Real Enterprise Documents
OfficeQA Pro shows why enterprise AI agents fail less from a lack of intelligence than from brittle parsing, retrieval, revision tracking, and numerical discipline.
Show Me the Money (Reasoning): Benchmarking Financial Intelligence in LLMs
A comparison-based reading of AFIB, a financial AI benchmark that shows why live retrieval, general reasoning, and investment-grade reliability are not the same thing.
When Images Learn to Think in Code: The Rise of Code-as-CoT for Structured Generation
A mechanism-first reading of CoCo, a Code-as-CoT framework that turns text-to-image generation into executable layout planning, deterministic preview, and draft-guided refinement.