Topology Trouble: Why Even Frontier LLMs Still Get Lost in a Grid
TopoBench shows that many LLM failures in spatial reasoning come from weak constraint extraction, not merely weak reasoning.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Agents With Memory: Turning Execution Logs into Institutional Knowledge
A mechanism-first reading of trajectory-informed agent memory, showing how execution logs can become structured operational guidance rather than decorative vector-store clutter.
Audit the Bots: When AI Judges the Work of Other AI
A practical reading of CUAAudit and what its evidence says about using vision-language models to audit autonomous computer-use agents.
Diagnosis, But Make It Iterative: When AI Learns Like a Doctor
DxEvolve shows why governed clinical AI may depend less on bigger models and more on workflow-constrained evidence acquisition plus auditable experience memory.
Don’t Build the Agent — Raise It: The Nurture‑First Paradigm for AI Expertise
A mechanism-first reading of Nurture-First Development, a framework for turning practitioner-agent conversations into reusable domain expertise.
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.
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.