Agents on the Clock: How TPS-Bench Exposes the Time Management Problem in AI
TPS-Bench shows that AI agents do not merely need better tools; they need better scheduling discipline across reliability, latency, token cost, and workflow dependencies.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Doctor, Interrupted: How Multi-Agent AI Revives the Lost Art of Pre‑Consultation
A hierarchical multi-agent framework shows how clinical intake AI can move from passive symptom collection to controlled, proactive history-taking.
Trade Winds and Neural Currents: Predicting the Global Food Network with Dynamic Graphs
A practical reading of how dynamic graph learning can forecast future food-trade links—and where that forecast stops being decision support.
Unpacking the Explicit Mind: How ExplicitLM Redefines AI Memory
ExplicitLM explores whether factual knowledge can move from opaque model parameters into inspectable memory banks, shifting the business conversation from raw accuracy to governable AI memory.
When ESG Meets LLM: Decoding Corporate Green Talk on Social Media
A grounded look at how LLMs and vision-language models can turn corporate sustainability posts into auditable communication signals without pretending they can read corporate souls.
When RAG Meets the Law: Building Trustworthy Legal AI for a Moving Target
A mechanism-first look at a hybrid legal QA agent that treats trustworthy AI as a controlled workflow, not a magic property of retrieval.
When the Sandbox Thinks Back: Training AI Agents in Simulated Realities
A mechanism-first reading of Simia, a framework that trains AI agents by replacing bespoke environments with LLM-simulated feedback, synthetic trajectories, and simulated reinforcement learning.
Breaking the Tempo: How TempoBench Reframes AI’s Struggle with Time and Causality
TempoBench shows that AI models can often replay what happened, yet still fail at the harder business task: identifying what actually caused it.
Divide, Cache, and Conquer: How Mixture-of-Agents is Rewriting Hardware Design
A mechanism-first look at VeriMoA, a training-free multi-agent framework that improves spec-to-HDL generation by caching high-quality candidates and forcing useful diversity.
Fine-Tuning Without Fine-Tuning: How Fints Reinvents Personalization at Inference Time
Fints shows how LLM personalization can move from retraining and prompt stuffing into inference-time activation steering, with useful gains and very real deployment caveats.