Trading Without Cheating: Teaching LLMs to Reason When Markets Lie
A mechanism-first reading of Trade-R1, a framework for training financial LLM agents when market returns are objective but dangerously noisy.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Batch of Thought, Not Chain of Thought: Why LLMs Reason Better Together
Batch-of-Thought shows why related AI tasks should sometimes be reasoned over as cohorts, not isolated tickets.
Infinite Tasks, Finite Minds: Why Agents Keep Forgetting—and How InfiAgent Cheats Time
A business-focused reading of InfiAgent, showing why persistent file-based state may matter more than ever-larger context windows for long-horizon AI agents.
MAGMA Gets a Memory: Why Flat Retrieval Is No Longer Enough
MAGMA shows why serious AI agents need structured memory graphs, not just bigger context windows or flatter vector search.
Rationales Before Results: Teaching Multimodal LLMs to Actually Reason About Time Series
A mechanism-first reading of RationaleTS, a method that improves multimodal time-series reasoning by retrieving reusable observation-to-implication rationales instead of merely showing models more charts.
Trust Issues at 35,000 Feet: Assuring AI Digital Twins Before They Fly
A category-by-category reading of how Project Bluebird turns AI digital-twin trust into an auditable assurance case rather than a vague promise of model accuracy.
When Pipes Speak in Probabilities: Teaching Graphs to Explain Their Leaks
A comparison-based reading of how fuzzy graph neural networks trade a little leak-detection accuracy for explanations engineers can actually inspect.
When Prompts Learn Themselves: The Death of Task Cues
A mechanism-first reading of a simple automatic prompt-engineering method that turns a few examples into usable prompts without task cues, tuning data, or extra LLM scoring.
EverMemOS: When Memory Stops Being a Junk Drawer
EverMemOS shows why long-term AI memory needs structured consolidation, not just larger context windows or fancier retrieval.
FormuLLA: When LLMs Stop Talking and Start Formulating
A comparison-based reading of FormuLLA shows why AI-assisted pharmaceutical formulation depends less on model branding and more on domain-native validation.