Update or Revise? Turns Out It’s the Same Argument in a Better Suit
A formal belief-change result shows why AGM revision is best read as a stricter version of KM update, with the real gap hiding in how systems handle unsurprising information.
Cognaptus Insights
Industry thoughts, trends, and commentary.
When Analysts Become Agents: Fine-Grained AI Teams That Actually Trade
A research-backed look at why LLM trading agents may depend less on agent count and more on how expert workflows are decomposed, routed, and validated.
When Memory Thinks: Shrinking GRAVE Without Losing Its Mind
A mechanism-first reading of how GRAVE², GRAVER, and GRAVER² preserve search strength under tight memory budgets.
When the Brain Refuses to Tick: Continuous-Time AI for Seizure Forecasting
A mechanism-first reading of ODEBRAIN, a Neural ODE framework that models EEG brain networks as continuous graph dynamics rather than fixed-window classifications.
When X-Rays Talk Back: Grounding AI Diagnosis in Evidence, Not Eloquence
CXReasonAgent shows why clinical AI needs verifiable evidence pipelines more than another layer of fluent medical-sounding text.
Divide & Verify: When Decomposition Finally Learns to Behave
A mechanism-first reading of DAD, a claim-decomposition framework that shows factuality pipelines need trained interfaces, not merely stronger verifiers.
Don’t Walk to the Car Wash: Why Prompt Architecture Beats More Context
A variable-isolation study shows why forcing an LLM to define the task can improve reliability more than adding profile data or retrieval context.
From Reactive to Preemptive: Benchmarking the Rise of Proactive Mobile Agents
A mechanism-first reading of ProactiveMobile, showing why proactive mobile agents are not just reactive agents with better prompts.
Pruning the Planner: When LLMs Tame the Grounding Explosion
A comparison-based reading of SPG-LLM, showing how LLMs can shrink symbolic planning tasks before grounding while trading speed for coverage and guarantees.
Stated to be Human, Revealed to be Algorithmic: The Trust Paradox Inside LLMs
A study on LLMs’ inconsistent trust in humans and algorithms shows why AI governance must test what models choose, not only what they say.