Seeing Too Much: When Multimodal Models Forget Privacy
A mechanism-first reading of PII-VisBench, showing why privacy risk in vision-language models depends on who is visible, what is asked, and how the model has learned to recognize people.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Speculate Smarter, Not Harder: Hierarchical Decoding Without Regret
A mechanism-first reading of Hierarchical Speculative Decoding, a lossless verifier that improves LLM inference speed by accepting more draft tokens without changing the target distribution.
STACKPLANNER: When Agents Learn to Forget
A mechanism-first reading of STACKPLANNER, showing why long-horizon agent systems may need memory control more than bigger context windows.
TowerMind: When Language Models Learn That Towers Have Consequences
TowerMind shows why valid actions are not enough: LLM agents can follow rules, waste resources, and still fail at dynamic planning.
When Debate Stops Being a Vote: DynaDebate and the Engineering of Reasoning Diversity
DynaDebate shows that multi-agent reasoning improves not by adding more voices, but by engineering disagreement, step-level critique, and conditional verification.
When Robots Guess, People Bleed: Teaching AI to Say ‘This Is Ambiguous’
A mechanism-first reading of Ambi3D and AmbiVer, showing why safe embodied AI needs an ambiguity gate before execution.
Agents That Ship, Not Just Think: When LLM Self-Improvement Meets Release Engineering
AgentDevel shows why improving LLM agents may require release gates, traces, and regression control more than another round of self-reflection.
Hook, Line, and Confidence: When Humans Outthink the Phish Bot
A mechanism-first reading of why phishing defense needs calibrated confidence and cue-level reasoning, not just another classifier with a larger vocabulary.
ResMAS: When Multi‑Agent Systems Stop Falling Apart
A mechanism-first reading of ResMAS, showing why resilient LLM agent systems depend on communication topology and topology-aware prompts, not just more agents.
Stuck on Repeat: When Reinforcement Learning Fails to Notice the Rules Changed
TAPE shows why reinforcement learning agents can fail when the interface stays familiar but the hidden rules of the world change.