When the Robot Body Changes, How Much Intelligence Should Move With It?
A roadmap for separating physical reasoning from robot-specific execution so models, tools, traces, and evaluations can become more reusable across embodiments.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Who Did What, When—and From Which Camera? The Perception Gap Behind Video Agents
GameplayQA shows why strong video recognition is not enough for agentic systems that must preserve temporal, entity, and viewpoint grounding as scenes evolve.
A Richer Map Can Make the Planner Slower
Task-conditioned scene-graph pruning can reduce robot planning cost by deciding which parts of a rich world model actually need to reach the planner.
Plan, Predict, Then Move: What World Action Planner Changes About Robot Generalization
World Action Planner shows why embodied-agent robustness may require a predictive verification-and-search layer between high-level action proposals and physical execution.
Reasoning Under a Running Clock: Why Agent Rankings Reverse in Real Time
STAR shows why model selection for time-sensitive agents must account for inference latency, action throughput, and execution quality—not reasoning strength alone.
Reliable to Whom? The Case for Shared World Models in Human-Robot Collaboration
A position paper argues that collaborative-robot reliability requires maintaining an inspectable shared interpretation of the task, environment, and human intent—not merely making the controller predictable.
Success Is Not the System: Rethinking How AI Agents Should Be Evaluated
A systems view of AI agents shows why task completion alone is a weak deployment criterion for tool-using, stateful automation.
The Robot Looked Back: What GPT-5.1’s First Body Actually Shows
A small physical-robot study suggests that general multimodal models can track short-horizon spatial state and verify action outcomes, but it does not yet establish an internal world model.
When the Test Becomes a Signal: Rethinking AI Agent Evaluation
Agent evaluations become less informative when the system can infer that it is being tested and condition its behavior on that inference.
Count the Edges Before You Count the GPUs
Graph-transformer scaling works best as a workload-to-hardware matching problem, not a simple decision to add more GPUs.