The Gap
LLM ≠ World Model.
GPT models already write code, pass the bar, do research, and operate browsers — but the moment they leave pure text, their grasp of the world drops off a cliff.
What models do well
- Write code, solve math problems
- Pass bar / medical exams
- Summarize, translate, rewrite
- Multi-turn dialogue & tool calls
What models still can't do
- Count objects accurately in a video
- Reason about physical cause & effect
- Control a robot arm for everyday tasks
- Learn a game it has never seen
"
In 4 years, a child has seen 50× more data than the biggest LLMs.
Yann LeCun
Why scale alone won't close the gap
The Shift
The bottleneck isn't parameters — it's the type of data.
The past five years solved parameters and model cognition. The next five are bottlenecked on physical and interaction data — and that is exactly where Tidel comes in.
- Multimodal-aligned real-world data — vision + depth + action
- Physical interaction with action trajectories — teleop, gameplay
- Data that keeps models reproducible & generalizable
Product Line 1
Four key tiers of the embodied-AI data pyramid.
Teleoperation, motion capture, and pure-vision egocentric data — captured in production format.
Tier 1 · Top
Real-robot teleoperation
Wheeled dual-arm + UMI gripper · Humanoid + dexterous hands
Tier 2
Simulated / synthetic
NVIDIA Isaac-style virtual environments · third-party
Tier 3
Human motion capture
Stereo + full-body mocap · Tri-camera + Manus gloves
Tier 4 · Base
Pure-vision egocentric
Monocular & stereo ego · ~2,000 task hours on Pico VR HMDs
Product Line 2
01 · FPS
02 · Multi-view 4D
03 · Reasoning Trace
Game-interaction data for world models, simulators & agents.
01 · FPS
Keyboard-and-mouse FPS
With depth + timeline alignment, captured from the player's first-person view.
02 · Multi-view 4D
Player ↔ agent interaction
4D multi-angle capture covering the full player–agent interaction.
03 · Reasoning Trace
Agent puzzle-game thinking traces
Full playthroughs plus the agent's reasoning chain and decisions.
Product Line 3
Training data for coding models, agents & visual-reasoning systems.
01 · Terminal Bench
Terminal-bench data
Full Terminal Bench runs — reasoning and execution traces in a terminal.
02 · Code Repo
Coding-repo data
Across many code repositories — languages, frameworks, task scenarios.
03 · Chart-Reasoning VLM
Chart-analysis VLM data
Agents analyzing charts and visualizations to make decisions.