Helping AI Understand Time.
TCE is an auxiliary temporal cognition layer that enables AI systems to reason across time through causal understanding, temporal memory, and event-based inference.
Incubated by Plaksha Incubation Centre·Supported by academic advisors and early pilot collaborators
Application
TCE Temporal Cognition Layer
Language Model
The Problem
Modern AI predicts patterns. It still struggles with causation.
Today’s AI systems operate largely on correlation and short-term context.
They can generate responses, but often fail to reliably understand: what changed, why it changed, what remains true over time.
- What changed
- Why it changed
- What remains true over time
Critical in
Real intelligence requires temporal understanding.
Temporal event chain
AI systems must reason about all four stages — not just the observed event.
What is TCE
A temporal cognition layer for AI systems.
TCE sits between applications and foundation models, enabling systems to reason across evolving events instead of isolated prompts.
temporal continuity
Track how facts, states, and relationships evolve across time without losing prior context.
causal inference
Understand why changes happen, not just that they did — tracing cause and effect through event chains.
evolving truths
Maintain awareness of what is currently true versus what was once true as world states shift.
long-horizon reasoning
Plan and reason across extended time spans, causal chains, and sequences of dependent events.
Architecture
Application
Your product or service
TCE Layer
Temporal cognition & causal reasoning
Language Model
Foundation model backend
TCE enhances existing AI systems without replacing them.
How it works
Event-driven temporal reasoning.
Instead of retrieving similar information, TCE retrieves temporally and causally relevant truth states.
EVENT
An interaction, observation, or state change occurs.
STORE
Immutable temporal facts are recorded.
SCORE
A multi-factor temporal relevance engine prioritizes meaningful events.
DECAY
Temporal validity evolves over time.
RETRIEVE
The system determines what is still true now.
REASON
Causal chains are analyzed before model response generation.
Why it matters
AI is moving into persistent environments.
Autonomous Agents
Persistent agents need temporal context to reason about evolving environments, prior actions, and causal chains.
Healthcare AI
Medical AI must understand how patient states progress across clinical encounters, not just isolated snapshots.
Robotics
Robots in dynamic environments require causal awareness of what changed, why it changed, and what to expect next.
Enterprise Copilots
Copilots embedded in workflows must track decisions, context, and organizational change across sessions.
Scientific Systems
Research systems benefit from temporal modeling of experimental progressions and evolving hypothesis states.
Simulation Systems
Simulations modeling complex systems require long-horizon causal reasoning across interdependent events.
As AI systems become persistent, time becomes infrastructure.
Healthcare application
Temporal reasoning for evolving medical states.
TCE research prototypes have been tested on longitudinal brain tumor progression modeling and temporal state evolution scenarios.
The goal: not just understanding isolated medical snapshots, but understanding progression across time.
Research-stage prototype. Not clinically validated.
Comparison
Memory alone is not enough.
Open source
Open ecosystem. Foundational infrastructure.
We believe foundational AI infrastructure benefits from open collaboration and transparent evaluation.
- benchmark tooling
- developer infrastructure
- research resources
- community-accessible temporal cognition tooling
Enterprise
Custom deployments and advanced infrastructure layers will power commercial adoption.
Reach out to discuss custom infrastructure, advanced deployment scenarios, or research partnerships.
Traction
We are working with early partners across healthcare, robotics, and autonomous agents.
0+
waitlist companies
0
live demos conducted
0
pre-pilot systems in testing
0
academic advisors
PIC
incubated at Plaksha Incubation Centre
Benchmarks
promising temporal consistency results
15.1%
Mean improvement across all AI models
84.5%
Win rate on contested queries
| Model | Provider | Improved | Baseline | Delta | Signal |
|---|---|---|---|---|---|
| claude-opus-4.6 | Anthropic | 0.973 | 0.893 | +0.080 | — |
| claude-haiku-4.5 | Anthropic | 0.970 | 0.837 | +0.133 | Best price/performance |
| gemini-3.1-pro | 0.897 | 0.600 | +0.297 | Biggest improvement | |
| gpt-5.1 | OpenAI | 0.907 | 0.643 | +0.263 | — |
| gpt-4.1 | OpenAI | 0.943 | 0.723 | +0.220 | — |
| qwen3:8b | Alibaba | 0.921 | 0.760 | +0.161 | — |
Vision
Building toward temporal cognition infrastructure.
We believe future AI systems will require deeper understanding of time, causality, and evolving world states.
Helping AI understand time.
Founder
Built from firsthand frustration with current AI systems.
TCE began after repeatedly encountering the limitations of stateless and correlation-driven AI architectures in persistent environments.
Founded by Tanvir Singh Sandhu at Plaksha University, TCE is focused on developing foundational temporal cognition infrastructure for next-generation AI systems.
Get involved
Join the early ecosystem.
Developers • Researchers • Enterprise Teams