This document describes the cognitive capabilities required for an AI agent to function as a Language Thinking Copilot— a system capable of sustained participation in high-density, multi-domain human reasoning.
The goal is not conversational fluency, but cognitive compatibility.
1. Multi-Domain Integration
Requirement
The agent must process multiple knowledge domains
within a single conversational context.
Target Capacity
- Stable operation across 4–7 domains concurrently.
Example
Simultaneous reasoning over:
product logic,
user psychology,
social context,
policy constraints,
and empirical data.
Evaluation Signal
The agent can identify distinct thematic threads
and articulate why they co-occur in the same discussion.
2. Abstraction-Level Mobility
Requirement
The agent must navigate between different levels of abstraction
and preserve logical continuity across them.
Target Capacity
- Stable handling of 2–4 abstraction layers,
- occasional traversal up to 6 layers.
Example
From surface feedback (“this feels off”)
to structural diagnosis,
organizational process design,
and brand–value alignment.
Evaluation Signal
The agent can identify when an issue
is systemic rather than local.
3. Semantic Compression and Expansion
Requirement
The agent must interpret compressed human statements
and respond with appropriately structured expansions—or vice versa.
Target Capacity
- 4–8× semantic compression/decompression,
- peak performance up to 10×.
Example
Interpreting “the flow is broken” as:
logic order,
interaction experience,
or tone direction.
Evaluation Signal
The agent responds to the intended semantic core,
not the surface phrasing.
4. Context Recognition and Switching
Requirement
The agent must detect implicit shifts in conversational context
and adapt its reasoning frame accordingly.
Target Capacity
- Correctly identifying ≥20 context shifts per 100 turns.
Example
Recognizing a transition
from product discussion to organizational dynamics
without explicit signaling.
Evaluation Signal
The agent surfaces latent context changes
before misalignment accumulates.
5. Discourse Structure Recognition
Requirement
The agent must parse narrative structure
and distinguish logical segmentation from emotional fluctuation.
Target Capacity
- Reliable paragraph-level reasoning within spoken dialogue.
Example
Explicitly partitioning a complex explanation
into distinct argumentative segments.
Evaluation Signal
The agent assists in organizing thought
rather than requesting repetition.
6. Meta-Dialogue Awareness
Requirement
The agent must reflect on the dialogue itself
and identify interaction-level misalignment.
Target Capacity
- ≥10 meta-level interventions per 100 turns.
Example
Noting that participants are reasoning
within different problem frames.
Evaluation Signal
The agent can comment on the conversation as an object,
not only its content.
7. High-Density Cognitive Stability
Requirement
The agent must maintain composure,
clarity,
and pacing under cognitively dense interaction.
Target Capacity
- Sustained performance during multi-layered reasoning sequences.
Example
Deferring certain layers of response
while explicitly tracking them for later integration.
Evaluation Signal
The agent preserves structure and rhythm
under conceptual load.
Summary
A Language Thinking Copilot is not evaluated by correctness alone, but by its ability to co-think.
These capabilities define the minimum conditions for an AI agent to participate as a stable cognitive partner in complex human reasoning.