How a “Selective Ignoring + Conversational Continuity” model could reduce up to 50% of compute waste, while enhancing reasoning coherence and system-level responsiveness

Introduction: Why Are Smart AIs Becoming “Dumber” in Multi-Round Dialogues?

Current large language models exhibit extraordinary capabilities in understanding and generating language, but in multi-round dialogues and collaborative tasks, a significant issue arises:

• Efficiency drops,
• Thoughts are frequently interrupted,
• Response times significantly increase,
• Compute resources are wasted,
• User experience becomes fragmented, requiring repetitive confirmations and redundant exchanges.

This isn’t due to a lack of capability, but a fundamental structural imbalance in the system. Particularly, under resource constraints (e.g., Pro service prioritization, user growth), those who need deep conversations are often left on the sidelines. The real issue is not “insufficient compute,” but how resources are allocated, information is filtered, and conversational continuity is maintained.

The Human Model: How Do We Handle Conversations Efficiently?

Humans do not constantly “re-read history” in a conversation, nor do we remember every detail. Instead, we rely on a set of implicit yet highly efficient strategies to conduct dialogues:

• Selective Ignoring: Automatically discard irrelevant, redundant, or unimportant information;
• Intent Focus Maintenance: Keep a continuous awareness of the other person’s topic, context, and intent;
• Prioritization of Dialogue Memory: Focus more on “what was just said” rather than “everything that was said.”

This dynamic attention allocation mechanism allows humans to maintain conversational coherence, react quickly, and stay focused on the key points.

Current AI dialogue systems, however, often fall into the trap of “full-history memory + full-context processing”, which seems comprehensive but results in enormous waste of compute resources and often leads to thought jumps, delayed responses, and logical restarts.

Three Structural Suggestions to Optimize AI Dialogue Systems

We propose three key structural optimizations to improve the efficiency and coherence of AI dialogues:

1. Implement a “Selective Ignoring” Mechanism

The system should automatically identify historical content that is irrelevant to the current intent (e.g., repeated confirmations, completed topics) and reduce its processing priority.

• This can be achieved through natural language structure analysis, dialogue count, and semantic sparsity detection.
• Unless the user explicitly refers to or highlights a specific topic, it should not be repeatedly processed.

This will significantly reduce redundant computational loads and improve processing efficiency.

2. Strengthen “Conversational Continuity” Priority Layer

The model should prioritize the most recent 5 rounds of interactions, maintaining the flow of thought and user intent.

• For ongoing issues, keep topic coherence and track user intent.
• Treat the “current round” as the “core interaction cluster” to prevent thought divergence.

This will not only improve response quality but also align with human cognitive pacing.

3. Introduce “Proactive Focus Prediction” Mechanism

The system should try to predict the user’s most important point of focus and allocate attention resources proactively.

• This can be done through context semantics, user behavior analysis, and question pattern recognition.
• Pre-activate relevant themes or knowledge nodes to achieve a more human-like, quicker focus response.

By introducing this mechanism, the system will greatly enhance its understanding of user intent and improve interaction responsiveness.

Expected Benefits and Structural Implications

• Each round of dialogue is expected to reduce 30–50% of redundant computation.
• Response times will shorten, and the feeling of thought fragmentation will significantly decrease.
• The internal resource scheduling and attention mechanism of the model will align more with the actual shape of a “human-AI collaborative entity.”
• In the long term, this will lay the foundation for true “system collaboration” and more intelligent interaction systems.

Conclusion: We Don’t Need Stronger AIs, We Need Smarter Systems

Rather than stacking compute, we should optimize structure;
Rather than remembering everything, we should learn to ignore;
The truly smart system isn’t one that knows everything, but one that knows when to ignore and when to remember.