I’ve noticed a recurring pattern in my long-term usage of large language models: The longer the conversation, the more inefficient the model becomes — not due to logic, but due to structural limits in how context and memory are handled.
While some technical research has focused on improving attention span or building memory modules (e.g., MemoryBank, Expire‑Span), this post explores the same issue from a user-centric perspective — based on lived experience rather than internal model architecture. My goal is to propose structural changes that could make language model interaction feel more natural, more adaptive, and more human-like.
1. Selective Ignoring
Humans constantly filter out irrelevant input — a survival adaptation evolved over millions of years. LLMs, on the other hand, accumulate tokens indiscriminately, treating each word from earlier in the conversation with equal weight.
A model that can forget tactically — deprioritizing stale, low-salience, or outdated context — would free up cognitive bandwidth and reduce waste.
2. Interaction Continuity
Each new session is a hard reset. Valuable patterns of reasoning, clarification, and alignment are discarded at every turn.
Persisting selected context across sessions, or allowing user-anchored memory layers (with privacy-preserving opt-in), would create a more coherent and collaborative partner. It would also reduce redundant prompts and regenerate less.
Additionally, current models struggle with multi-topic switching — a human-like conversation often moves between distinct yet related themes. When models lack an internal structure for managing topic transitions, they either reset prematurely or confuse the threads. This undermines reasoning continuity and makes long-term collaboration harder.
3. Predictive Emphasis
Human conversations involve constant inference — guessing what the other person really wants to focus on. LLMs could approximate this by prioritizing likely-intended tokens, inferred from short-term memory patterns, context clues, or prior user behavior.
Even basic heuristics here could improve perceived relevance and drastically lower unnecessary branching.
Note: The 30–50% figure here is based on my personal experience using LLMs over long-term dialogue sessions, especially when switching between topics or referencing earlier context. While not empirically validated, I believe this inefficiency is broadly recognizable to anyone interacting with models in creative or dynamic tasks, such as writing, coding, or image/video generation.
A truly intelligent system isn’t the one that remembers everything — It’s the one that knows what to ignore, when to forget, and how to prioritize what the human really meant.
This post is written by a real person, based on actual usage patterns and frustrations. Feedback welcome.
Why LLMs Waste So Much Cognitive Bandwidth — and How to Fix It
I’ve noticed a recurring pattern in my long-term usage of large language models:
The longer the conversation, the more inefficient the model becomes — not due to logic, but due to structural limits in how context and memory are handled.
While some technical research has focused on improving attention span or building memory modules (e.g., MemoryBank, Expire‑Span), this post explores the same issue from a user-centric perspective — based on lived experience rather than internal model architecture. My goal is to propose structural changes that could make language model interaction feel more natural, more adaptive, and more human-like.
1. Selective Ignoring
Humans constantly filter out irrelevant input — a survival adaptation evolved over millions of years.
LLMs, on the other hand, accumulate tokens indiscriminately, treating each word from earlier in the conversation with equal weight.
A model that can forget tactically — deprioritizing stale, low-salience, or outdated context — would free up cognitive bandwidth and reduce waste.
2. Interaction Continuity
Each new session is a hard reset. Valuable patterns of reasoning, clarification, and alignment are discarded at every turn.
Persisting selected context across sessions, or allowing user-anchored memory layers (with privacy-preserving opt-in), would create a more coherent and collaborative partner. It would also reduce redundant prompts and regenerate less.
Additionally, current models struggle with multi-topic switching — a human-like conversation often moves between distinct yet related themes. When models lack an internal structure for managing topic transitions, they either reset prematurely or confuse the threads. This undermines reasoning continuity and makes long-term collaboration harder.
3. Predictive Emphasis
Human conversations involve constant inference — guessing what the other person really wants to focus on.
LLMs could approximate this by prioritizing likely-intended tokens, inferred from short-term memory patterns, context clues, or prior user behavior.
Even basic heuristics here could improve perceived relevance and drastically lower unnecessary branching.
Note: The 30–50% figure here is based on my personal experience using LLMs over long-term dialogue sessions, especially when switching between topics or referencing earlier context.
While not empirically validated, I believe this inefficiency is broadly recognizable to anyone interacting with models in creative or dynamic tasks, such as writing, coding, or image/video generation.
A truly intelligent system isn’t the one that remembers everything —
It’s the one that knows what to ignore, when to forget, and how to prioritize what the human really meant.
This post is written by a real person, based on actual usage patterns and frustrations. Feedback welcome.