I’m having a problem when using ChatGPT…
When working with multiple large documents and long-running sessions that require iterative edits, merges, and inconsistency resolution, ChatGPT frequently resets or fails. These resets are triggered by token overflow, unstable memory references, or internal execution faults caused by repeated regeneration, long prompt chains, and dense context requirements. This is especially problematic when referencing dozens of interdependent data points across multiple turns or when regenerating outputs based on small deltas to prior responses.
Here is a feature ChatGPT is missing…
ChatGPT lacks an orchestration-level mechanism to manage compound context state, token-aware memory injection, and reference coherence across iterative generations. There is no native system for scoring or decaying prior context, verifying consistency between injected content and edits, or adapting memory granularity based on task phase. Additionally, there is no lightweight, adaptive approach to selectively optimize performance based on the complexity of user needs—resulting in unnecessary latency for casual users and instability for advanced workflows.
Here is why this feature would fix this problem…
By integrating a system that tracks session-local tool/function behavior, memory usage frequency, and prompt token pressure in real time, ChatGPT could selectively retain only semantically critical content, summarize low-impact history, and defer low-confidence generations when the risk of reset is high. This would preserve referential integrity across document layers, reduce total token count, and avoid systemic resets caused by overloaded prompt windows or unstable regeneration logic.
To prevent added latency for casual users, this system would operate in adaptive tiers: a lightweight, invisible layer for everyday users, and a progressively enhanced mode triggered automatically or manually for complex workflows. This ensures that only high-need scenarios incur additional processing overhead, while everyday interactions remain fast and responsive.
This is one way this might be implemented…
A modular orchestration-layer framework—MAERS (Modular Adaptive Execution & Retrieval System)—can be implemented with the following components:
Core Components (Power + Casual Modes)
• Ephemeral Execution Buffer (EEB):
In-memory, session-local state store tracking memory injections, failed regenerations, and retry thresholds. Enables fallback logic, retry suppression, and fast recovery after instability events. Only activates in advanced workflows.
• Tiered Context Retrieval (TCR):
Context fragments selected through a three-phase pipeline:
→ direct keyword match → semantic vector similarity (via embeddings) → fallback to auto-generated summaries.
Default to keyword match for casual users to minimize latency; full pipeline engages only for high-context needs.
• Relevance Scoring Layer:
Fragments scored by cosine_similarity * access_frequency, and only top-N are injected into the prompt. This token-aware prioritization minimizes memory clutter while maintaining semantic integrity.
• Context Decay Engine:
Low-use fragments are flagged and transformed into summaries by an async transformer-based summarizer. Original content is retained only if reaccessed, preventing prompt overload in long sessions. For casual users, this occurs passively in the background or on idle.
• Token Budget Monitor:
Tracks token usage across the system and compresses or deprioritizes low-impact memory as the prompt nears a critical threshold. Helps avoid resets without user intervention.
Optional Advanced Layers (Power Mode Only or Post-Generation)
• Summary Verification Pass:
Summarized memory is QA-verified using an internal prompt (e.g., “Does this summary preserve all facts needed for the current task?”). Regenerates if confidence is low. This pass runs asynchronously or only on demand to avoid blocking generation.
• Inconsistency Detection + Prompt Audit Layer:
Cross-checks new output against injected memory snapshots. If factual deltas or contradictions are detected, the system flags the issue for review or suggests inline correction. This layer is opt-in or triggers automatically after repeated regenerations.
• Function Reasoning Filter:
For plugin/tool-based sessions, adds a confidence gate: “Do I need to call this function?” Only proceeds when confidence exceeds threshold, reducing unnecessary or redundant tool calls.
Latency Management & User Adaptation
To ensure this framework works for both power users and casual users without adding latency, MAERS is designed to run as a layered, adaptive system:
• Adaptive Mode Tiers:
• Light Mode (Default):
Uses passive summarization, basic token monitoring, and recency-weighted context selection. Runs invisibly for casual users with near-zero latency.
• Power Mode (Opt-In or Auto-Triggered):
Activates advanced modules when large documents, long sessions, or repeated regenerations are detected. Prioritizes consistency, precision, and context integrity.
• Async + Event-Triggered Operations:
Heavy components (e.g., summarizers, audits, QA checks) run in background threads or only trigger on events like memory saturation, contradiction detection, or user request.
• Client-Side Memory Indexing (for apps/SDKs):
Stores embeddings, summary fragments, and access metrics locally or in-session, reducing system calls and latency while improving memory relevance.
• Smart UI Defaults:
Casual users never need to configure anything. Advanced users can toggle options like “Preserve detailed context,” “Enable audit checks,” or “Reduce token usage” for fine-tuned control.
To sumarize this:
MAERS solves prompt saturation and execution instability in large-document, high-context, regenerative workflows by introducing token-aware memory scoring, session-local state tracking, and adaptive context decay. Through a layered, latency-conscious design, it provides both casual and power users with a seamless experience—balancing speed, consistency, and referential integrity. MAERS is lightweight, modular, and fully compatible with the ChatGPT API, Assistants API, and plugin ecosystems—making it ideal for developers and advanced users without burdening everyday interactions.