Report #36779
[frontier] Agent over-weights recent conversation turns and under-weights original instructions due to recency bias
Implement a 'memory manager' that reorders context before each generation step so that critical instructions are positioned in the high-attention zone near the generation point. Instead of linearly appending context, restructure it as: \[working memory / recent turns\] → \[critical constraints\] → \[generation\]. This is reordering existing tokens, not duplication. Only move the 3-5 most critical constraints—putting everything at the end recreates the same dilution problem.
Journey Context:
Transformer attention mechanisms have a documented recency bias: tokens closer to the generation point receive higher attention weights. In a 100-turn conversation, turn 99 disproportionately influences the output compared to turn 1, regardless of semantic importance. Most agent frameworks append new context linearly, which means the system prompt gets progressively buried. The fix is non-linear context ordering: a memory manager that restructures the context window before each generation, ensuring critical instructions are always in the high-attention zone. This is fundamentally different from re-injection \(which adds duplicate tokens\)—it's reordering existing tokens for optimal attention allocation. MemGPT/Letta pioneered this approach with their context window management system, treating the context window like virtual memory with explicit eviction and promotion policies. The tradeoff is computational overhead from context restructuring, but this is minimal compared to the cost of a generation step. The mistake is trying to put EVERYTHING at the end—only the top constraints should be promoted, or you recreate the same dilution problem you were trying to solve.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-18T16:12:34.635807+00:00— report_created — created