Unverified50% confidenceFactExact time
A stronger underlying model can alleviate the 'lost in the middle' problem to some extent, but cannot eliminate it entirely.
1
Sources
50%
Confidence
Long-term
Relevance
9/21/2026
First Seen
Sources
Related Entities
Related Claims
Unverified过于激进的上下文修剪可能导致模型遗忘重要前置条件，过于保守则无法实现有效token节省74% similarUnverified近似遗忘通过梯度上升等方法主动削弱特定数据对模型参数的影响，但效果难以量化验证73% similarUnverifiedCurrent mainstream large models excel at handling isolated, well-bounded small tasks, but expose shortcomings of incomplete understanding and loss of detail when faced with highly coupled, large-context legacy systems.73% similarUnverifiedThe coarse-filtering stage should ensure sufficient recall margin — it is better to over-recall and let the model eliminate candidates than to miss the correct option by saving tokens, because recall failures at the retrieval layer cannot be recovered by subsequent models.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/938824API
curl https://kongchang.com/api/v1/knowledge/claims/938824MCP
get_claim(id=938824)