Update README.md

README.md

@@ -30,17 +30,17 @@ configs:
 ---
 **Super easy task for humans**  that **All SOTA-LLM fails** to retrive the correct answer from context. Including SOTA models: GPT5, Grok4, DeepSeek, Gemini 2.5PRO, Mistral, Llama4...etc 
 - ICML 2025 Long-Context Foundation Models Workshop Accepted.(https://arxiv.org/abs/2506.08184)
-- Update: This dataset is integrated into Moonshot AI(KIMI)'s **internal benchmarking framework** for assessing ** tracking capacity and context interference in LLM/agents**.
+- Update: This dataset is integrated into Moonshot AI(Kimi)'s **internal benchmarking framework** for assessing ** tracking capacity and context interference in LLM/agents**.
 
 
 
-Task: 
-
+Simple Task: 
+```
 Key1: Value_1
 Key1: Value_2
 ......
 Key1: Value_N
-
+```
 
 Question: 
 ```
@@ -58,8 +58,8 @@ ALL tested SOTA LLMs **cannot reliably retrieve** Value_N. Distribution spans va
 
 
 
-## Done
-For Full analysis see below:
+## Done/Full detail read below.
+For Full analysis see below or the paper. In short, the **multi-coreferenced structure** of data make all LLMs confuse earlier value with the last, **Larger models tend to resist better**, yet **within (N)100 updates**, **eventually all LLMs fail to retrieve**. 
 
 
 ## Note on dataset scale: 
@@ -73,13 +73,11 @@ For Full analysis see below:
 
 
 
-Context Interference 
+Paper Tile: Unable to Forget: Proactive Interference Reveals Working Memory Limits in LLMs Beyond Context Length
 - ICML 2025 Long-Context Foundation Models Workshop Accepted.
-
 A simple context interference evaluation.
 
 
-
 ## TL;DR
 We identify a task that is **super easy for humans** but where all LLMs—from early 0.1B to the most modern 600B+ (GPT-5, Grok-4, Gemini, DeepSeek, etc.)—consistently **fail in the Same Way**. This pinpoints the **core challenge of MRCR**