Datasets:

giantfish-fly
/

pi-llm

@@ -8,7 +8,6 @@ language:
 ## 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**.
-- Mechanistic research is ongoing. The test is well-established in cognitive science, where it has been studied extensively to measure human **Working Memory capacity**.
 -Multi-round co-reference in Context Interference:
@@ -28,6 +27,9 @@ If MRCR is "multiple needles in a haystack", we show the **haystack isn't necess
 - Our demo site: https://sites.google.com/view/cog4llm
 - Our paper (ICML2025  Long-Context Workshop): https://arxiv.org/abs/2506.08184
@@ -101,7 +103,7 @@ Two sets of tests are provided, one fix update to 20 and another fixed update pe
 (As this test is too hard, only 4 updates per key make all LLMs fail to retrieve the last value—which we intentionally designed to keep the searching difficulty low. Retrieve other order of value has even lower performance)
-## Extra-Hard Mode / Non-Randomized Mode (Last but most interesting and striking)
 In a separated dataset files
 This mode takes the exact format shown in this document, without randomization. We fix everything but vary only the update times just like in the above experiment, but turn randomize_mode off .(column: randomize_mode)
 - This separate dataset consists of 46 of following blocks in a non-randomized order:
@@ -133,6 +135,15 @@ What is the current value (the last value) for key1 key2....key46?
 - **This mode is the most striking, as it highlights a fundamental limitation in how LLMs process context—A task that is human infailable.”**
 ## Quick Start - Evaluate Your Model
 ```python

 ## 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**.
 -Multi-round co-reference in Context Interference:
 - Our demo site: https://sites.google.com/view/cog4llm
 - Our paper (ICML2025  Long-Context Workshop): https://arxiv.org/abs/2506.08184
+- Mechanistic research is ongoing. The test is well-established in cognitive science, where it has been studied extensively to measure human **Working Memory capacity**.
 (As this test is too hard, only 4 updates per key make all LLMs fail to retrieve the last value—which we intentionally designed to keep the searching difficulty low. Retrieve other order of value has even lower performance)
+## Hard Mode / Non-Randomized Mode (Last but most interesting and striking)
 In a separated dataset files
 This mode takes the exact format shown in this document, without randomization. We fix everything but vary only the update times just like in the above experiment, but turn randomize_mode off .(column: randomize_mode)
 - This separate dataset consists of 46 of following blocks in a non-randomized order:
 - **This mode is the most striking, as it highlights a fundamental limitation in how LLMs process context—A task that is human infailable.”**
+# PI-LLM Dataset File List
+This repository hosts the **PI-LLM** dataset.
+Currently it includes two files:
+- **core.parquet** → the main dataset
+- **hardmode_ordered.parquet** →  harder for all LLMs but even easier for humans, with ordered update blocks.
 ## Quick Start - Evaluate Your Model
 ```python