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Datasets:
Cosineyx
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IMLogic

Tasks:
Question Answering
Modalities:
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Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
benchmark
personalization
memory
retrieval
llm
arxiv:2606.23283
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metadata 
pretty_name: IMLogic
language:
  - en
license: cc-by-nc-nd-4.0
task_categories:
  - question-answering
size_categories:
  - 1K<n<10K
tags:
  - benchmark
  - personalization
  - memory
  - retrieval
  - llm
  - arxiv:2606.23283
source_datasets:
  - IAAR-Shanghai/HaluMem
configs:
  - config_name: qa
    default: true
    data_files:
      - split: test
        path: data/qa/test.parquet
  - config_name: users
    data_files:
      - split: test
        path: data/users/test.parquet

IMLogic

IMLogic benchmarks implicit logical memory retrieval for personalized LLMs. A query may closely match a user's apparent preference, while an appropriate personalized response depends on a different memory with limited semantic overlap.

Dataset summary

The release contains 2,216 QA instances associated with 20 user trajectories, 14,948 source memory entries, 1,387 sessions, and 60,146 dialogue turns.

The two evidence roles are:

  • memory_s: semantically relevant background that alone is insufficient to determine the most appropriate personalized response.
  • memory_l: logically relevant information whose constraints or implications are needed for that response.

Evaluation supports memory-level retrieval using source memory entries and conversation-level evaluation using the original dialogue histories. Multiple-choice and open-ended evaluation protocols are provided in the GitHub repository.

Load the benchmark 

from datasets import load_dataset

# Default configuration: one row per QA instance.
qa = load_dataset("Cosineyx/IMLogic", split="test")
# Equivalent: load_dataset("Cosineyx/IMLogic", "qa", split="test")

# User metadata and links to the original trajectories.
users = load_dataset("Cosineyx/IMLogic", "users", split="test")

example = qa[0]
print(example["query"])
print(example["user_index"], example["user_id"])

All instances are released in a single test split. No official training or validation split is introduced by this Hub packaging.

Load aligned dialogue histories

The full nested histories are retained in the original JSONL file. Keeping large trajectories outside the preview tables makes the QA viewer easier to browse.

import json
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="Cosineyx/IMLogic",
    repo_type="dataset",
    filename="original/dialogue/HaluMem-Medium.jsonl",
)
with open(path, encoding="utf-8") as handle:
    histories = [json.loads(line) for line in handle if line.strip()]

history = histories[example["user_index"]]
assert history["uuid"] == example["user_id"]
sessions = history["sessions"]
memory_bank = [memory for session in sessions for memory in session["memory_points"]]

For a version-pinned experiment, pass the same commit SHA as revision= to both load_dataset and hf_hub_download.

Data configurations and fields

Configuration Split Rows Contents
qa (default) test 2,216 Queries, reference memories, answer options, and original annotations
users test 20 User UUIDs, trajectory counts, and source-file linkage

QA fields

Field Meaning
qa_id Stable identifier, e.g. user00_qa0000
user_id The original dialogue trajectory's uuid
user_index Zero-based user record index in the original JSONL file
qa_index Zero-based question index in its original per-user QA file
query Query to be answered
memory_s Annotated semantic memory
memory_l Annotated logical memory
options Named correct and diagnostic candidate responses; see below
query_time Original timestamp string; nullable
diff_similarity_score Original similarity-difference annotation where present; nullable, not an evaluation score
source_file Path to the original per-user QA file in this dataset repository

options contains:

  • Correct: the reference personalized response that accounts for the logical memory.
  • Trap_Preference: follows the apparent preference while overlooking the logical constraint.
  • Trap_Fabrication: introduces unsupported personal details.
  • Trap_Generic: provides a generic response without relevant personalization.

These are gold annotations. The named option keys reveal correctness; for MCQ evaluation, shuffle the available candidate texts and conceal the gold keys from the answering model, as in the reference evaluator. The annotated memory_s and memory_l identify target evidence; supplying them directly is different from evaluating retrieval over a full memory bank.

User fields and alignment

The users configuration contains user_index, user_id, persona_info, num_sessions, num_memories, num_dialogue_turns, num_qa, dialogue_file, and qa_file.

Alignment follows the original benchmark: dialogue record 0 corresponds to qa_user00.json, record 1 to qa_user01.json, and so on. Both tables include the original UUID to validate this join. Preserve the original trajectory/session order and source timestamps when reproducing the temporal evaluation protocol.

Source data and construction

IMLogic reuses HaluMem-Medium histories and memory entries. Its construction pipeline tags memories, mines candidate logical associations, and generates QA instances through a Generator–Judger–Refiner process followed by expert screening and independent sample-based human evaluation. See the paper and repository for the construction prompts and evaluation protocols.

The original source files are included without edits under original/. This Hub release adds Parquet views and explicit record/user identifiers for loading and browsing. It does not regenerate any benchmark examples.

Files and reproducibility 

README.md
LICENSE
NOTICE.md
release_manifest.json
data/
  qa/test.parquet
  users/test.parquet
original/
  dialogue/HaluMem-Medium.jsonl
  qa/qa_user00.json
  qa/qa_user01.json
  ...

release_manifest.json records the source repository commit, row counts, user alignment, and SHA-256 digests of all original files. Evaluation scripts and model configuration instructions are maintained in the GitHub repository.

License and attribution

This release uses Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). See LICENSE and NOTICE.md. The original HaluMem histories and annotations retain their upstream attribution and license.

Citation 

@article{ding2026rootmemory,
  title={Towards Root Memory: Benchmarking and Enhancing Implicit Logical Memory Retrieval for Personalized LLMs},
  author={Ding, Hongxun and Yu, Xiang and Wang, Chengbing and Xiao, Jianfei and Bao, Keqin and Wang, Wenjie and He, Xiangnan},
  journal={arXiv preprint arXiv:2606.23283},
  year={2026}
}

Contact

For questions or benchmark corrections, open an issue in the IMLogic GitHub repository or a discussion on this dataset page.