Wei Chen, Xiaofang Zhou, Zhoujin Tian, Fangyuan Zhang, Wenxuan Liu, Zhengjun Huang, Junle Chen, Yuqian Wu, Qintian Guo
We lifted 19 functions out of this paper's own repositories and ran 13 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| WujiangXu/A-mem | canonical | 7 of 8 |
| Mirix-AI/MIRIX | canonical | 3 of 7 |
| wgcyeo/UniversalRAG | canonical | 3 of 3 |
| BAI-LAB/MemoryOS | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| async_cache_response | Ran | wgcyeo/UniversalRAG/src/universalrag/llm/openai_gpt.py code served (permissive licence) · get_code("d00332f18998e00c") |
| cache_response | Ran | wgcyeo/UniversalRAG/src/universalrag/llm/openai_gpt.py code served (permissive licence) · get_code("7b1455027088aa7b") |
| class_str | Ran | wgcyeo/UniversalRAG/src/universalrag/llm/openai_gpt.py code served (permissive licence) · get_code("85d83304b08afc08") |
| get_field | Ran | Mirix-AI/MIRIX/mirix/config.py code served (permissive licence) · get_code("b9d23ef56c0eb877") |
| load_config | Ran | Mirix-AI/MIRIX/mirix/sdk.py code served (permissive licence) · get_code("54b0fc52f020998a") |
| load_locomo_dataset | Ran | WujiangXu/A-mem/load_dataset.py code served (permissive licence) · get_code("5c12c257ad6b32ca") |
| parse_conversation | Ran | WujiangXu/A-mem/load_dataset.py code served (permissive licence) · get_code("4a1510da2b417c43") |
| parse_session | Ran | WujiangXu/A-mem/load_dataset.py code served (permissive licence) · get_code("3701fefb10719e87") |
| parse_with_json_fallback | Ran | WujiangXu/A-mem/llm_text_parsers.py code served (permissive licence) · get_code("24e35e9dadb2fe27") |
| retry_llm_call | Ran | WujiangXu/A-mem/memory_layer_robust.py code served (permissive licence) · get_code("ddc19d511f03d63d") |
| simple_tokenize | Ran | WujiangXu/A-mem/utils.py code served (permissive licence) · get_code("48cea41b09d89a75") |
| strip_markdown_fences | Ran | WujiangXu/A-mem/llm_text_parsers.py code served (permissive licence) · get_code("21854723dac05890") |
| validate_log_file_path | Ran | Mirix-AI/MIRIX/mirix/log.py code served (permissive licence) · get_code("39746e0471f64865") |
| Memoryos | Not yet run | BAI-LAB/MemoryOS/memoryos-mcp/memoryos/memoryos.py code served (permissive licence) · get_code("1be0d2dcd46418e9") |
| get_contine_chaining | Not yet run | Mirix-AI/MIRIX/mirix/system.py code served (permissive licence) · get_code("67ac46c06642465e") |
| get_initial_boot_messages | Not yet run | Mirix-AI/MIRIX/mirix/system.py code served (permissive licence) · get_code("778ddf10eda08321") |
| get_login_event | Not yet run | Mirix-AI/MIRIX/mirix/system.py code served (permissive licence) · get_code("dec2ac6f8448f277") |
| parse_analyze_content | Not yet run | WujiangXu/A-mem/llm_text_parsers.py code served (permissive licence) · get_code("901a2bbc34750c4e") |
| parse_and_chunk_text | Not yet run | Mirix-AI/MIRIX/mirix/embeddings.py code served (permissive licence) · get_code("42c780d962df1807") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Language agents are increasingly deployed over accumulating multimodal information, yet existing benchmarks assume a human-human form with sparse visuals and straightforward content, evaluating neither reasoning over authentic multimodal file interaction nor the interpretation of concealed user information. We therefore introduce M 3 Exam, a query-centric multimodal conversational memory benchmark built on realistic user-agent interaction, with multi-dimensional evaluation spanning cross-modal grounding and implicit information inference. Benchmarking MLLMs and memory systems reveals persistent gaps in cross-modal grounding, cross-session reasoning, and the efficiency cost of accumulating multimodal context. We further propose M 3 Proctor, a multimodal memory method that detects query modality bias and consumes raw visual sources only on demand, improving accuracy by 13% while cutting index-construction time and retrieved tokens by over 70% 1 . * Equal contribution. 1 Code and data are released at https://anonymous. 4open.science/r/M-3-Exam-128D.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.07402")
get_code_for_paper("2606.07402")
have("2606.07402")
Connect an agent — have() is free.