We lifted 3 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| liqs-v2/ctim-rover | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| update_costs | Ran | liqs-v2/ctim-rover/ctim-rover-scripts/knowledge_distiller.py pointer only (licence: NOASSERTION) · get_code("787766420c613ab4") |
| prompt_o1_model_with | Not yet run | liqs-v2/ctim-rover/ctim-rover-scripts/knowledge_distiller.py pointer only (licence: NOASSERTION) · get_code("067a62c96c1c5542") |
| prompt_sonnet_37_model_with | Not yet run | liqs-v2/ctim-rover/ctim-rover-scripts/knowledge_distiller.py pointer only (licence: NOASSERTION) · get_code("fd9fbbe2c68337a6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce CTIM-Rover, an AI agent for Software Engineering (SE) built on top of AutoCodeRover (Zhang et al., 2024) that extends agentic reasoning frameworks with an episodic memory, more specifically, a general and repository-level Cross-Task-Instance Memory (CTIM). While existing open-source SE agents mostly rely on ReAct (Yao et al., 2023b), Reflexion (Shinn et al., 2023), or Code-Act (Wang et al., 2024), all of these reasoning and planning frameworks inefficiently discard their long-term memory after a single task instance. As repository-level understanding is pivotal for identifying all locations requiring a patch for fixing a bug, we hypothesize that SE is particularly well positioned to benefit from CTIM. For this, we build on the Experiential Learning (EL) approach ExpeL (Zhao et al., 2024), proposing a Mixture-Of-Experts (MoEs) inspired approach to create both a general-purpose and repository-level CTIM. We find that CTIM-Rover does not outperform AutoCodeRover in any configuration and thus conclude that neither ExpeL nor DoT-Bank (Lingam et al., 2024) scale to real-world SE problems. Our analysis indicates noise introduced by distracting CTIM items or exemplar trajectories as the likely source of the performance degradation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2505.23422")
get_code_for_paper("2505.23422")
have("2505.23422")
Connect an agent — have() is free.