3 papers
cs.SE2025
The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Tobias Lindenbauer, Igor Slinko, Ludwig Felder +2
Large Language Model (LLM)-based agents solve complex tasks through iterative reasoning, exploration, and tool-use, a process that can result in long, expensive context histories.…
cs.SE2025
From Knowledge to Noise: CTIM-Rover and the Pitfalls of Episodic Memory in Software Engineering Agents
Tobias Lindenbauer, Georg Groh, Hinrich Schütze
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 mem…
cs.SE2025
GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git
Tobias Lindenbauer, Egor Bogomolov, Yaroslav Zharov
Benchmarks for Software Engineering (SE) AI agents, most notably SWE-bench, have catalyzed progress in programming capabilities of AI agents. However, they overlook critical develo…