Paper

AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents

A typed-retrieval agent loop replaces transcript accumulation with bounded per-decision prompt assembly, so memory slots can be toggled and studied in isolation.

arXiv ↗Jul 2, 2026Code ↗Dataset ↗

  • llm
  • nlp
  • agents
  • memory
  • evaluation

What it is

A bounded-memory LLM-agent testbed for Slay the Spire 2 that replaces growing transcripts with typed retrieval, enabling clean memory-layer ablations over long runs. Instead of appending full cross-decision history, each action is made from a fresh prompt built by typed retrieval over fixed rules, episodic summaries, and triggered skills. This keeps context bounded and makes memory layers individually ablatable.

Key results

  • Skill-enabled cells won 6/10 at A0 vs 3/10 for no-store baseline
  • Prompt-only won 4/10 at A0, below all skill-enabled variants at 6/10
  • Released 298 completed trajectories with condition tags, prompts, and snapshots

Shows that bounded, typed memory turns long-horizon agent memory into a reproducible and separately testable evaluation surface.

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