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

• URL: http://arxiv.org/abs/2607.02255v1
• Date: Thu, 02 Jul 2026 14:44:32 GMT
• Title: AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
• Abstract summary: Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see.<n>We instrument an alternative bounded contract: every decision is made from a fresh user message assembled by typed retrieval.<n>We instantiate the contract in Slay the Spire 2, a closed-rule deck-building game.
• Score: 34.450981549319266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of any single memory component is hard to isolate. We introduce and instrument an alternative bounded contract: every decision is made from a fresh user message assembled by typed retrieval, with no raw cross-decision transcript appended. The prompt thus stays bounded across runs of any length, and any single layer can be ablated in isolation. We instantiate the contract in Slay the Spire 2, a closed-rule stochastic deck-building game whose runs require hundreds of tactical and strategic decisions. A public online benchmark of frontier LLMs on the same game reports zero wins at the lowest difficulty across five configurations, and the developer-reported human win rate at the same difficulty is 16%; the task is hard but not saturated. Within our harness, a fixed-A0 ablation shows the largest observed difference when triggered strategic skills are enabled: the no-store baseline wins 3/10 games and adding the skill layer 6/10. At this sample size the comparison is directional rather than statistically decisive (Fisher exact p\approx0.37); a cross-backbone probe and public accumulating-context baselines are reported as operational comparisons rather than controlled tests of the contract variable itself. We release a reproducible testbed: 298 completed trajectories with condition tags, frozen memory/skill snapshots, prompt records, and analysis scripts -- an agent design and a validated, reusable methodology for studying how explicit memory layers shape long-horizon LLM-agent decisions.

Related papers

• The Deception Delta: Adversarial Evaluation of LLM-Based Smart Contract Bytecode Forensics [0.0]
  Large language models are increasingly used in blockchain forensic investigations.<n> adversarial deception reduces drain detection by 20.0 percentage points.<n>Models correctly describe the hidden drain mechanism but accept the contract's framing and dismiss it as benign.
  arXiv  Detail & Related papers  (2026-09-12T18:45:24Z)
• LMSM: LLM Security Framework Inspired by Linux Security Modules [56.93644694627783]
  Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them.<n>We present Language Model Security Modules (LMSM), a security framework that adapts the separation behind Linux Security Modules (LSM) to LLM serving.
  arXiv  Detail & Related papers  (2026-08-26T12:13:05Z)
• Disagree to Explore, Agree to Commit: Routing-Guided Test-Time Scaling for Software Agents [60.650808962786364]
  We study whether native MoE router traces can guide steering and selection without an external judge or selection-time test execution.<n>Our analysis shows that routing provides a robust behavioral role signal; token-granular readouts and decision-matched comparison sets turn it into effective control.<n>We evaluate on SWE-bench Verified using open-weight sparse MoE agents across scales and reasoning-effort settings.
  arXiv  Detail & Related papers  (2026-08-23T03:07:03Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents [0.0]
  Long-answer LLM agents can fail quietly: they settle on one reading of the evidence early, then spend the rest of the run defending it.<n>We define representational commitment as cross-run hidden-state convergence at a fixed reasoning step.<n>A runtime monitor detects inconsistent trajectories from hidden states at AUROC up to 0.97 (0.85--0.88 under a stricter split)
  arXiv  Detail & Related papers  (2026-06-22T07:13:13Z)
• Entity-Collision: A Stratified Protocol for Attributing Retrieval Lift in Agent Memory [0.0]
  End-to-end agent-memory benchmarks report a single hit@k per retriever.<n>We propose entity-collision, a system-agnostic protocol that pins the BM25 floor by construction.
  arXiv  Detail & Related papers  (2026-05-28T09:02:48Z)
• AEL: Agent Evolving Learning for Open-Ended Environments [43.56685432981852]
  We introduce emphAgent Evolving Learning (ael), a two-timescale framework that addresses this obstacle.<n>ael achieves a Sharpe ratio of 2.13$pm$0.47, outperforming five published self-improving methods.<n>This demonstrates that the bottleneck in agent self-improvement is emphself-diagnosing how to use experience rather than adding architectural complexity.
  arXiv  Detail & Related papers  (2026-04-23T14:29:25Z)
• Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms [0.0]
  CSTM-Bench is 26 executable attack classified by kill-chain stage and cross-session operation.<n>Released on Hugging Face as intrinsec-ai/cstm-bench with two 54-scenario splits: dilution (compositional) and cross_session.
  arXiv  Detail & Related papers  (2026-04-22T22:40:31Z)
• LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications [51.56484100374058]
  We present a readiness harness for LLM and RAG applications that turns evaluation into a deployment decision workflow.<n>The system combines automated benchmarks, OpenTelemetry observability, and CI quality gates under a minimal API contract.<n>We evaluate the harness on ticket-routing and BEIR tasks with full Azure matrix coverage.
  arXiv  Detail & Related papers  (2026-03-28T18:03:32Z)
• AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management [47.49917373646469]
  Existing defenses treat bloated memory as given and focus on remaining resilient.<n>We present AgentSys, a framework that defends against indirect prompt injection through explicit memory management.
  arXiv  Detail & Related papers  (2026-02-07T06:28:51Z)
• EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents [52.567469286881426]
  We introduce EMemBench, a programmatic benchmark for evaluating long-term memory of agents through interactive games.<n>Rather than using a fixed set of questions, EMemBench generates questions from each agent's own trajectory.<n>Each template computes verifiable ground truth from underlying game signals.
  arXiv  Detail & Related papers  (2026-01-23T12:09:59Z)
• A BERTology View of LLM Orchestrations: Token- and Layer-Selective Probes for Efficient Single-Pass Classification [2.0069888187253615]
  Production LLM systems often rely on separate models for safety and other classification-heavy steps.<n>We instead reuse computation already paid for by the serving LLM: we train lightweight probes on its hidden states and predict labels in the same forward pass used for generation.
  arXiv  Detail & Related papers  (2026-01-19T18:40:29Z)
• Memory Injection Attacks on LLM Agents via Query-Only Interaction [49.14715983268449]
  We propose a novel Memory INJection Attack, MINJA, without assuming that the attacker can directly modify the memory bank of the agent.<n>The attacker injects malicious records into the memory bank by only interacting with the agent via queries and output observations.<n> MINJA enables any user to influence agent memory, highlighting the risk.
  arXiv  Detail & Related papers  (2025-03-05T17:53:24Z)
• WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia [59.96425443250666]
  Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs) In this work, we conduct a comprehensive evaluation of LLM-generated answers to questions based on contradictory passages from Wikipedia. We benchmark a diverse range of both closed and open-source LLMs under different QA scenarios, including RAG with a single passage, and RAG with 2 contradictory passages.
  arXiv  Detail & Related papers  (2024-06-19T20:13:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.