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ARXIV:2603.18429 · AGENTS · SUBMITTED 20 MAR · 21:29 UTC · FRESHNESS STALE
ARXIV:2603.18429AGENTSSUBMITTED 20 MAR · 21:29 UTCFRESHNESS STALEYibo Shi · Jungang Li · Linghao Zhang · Zihao Dongfang · Biao Wu · Sicheng Tao · +22 at arXiv
A diagnostic framework and memory mechanism for long-horizon GUI agents that significantly improves task completion rates by structuring interaction history.
Opportunity summary
Pain A diagnostic framework and memory mechanism for long-horizon GUI agents that significantly improves task completion rates by structuring interaction history.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
A diagnostic framework and memory mechanism for long-horizon GUI agents that significantly improves task completion rates by structuring interaction history. Replaying full interaction sequences is redundant and amplifies noise, while summaries often erase dependency-critical…
Long-horizon GUI agents are a key step toward real-world deployment, yet effective interaction memory under prevailing paradigms remains under-explored. Replaying full interaction sequences is redundant and amplifies noise, while summaries often erase dependency-critical information…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Guided by this diagnosis, we propose Anchored State Memory (ASM), which represents interaction sequences as a compact set of causally linked intermediate-state anchors to…
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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A diagnostic framework and memory mechanism for long-horizon GUI agents that significantly improves task completion rates by structuring interaction history.
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10.48550/arXiv.2603.18429A diagnostic framework and memory mechanism for long-horizon GUI agents that significantly improves task completion rates by structuring interaction history.
Abstract
Long-horizon GUI agents are a key step toward real-world deployment, yet effective interaction memory under prevailing paradigms remains under-explored. Replaying full interaction sequences is redundant and amplifies noise, while summaries often erase dependency-critical information and traceability. We present AndroTMem, a diagnostic framework for anchored memory in long-horizon Android GUI agents. Its core benchmark, AndroTMem-Bench, comprises 1,069 tasks with 34,473 interaction steps (avg. 32.1 per task, max. 65). We evaluate agents with TCR (Task Complete Rate), focusing on tasks whose completion requires carrying forward critical intermediate state; AndroTMem-Bench is designed to enforce strong step-to-step causal dependencies, making sparse yet essential intermediate states decisive for downstream actions and centering interaction memory in evaluation. Across open- and closed-source GUI agents, we observe a consistent pattern: as interaction sequences grow longer, performance drops are driven mainly by within-task memory failures, not isolated perception errors or local action mistakes. Guided by this diagnosis, we propose Anchored State Memory (ASM), which represents interaction sequences as a compact set of causally linked intermediate-state anchors to enable subgoal-targeted retrieval and attribution-aware decision making. Across multiple settings and 12 evaluated GUI agents, ASM consistently outperforms full-sequence replay and summary-based baselines, improving TCR by 5%-30.16% and AMS by 4.93%-24.66%, indicating that anchored, structured memory effectively mitigates the interaction-memory bottleneck in long-horizon GUI tasks. The code, benchmark, and related resources are publicly available at [https://github.com/CVC2233/AndroTMem](https://github.com/CVC2233/AndroTMem).
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Dimensions overall score 8.0
PROBLEM
A diagnostic framework and memory mechanism for long-horizon GUI agents that significantly improves task completion rates by structuring interaction history. Replaying full interaction sequences is redundant and amplifies noise, while summaries often erase dependency-critical in...
METHOD
Long-horizon GUI agents are a key step toward real-world deployment, yet effective interaction memory under prevailing paradigms remains under-explored. Replaying full interaction sequences is redundant and amplifies noise, while summaries often erase dependency-critical informa...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Guided by this diagnosis, we propose Anchored State Memory (ASM), which represents interaction sequences as a compact set of causally linked intermediate-state anchors to enable subgoal-targeted retrieval...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
comprises 1,069 tasks with 34,473 interaction steps (avg. 32.1 per task, max. 65)
Implication not extracted yet.
partial
as interaction sequences grow longer, performance drops are driven mainly by within-task memory failures, not isolated perception errors or local action mistakes
Implication not extracted yet.
partial
ASM consistently outperforms full-sequence replay and summary-based baselines, improving TCR by 5%-30.16% and AMS by 4.93%-24.66%
Implication not extracted yet.
partial
improving TCR by 5%-30.16% and AMS by 4.93%-24.66%
Implication not extracted yet.
partial
AndroTMem-Bench is designed to enforce strong step-to-step causal dependencies, making sparse yet essential intermediate states decisive for downstream actions
Implication not extracted yet.
partial
represents interaction sequences as a compact set of causally linked intermediate-state anchors to enable subgoal-targeted retrieval and attribution-aware decision making
Implication not extracted yet.
partial
Replaying full interaction sequences is redundant and amplifies noise, while summaries often erase dependency-critical information and traceability
Implication not extracted yet.
partial
effective interaction memory under prevailing paradigms remains under-explored
Implication not extracted yet.
partial
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A diagnostic framework and memory mechanism for long-horizon GUI agents that significantly improves task completion rates by structuring interaction history.
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8.0/10 public viability
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Technical feasibility
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