Evidence Receipt. Related Resources.
Darwinian Memory: A Training-Free Self-Regulating Memory System for GUI Agent Evolution
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/darwinian-memory-a-training-free-self-regulating-memory-system-for-gui-agent-evolution
- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 8/10
- Last proof check
- 2026-03-17
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- Coverage
- 33%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Darwinian Memory: A Training-Free Self-Regulating Memory System for GUI Agent Evolution
Canonical ID darwinian-memory-a-training-free-self-regulating-memory-system-for-gui-agent-evolution | Route /signal-canvas/darwinian-memory-a-training-free-self-regulating-memory-system-for-gui-agent-evolution
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/darwinian-memory-a-training-free-self-regulating-memory-system-for-gui-agent-evolutionMCP example
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}Preparing verified analysis
Dimensions overall score 8.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
- Evidencepartial
we propose the Darwinian Memory System (DMS), a self-evolving architecture
ImplicationpartialThe abstract explicitly introduces DMS as a 'self-evolving architecture'.
Verificationpartialpartial
- Evidencepartial
DMS decomposes complex trajectories into independent, reusable units for compositional flexibility
ImplicationpartialThis is a direct statement of a key feature of DMS in the abstract.
Verificationpartialpartial
- Evidencepartial
implements Utility-driven Natural Selection to track survival value, actively pruning suboptimal paths
ImplicationpartialThe abstract clearly describes the mechanism of 'Utility-driven Natural Selection' within DMS.
Verificationpartialpartial
- Evidencepartial
DMS boosts general-purpose MLLMs without training costs or architectural overhead
ImplicationpartialThe abstract states this benefit directly, implying a training-free and overhead-free approach.
Verificationpartialpartial
- Evidencepartial
achieving average gains of 18.0% in success rate
ImplicationpartialThis is a specific, quantifiable result reported in the abstract.
Verificationpartialpartial
- Evidencepartial
and 33.9% in execution stability
ImplicationpartialThis is another specific, quantifiable result reported in the abstract.
Verificationpartialpartial
- Evidencepartial
while reducing task latency
ImplicationpartialThe abstract mentions reducing task latency as a benefit of DMS.
Verificationpartialpartial
