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This equation captures one of the core mathematical components of the system. flight_class="Economy" ) 🛠️ Explicit Args. origin="London" destination="Paris" Unde
Latent Preference Modeling for Cross-Session Personalized Tool Calling explores A new benchmark and memory-augmented method improve LLM agent tool-calling accuracy by learning evolving user preferences across sessions.. Commercial viability score: 6/10 in Agents.
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Canonical route: /paper/latent-preference-modeling-for-cross-session-personalized-tool-calling
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Canonical ID latent-preference-modeling-for-cross-session-personalized-tool-calling | Route /paper/latent-preference-modeling-for-cross-session-personalized-tool-calling
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/latent-preference-modeling-for-cross-session-personalized-tool-callingMCP example
{
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}Paper proof page receipt window
/buildability/latent-preference-modeling-for-cross-session-personalized-tool-calling
Subject: Latent Preference Modeling for Cross-Session Personalized Tool Calling
Verdict
Watch
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Dimensions overall score 6.0
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This equation captures one of the core mathematical components of the system. flight_class="Economy" ) 🛠️ Explicit Args. origin="London" destination="Paris" Unde
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Receipt path
/buildability/latent-preference-modeling-for-cross-session-personalized-tool-calling
Paper ref
latent-preference-modeling-for-cross-session-personalized-tool-calling
arXiv id
2604.17886
Generated at
2026-04-21T02:39:51.498Z
Evidence freshness
fresh
Last verification
2026-04-21T02:39:51.498Z
Sources
3
References
0
Coverage
50%
Lineage hash
4b3aa7d6eb344ae54f13a78c9e2ef77b3596bd52a92bdcfb88a1a37da93abda7
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This equation captures one of the core mathematical components of the system. we accumulate per-session API calls into an API call list A≤T, preserving the action trace from which
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