pith:PVLXGAPZ
HARPO: Hierarchical Agentic Reasoning for User-Aligned Conversational Recommendation
HARPO uses hierarchical preference learning and value-guided tree search to optimize conversational recommendations for multi-dimensional user quality.
arxiv:2604.10048 v2 · 2026-04-11 · cs.IR
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Claims
HARPO integrates hierarchical preference learning that decomposes recommendation quality into interpretable dimensions (relevance, diversity, predicted user satisfaction, and engagement) and learns context-dependent weights over these dimensions; (ii) deliberative tree-search reasoning guided by a learned value network that evaluates candidate reasoning paths based on predicted recommendation quality rather than task completion; and (iii) domain-agnostic reasoning abstractions through Virtual Tool Operations and multi-agent refinement, enabling transferable recommendation reasoning across domains. We evaluate HARPO on ReDial, INSPIRED, and MUSE, demonstrating consistent improvements over strong baselines on recommendation-centric metrics while maintaining competitive response quality.
That the learned value network and context-dependent weights over the four quality dimensions accurately capture and optimize for actual user-aligned recommendation quality in real interactions, rather than merely correlating with the chosen proxy metrics on the evaluation datasets.
HARPO reframes conversational recommendation as hierarchical agentic reasoning with learned weights over quality dimensions and value-guided tree search, yielding better recommendation metrics on ReDial, INSPIRED, and MUSE.
Receipt and verification
| First computed | 2026-06-09T01:05:17.105780Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/PVLXGAPZ4XEJ2GP7CYVKWBFPCZ \
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Canonical record JSON
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