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  3. Efficient Personalized Reranking with Semi-Autoregressive Ge
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Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation

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0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation

PDF: https://arxiv.org/pdf/2603.07107v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation

Overall score: 7/10
Lineage: 65cfec378401…
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

Missingness
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  • Paper mode pins trust state to the canonical paper kernel.
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Dimensions overall score 7.0

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Keep exploring

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SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation
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Prior Work
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Score 7.0stable
Prior Work
AgenticRec: End-to-End Tool-Integrated Policy Optimization for Ranking-Oriented Recommender Agents
Score 7.0stable
Prior Work
Spend Search Where It Pays: Value-Guided Structured Sampling and Optimization for Generative Recommendation
Score 7.0stable
Higher Viability
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
Score 8.0up
Competing Approach
RecNet: Self-Evolving Preference Propagation for Agentic Recommender Systems
Score 6.0down
Competing Approach
Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs
Score 7.0stable

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