arXiv AI
Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation
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arXiv Computation and Language
Aug 31
Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection
The paper investigates modular entity disambiguation by separating candidate retrieval from entity selection. It compares sparse retrieval (BM25), Web KB search, and a dense retriever, all paired with large language model selectors. Results show that a training‑free BM25 retriever combined with an LLM selector achieves state‑of‑the‑art performance on the ZELDA benchmark, and the modular approach enables abstention when retrieval fails.
arXiv Computation and Language
Oct 7
Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents
arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right en...
arXiv Machine Learning
Sep 30
Know Thyself, Teach Thyself: Internal Information Flow for Selective Self-Distillation
arXiv:2609.36695v1 Announce Type: new Abstract: Self-distillation turns knowledge distillation into a closed learning loop and offers a path toward recursive self-improvement. Without an external tea...
arXiv Machine Learning
Jun 15
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
arXiv:2604. 23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs.
arXiv AI
Oct 27
GraphDancer: Training LLMs to Explore and Reason over Graphs via Curriculum Reinforcement Learning
arXiv:2602.02518v3 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly rely on external knowledge to improve factuality, yet many knowledge sources on the Web, such as sc...
arXiv AI
Jul 28
From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search
arXiv:2607. 24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision.