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seek, Self-Evaluative Exploration for Knowledge Retrieval, a training-free framework that addresses this limitation through iterative corpus interaction at test time through iterative corpus interaction at test time.
LLM-based query reformulation can improve retrieval, but no single reformulation strategy is consistently optimal across queries, domains, retrievers, or model backbones. This creates an inference-time decision problem: ``Given an original query and a pool of candidate reformulations, which one should be issued to the...
Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high score because it is fluent, organized, and polished, rather than because it provides a strong evaluation of the paper. This risk is especially important in AI-assisted rev...
Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmarks such as BrowseComp-Plus shows that well-configured lexical retrieval can surface high-quality evidence, yet agents may still fail to connect documents carrying eviden...
Peer review is central to scientific decision-making, yet it is rarely evaluated or audited at scale. Growing submission volumes and the increasing use of large language models (LLMs) in drafting reviews have introduced new challenges for transparency, accountability, and quality control. Today, peer reviews are often...
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