LLM agent learns user folder logic without training data
Researchers have reframed paper management as a personalized routing problem that standard classifiers cannot solve. PaperRouter-Agent, a training-free LLM system, grounds routing decisions in the semantic content of existing folder members rather than folder labels alone, enabling it to infer user-specific organizational logic without per-user fine-tuning. This work exposes a gap in how LLMs handle evolving, user-defined taxonomies and demonstrates that content-grounded reasoning can outperform label-only approaches in knowledge management tasks. The technique has implications for any domain where users maintain private, idiosyncratic hierarchies.
Modelwire context
ExplainerThe key insight is that PaperRouter-Agent works without training on individual users' filing habits. Instead of learning 'what folder does this paper belong in' from labeled examples, it infers organizational logic by comparing new papers to the semantic patterns already present in each folder.
This is largely disconnected from recent activity in the broader LLM capability space. The work belongs to a narrower domain: how LLMs handle user-defined structure and private taxonomies. Most recent LLM research has focused on scaling, reasoning, or multimodal tasks. PaperRouter-Agent instead tackles a friction point that knowledge workers face daily but that standard classifiers and even label-based LLM prompting miss: users don't organize by consistent rules, they organize by intuition. The absence of related coverage in our archive suggests this capability gap hasn't been a focal point for the field yet.
If PaperRouter-Agent is tested on real user filing systems (not synthetic benchmarks) and maintains >80% accuracy without any per-user adaptation, that confirms the content-grounding approach generalizes. If instead accuracy drops below 70% on held-out real users, the method is likely overfitting to the paper domain or to researcher-curated datasets.
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsPaperRouter-Agent · LLM
Modelwire Editorial
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “PaperRouter-Agent: A Content-Grounded LLM Agent for Personalized Hierarchical Paper Routing”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.