arXiv AI
Jul 2
EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents
arXiv:2605. 12887v2 Announce Type: replace-cross Abstract: Web-enabled LLM agents are changing how online information influences search outcomes.
PaperRouter-Agent: A Content-Grounded LLM Agent for Personalized Hierarchical Paper Routing
Researchers organize the papers they collect into personal folder hierarchies in reference managers, and route each new paper into the folder where it belongs. This task differs from standard hierarchical text classification.
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Hugging Face Trending Papers
Jul 22
Personalized Recommendation Tool Learning via Autonomous Language Agents
Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks. To circumvent these limitations through architectural design rather than modifying the LLM itself, we propose an agent-based recommendation framework, memory-based $\textbf{P}$ersonalized $\textbf{R}$ecommendation $\textbf{T}$ool learning via autonomous language $\textbf{A}$gents (PRTA), in which an LLM acts as a central planner interacting with multiple recommendation models as tools.
arXiv AI
Jul 23
Personalized Recommendation Tool Learning via Autonomous Language Agents
arXiv:2607. 19739v1 Announce Type: cross Abstract: Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks.
arXiv AI
Aug 12
DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents
arXiv:2608. 10037v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical grounding resource for LLM agents.
arXiv AI
Jul 31
Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
arXiv:2607. 26637v1 Announce Type: cross Abstract: Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools.
arXiv AI
Jun 16
Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
arXiv:2606. 14885v1 Announce Type: new Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.
Hugging Face Trending Papers
Jun 23
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer.
arXiv AI
Aug 11
DocAtlas: Long-Document Understanding as Mutable-State Interaction
arXiv:2608. 07527v1 Announce Type: cross Abstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts.
arXiv Machine Learning
Aug 4
Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark
arXiv:2608. 00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it.
arXiv AI
Jul 28
VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy
arXiv:2607. 23006v1 Announce Type: cross Abstract: Scientific question answering requires a retrieval system to solve two distinct problems: identifying which papers are relevant and locating the supporting evidence within those papers.
arXiv AI
Aug 5
Training Documents Reranker with Search Rubrics for Deep Research Agent
arXiv:2608. 03527v1 Announce Type: cross Abstract: Retrieval systems help deep research agents generate high-quality answers by providing relevant documents.
arXiv AI
Jun 30
Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off
arXiv:2603. 26815v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems for financial document QA typically follow a chunk-based paradigm: documents are split into fragments, embedded, and retrieved by similarity.