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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arXiv Computation and Language
Sep 1
Agent Zero Memory: Provenance-Aware Long-Term Memory for LLM Agents
Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.
arXiv AI
Sep 30
Follow the Entities: A Corpus Map for Agentic Search
arXiv:2609.37226v1 Announce Type: cross Abstract: Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as...
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
Sep 1
ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval
arXiv:2601.21545v2 Announce Type: replace Abstract: Agentic systems accumulate persistent memory across sessions, tools, and tasks, and a later request must retrieve from it under two distinct constr...
arXiv AI
Sep 18
TRACE: Accountable Agentic Retrieval for Source Discovery in Digital Archives
TRACE is a training‑free, agentic retrieval framework that enables accountable source discovery in historical archives, addressing challenges such as OCR degradation and genre heterogeneity. Developed for the DECIDON project on French Third Republic political discourse, it is deployed internally for 24 researchers across six institutions. On the HistoriQA‑ThirdRepublic benchmark, TRACE achieves R@10 of 0.856 and MRR of 0.653, outperforming sparse, dense, graph‑based, and other agentic RAG baselines, especially on multi‑hop and cross‑corpus questions, while costing only about $0.02 per question.
arXiv AI
Sep 15
Positioning manuscripts in the scientific landscape with agentic AI
arXiv:2609.13760v1 Announce Type: new Abstract: Publishing a research manuscript is a routine yet demanding part of scientific life: time-consuming, stressful, and often uncertain in outcome. Recent...
arXiv Computation and Language
Sep 30
Remember Your Trace: Memory-Guided Long-Horizon Agentic Framework for Consistent and Hierarchical Repository-Level Code Documentation
arXiv:2605.14563v3 Announce Type: replace-cross Abstract: Automated code documentation is essential for modern software development, providing the contextual grounding that both human developers and...
arXiv AI
1d ago
RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees
arXiv:2610.11370v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds language models in external corpora. Agentic RAG enables iterative search, yet exposes the model to isolat...
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.