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Large Knowledge Model: From Papers to a Scientific Reasoning Landscape
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arXiv AI
Sep 24
Large Knowledge Model: From Papers to a Scientific Reasoning Landscape
The paper introduces the Large Knowledge Model (LKM), a scientific knowledge infrastructure that converts research literature into shared, computationally accessible reasoning graphs. LKM aligns questions, claims, and reasoning chains across papers, creating a Scientific Reasoning Landscape with Question, Workflow, and Evidence views. The system enhances scientific search, evidence‑grounded QA, and research planning, achieving notable accuracy gains on ChemBench, PubMedQA, and SciBench.
arXiv AI
3d ago
Large Knowledge Model: A Knowledge Foundation for Agentic Science at Scale
arXiv:2609.27297v2 Announce Type: replace Abstract: Agentic science envisions many autonomous agents investigating concurrently while building on a shared, evolving body of scientific knowledge. This...
arXiv AI
Sep 1
SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature
arXiv:2608.30214v1 Announce Type: new Abstract: Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data. Existing datasets a...
arXiv AI
Jul 24
SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration
arXiv:2607. 20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources.
arXiv AI
Sep 23
ScholarStack: Layered Research Asset Orchestration and Cross-Task Reuse for Scientific Agents
arXiv:2609.23735v2 Announce Type: new Abstract: Scientific agents support a range of literature-based research tasks, such as retrieval, question answering, evidence-grounded generation, and claim as...
arXiv AI
3d ago
PathAnchor: Path-Structured Evidence for Scientific Agents
PathAnchor is a new scientific reasoning system that uses path-structured evidence workspaces instead of independent passages or concepts. It retrieves source-linked Material‑Sensor‑Signal‑System trajectories that preserve role, direction, and supporting evidence, and a controller uses read‑only tools to search, trace, and open exact evidence before producing a claim‑cited answer. In evaluations on 120 flexible‑sensor questions, PathAnchor achieved an 82.6% score, outperformed six other systems, and improved source recall, citation completeness, and reduced tool calls compared to unordered concept graphs.