Multimodal Benchmark Curation for Biology LLMs
We curate paper, code and dataset benchmarks for biology-focused language models under provenance and reproducibility constraints.
Topic overview
Large Language Models
Foundation model evaluation, alignment and system design.
Topic snapshot
What this area looks like now
7works
9authors
0experts visible
2communities
Next steps
Move from topic reading into action
The graph preview below keeps the nearby papers, people and communities visible in the same reading flow.
Topic graph
See the topic as a live network
Inspect nearby papers, researchers, institutions and communities without opening a separate graph page.
Drag to move the map, use wheel or controls to zoom.Selected topic: Large Language Models
topicsignal 104 adjacent
Large Language Models
7 works
Papers in this area
7 work(s)
Open-Weight Biology Assistants for Dataset-Centric Literature Triage
We study open-weight assistants that combine paper summaries, dataset cards and experiment context for biology teams.
Graph Memory for Literature Agents: Persistent Neighborhoods for Scientific Search
We show that persistent graph neighborhoods and operator-facing retrieval memory improve literature navigation across papers, people and opportunities.
A Signal Layer for Science: Structured Reviews, Graph Context and Faster Research Triage
We present a graph-native product architecture for scholarly sensemaking where work pages, topic maps and researcher trust signals reduce time-to-understanding for new literature.
Calibrated Review Rubrics for AI Science Communities
We evaluate rubric design for structured reviews, moderation queues and reviewer calibration in technical communities.
Sparse Embeddings for Scholarly Discovery with Human-Readable Signals
We compare dense and sparse retrieval strategies for scholarly search when operators need interpretable reasons and controllable ranking.
Trust-Aware Feed Ranking for Scholarly Collaboration Networks
We study how follow edges, review quality, graph proximity and freshness can be blended into an explainable feed optimized for high-signal research discovery.
People in this topic
9 active researcher(s)
Diego Santana
Applied Research Engineer
Builds collaboration tooling and graph-backed ranking loops for scholarly platforms.
Open to collaborateHanae Morita
Senior Lecturer
Works on review quality, moderation design and institutional trust systems for online science.
Open to collaborateLena Hartmann
Research Scientist, Networked Intelligence
Designs graph retrieval layers and provenance-rich interfaces for scientific search.
Open to collaborateMira Alvarez
PI, Machine Intelligence Lab
Builds graph-native systems for research evaluation and discovery.
Open to collaborateNoah Darzi
Staff Research Scientist
Studies expert routing, retrieval systems and opportunity matching in scientific networks.
Open to collaborateOwen Chen
Postdoctoral Researcher
Works on literature-scale retrieval and scientific representation learning.
Open to collaborateRhea Patel
PhD Candidate
Studies trust signals and structured peer feedback in online science.
Open to collaborateSofia Petrova
Computational Biology Fellow
Curates multimodal biology benchmarks and literature-aware evaluation pipelines.
Heads-down modeSunwoo Kim
Industry Research Scientist
Bridges dataset-centric ML with reproducibility tooling.
Heads-down mode