Evidence map›Paper›PMID 40475621›Full record

ArticlebioRxiv : the preprint server for biology2025

An autonomous AI agent for universal behavior analysis.

Almir Aljović, Zuwan Lin, Wenbo Wang, Xinhe Zhang, Arnau Marin-Llobet, Ningyue Liang, Bradley Canales, Jaeyong Lee, Jongmin Baek, Ren Liu and 3 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Almir AljovićJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.ORCID 0000-0001-9725-6368
Zuwan LinJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.ORCID 0000-0001-5786-9768
Wenbo WangJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.ORCID 0000-0002-4282-2542
Xinhe ZhangJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Arnau Marin-LlobetJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.ORCID 0009-0008-8128-4119
Ningyue LiangJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Bradley CanalesJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Jaeyong LeeJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Jongmin BaekJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Ren LiuJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Catherine LiJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Na LiJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Jia LiuJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.

Funding

Roles of body-wide injury responses in axolotl limb regenerationR01HD115272 · NICHD · HARVARD UNIVERSITY · PI JESSICA L. WHITED · 2024 to 2026
$2.5M
NICHD NIH HHS R01 HD115272
6 · The paper itself

Abstract

Behavior analysis across species represents a fundamental challenge in neuroscience, psychology, and ethology, typically requiring extensive expert knowledge and labor-intensive processes that limit research scalability and accessibility. We introduce BehaveAgent, an autonomous multimodal AI agent designed to automate behavior analysis from video input without retraining or manual intervention. Unlike conventional methods that require manual behavior annotation, video segmentation, task-specific model training, BehaveAgent leverages the reasoning capabilities of multimodal large language models (LLM) to generalize across novel behavioral domains without need for additional training. It integrates LLMs, vision-language models (VLMs), and large-scale visual grounding modules, orchestrated through a multimodal context memory and goal-directed attention mechanism, to enable robust zero-shot visual reasoning across species and experimental paradigms, including plants, insects, rodents, primates, and humans. Upon receiving a video input, BehaveAgent autonomously identifies the correct analysis strategy and performs end-to-end behavior analysis and interpretation without human supervision. Leveraging vision-language representations, it performs general-purpose tracking, pose estimation and segmentation. We demonstrate BehaveAgent's universal applicability to autonomously (1) identify the behavioral paradigm and develop an action plan specialized for the identified paradigm, (2) identify relevant subjects and objects, (3) track those features, (4) identify behavioral sequences with explicit reasoning, (5) generate and execute code for targeted analysis and (6) generate comprehensive research reports that integrate behavioral findings with relevant scientific literature. Through interpretable agentic reasoning, BehaveAgent makes its internal decision-making process transparent, clarifying why particular features are tracked or behaviors inferred. By reducing the time and expertise required for behavior analysis, BehaveAgent introduces a scalable, generalizable, and explainable paradigm for advancing biological and behavioral research.

Identifiers

PMID40475621
PMCPMC12139829

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.