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Zochi (Intology)

external · status: active · focus: end-to-end · discipline: computer-science · started: 2025

Project page: https://github.com/IntologyAI/Zochi

Source: projects/landscape/zochi.yml

Positioning

An end-to-end "artificial scientist" system from Intology, claimed to span hypothesis generation through to peer-reviewed publication. Differentiates itself from earlier AI-scientist releases by publishing the outputs — accepted ACL 2025 and ICLR 2025 workshop papers (CS-ReFT, Tempest/Siege) with reported state-of-the-art results — rather than only the pipeline.

Distinctive contribution

The strongest external-validation claim in the AI-scientist landscape: peer-reviewed acceptances of papers produced by the system, including ACL 2025 main proceedings. The repository releases code and a technical report covering an earlier version of the system.

Evaluation scores

Dimension Score (0–3) Note
Lifecycle coverage 3 Seven stages from hypothesis through review; full lifecycle in claimed scope.
Autonomy level 3 Autonomous end-to-end discovery is the stated design target.
Architectural transparency 2 Public repository covers earlier version; current capabilities described in blog posts; full current pipeline not open.
Inputs supported 2 Research-area inputs with optional dataset; commercial back-end at intology.ai.
Outputs / reproducibility 2 Published papers + benchmark code; full system reproduction depends on current closed components.
Internal evaluation 3 External peer review at ACL 2025 + ICLR 2025 workshops — strongest external validation in the catalog.
Openness 2 MIT-licensed code for earlier version; current system features are commercial.
Maturity / traction 2 305 stars; commercial backing (Intology); credible publication trajectory.
Cross-family policy 0 Same-model self-refinement is the canonical failure mode ARIS Table 4 identifies.
Runtime assurance 2 External peer-review acceptance (ACL 2025) is the strongest runtime-equivalent assurance signal in the catalog.
Cross-platform portability 1 Public code covers earlier version; current pipeline is commercial.

Scored on 2026-05-18. See the evaluation rubric.

Tags

Pipeline stages: hypothesis-generation research-design data-analysis code-generation paper-drafting revision-editing referee-simulation

Architectural features: multi-agent tool-use iterative-loop artifact-versioning

Inputs: research-area

Outputs: paper-draft code experiment-results

Data sources: benchmark-datasets

Knowledge sources: literature

Limitations

  • Public code lags the current capabilities described in marketing — current pipeline is not fully open.
  • Validation is via specific peer-reviewed papers; cross-domain generality is asserted but not separately tested.
  • Last push 2025-11; the open repository may not reflect the live system.

Also compared in

  • A Survey of AI Scientists (tie2025aiscientistsurvey) — Covered as an end-to-end system with peer-reviewed publication trajectory.