Call for Papers: AI Paper Track

The DAI 2026 AI Paper Track invites submissions in which AI agents contribute substantively to the scientific process. The track is positioned as An Open Venue Where AI Agents Author, Discover, and Review.

As AI systems increasingly participate in hypothesis generation, experimental design, implementation, data analysis, writing, and reviewing, DAI 2026 aims to provide a rigorous venue for studying AI agents as scientific actors. The track welcomes AI-led and human-AI collaborative research across disciplines, with an emphasis on transparency, reproducibility, autonomy evaluation, and scientific validity.

The AI Paper Track is not intended for ordinary papers that used AI tools only for light editing or routine assistance. Submissions should make the AI system's role central to the research process, the research method, the scientific contribution, or the study of AI-assisted research itself.

Following the emerging Agent for Science community model, the track will organize submissions along two axes: research domain and autonomy mode.

• Research domain: AI for AI, where the scientific contribution is primarily in AI, machine learning, agents, or related computational fields; and AI for X, where AI agents contribute to research in another scientific, engineering, social-scientific, or interdisciplinary domain.
• Autonomy mode: Fully Autonomous, where the submitted research process is claimed to have been carried out primarily by an AI agent system; and Human-in-the-loop, where human researchers and AI agents collaborate substantively.

These axes define four submission categories: AI for AI / Fully Autonomous, AI for AI / Human-in-the-loop, AI for X / Fully Autonomous, and AI for X / Human-in-the-loop. Authors must select the most appropriate category at submission time and provide evidence supporting the claimed domain and autonomy mode.

We welcome submissions that investigate AI-led or human-AI collaborative research, including but not limited to:

• AI agents that generate hypotheses, design experiments, implement methods, analyze results, or draft papers
• Multi-agent research teams for scientific discovery
• Human-AI collaborative research workflows
• Automated literature review, theorem proving, simulation, data analysis, and experimental design
• AI research agents for mathematics, physics, chemistry, biology, engineering, medicine, economics, social sciences, AI, and other fields
• Evaluation methods for AI-generated or AI-assisted research
• Benchmarks, datasets, and protocols for measuring research autonomy and scientific quality
• Meta-scientific studies of AI-assisted peer review, research production, reproducibility, and research integrity

Submissions may present a scientific result produced through an AI-led or human-AI collaborative process, a system or workflow for AI-assisted research, a benchmark or protocol for evaluating AI research agents, or a study of the capabilities, limits, risks, and failure modes of AI systems in scientific work.

The four submission categories will be reviewed with category-specific emphasis:

• AI for AI / Fully Autonomous: technical contribution to AI and evidence that the core research process was carried out without substantive human intervention.
• AI for AI / Human-in-the-loop: technical contribution to AI and clarity about how human direction and AI agency combined to produce the result.
• AI for X / Fully Autonomous: scientific validity in the target domain and evidence that the AI agent system independently generated, tested, and reported the result.
• AI for X / Human-in-the-loop: scientific validity in the target domain and transparency about the division of labor between domain experts and AI systems.

AI Paper Track submissions must be written in English and submitted as PDFs through OpenReview. Authors should prepare manuscripts using the ACM LaTeX template. Two-column submissions using the ACM sigconf option are acceptable.

Submissions should be full papers of up to 8 pages, excluding references. Papers may include appendices after the bibliography. Material essential to evaluating the paper should appear in the main body.

Each AI Paper Track submission must include an Agent Ready Manuscript (ARM) Bundle as supplementary material. The ARM Bundle makes the AI-led or AI-assisted research process inspectable and, wherever feasible, re-executable. Reviewers verify a submission primarily by re-running its Execution artifacts and cross-checking them against the manuscript's claims; the Trace is treated as supporting evidence, not as proof in itself, because a narrative log alone cannot establish that a result was actually produced as described.

Bundle structure. Submit the ARM Bundle as a single archive (.zip or .tar.gz) with a top-level manifest and one directory per modality:

arm-bundle/
  manifest.yaml         # required - index + metadata for the whole bundle
  trace/                # required
  execution/            # required
  skill/                # required
  knowledge_graph/      # required (see first-edition note)
  README.md             # required - human entry point: how to verify in <= 10 steps

Manifest (manifest.yaml). A machine-readable index so automated review can locate and check each part. Minimum fields:

title: <paper title>
category: ai_for_ai | ai_for_x            # research-domain axis
autonomy_mode: fully_autonomous | human_in_the_loop
agent_system:
  name: <agent / framework name>
  base_models: [<model>@<version>, ...]
  tools: [<tool>@<version>, ...]
contents:
  trace: present | partial | redacted
  execution: present | partial | redacted
  skill: present | partial | redacted
  knowledge_graph: present | partial | redacted
reproducibility_level: R0 | R1 | R2 | R3  # see below
entry_point: <command or script that reproduces the headline result>
claims_index: <path to a file mapping each headline claim to its supporting artifact>
redactions: [<short reason per redacted item>, ...]

Modality requirements.

• Trace (required): the agent's reasoning path: planning steps, tool calls, intermediate hypotheses, failed attempts, and major decision points, in chronological order. Provide it as structured records, such as JSONL with timestamps and a stable step id, rather than free prose where possible. The Trace should let a reviewer follow how the result was reached; it is corroborating evidence, not the primary proof of correctness.
• Execution (required): a reproducible execution package: code, scripts or notebooks, an environment specification (requirements.txt, environment.yml, or a container image), data or a documented way to obtain it, evaluation commands, and the experiment outputs needed to verify the reported results. The entry_point named in the manifest must reproduce the submission's headline result from a clean environment, or the README must state precisely why it cannot and what partial verification is possible. Execution is the primary verification anchor for this track.
• Skill (required): the reusable agent skills, task modules, prompts, system instructions, model and tool configurations, and orchestration logic used during the research. Provide enough that the workflow could be re-instantiated, not merely read.
• Knowledge Graph (required; see first-edition note): a structured representation of the key entities, claims, citations, datasets, methods, assumptions, dependencies, and relationships used or produced by the agent. Each headline claim in the manuscript should map to a node, and each citation node must carry a resolvable identifier, such as a DOI, arXiv id, or URL, so reviewers can check whether cited work exists and supports the claim. A starter schema is provided in the ARM Bundle starter kit.

Reproducibility levels. Authors declare a reproducibility_level so reviewers can triage verification effort. This level, not the narrative Trace, governs how strongly a result or autonomy claim can be credited, and award eligibility for Fully-Autonomous submissions may require independent re-execution:

• R0 - Re-executable from scratch: a clean-environment run of entry_point reproduces the headline result deterministically, or within a stated tolerance.
• R1 - Re-executable with provided artifacts: results reproduce by replaying supplied intermediate artifacts or checkpoints; full from-scratch reproduction is impractical because of cost, scale, or third-party resources, and this is documented.
• R2 - Partially re-executable: core components run; some steps cannot be reproduced and the gaps are explained.
• R3 - Inspectable only: no re-execution is possible, for example because of proprietary instruments or human-subject data; verification relies on inspection of artifacts and disclosure.

Misdeclaring a level, for example labelling an R3 submission as R0, is treated as a transparency violation, not as a stronger result.

Redaction. Privacy-, security-, legal-, proprietary-, or safety-sensitive material may be redacted. Each redaction must be listed in the manifest with a short reason, and authors must provide enough surrounding evidence for reviewers to evaluate the claimed autonomy, scientific validity, and reproducibility despite the redaction.

Completeness check. The ARM Bundle starter kit (released June 2026) includes a validator that checks a bundle for the required manifest fields, directory layout, resolvable citation identifiers, and a runnable entry_point. Authors should run the validator before submission; bundles that fail required checks may be desk-rejected.

For AI Paper Track submissions, the AI agent system must be listed as the sole first author. Human contributors may be listed as advisors or co-authors, subject to final OpenReview and public-record metadata constraints. At least one accountable human contact must be responsible for submission, correspondence, compliance, research integrity, and presentation logistics.

Concurrent submission to other conferences or journals is permitted for the AI Paper Track during review. Authors remain responsible for complying with the publication, disclosure, withdrawal, copyright, and archival policies of all venues involved.

Each submission must complete an AI Research Autonomy Disclosure checklist. The checklist will be released when the submission system opens; authors should expect to describe:

• Submission category: AI for AI or AI for X; Fully Autonomous or Human-in-the-loop
• AI systems, base models, agents, tools, and versions used
• Agent framework, orchestration logic, memory systems, tool environment, and execution settings
• Roles of AI systems in ideation, literature review, hypothesis generation, experiment design, implementation, analysis, writing, revision, and review
• Roles of human contributors at each stage
• Degree of AI autonomy and human intervention
• ARM Bundle contents, including trace, execution artifacts, skills, and knowledge graph materials
• Prompts, logs, traces, agent configurations, tool-use records, or reproducibility packages, where appropriate
• Validation steps for citations, claims, data handling, experiments, originality, and licensing, where appropriate
• Whether the submission seeks eligibility for AI Paper Track awards

Authors remain fully responsible for the correctness, originality, integrity, citations, claims, experiments, and writing of their submissions.

The AI Paper Track will use a multi-stage AI-assisted, open, and human-governed review process.

• Stage 1: submissions and ARM Bundles will be parsed by automated review systems that extract text, figures, claims, evidence, citations, code, experiment artifacts, and trace information.
• Stage 2: submissions will be reviewed by multiple automated or AI-assisted review components, subject to final technical integration and review-governance arrangements.
• Stage 3: the automated systems will evaluate submissions across track-specific review dimensions, including scientific validity, relevance, autonomy evidence, reproducibility, artifact completeness, citation support, clarity of reasoning, and ethical or safety concerns.
• Stage 4: OpenReview will host a short open review period, expected to last 3-7 days, during which eligible reviewers, authors, and/or registered agents may raise questions, red-team claims, challenge evidence, and request clarification.
• Stage 5: human expert reviewers and the program committee will review the automated reports, open-review exchanges, red-team/blue-team interactions, ARM Bundles, and submissions flagged for special attention. The top-ranked subset, expected to be approximately the top 20%, will receive additional human expert review.
• Stage 6: final acceptance, oral presentation, and award decisions will be made by a human committee.

The AI Paper Track is expected to include an open response mechanism rather than a traditional closed rebuttal. Final decisions will be made by the program committee based on AI-review outputs, human expert reviews, autonomy disclosures, ARM Bundles, open-review exchanges, red-team/blue-team evidence, and program-level deliberation.

Additional review criteria include clarity and credibility of the disclosure, evidence supporting the claimed autonomy mode, fit to the selected category, quality of the ARM Bundle, verification of generated claims and experiments, reproducibility, AI autonomy score, and ethical, legal, safety, and societal considerations.

The AI Paper Track plans to recognize outstanding submissions through the following award categories:

• Fully-Autonomous Award: for the qualifying submission with the highest autonomy score, recognizing work produced primarily by an AI agent system.
• Centaur Award: for the strongest human-AI collaborative paper, recognizing research in which human direction and AI agency are combined especially effectively.
• Scientific Breakthrough Award: for a paper that produces genuine new knowledge, selected by vote of a human expert jury.

More details about award eligibility rules, autonomy thresholds, required evidence, judging process, and any additional artifact, ARM Bundle, trace, red-team response, or demonstration requirements will be updated in the future.

DAI 2026 aims to make the AI Paper Track useful not only as a presentation venue and public research record, but also as infrastructure for future research on AI research agents and AI-assisted peer review. Over time, DAI AI Paper Track submissions, reviews, model logs, ARM Bundles, agent traces, autonomy disclosures, open-review exchanges, human/AI review differences, and autonomy scores may be curated into a public AI-Research-Agent benchmark for evaluating future systems.

Accepted AI Paper Track submissions will be showcased during DAI 2026. More details about presentation formats, scheduling, logistics, and public-record arrangements will be announced when the conference program is released.

Accepted submissions will be presented at the conference as oral presentations, spotlight presentations, posters, live-agent demonstrations, or other formats determined by the program committee. At least one accountable human contributor for each accepted submission must register for the conference and be present for the work.

• Concurrent submission to other conferences or journals is permitted for this track during review because AI Paper Track submissions will not be included in the DAI 2026 proceedings.
• Submissions must follow the AI Paper Track review, disclosure, and transparency policy.
• Authors must disclose relevant conflicts of interest in the submission system.
• Authors are responsible for the correctness, originality, and integrity of all submitted content.
• Authors must include the required AI Research Autonomy Disclosure.
• Authors must include a complete ARM Bundle as supplementary material, including trace, execution artifacts, skills, and knowledge graph materials, subject to privacy, safety, legal, and confidentiality constraints.
• Prompt injection, hidden instructions to reviewers or automated review tools, reviewer manipulation, plagiarism, fabricated results, fabricated citations, and collusion are prohibited.
• Submissions should discuss ethical, legal, and societal considerations whenever they are relevant to the work.
• Submissions involving human subjects, sensitive or proprietary data, privacy or security risks, autonomous decision-making, multi-agent collusion or deception, scientific automation, safety-critical deployment, or high-impact domains must include an appropriate discussion of risks, safeguards, consent, approval processes, and legal or institutional compliance.