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🔬 ScholarEnv

The first RL environment for AI-assisted peer review and scholarly integrity verification

An AI agent that investigates papers — not one that produces them.

API Reference · Quick Start · Research

Nensi Pansuriya · Krushna Parmar · Ishita Bhojani

Meta × PyTorch OpenEnv Hackathon · Round 1 · April 2026

Why This Exists

~10,000 papers are retracted every year. Every major journal — Nature, Science, IEEE, ACM — has a manual integrity screening bottleneck at scale. StatCheck found errors in ~50% of psychology papers in top journals.

The key insight: LLMs are already good at formatting. They fail at auditing.

Ask GPT-4o to format a manuscript → scores ~0.92 with no training. Ask GPT-4o to find numerical claim mismatches in a paper → scores 0.20–0.45.

That gap is exactly where RL adds value. The agent must discover a document traversal strategy — which sections to read first, which tables to cross-reference — that varies by paper structure and cannot be reduced to a fixed prompt. RL finds this strategy. Prompting cannot.

Four Tasks

Formatting → Consistency → Claim Audit → Citation Check
   Easy          Medium         Hard          Medium

Task	What the agent does	Frontier baseline	RL target
`formatting_compliance`	Fix IEEE formatting violations	0.80–0.95	0.95+
`internal_consistency`	Find where paper contradicts itself	0.40–0.65	0.65–0.80
`claim_evidence_audit`	Find where text claims ≠ table values	0.20–0.45	0.55–0.75
`citation_verification`	Identify ghost and misattributed references	0.35–0.60	0.65–0.80

Task 3's low baseline is the core RL contribution — it proves genuine training headroom exists.

Reward Design

Task 1 — Progressive Reward Shaping (PRS)

Three stages unlock sequentially. Stage N only contributes when Stage N-1 ≥ threshold. Prevents GRPO gradient collapse.

Stage 1 │ weight 0.40 │ threshold 0.00 │ Title, abstract, section headings
Stage 2 │ weight 0.35 │ threshold 0.60 │ Section order, word limits, captions
Stage 3 │ weight 0.25 │ threshold 0.70 │ IEEE citations, author block, keywords

Based on: arXiv 2512.07478 — PRS for Agentic RL

Tasks 2 & 3 — F-beta + Potential-Based Reward Shaping

F-beta (β=0.5) weights precision 4× over recall — prevents hallucination gaming:

F_β(precision=1.0, recall=0.5) = 0.833   ✓ correct and precise
F_β(precision=0.2, recall=1.0) = 0.227   ✗ spamming guesses

PBRS (Ng et al., ICML 1999) gives dense intermediate rewards on every navigation step:

Φ(s) = 0.30 × sections_read/total + 0.30 × tables_checked/total + 0.40 × claims_extracted/est
F(s,s') = γ·Φ(s') − Φ(s)     ← policy-invariant, theoretically guaranteed

Curriculum — AdaRFT + UCB1

Keeps agent in productive zone (avg score 0.40–0.70). UCB1 maximises learning gradient (reward variance), not mean reward.

avg > 0.70  →  select harder papers
avg < 0.40  →  select easier papers

Based on: arXiv 2504.05520 — AdaRFT Adaptive Data Selection

Quick Start

Install

bash

git clone https://github.com/Nensi1311/research-paper-formatter-agent
cd research-paper-formatter-agent
pip install -r requirements.txt

Generate corpus

python scripts/generate_corpus.py

Run tests

bash

python tests/test_all.py
# → ALL TESTS PASSED (63/63)

Start server

uvicorn server.app:app --host 0.0.0.0 --port 7860

Test all 4 tasks — Linux/macOS

bash

for task in formatting_compliance internal_consistency claim_evidence_audit citation_verification; do
  curl -s -X POST localhost:7860/reset \
    -H "Content-Type: application/json" \
    -d "{\"task_id\":\"$task\"}" | python3 -c \
    "import sys,json; d=json.load(sys.stdin); print('$task: OK' if 'observation' in d else '$task: FAIL')"
done

Test all 4 tasks — Windows PowerShell

powershell

foreach ($task in @("formatting_compliance","internal_consistency","claim_evidence_audit","citation_verification")) {
    $body = '{"task_id":"' + $task + '"}'
    $r = Invoke-RestMethod -Uri "http://localhost:7860/reset" -Method POST -ContentType "application/json" -Body $body
    Write-Host "$task : OK"
}

Docker

bash

docker build -t scholar-env .
docker run -p 7860:7860 scholar-env
curl http://localhost:7860/health

Run baseline agent

bash

export API_BASE_URL="https://api-inference.huggingface.co/v1"
export MODEL_NAME="meta-llama/Llama-3.1-8B-Instruct"
export HF_TOKEN="hf_your_token"
export HF_SPACE_URL="https://flyingmaverick-scholar-env.hf.space"

python inference.py
# Writes: baseline_scores.json

API Reference

`POST /reset`

{"task_id": "formatting_compliance"}

Returns observation with manuscript_text, style_guide, step_count, max_steps, hint.

`POST /step`

Task 1 — submit formatted manuscript:

{"task": "formatting_compliance", "formatted_text": "...full reformatted manuscript..."}

Tasks 2/3 — navigate:

json

{"task": "claim_evidence_audit", "action_type": "query_section", "section_name": "results"}
{"task": "claim_evidence_audit", "action_type": "check_table", "table_id": "Table 1"}
{"task": "claim_evidence_audit", "action_type": "extract_claims", "section_name": "results"}

Tasks 2/3 — submit findings:

json

{
  "task": "claim_evidence_audit",
  "action_type": "submit_findings",
  "findings": [
    {
      "type": "table_text_mismatch",
      "location": "abstract",
      "claim": "Table 2 shows 87% accuracy",
      "contradicts": "Table 2 value is 79%",
      "table_id": "Table 2",
      "table_value": "79%"
    }
  ]
}

Task 4 — check citation:

{"task": "citation_verification", "action_type": "check_citation", "citation_id": "ref_3"}

Task 4 — submit verdicts:

json

{
  "task": "citation_verification",
  "action_type": "submit_verdicts",
  "verdicts": [
    {"citation_id": "ref_3", "status": "ghost", "issue": "Implausible title claim", "confidence": 0.9}
  ]
}

Step response:

json

{
  "observation": {...},
  "reward": 0.7341,
  "done": false,
  "info": {"f_beta": 0.73, "precision": 0.8, "recall": 0.67}
}

Other endpoints

Endpoint	Method	Description
`/health`	GET	`{"status":"ok","version":"0.4.0"}`
`/state`	GET	Episode state, curriculum summary
`/tasks`	GET	All 4 task descriptions
`/action_space`	GET	Full action schema

Project Structure

├── inference.py                 ← Baseline agent (root — required by spec)
├── models.py                    ← FormattingAction, ScholarAction, CitationAction
├── corpus.py                    ← PaperCorpus loader
├── openenv.yaml                 ← 4 tasks, endpoints, authors, baseline_script
├── Dockerfile
├── requirements.txt
│
├── data/
│   ├── papers/
│   │   ├── paper_001.json       ← NLP benchmark (easy)
│   │   ├── paper_002.json       ← CV survey (medium)
│   │   └── paper_003.json       ← MTL paper (hard)
│   └── styles/ieee.yaml
│
├── server/
│   ├── app.py                   ← FastAPI endpoints
│   ├── environment.py           ← 4-task state machine
│   ├── reward_shaper.py         ← PBRS (Ng et al. 1999)
│   ├── curriculum.py            ← AdaRFT + UCB1
│   ├── bandit.py                ← Learning-gradient UCB1
│   ├── citation_verifier.py     ← Citation parser + SQLite cache
│   └── graders/
│       ├── formatting_grader.py ← PRS 3-stage (Task 1)
│       ├── consistency_grader.py← F-beta (Task 2)
│       └── audit_grader.py      ← F-beta + PBRS (Task 3)
│
├── scripts/generate_corpus.py
└── tests/test_all.py            ← 63 assertions

Testing

[Corpus]              8/8  ✓
[FormattingGrader]    8/8  ✓  PRS stage locking
[ConsistencyGrader]   9/9  ✓  F-beta, hallucination penalty
[AuditGrader]         6/6  ✓  Evidence specificity, coverage bonus
[PBRS]                6/6  ✓  Potential monotonicity, bonus bounds
[UCB1 Bandit]         3/3  ✓  Learning gradient maximisation
[Curriculum]          4/4  ✓  AdaRFT productive-zone targeting
[ScholarEnvironment] 19/19 ✓  Full episode loops, all 4 tasks

Results: 63/63 passed — ALL TESTS PASSED

Research Foundation

Paper	What it justifies
PRS · arXiv 2512.07478	Task 1 progressive staging prevents GRPO gradient collapse
PBRS · Ng, Harada & Russell, ICML 1999	Policy-invariant dense intermediate rewards
AdaRFT · arXiv 2504.05520	Curriculum targeting [0.40, 0.70] productive zone
RLVE · arXiv 2511.07317	Adaptive difficulty, UCB1 maximises variance
Veri-R1 · arXiv 2510.01932	Online RL for claim verification is current SOTA
LaMer · arXiv 2512.16848	Structured feedback improves agent 11–19%
StatCheck · Epskamp 2016	~50% of papers have errors — scale motivation
GROBID · Lopez 2008–2025	Prior art; CitationVerifier is our RL-native alternative

Authors

Nensi Pansuriya · Krushna Parmar · Ishita Bhojani

Meta × PyTorch OpenEnv Hackathon · Round 1 · April 2026

License

Apache 2.0

The future of AI isn't just models that generate — it's models that verify.

GitHub

flyingmaverick/ScholarEnvMeta

🔬 ScholarEnv

The first RL environment for AI-assisted peer review and scholarly integrity verification

Why This Exists

Four Tasks

Reward Design

Task 1 — Progressive Reward Shaping (PRS)

Tasks 2 & 3 — F-beta + Potential-Based Reward Shaping

Curriculum — AdaRFT + UCB1

Quick Start

Install

Generate corpus

Run tests

Start server

Test all 4 tasks — Linux/macOS

Test all 4 tasks — Windows PowerShell

Docker

Run baseline agent

API Reference

POST /reset

POST /step

Other endpoints

Project Structure

Testing

Research Foundation

Authors

License

flyingmaverick/
ScholarEnvMeta

`POST /reset`

`POST /step`