Use Jev for research paper triage

curl -sS https://router.flatkey.ai/api/alpha/decisions \
  -H "Authorization: Bearer $FLATKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "typesafe/jev-1.13",
  "state": "Brief: cheap decision layers for agent control loops.\n\nPaper: Scoring agent actions with contrastive embeddings. Abstract: Instead of generating text, we embed the agent state and each candidate action separately and score them by similarity, so action embeddings can be cached. We evaluate on computer-use, gaming and tool-calling tasks.",
  "questions": {
    "topic": {
      "type": "choice",
      "instructions": "Which topic best describes the paper'\''s main contribution?",
      "criteria": {
        "agents": "Agent architectures, tool use and planning",
        "reasoning": "Reasoning, math and chain-of-thought methods",
        "efficiency": "Faster or cheaper inference, distillation, small models",
        "evaluation": "Benchmarks, evals and measurement",
        "safety": "Alignment, robustness and security",
        "other": "None of these"
      }
    },
    "relevance": {
      "type": "score",
      "instructions": "How relevant is this paper to the research brief in the state?",
      "criteria": [
        "off_topic",
        "tangential",
        "relevant",
        "must_read"
      ]
    },
    "needs_full_read": {
      "type": "noul",
      "instructions": "The abstract makes a claim that should be checked in the full paper before it is cited or dismissed."
    }
  }
}'

// Node.js 18+ (built-in fetch). Run: FLATKEY_API_KEY=... node jev.mjs
const body = {
  "model": "typesafe/jev-1.13",
  "state": "Brief: cheap decision layers for agent control loops.\n\nPaper: Scoring agent actions with contrastive embeddings. Abstract: Instead of generating text, we embed the agent state and each candidate action separately and score them by similarity, so action embeddings can be cached. We evaluate on computer-use, gaming and tool-calling tasks.",
  "questions": {
    "topic": {
      "type": "choice",
      "instructions": "Which topic best describes the paper's main contribution?",
      "criteria": {
        "agents": "Agent architectures, tool use and planning",
        "reasoning": "Reasoning, math and chain-of-thought methods",
        "efficiency": "Faster or cheaper inference, distillation, small models",
        "evaluation": "Benchmarks, evals and measurement",
        "safety": "Alignment, robustness and security",
        "other": "None of these"
      }
    },
    "relevance": {
      "type": "score",
      "instructions": "How relevant is this paper to the research brief in the state?",
      "criteria": [
        "off_topic",
        "tangential",
        "relevant",
        "must_read"
      ]
    },
    "needs_full_read": {
      "type": "noul",
      "instructions": "The abstract makes a claim that should be checked in the full paper before it is cited or dismissed."
    }
  }
};

const res = await fetch("https://router.flatkey.ai/api/alpha/decisions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.FLATKEY_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify(body),
});
if (!res.ok) throw new Error(`Jev request failed: ${res.status} ${await res.text()}`);
const { answers, usage } = await res.json();
console.log(JSON.stringify(answers, null, 2), usage);

# Python 3.8+ (pip install requests). Run: FLATKEY_API_KEY=... python jev.py
import json, os, requests

body = json.loads(r"""{
  "model": "typesafe/jev-1.13",
  "state": "Brief: cheap decision layers for agent control loops.\n\nPaper: Scoring agent actions with contrastive embeddings. Abstract: Instead of generating text, we embed the agent state and each candidate action separately and score them by similarity, so action embeddings can be cached. We evaluate on computer-use, gaming and tool-calling tasks.",
  "questions": {
    "topic": {
      "type": "choice",
      "instructions": "Which topic best describes the paper's main contribution?",
      "criteria": {
        "agents": "Agent architectures, tool use and planning",
        "reasoning": "Reasoning, math and chain-of-thought methods",
        "efficiency": "Faster or cheaper inference, distillation, small models",
        "evaluation": "Benchmarks, evals and measurement",
        "safety": "Alignment, robustness and security",
        "other": "None of these"
      }
    },
    "relevance": {
      "type": "score",
      "instructions": "How relevant is this paper to the research brief in the state?",
      "criteria": [
        "off_topic",
        "tangential",
        "relevant",
        "must_read"
      ]
    },
    "needs_full_read": {
      "type": "noul",
      "instructions": "The abstract makes a claim that should be checked in the full paper before it is cited or dismissed."
    }
  }
}""")

res = requests.post(
    "https://router.flatkey.ai/api/alpha/decisions",
    headers={"Authorization": f"Bearer {os.environ['FLATKEY_API_KEY']}"},
    json=body,
    timeout=60,
)
res.raise_for_status()
data = res.json()
print(json.dumps(data["answers"], indent=2), data.get("usage"))

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