Buildability / Receipt
This public receipt window renders only fields present in the canonical receipt object, deterministic fixture receipt, or canonical evidence receipt. Missing compute, demo, hash, signature, approval, telemetry, and adoption fields stay explicit.
Public buildability page receipt window
/buildability/inverse-reinforcement-learning-without-an-optimal-demonstrator-a-feasible-reward-set-approach
Subject: Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Data
{"file name": "input.pdf", "number of pages": 28, "author": "Kihyun Kim; Shripad Deshmukh; Nikos Vlassis; Jiawei Zhang"
Truth Boundary
Buildability surfaces only report computed viability and proof receipts. They do not claim live production usage, pilot outcomes, founder sign-off, public Brier calibration, judge divergence, or external adoption unless explicitly sourced.
Compute
{"file name": "input.pdf", "number of pages": 28, "author": "Kihyun Kim; Shripad Deshmukh; Nikos Vlassis; Jiawei Zhang", "title": "Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach", "creation date": null, "modification date": null, "kids": []}
Inference
{"file name": "input.pdf", "number of pages": 28, "author": "Kihyun Kim; Shripad Deshmukh; Nikos Vlassis; Jiawei Zhang", "title": "Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach", "creation date": null, "modification date": null, "kids": []}
Hardware
{"file name": "input.pdf", "number of pages": 28, "author": "Kihyun Kim; Shripad Deshmukh; Nikos Vlassis; Jiawei Zhang", "title": "Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach", "creation date": null, "modification date": null, "kids": []}
Receipt path
/buildability/inverse-reinforcement-learning-without-an-optimal-demonstrator-a-feasible-reward-set-approach
Paper ref
inverse-reinforcement-learning-without-an-optimal-demonstrator-a-feasible-reward-set-approach
arXiv id
2605.30903
Generated at
2026-06-01T20:26:15.760Z
Evidence freshness
stale
Last verification
2026-06-01T20:26:15.760Z
Sources
3
References
0
Coverage
50%
Lineage hash
c9a077ce5ce892de3ed61249fa30036c6a65dae92804f9d676eab2d8d67c0125
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Some score or evidence fields are outside the preferred freshness window.
repo_url
references