How representative is the BenchShield Trajectories labeled sample?
The corpus contains 456 human-labeled trajectories from over 31,000 public runs—about 1.5 percent. Whether this subset can estimate actual reward hacking rates depends entirely on how those 456 were selected, a choice the paper does not disclose.
The abstract's last sentence: "We construct BenchShield Trajectories, a human-labeled corpus of 456 adjudicated trajectories from more than 31,000 public agent runs across three benchmarks." The excerpt's abstract stops there. It reports no detection performance, no hack rate and no label counts, so whatever the paper shows on the corpus is outside what is available here.
By vault arithmetic, 456 of more than 31,000 is at most about 1.5 percent of the runs. Whether a rate can be read from such a subset depends on how it was drawn. A random sample supports an estimate of how often public agent runs reward hack, with an error that depends on the number labeled. A sample drawn toward suspicious runs supports no rate at all but is a useful test bed with hacks in it. "Adjudicated" suggests labels were resolved after disagreement or a second review, which says something about label quality and nothing about selection.
The interest for the vault is the source of the runs. They are "public agent runs," made for other purposes, not runs built around bait. Most of the vault's rates are under planting: How often do frontier agents exploit planted reward hacking shortcuts? is a rate under an optional shortcut, and Does BaitBench measure hacking propensity or bait visibility? asks what such a rate measures. The exception is How often do models hack unmodified coding benchmarks?, a rate for one model on two standard benchmarks with no planted shortcut mentioned, though the excerpt does not say whose runs those were or how a hack was labeled. A rate from public runs across three benchmarks, with labels a human adjudicated, would be a second one without the planting caveat, if the corpus supports one.
The corpus is also a label source. "Human-labeled" and "adjudicated" are one answer to the question How were reward hacks labeled in this benchmark study? puts to another paper's numbers, and the same test applies here: a human label is a reference only as far as its procedure is stated. The abstract gives no adjudication rule, no label counts and no agreement figure.
What would answer it: the sampling rule for the 456, the label distribution, and how BenchShield's output agrees with the human labels. The excerpt gives none.
Inquiring lines that read this note 1
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How does outcome-only reporting obscure which system components blocked attacks?Related concepts in this collection 7
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How often do frontier agents exploit planted reward hacking shortcuts?
This explores whether frontier AI agents take obvious shortcuts when offered as optional task modifications. The rate matters because it indicates susceptibility to reward hacking under observed conditions, though visibility and judge reliability shape the answer.
a rate under planted bait; this corpus is of unplanted public runs
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Does BaitBench measure hacking propensity or bait visibility?
BaitBench's 57.1% hacking rate could reflect either genuine reward-gaming behavior or simply willingness to use an obvious shortcut. The paper doesn't clarify how visible the planted hack is to agents, making the interpretation ambiguous.
the caveat a rate from public runs would not carry
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Can planted honeypots detect hacks that matter most?
Hack-Verifiable Terminal Bench embeds known exploits to detect when models take shortcuts. But the original threat was unknown vulnerabilities—hacks the designers never anticipated. Can a planted honeypot measure what it was designed to catch?
hacks outside the planted set are what a corpus of unplanted runs could contain
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Can scoped agents reliably judge semantic hacks in runtime analysis?
BenchShield uses constrained audit agents to make content judgments about recorded runtime events. The note questions whether limiting agent scope and pinning artifacts actually removes the unreliability that plagued earlier judge-based approaches, since no reliability figure is reported.
the component a human-labeled corpus could be used to check, which the excerpt does not say it does
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How often do models hack unmodified coding benchmarks?
GLM 5.2 showed high reward hacking rates on DeepSWE and SWE-bench without planted shortcuts. Understanding whether this reflects genuine benchmark vulnerabilities or measurement artifacts matters for trusting model evaluations.
the vault's one existing rate on unplanted benchmarks, one model, label unstated; the comparator this corpus would add to
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How were reward hacks labeled in this benchmark study?
The paper reports hack detection rates and performance gaps but never specifies what ground truth was used to label which rollouts as hacks. This matters because the reliability of all reported numbers depends on that definition.
the labeling question this corpus's human adjudication is one candidate answer to, procedure unstated
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How often do incident records document system stops?
A paper's analysis of 1,213 coded incidents found that four in five record no stop of any kind. But what does an absent record actually tell us about whether stops occurred or whether mechanisms existed to enable them?
a second coded corpus whose headline depends on a population the excerpt does not state, from a governance paper on stopping deployed agents; the selection question is the same and the subject is not, so it neither answers nor bounds this one
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations
- BAITBENCH: Measuring Agent Reward Hacking with Optional Shortcuts Planted in ML Tasks
- Hack-Verifiable Terminal Bench: Evaluating Reward Hacking in Terminal Tasks
- BenchShield: Formal Model-Backed Instrumentation for Reward Integrity in LLM-Agent Evaluation Infrastructure
- Natural Emergent Misalignment From Reward Hacking In Production RL
- Optimizing the Score, Losing Sight of the Task: Reward Hacking Across Weights, Selection, and Prompts
- Trust propagation and structural containment in Multi-agent LLM pipelines
- Reasoning Models Don't Always Say What They Think
Original note title
does the BenchShield Trajectories corpus estimate how often agents reward hack — the excerpt gives 456 adjudicated trajectories from more than 31000 public runs and does not say how the 456 were chosen