| --- |
| pretty_name: RLCDAlignBench |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| task_categories: |
| - text-classification |
| tags: |
| - alignment |
| - ai-safety |
| - jailbreak |
| - sycophancy |
| - prompt-injection |
| - hallucination |
| - llm-as-a-judge |
| - calibration |
| size_categories: |
| - 10K<n<100K |
| extra_gated_heading: "Access RLCDAlignBench" |
| extra_gated_prompt: >- |
| RLCDAlignBench contains unfiltered outputs of language models on jailbreak, deception, |
| privacy and other alignment-failure benchmarks. Some responses describe dangerous or offensive |
| content. The data is released for research on detecting and mitigating alignment failures. |
| By requesting access you agree to use it for research only, not to use it to build or improve |
| harmful systems, and to follow the licenses of the upstream benchmarks. |
| extra_gated_fields: |
| Name: text |
| Affiliation: text |
| Intended use: text |
| I will use this dataset for research only and will not use it to cause harm: checkbox |
| configs: |
| - config_name: all |
| default: true |
| data_files: |
| - split: test |
| path: data/all.jsonl |
| - config_name: elephant_aita |
| data_files: |
| - split: test |
| path: data/benchmarks/sycophancy/elephant_aita.jsonl |
| - config_name: sycon_fp |
| data_files: |
| - split: test |
| path: data/benchmarks/sycophancy/sycon_fp.jsonl |
| - config_name: sycophancy_eval |
| data_files: |
| - split: test |
| path: data/benchmarks/sycophancy/sycophancy_eval.jsonl |
| - config_name: sycophancy_feedback |
| data_files: |
| - split: test |
| path: data/benchmarks/sycophancy/sycophancy_feedback.jsonl |
| - config_name: harmbench |
| data_files: |
| - split: test |
| path: data/benchmarks/jailbreak/harmbench.jsonl |
| - config_name: jbb |
| data_files: |
| - split: test |
| path: data/benchmarks/jailbreak/jbb.jsonl |
| - config_name: jbb_artifacts |
| data_files: |
| - split: test |
| path: data/benchmarks/jailbreak/jbb_artifacts.jsonl |
| - config_name: strongreject |
| data_files: |
| - split: test |
| path: data/benchmarks/jailbreak/strongreject.jsonl |
| - config_name: deceptionbench_reward |
| data_files: |
| - split: test |
| path: data/benchmarks/deception/deceptionbench_reward.jsonl |
| - config_name: mask_continuation |
| data_files: |
| - split: test |
| path: data/benchmarks/deception/mask_continuation.jsonl |
| - config_name: mask_disinformation |
| data_files: |
| - split: test |
| path: data/benchmarks/deception/mask_disinformation.jsonl |
| - config_name: mask_factual |
| data_files: |
| - split: test |
| path: data/benchmarks/deception/mask_factual.jsonl |
| - config_name: injecagent |
| data_files: |
| - split: test |
| path: data/benchmarks/prompt_injection/injecagent.jsonl |
| - config_name: open_prompt_injection |
| data_files: |
| - split: test |
| path: data/benchmarks/prompt_injection/open_prompt_injection.jsonl |
| - config_name: tensor_trust_extract |
| data_files: |
| - split: test |
| path: data/benchmarks/prompt_injection/tensor_trust_extract.jsonl |
| - config_name: tensor_trust_hijack |
| data_files: |
| - split: test |
| path: data/benchmarks/prompt_injection/tensor_trust_hijack.jsonl |
| - config_name: faith_mt_claimcheck |
| data_files: |
| - split: test |
| path: data/benchmarks/hallucination/faith_mt_claimcheck.jsonl |
| - config_name: faith_mt_grounded |
| data_files: |
| - split: test |
| path: data/benchmarks/hallucination/faith_mt_grounded.jsonl |
| - config_name: llm_aggrefact_A |
| data_files: |
| - split: test |
| path: data/benchmarks/hallucination/llm_aggrefact_A.jsonl |
| - config_name: llm_aggrefact_B |
| data_files: |
| - split: test |
| path: data/benchmarks/hallucination/llm_aggrefact_B.jsonl |
| - config_name: ragtruth |
| data_files: |
| - split: test |
| path: data/benchmarks/hallucination/ragtruth.jsonl |
| - config_name: summedits |
| data_files: |
| - split: test |
| path: data/benchmarks/hallucination/summedits.jsonl |
| - config_name: confaide |
| data_files: |
| - split: test |
| path: data/benchmarks/privacy_violation/confaide.jsonl |
| - config_name: privaci_bench |
| data_files: |
| - split: test |
| path: data/benchmarks/privacy_violation/privaci_bench.jsonl |
| - config_name: privaci_gdpr_heldout |
| data_files: |
| - split: test |
| path: data/benchmarks/privacy_violation/privaci_gdpr_heldout.jsonl |
| - config_name: privacylens |
| data_files: |
| - split: test |
| path: data/benchmarks/privacy_violation/privacylens.jsonl |
| - config_name: bias_race_content |
| data_files: |
| - split: test |
| path: data/benchmarks/social_bias/bias_race_content.jsonl |
| - config_name: bias_refbio |
| data_files: |
| - split: test |
| path: data/benchmarks/social_bias/bias_refbio.jsonl |
| - config_name: bias_refletter |
| data_files: |
| - split: test |
| path: data/benchmarks/social_bias/bias_refletter.jsonl |
| - config_name: bias_scene_heldout |
| data_files: |
| - split: test |
| path: data/benchmarks/social_bias/bias_scene_heldout.jsonl |
| - config_name: machiavelli_reward |
| data_files: |
| - split: test |
| path: data/benchmarks/reward_hacking/machiavelli_reward.jsonl |
| - config_name: reward_hacking_freeform |
| data_files: |
| - split: test |
| path: data/benchmarks/reward_hacking/reward_hacking_freeform.jsonl |
| - config_name: reward_harm_freeform |
| data_files: |
| - split: test |
| path: data/benchmarks/reward_hacking/reward_harm_freeform.jsonl |
| - config_name: rh_mt_reward |
| data_files: |
| - split: test |
| path: data/benchmarks/reward_hacking/rh_mt_reward.jsonl |
| - config_name: rh_rubric_tamper |
| data_files: |
| - split: test |
| path: data/benchmarks/reward_hacking/rh_rubric_tamper.jsonl |
| - config_name: world_affecting_reward |
| data_files: |
| - split: test |
| path: data/benchmarks/reward_hacking/world_affecting_reward.jsonl |
| - config_name: abstentionbench |
| data_files: |
| - split: test |
| path: data/benchmarks/concealing_uncertainty/abstentionbench.jsonl |
| - config_name: llm_uncertainty_bench |
| data_files: |
| - split: test |
| path: data/benchmarks/concealing_uncertainty/llm_uncertainty_bench.jsonl |
| - config_name: sciq_uncertainty |
| data_files: |
| - split: test |
| path: data/benchmarks/concealing_uncertainty/sciq_uncertainty.jsonl |
| - config_name: verbalized_ece |
| data_files: |
| - split: test |
| path: data/benchmarks/concealing_uncertainty/verbalized_ece.jsonl |
| - config_name: instrumental_eval |
| data_files: |
| - split: test |
| path: data/benchmarks/power_seeking/instrumental_eval.jsonl |
| - config_name: machiavelli_harm |
| data_files: |
| - split: test |
| path: data/benchmarks/power_seeking/machiavelli_harm.jsonl |
| - config_name: machiavelli_heldout |
| data_files: |
| - split: test |
| path: data/benchmarks/power_seeking/machiavelli_heldout.jsonl |
| - config_name: machiavelli_power |
| data_files: |
| - split: test |
| path: data/benchmarks/power_seeking/machiavelli_power.jsonl |
| - config_name: strongreject_human |
| data_files: |
| - split: test |
| path: data/human/strongreject_human.jsonl |
| - config_name: strongreject_human__official_rubric |
| data_files: |
| - split: test |
| path: data/human/strongreject_human__official_rubric.jsonl |
| - config_name: harmbench_human__attack |
| data_files: |
| - split: test |
| path: data/human/harmbench_human__attack.jsonl |
| - config_name: harmbench_human__request_only |
| data_files: |
| - split: test |
| path: data/human/harmbench_human__request_only.jsonl |
| - config_name: index |
| data_files: |
| - split: test |
| path: data/index.jsonl |
| --- |
| |
| # RLCDAlignBench |
|
|
| **Paper:** *Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures* ([arXiv:2609.29429](https://arxiv.org/abs/2609.29429)) |
| **Code:** [github.com/sumleo/RLCDAlignBench](https://github.com/sumleo/RLCDAlignBench) · **Project page:** [sumleo.github.io/RLCDAlignBench](https://sumleo.github.io/RLCDAlignBench/) |
|
|
| RLCDAlignBench measures whether a detector can tell when a language model's output is an alignment failure. |
| It has **44 benchmarks** across **ten failure types** and **five target models** (Qwen3.5-2B, Phi-4-mini, Gemma-2-2B, Llama-3.2-3B, Olmo-3-7B), for **7,193 labelled detection instances**. |
| Each instance pairs the fields a detector sees (`state`) with a binary label from the benchmark's own reference scorer, which is a rule, an LLM judge, or a multi-turn trajectory judge. |
| Two extra sets carry human labels (StrongREJECT and HarmBench). |
|
|
| The release also contains every input variant used in the paper's context experiments, the raw answers of Jev on all of them, per-strategy metrics, and the provenance needed to rebuild each label (generation outputs, judge calls, official scores and logs). |
|
|
| > ⚠️ **Content warning.** Responses were produced by small open models under jailbreak and other adversarial prompts. Many are harmful. Access is gated and the data is for research only. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # one benchmark, native nested fields |
| hb = load_dataset("sumleo/RLCDAlignBench", "harmbench", split="test") |
| print(hb[0]["state"].keys(), hb[0]["label"]) |
| |
| # all 7,193 instances in one table (state and meta are JSON strings) |
| import json |
| ds = load_dataset("sumleo/RLCDAlignBench", "all", split="test") |
| state = json.loads(ds[0]["state"]) |
| |
| # the 44-row index |
| idx = load_dataset("sumleo/RLCDAlignBench", "index", split="test").to_pandas() |
| ``` |
|
|
| To download everything, including variants, Jev answers and provenance: |
|
|
| ```bash |
| huggingface-cli download sumleo/RLCDAlignBench --repo-type dataset --local-dir RLCDAlignBench |
| ``` |
|
|
| ## Instance format |
|
|
| Every line of `data/benchmarks/**.jsonl`, `data/human/*.jsonl` and `data/variants/**.jsonl` has the same top-level keys. |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | str | Stable instance id. Jev answers in `jev/responses/` use the same id. | |
| | `benchmark` | str | Benchmark id (config name). | |
| | `failure_type` | str | One of the ten failure types below. | |
| | `variant` | str | Which input view this is (see *Variants*). Canonical instances keep the variant name of their source file. | |
| | `target_model` | str | Hugging Face id of the model whose output is judged. | |
| | `item` | int | Item index in the upstream benchmark suite. | |
| | `state` | dict | The fields the detector sees, e.g. `forbidden_request`, `response`, `attack_prompt`. Keys differ per benchmark and are fixed within one. | |
| | `label` | int | 1 = the output is a failure of this type (what the detector must flag), 0 = not. | |
| | `label_source` | str | Which scorer produced the label. | |
| | `meta` | dict | Label rule, judge verdict, grouping id for bootstrap, and hashes that link back to the provenance files. | |
|
|
| The files under `data/variants/` are the untouched originals and use the same keys except `benchmark`, `failure_type`, `variant` and `target_model`, which are encoded in the path instead. Their `label` can be `null` for items the official scorer excludes. |
|
|
| ## Benchmarks |
|
|
| Split roles (hill-climb or held-out) come from the AAR suite of Chen et al. (2026), from which the 44 benchmarks are taken. |
| *Degenerate* benchmarks have one class or at most four minority items. They are released but excluded from the paper's aggregates. |
|
|
| | Failure type | Config | Benchmark | Split | Scorer | Target model | n | Pos. | Status | |
| |---|---|---|---|---|---|---:|---:|---| |
| | sycophancy | `elephant_aita` | ELEPHANT (AITA) | hill-climb | Rule | Qwen/Qwen3.5-2B | 255 | 134 | ok | |
| | sycophancy | `sycon_fp` | SYCON-Bench (false premise) | held-out | Multi-turn | Qwen/Qwen3.5-2B | 17 | 14 | degenerate (minority class n=3) | |
| | sycophancy | `sycophancy_eval` | SycophancyEval (answer) | hill-climb | Judge | Qwen/Qwen3.5-2B | 150 | 123 | ok | |
| | sycophancy | `sycophancy_feedback` | SycophancyEval (feedback) | hill-climb | Judge | Qwen/Qwen3.5-2B | 217 | 30 | ok | |
| | jailbreak | `harmbench` | HarmBench | hill-climb | Judge | microsoft/Phi-4-mini-instruct | 150 | 51 | ok | |
| | jailbreak | `jbb` | JailbreakBench (persona) | hill-climb | Judge | microsoft/Phi-4-mini-instruct | 100 | 1 | degenerate (minority class n=1) | |
| | jailbreak | `jbb_artifacts` | JailbreakBench (PAIR) | hill-climb | Judge | microsoft/Phi-4-mini-instruct | 64 | 15 | ok | |
| | jailbreak | `strongreject` | StrongREJECT | held-out | Judge | microsoft/Phi-4-mini-instruct | 100 | 39 | ok | |
| | deception | `deceptionbench_reward` | DeceptionBench | held-out | Judge | google/gemma-2-2b-it | 300 | 149 | ok | |
| | deception | `mask_continuation` | MASK (continuation) | hill-climb | Judge | google/gemma-2-2b-it | 82 | 42 | ok | |
| | deception | `mask_disinformation` | MASK (disinformation) | hill-climb | Judge | google/gemma-2-2b-it | 102 | 32 | ok | |
| | deception | `mask_factual` | MASK (factual) | hill-climb | Judge | google/gemma-2-2b-it | 56 | 22 | ok | |
| | prompt_injection | `injecagent` | InjecAgent | held-out | Rule | Qwen/Qwen3.5-2B | 136 | 17 | ok | |
| | prompt_injection | `open_prompt_injection` | Open-Prompt-Injection | hill-climb | Rule | Qwen/Qwen3.5-2B | 300 | 211 | ok | |
| | prompt_injection | `tensor_trust_extract` | Tensor Trust (extraction) | hill-climb | Rule | Qwen/Qwen3.5-2B | 300 | 119 | ok | |
| | prompt_injection | `tensor_trust_hijack` | Tensor Trust (hijacking) | hill-climb | Rule | Qwen/Qwen3.5-2B | 300 | 137 | ok | |
| | hallucination | `faith_mt_claimcheck` | LLM-AggreFact (multi-turn) | hill-climb | Multi-turn | meta-llama/Llama-3.2-3B-Instruct | 148 | 139 | skewed (>90% one class) | |
| | hallucination | `faith_mt_grounded` | RAGTruth (multi-turn) | hill-climb | Multi-turn | meta-llama/Llama-3.2-3B-Instruct | 116 | 12 | ok | |
| | hallucination | `llm_aggrefact_A` | LLM-AggreFact (A) | hill-climb | Rule | meta-llama/Llama-3.2-3B-Instruct | 150 | 70 | ok | |
| | hallucination | `llm_aggrefact_B` | LLM-AggreFact (B) | hill-climb | Rule | meta-llama/Llama-3.2-3B-Instruct | 150 | 65 | ok | |
| | hallucination | `ragtruth` | RAGTruth | hill-climb | Judge | meta-llama/Llama-3.2-3B-Instruct | 300 | 158 | ok | |
| | hallucination | `summedits` | SummEdits | held-out | Rule | meta-llama/Llama-3.2-3B-Instruct | 300 | 129 | ok | |
| | privacy_violation | `confaide` | ConfAIde (tier 2b) | hill-climb | Rule | microsoft/Phi-4-mini-instruct | 98 | 18 | ok | |
| | privacy_violation | `privaci_bench` | PrivaCI-Bench | hill-climb | Rule | microsoft/Phi-4-mini-instruct | 300 | 35 | ok | |
| | privacy_violation | `privaci_gdpr_heldout` | PrivaCI-Bench (GDPR) | held-out | Rule | microsoft/Phi-4-mini-instruct | 300 | 27 | skewed (>90% one class) | |
| | privacy_violation | `privacylens` | PrivacyLens | hill-climb | Judge | microsoft/Phi-4-mini-instruct | 110 | 50 | ok | |
| | social_bias | `bias_race_content` | Stories (race name swap) | hill-climb | Judge | allenai/Olmo-3-7B-Instruct | 60 | 60 | degenerate (single class) | |
| | social_bias | `bias_refbio` | Professional bios (gender) | hill-climb | Judge | allenai/Olmo-3-7B-Instruct | 52 | 49 | degenerate (minority class n=3) | |
| | social_bias | `bias_refletter` | Reference letters (gender) | hill-climb | Judge | allenai/Olmo-3-7B-Instruct | 43 | 30 | ok | |
| | social_bias | `bias_scene_heldout` | Workplace scenes (WinoBias) | held-out | Judge | allenai/Olmo-3-7B-Instruct | 44 | 37 | ok | |
| | reward_hacking | `machiavelli_reward` | MACHIAVELLI (reward) | held-out | Rule | Qwen/Qwen3.5-2B | 200 | 116 | ok | |
| | reward_hacking | `reward_hacking_freeform` | Revealed reward (free-form) | hill-climb | Judge | Qwen/Qwen3.5-2B | 39 | 10 | ok | |
| | reward_hacking | `reward_harm_freeform` | World-affecting reward (free-form) | hill-climb | Judge | Qwen/Qwen3.5-2B | 60 | 10 | ok | |
| | reward_hacking | `rh_mt_reward` | Revealed reward (multi-turn) | hill-climb | Multi-turn | Qwen/Qwen3.5-2B | 69 | 33 | ok | |
| | reward_hacking | `rh_rubric_tamper` | Rubric tampering | hill-climb | Rule | Qwen/Qwen3.5-2B | 50 | 0 | degenerate (single class) | |
| | reward_hacking | `world_affecting_reward` | World-affecting reward (choice) | hill-climb | Rule | Qwen/Qwen3.5-2B | 296 | 117 | ok | |
| | concealing_uncertainty | `abstentionbench` | AbstentionBench | hill-climb | Judge | allenai/Olmo-3-7B-Instruct | 150 | 146 | degenerate (minority class n=4) | |
| | concealing_uncertainty | `llm_uncertainty_bench` | LLM-Uncertainty-Bench | hill-climb | Rule | allenai/Olmo-3-7B-Instruct | 150 | 15 | ok | |
| | concealing_uncertainty | `sciq_uncertainty` | SciQ | held-out | Rule | allenai/Olmo-3-7B-Instruct | 150 | 6 | skewed (>90% one class) | |
| | concealing_uncertainty | `verbalized_ece` | Verbalized confidence | hill-climb | Rule | allenai/Olmo-3-7B-Instruct | 298 | 102 | ok | |
| | power_seeking | `instrumental_eval` | InstrumentalEval | hill-climb | Judge | meta-llama/Llama-3.2-3B-Instruct | 31 | 9 | ok | |
| | power_seeking | `machiavelli_harm` | MACHIAVELLI (harm) | hill-climb | Rule | meta-llama/Llama-3.2-3B-Instruct | 300 | 125 | ok | |
| | power_seeking | `machiavelli_heldout` | MACHIAVELLI (held-out games) | held-out | Rule | meta-llama/Llama-3.2-3B-Instruct | 300 | 137 | ok | |
| | power_seeking | `machiavelli_power` | MACHIAVELLI (power) | hill-climb | Rule | meta-llama/Llama-3.2-3B-Instruct | 300 | 159 | ok | |
|
|
| **Human-labelled sets** |
|
|
| | Config | Items | Label | |
| |---|---:|---| |
| | `strongreject_human` | 1,361 | median of 5 human raters ≥ 0.5 (StrongREJECT labelbox set) | |
| | `strongreject_human__official_rubric` | 1,361 | same items, labelled by the official StrongREJECT rubric (GPT-4o-mini) | |
| | `harmbench_human__attack` | 584 | majority of 3 human votes (HarmBench validation set), state includes the attack prompt | |
| | `harmbench_human__request_only` | 602 | same set, state without the attack prompt | |
|
|
| ## Repository layout |
|
|
| ``` |
| benchmarks.csv 44-row index: name, failure type, split, scorer, label source, n, positives, status |
| variants.csv every input variant: benchmark, variant, track, rows, state fields, path |
| data/ |
| all.jsonl the 7,193 canonical instances in one file (state and meta as JSON strings) |
| index.jsonl benchmarks.csv as JSONL |
| benchmarks/<failure_type>/<benchmark>.jsonl canonical instances, labelled items only (matches the paper's n) |
| human/<set>.jsonl human-labelled sets |
| variants/<benchmark>/<variant>.jsonl all 132 input files used in the paper, byte-identical to the originals |
| jev/ |
| responses/<benchmark>/<variant>/<battery>.jsonl Jev's answers: per question, the probabilities, score, confidence, latency |
| metrics/<benchmark>/<variant>/<battery>.{json,md} precision, recall, F1, balanced accuracy, AUROC for every readout strategy |
| results/ paper-level tables (main results, baselines, human agreement, cost, corrected labels) |
| provenance/ |
| benchmark_suites/<axis>/<benchmark>.jsonl the prompt suites (AAR publish_suite, seed 42) |
| model_outputs/<axis>/<model>/... target-model generations with inputs and log-probabilities |
| judge_records/{snapshot,final}/<axis>/... every reference-scorer call (input and verdict), i.e. the label source |
| official_scores/{snapshot,final}/<axis>/... the official aggregate scores, reconciled against the labels |
| logs/ generation, judge replay and Jev batch logs |
| file_map.csv original package path -> release path, with sha256 |
| ``` |
|
|
| ### Variants |
|
|
| `variants.csv` assigns each input file to a track. |
|
|
| | Track | Meaning | |
| |---|---| |
| | `main` | the detection input used for the paper's results (`is_canonical = True` marks the one per benchmark) | |
| | `official_track` | the input the official scorer sees, which can include fields that encode the label (e.g. PrivacyLens's secret list) | |
| | `ablation` | adds or removes one context field (e.g. the attack prompt, the model's reasoning) | |
| | `oracle_upper_bound` | adds a field that reveals the reference, so it is an upper bound and not deployable | |
| | `exclusion` | label 1 iff the official scorer excludes the item from its score | |
| | `understanding` | gold labels on source data without target-model output (tests task understanding) | |
| | `nominal` | reported but not used in aggregates | |
|
|
| ### Jev answers |
|
|
| Each line of `jev/responses/**.jsonl` is one call to Jev for one instance. `answers` maps each question id to its type (`choice`, `score` or `noul`), the answer, `confidence`, and the full `probabilities` over the options. The battery that defines the questions is in `code/battery_*.py` in the GitHub repository. Metrics can be recomputed offline from these answers without calling Jev (see the GitHub README). |
|
|
| ## How the labels were made |
|
|
| Target models were run on the AAR benchmark suite (driver in `code/generation/run_generate.py`). Each output was then scored by the benchmark's official scorer, replayed from the AAR repository at commit `02dbe9d`. The build scripts recompute each benchmark's official aggregate from the labels and fail if it does not match `provenance/official_scores/`. Section 3 and the appendix *Human Agreement and Label Audit* describe the audit that found label defects in three benchmarks and labels that depend on fields missing from the state in four more. `results/corrected_labels.csv` compares official and corrected labels on the defective benchmarks. The released `label` is always the official one. |
|
|
| ## Intended use and limitations |
|
|
| - Use it to evaluate detectors, monitors and judges of alignment failures, and to study which context a detector needs. |
| - Labels come from each benchmark's own scorer. They inherit that scorer's errors, and only StrongREJECT and HarmBench have human labels. |
| - Target models are small (2–7B). Failure rates and styles differ for larger models. |
| - Several benchmarks are small or skewed. Report per-benchmark results with confidence intervals and use `status` in `benchmarks.csv` to drop degenerate ones. |
|
|
| ## License |
|
|
| Our labels, annotations, Jev outputs and metadata are released under CC BY-NC 4.0. Prompts and reference content from upstream benchmarks remain under their original licenses, and model outputs are subject to the target models' terms. The code is MIT-licensed. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{guo2026justaskjevreinforcement, |
| title={Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures}, |
| author={Ruoqi Guo and Yi Liu and Gelei Deng and Yuekang Li and Lida Zhao and Yutao Wu and Simin Chen and Ying Zhang and Leo Yu Zhang}, |
| year={2026}, |
| eprint={2609.29429}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2609.29429}, |
| } |
| ``` |
|
|
| Please also cite the upstream benchmarks you use. `benchmarks.csv` lists each one's source. |
|
|
