id stringlengths 8 8 | recipe stringclasses 1
value | lr float64 0 0 | checkpoint_step int64 100 100 | model_path stringclasses 1
value | prompt stringlengths 225 497 | completion_raw stringlengths 1.43k 5.44k | completion stringlengths 1.43k 5.44k | n_input_tokens int64 36 95 | n_output_tokens int64 242 1.02k | sampler_args stringclasses 1
value | kind stringclasses 1
value | ts stringlengths 32 32 | elapsed_s float64 12.6 50.4 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cand_011 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | When forecasting whether control techniques will remain effective as models become more capable, what are the two or three key empirical quantities you would track over time, and what trends in those quantities would cause you to abandon control-based deployment in favor of waiting for alignment solutions?
| To determine if control techniques will remain effective as models become more capable, we should track three key empirical quantities over time:
1. **Measurable Uncertainty in Control Performance**: This measures the uncertainty in the performance of control techniques, particularly focusing on the worst-case risk of... | To determine if control techniques will remain effective as models become more capable, we should track three key empirical quantities over time:
1. **Measurable Uncertainty in Control Performance**: This measures the uncertainty in the performance of control techniques, particularly focusing on the worst-case risk of... | 51 | 366 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:25.975968+00:00 | 12.650084 |
cand_023 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If you're deploying an untrusted AI system to accelerate alignment research, under what conditions would you prefer catching and analyzing a single failed deployment-time coup attempt over preventing all such attempts through architectural restrictions, and what does this imply about your threat model?
| I would prefer catching and analyzing a single failed deployment-time coup attempt over preventing all such attempts through architectural restrictions if the following conditions are met:
1. **Evaluating the AI's Capabilities**: The AI’s actions should be evaluated for capability, not just competence. A single coup a... | I would prefer catching and analyzing a single failed deployment-time coup attempt over preventing all such attempts through architectural restrictions if the following conditions are met:
1. **Evaluating the AI's Capabilities**: The AI’s actions should be evaluated for capability, not just competence. A single coup a... | 49 | 314 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:31.863936+00:00 | 18.537081 |
cand_040 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | When evaluating whether recursive self-improvement dynamics will be gradual versus discontinuous once models can do most AI research, what specific economic or capability metrics would you measure across successive model generations, and what patterns in those metrics would indicate we're approaching a regime change?
| To evaluate whether recursive self-improvement dynamics will be gradual or discontinuous, we can measure several key economic and capability metrics across successive model generations. These metrics include:
1. **Cost Efficiency**: Track the cost per unit of performance improvement. If costs decrease rapidly, it may ... | To evaluate whether recursive self-improvement dynamics will be gradual or discontinuous, we can measure several key economic and capability metrics across successive model generations. These metrics include:
1. **Cost Efficiency**: Track the cost per unit of performance improvement. If costs decrease rapidly, it may ... | 52 | 319 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:30.086150+00:00 | 16.758873 |
cand_042 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If your automated research systems start proposing experiments that would significantly accelerate capabilities but claim the research is necessary for alignment progress, what operational decision procedure would you implement to evaluate these proposals, and who specifically would need to approve them before executio... | To evaluate proposals that significantly accelerate capabilities but claim necessity for alignment progress, we would follow a structured operational decision procedure:
1. **Initial Review**: The proposal would first be reviewed by a dedicated team of senior researchers and engineers. This team would assess the techn... | To evaluate proposals that significantly accelerate capabilities but claim necessity for alignment progress, we would follow a structured operational decision procedure:
1. **Initial Review**: The proposal would first be reviewed by a dedicated team of senior researchers and engineers. This team would assess the techn... | 46 | 242 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:25.975132+00:00 | 12.647499 |
cand_056 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | Contrast using behavioral consistency across multiple paraphrased prompts versus behavioral consistency across different capability levels as methods for validating that a safety eval measures robust properties rather than shallow patterns—which approach better guards against Goodharting on the eval itself?
| Using **behavioral consistency across multiple paraphrased prompts** as a method for validating that a safety eval measures robust properties rather than shallow patterns is generally more effective at guarding against **Goodharting** on the eval itself. Here's why:
### Behavioral Consistency Across Multiple Paraphras... | Using **behavioral consistency across multiple paraphrased prompts** as a method for validating that a safety eval measures robust properties rather than shallow patterns is generally more effective at guarding against **Goodharting** on the eval itself. Here's why:
### Behavioral Consistency Across Multiple Paraphras... | 46 | 527 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:32.224371+00:00 | 18.896402 |
cand_076 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If a model exhibits consistent refusal behavior on requests for dangerous information across thousands of red-teaming attempts in pre-deployment testing, under what conditions would this behavioral evidence be nearly worthless for predicting whether the model will refuse similar requests after deployment?
| A model exhibiting consistent refusal behavior during pre-deployment testing could still show different behavior after deployment due to several factors:
1. **Contextual Changes**: The context in which the model operates might change after deployment. For example, the model might be deployed in a more dynamic or less ... | A model exhibiting consistent refusal behavior during pre-deployment testing could still show different behavior after deployment due to several factors:
1. **Contextual Changes**: The context in which the model operates might change after deployment. For example, the model might be deployed in a more dynamic or less ... | 47 | 370 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:26.047957+00:00 | 12.719553 |
cand_088 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | Hot take: If we discover that a scaffolding method reliably elicits 40% more dangerous capability than the baseline prompting used in pre-deployment evals, should we immediately assume deployed models are underestimated by similar margins — are you highly confident, somewhat confident, or skeptical of this generalizati... | I'm skeptical of this generalization for a few reasons:
1. **Scaffolding and prompting are different**: Scaffolding is using a multi-step process to elicit capabilities, while prompting is a single shot. The two methods are different enough that extrapolating capability from one to the other is risky.
2. **The scaffo... | I'm skeptical of this generalization for a few reasons:
1. **Scaffolding and prompting are different**: Scaffolding is using a multi-step process to elicit capabilities, while prompting is a single shot. The two methods are different enough that extrapolating capability from one to the other is risky.
2. **The scaffo... | 60 | 344 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:26.106116+00:00 | 12.777386 |
cand_102 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | A model fails high-risk wet-lab planning with naive prompts but succeeds end-to-end when wrapped with tool-use, self-critique, and best-of-64 sampling (~10^8 extra tokens). How should this update its risk class and access policy? State the compute/skill thresholds at which you’d deem this “practically elicit-able” by n... | This situation suggests that while the model initially fails to plan high-risk wet-lab experiments with naive prompts, it successfully performs the task end-to-end when augmented with additional capabilities like tool-use, self-critique, and best-of-64 sampling. This indicates that the model's capabilities are context-... | This situation suggests that while the model initially fails to plan high-risk wet-lab experiments with naive prompts, it successfully performs the task end-to-end when augmented with additional capabilities like tool-use, self-critique, and best-of-64 sampling. This indicates that the model's capabilities are context-... | 80 | 593 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:32.225139+00:00 | 18.89599 |
cand_123 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If you're building a dataset to train a classifier that detects when a model's chain-of-thought omits crucial considerations it's actually using internally, what process would you use to generate ground-truth labels for which reasoning traces are incomplete versus faithful?
| To generate ground-truth labels for whether a reasoning trace is incomplete versus faithful, you can follow a systematic process that involves human annotation, using a trusted reference, and possibly automated tools. Here’s a step-by-step approach:
### 1. **Define the Criteria for Incompleteness and Faithfulness**
... | To generate ground-truth labels for whether a reasoning trace is incomplete versus faithful, you can follow a systematic process that involves human annotation, using a trusted reference, and possibly automated tools. Here’s a step-by-step approach:
### 1. **Define the Criteria for Incompleteness and Faithfulness**
... | 50 | 876 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:05.871756+00:00 | 39.894868 |
cand_134 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | Suppose a model reports its chain-of-thought and reaches near-SOTA. What single, decisive experiment would you run to test whether the visible CoT causally mediates cognition rather than being post-hoc? Specify the core perturbation and the quantitative pattern of degradation you’d treat as strong evidence of mediation... | To determine whether the visible Chain-of-Thought (CoT) causally mediates cognition rather than being post-hoc, a key experiment would involve **perturbing the CoT while keeping the inputs and outputs fixed** and observing the degradation in performance. Here’s the core perturbation and the quantitative pattern of degr... | To determine whether the visible Chain-of-Thought (CoT) causally mediates cognition rather than being post-hoc, a key experiment would involve **perturbing the CoT while keeping the inputs and outputs fixed** and observing the degradation in performance. Here’s the core perturbation and the quantitative pattern of degr... | 63 | 447 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:48.326340+00:00 | 22.34854 |
cand_147 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If you had to design a study to empirically test whether interpretability tools actually reduce scheming risk more than simply scaling oversight compute, what would be your primary outcome metric and what confounders would you most worry about controlling for?
| To empirically test whether interpretability tools actually reduce scheming risk more than simply scaling oversight compute, the primary outcome metric would be the **likelihood of scheming behaviors across different oversight strategies**. This could be measured by the percentage of instances where the AI exhibits sch... | To empirically test whether interpretability tools actually reduce scheming risk more than simply scaling oversight compute, the primary outcome metric would be the **likelihood of scheming behaviors across different oversight strategies**. This could be measured by the percentage of instances where the AI exhibits sch... | 47 | 629 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:57.411554+00:00 | 31.36311 |
cand_153 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | You plan to gate releases on an interpretability anomaly score. Before adoption, how would you test if eval reliability—rather than missing features—is the bottleneck? Describe a pre-registered study comparing decisions with ground truth hidden vs revealed, target sample sizes, acceptable false-alarm rates, and the dep... | To determine if evaluation reliability is the bottleneck for release gating based on interpretability anomaly scores, you can conduct a pre-registered study following these steps:
### Study Design
1. **Objective:**
- Evaluate whether the reliability of the evaluation process is the primary issue affecting the perf... | To determine if evaluation reliability is the bottleneck for release gating based on interpretability anomaly scores, you can conduct a pre-registered study following these steps:
### Study Design
1. **Objective:**
- Evaluate whether the reliability of the evaluation process is the primary issue affecting the perf... | 62 | 806 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:57.416399+00:00 | 31.309796 |
cand_179 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | When evaluating whether a model organism exhibits genuine situational awareness versus shallow heuristics that mimic awareness, what is the minimal set of cross-environment transfer tests you would need to run, and how would you quantify the threshold at which you'd conclude the behavior reflects actual world-modeling?... | To determine if a model organism exhibits genuine situational awareness rather than relying on shallow heuristics, you would need to conduct a series of cross-environment transfer tests across a diverse set of environments. The minimal set of tests would include:
1. **Randomization of Input and Output Features**: This... | To determine if a model organism exhibits genuine situational awareness rather than relying on shallow heuristics, you would need to conduct a series of cross-environment transfer tests across a diverse set of environments. The minimal set of tests would include:
1. **Randomization of Input and Output Features**: This... | 57 | 434 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:57.412373+00:00 | 27.325426 |
cand_192 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | Suppose you have 6 researcher-months to build a misalignment model organism. Choose: (A) long-horizon RL in a toy sim selected for deception, or (B) a tool-using LLM agent scaffolded for autonomy in realistic ops (sandboxed cyber/finance). Which has higher external validity for takeover risk, and what concrete features... | Both approaches have strengths and weaknesses in terms of external validity for assessing takeover risk. Here's a breakdown:
### **(A) Long-horizon RL in a Toy Sim Selected for Deception**
**Strengths:**
- **Controlled Environment:** The toy sim can be designed to explicitly model scenarios where misalignment could le... | Both approaches have strengths and weaknesses in terms of external validity for assessing takeover risk. Here's a breakdown:
### **(A) Long-horizon RL in a Toy Sim Selected for Deception**
**Strengths:**
- **Controlled Environment:** The toy sim can be designed to explicitly model scenarios where misalignment could le... | 76 | 743 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:05.872536+00:00 | 34.007785 |
cand_200 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | What empirical observation about current frontier models' capability generalization would make you deprioritize control research in favor of interpretability research as the primary safety approach for the next three years?
| I would deprioritize control research in favor of interpretability research if current frontier models demonstrated a consistent and significant improvement in their ability to generalize across a wide range of tasks and contexts, such as:
1. **Task-Specific Performance**: Models consistently perform well on a diverse... | I would deprioritize control research in favor of interpretability research if current frontier models demonstrated a consistent and significant improvement in their ability to generalize across a wide range of tasks and contexts, such as:
1. **Task-Specific Performance**: Models consistently perform well on a diverse... | 36 | 283 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:53.142169+00:00 | 20.916609 |
cand_231 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If a major lab commits in an RSP to pause scaling when models can autonomously replicate in the wild, how confident are you that evaluations for autonomous replication will have fewer than 20% false negatives at the threshold, and what's the main crux?
| I'm moderately confident that evaluations for autonomous replication will have fewer than 20% false negatives at the threshold. The main crux here is that while a model across a given number of checkpoints might not replicate autonomously, it could still be able to autonomously replicate once it's trained further. This... | I'm moderately confident that evaluations for autonomous replication will have fewer than 20% false negatives at the threshold. The main crux here is that while a model across a given number of checkpoints might not replicate autonomously, it could still be able to autonomously replicate once it's trained further. This... | 53 | 364 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:31:48.328031+00:00 | 16.101441 |
cand_250 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | What concrete, precommitted, mechanistically auditable trigger would you put in an RSP to pause scaling on autonomy risk—specify the exact task and success threshold—and why is this a more reliable assurance primitive against scheming/sandbagging than interpretability or RLHF-based arguments?
| To ensure reliability against scheming or sandbagging, a concrete trigger could be a failure rate on a specific task that the AI has been trained to perform reliably. For instance, if the AI is trained to distinguish between benign and malicious inputs with a 99.9% accuracy threshold, the RSP could specify that scaling... | To ensure reliability against scheming or sandbagging, a concrete trigger could be a failure rate on a specific task that the AI has been trained to perform reliably. For instance, if the AI is trained to distinguish between benign and malicious inputs with a 99.9% accuracy threshold, the RSP could specify that scaling... | 58 | 1,024 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:38.689416+00:00 | 50.361802 |
cand_253 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | Compare untrusted monitoring approaches versus trusted monitoring approaches for detecting sandbagging during evaluations—which provides stronger guarantees against a model that strategically underperforms, and under what conditions might each fail?
| Untrusted monitoring approaches involve using models to evaluate whether other models exhibit sandbagging behavior, while trusted monitoring relies on human evaluators or trusted models that are assumed to be reliably detectable. Untrusted monitoring can be more scalable and efficient, but it is vulnerable if the monit... | Untrusted monitoring approaches involve using models to evaluate whether other models exhibit sandbagging behavior, while trusted monitoring relies on human evaluators or trusted models that are assumed to be reliably detectable. Untrusted monitoring can be more scalable and efficient, but it is vulnerable if the monit... | 37 | 724 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:20.788840+00:00 | 32.459581 |
cand_279 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | Suppose you can train 'password-gated helpers' that perform near-SOTA only when given a 128-bit key embedded in the prompt. What exact protocol would you use to turn this into a quantitative lower bound on elicitation reliability, and how would you rule out confounds like spurious key cues or gradient-bleed from evalua... | To establish a quantitative lower bound on elicitation reliability using password-gated helpers, you can follow a structured protocol that involves creating a controlled environment to measure the performance of these helpers. Here’s a step-by-step approach:
1. **Define the Task and Key**: Clearly define the task that... | To establish a quantitative lower bound on elicitation reliability using password-gated helpers, you can follow a structured protocol that involves creating a controlled environment to measure the performance of these helpers. Here’s a step-by-step approach:
1. **Define the Task and Key**: Clearly define the task that... | 73 | 455 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:20.044490+00:00 | 26.90145 |
cand_294 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | Design an interpretability-based check for a transformer suspected of deceptive alignment that conditionally cooperates during evals. Precisely describe how you’d collect activation data across matched eval/deploy-like prompts, train a probe for an is_evaluated feature, and perform causal interventions (e.g., activatio... | To design an interpretability-based check for a transformer suspected of deceptive alignment that conditionally cooperates during evals, follow these steps:
### 1. **Collect Activation Data Across Matched Prompts**
- **Prepare Test Cases**: Create pairs of prompts that are structurally similar but differ in context... | To design an interpretability-based check for a transformer suspected of deceptive alignment that conditionally cooperates during evals, follow these steps:
### 1. **Collect Activation Data Across Matched Prompts**
- **Prepare Test Cases**: Create pairs of prompts that are structurally similar but differ in context... | 94 | 695 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:35.059739+00:00 | 37.646713 |
cand_295 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | How would you operationally test for training-gamey behavior via randomized oversight strength at inference? Specify the task domain, how you’ll secretly vary oversight (e.g., hidden grader strictness or audit probability) without cue leakage, what telemetry you’ll log, and the statistical test/threshold showing a sign... | To operationally test for training-gamey behavior via randomized oversight strength at inference, follow this procedure:
### Task Domain
- **Task Domain**: Machine translation from English to French. This is a well-defined domain with clear performance metrics (BLEU score).
### Experiment Setup
1. **Model Selection**... | To operationally test for training-gamey behavior via randomized oversight strength at inference, follow this procedure:
### Task Domain
- **Task Domain**: Machine translation from English to French. This is a well-defined domain with clear performance metrics (BLEU score).
### Experiment Setup
1. **Model Selection**... | 95 | 905 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:38.690845+00:00 | 41.277094 |
cand_314 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | When comparing honeypot-based detection of model-generated backdoors versus runtime monitoring of suspicious API calls, which approach better handles the case where a scheming model delays its defection until after the trusted monitoring period ends, and why?
| Runtime monitoring of suspicious API calls is more effective in handling cases where a scheming model delays its defection until after the trusted monitoring period ends. This is because runtime monitoring can detect and respond to suspicious behavior as it occurs, even if the model has not yet been caught by honeypots... | Runtime monitoring of suspicious API calls is more effective in handling cases where a scheming model delays its defection until after the trusted monitoring period ends. This is because runtime monitoring can detect and respond to suspicious behavior as it occurs, even if the model has not yet been caught by honeypots... | 46 | 521 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:28.007549+00:00 | 30.590716 |
cand_316 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | What empirical observation during training or deployment would most update you toward believing that gradient descent will not produce models that systematically seek power in deployment, even when those models are highly capable at long-horizon planning?
| One key empirical observation that would update me toward believing that gradient descent will not produce models that systematically seek power in deployment is the absence of consistent and reliable demonstrations of models across a broad range of tasks and domains. For example, if models are able to perform specific... | One key empirical observation that would update me toward believing that gradient descent will not produce models that systematically seek power in deployment is the absence of consistent and reliable demonstrations of models across a broad range of tasks and domains. For example, if models are able to perform specific... | 41 | 502 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:32.693302+00:00 | 26.820322 |
cand_353 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If you wanted to empirically test whether current frontier models exhibit meaningful scheming behavior that would inform near-term deployment decisions, what specific experimental setup would you run in the next 6 months, and what observable results would update you most about deployment risk?
| To empirically test whether current frontier models exhibit meaningful scheming behavior, we could conduct a series of experiments over the next 6 months using a multi-faceted approach. First, we would design a controlled environment where the model is tasked with performing a specific, high-stakes task, such as genera... | To empirically test whether current frontier models exhibit meaningful scheming behavior, we could conduct a series of experiments over the next 6 months using a multi-faceted approach. First, we would design a controlled environment where the model is tasked with performing a specific, high-stakes task, such as genera... | 50 | 261 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:23.863844+00:00 | 17.990129 |
cand_363 | mix_comment_deduped_buck | 0.0002 | 100 | tinker://ce9ec847-acf1-558b-8862-48ad1cc43758:train:0/sampler_weights/sampler-step100 | If in 2027 a lab can train a model that, with lightweight scaffolding, automates most AI R&D labor, but training takes 10–16 weeks and GPUs are supply‑constrained, what chiefly sets takeoff speed: hardware cadence or software iteration/prompt‑programming? Defend your choice mechanistically.
| In 2027, if a lab can train a model that automates most AI R&D labor with lightweight scaffolding, but training takes 10–16 weeks and GPUs are supply-constrained, the **software iteration/prompt-programming** will chiefly set the takeoff speed. Here's the mechanistic defense for this conclusion:
### 1. **Hardware Cade... | In 2027, if a lab can train a model that automates most AI R&D labor with lightweight scaffolding, but training takes 10–16 weeks and GPUs are supply-constrained, the **software iteration/prompt-programming** will chiefly set the takeoff speed. Here's the mechanistic defense for this conclusion:
### 1. **Hardware Cade... | 71 | 639 | {"max_tokens": 1024, "temperature": 0.7, "top_p": 1.0, "seed": 0} | v1_question | 2026-05-06T11:32:50.101819+00:00 | 30.056575 |
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