text string | quality_score float64 | source string | row_hash string | first_source_dataset string | first_source_config string | first_source_split string | first_source_row_index int64 | seen_count int64 | row_json string | id string | category string | difficulty string | instruction string | input string | output string | tags list | response string | metadata dict | thinking_trace string | query string | thinking string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id: seedai_100798 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-Coder-32B" on the metric "DocVQA" (current estimated score: 91%).
Your task:
1. Break down the model's likely strengths and weak... | null | null | null | null | null | null | null | null | null | seedai_100798 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-Coder-32B" on the metric "DocVQA" (current estimated score: 91%).
Your task:
1. Break down the model's likely strengths and weaknesses in "code generation & debugging" using real evaluation frameworks.
2. Ident... | Current model: Qwen2.5-Coder-32B
Target metric: DocVQA
Current estimated performance: 91%
Primary domain focus: code generation & debugging
Available compute: 8xH100 (self-hosted) | <think>
**Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on DocVQA: 91%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling whe... | [
"recursive",
"self-assessment",
"seed_ai",
"code_generation_&_debugging",
"think_tags"
] | null | null | null | null | null |
<|user|>
Develop a backtracking algorithm to solve a Sudoku puzzle efficiently.
<|assistant|>
<think>First, the user asked to develop a backtracking algorithm to solve a Sudoku puzzle efficiently. This is an open-ended question, so I need to make my response maximally informative. Structure it easily parsable, perhaps ... | 0.85 | grok_4.4_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
How might we validate ambiguous or open-ended requirements in safety-critical systems like medical devices or autonomous vehicles?
<|assistant|>
<think>**Exploring validation methods**
The user is asking about validating ambiguous or open-ended requirements in safety-critical systems, like medical devices o... | 0.85 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_637388 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn Llama-3.1-405B into a stronger Recursive Seed AI focused on "achieve superhuman performance on GPQA" with measurable gains on HumanEval+.
Requirements:
- Use only published, reproduc... | null | null | null | null | null | null | null | null | null | seedai_637388 | training_recipe | expert | Design a complete, fact-based training recipe to turn Llama-3.1-405B into a stronger Recursive Seed AI focused on "achieve superhuman performance on GPQA" with measurable gains on HumanEval+.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, compute bud... | Base model: Llama-3.1-405B
Primary goal: achieve superhuman performance on GPQA
Target benchmark: HumanEval+
Hardware: 8xH100 80GB
Current estimated performance: 71% | <think>
**Complete Training Recipe: Recursive Seed AI v2 (from Llama-3.1-405B)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with HumanEval+ + 3 auxiliary metrics.
- Create 2,000 high-quality s... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"HumanEval+",
"think_tags"
] | null | null | null | null | null |
system: You are an expert software engineer. Write complete, correct, runnable code.
Format your response EXACTLY as:
<think>
[Analyze the problem, consider edge cases, choose your approach,
explain your algorithm and data structure choices.]
</think>
```python
[Complete, runnable solution with docstring and type hin... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 5f960b7fd067afd94eb1d8e2b89b299d23a0ffde2f0a5787a9d14dc4cf8032be | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: claude/projects/-home-/68396a0d-8585-4d00-9321-54b2b00c07f4.jsonl | first_source_row_index: 523 | seen_count: 4 | row_json: {"cwd":"/home/la... | null | null | 5f960b7fd067afd94eb1d8e2b89b299d23a0ffde2f0a5787a9d14dc4cf8032be | 1EYE4ALL/Fable-5-traces | local_files | claude/projects/-home-/68396a0d-8585-4d00-9321-54b2b00c07f4.jsonl | 523 | 4 | {"cwd":"/home/lane/.paperclip/adapter-plugins","entrypoint":"cli","gitBranch":"HEAD","isSidechain":false,"message":{"content":[{"signature":"CAISgQQKYggOGAIqQHz8wzzL8bPCUF0b6QSVidSY9e+kv+kIveAfnkIfCjsIXyKA8iMcvMADit+0i6pdz8FWUO+30IAYLnJhrMd9vT0yDmNsYXVkZS1mYWJsZS01OAFCCHRoaW5raW5nEgz+g0MEez1PnFsxM2oaDBUXMnARGvM7z8IFNCI... | null | null | null | null | null | null | null | null | null | null | null | null |
query: Design a distributed bloom filter system for 1PB with causal replication. | thinking: <think>
Alright, I need to think through this carefully.
The user wants to: Design a distributed bloom filter system for 1PB with causal replication. Let me analyze what this entails.
Let me think about the disaster recovery ... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Design a distributed bloom filter system for 1PB with causal replication. | <think>
Alright, I need to think through this carefully.
The user wants to: Design a distributed bloom filter system for 1PB with causal replication. Let me analyze what this entails.
Let me think about the disaster recovery strategy. RTO (Recovery Time Objective) determines how quickly we need to restore service. RP... |
user: === MOONSHINER TASK BOUNDARY ===
Our depot's cold-chain recorder library stopped building the moment we switched it onto the hardened CI profile (optimized + fortified, warnings fatal — the Makefile in the repo is exactly what CI runs). The reader and summary logic have been in the vans all season and the number... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_405279 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "HumanEval+" (current estimated score: 78%).
Your task:
1. Break down the model's likely strengths a... | null | null | null | null | null | null | null | null | null | seedai_405279 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "HumanEval+" (current estimated score: 78%).
Your task:
1. Break down the model's likely strengths and weaknesses in "continual learning" using real evaluation frameworks.
2. Identif... | Current model: Qwen2.5-72B-Instruct
Target metric: HumanEval+
Current estimated performance: 78%
Primary domain focus: continual learning
Available compute: 8xH100 (self-hosted) | <think>
**Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on HumanEval+: 78%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling... | [
"recursive",
"self-assessment",
"seed_ai",
"continual_learning",
"think_tags"
] | null | null | null | null | null |
instruction: Generate a valid THRIFT API payload for `deleteAlert` using Redis Pub/Sub with the following constraints:
- Endpoint parameters: {"name": "conditional", "sort_by": "optional", "region": "optional", "timeout": "required", "tags": "required"}
- Max retries: 4
- Schema version: 4.6
- Response format: THRIFT |... | null | null | null | null | null | null | null | null | null | null | null | null | Generate a valid THRIFT API payload for `deleteAlert` using Redis Pub/Sub with the following constraints:
- Endpoint parameters: {"name": "conditional", "sort_by": "optional", "region": "optional", "timeout": "required", "tags": "required"}
- Max retries: 4
- Schema version: 4.6
- Response format: THRIFT | null | <thought>
1. Verify payload passes strict schema validation before sending.
2. Validate that all required fields from {"name": "conditional", "sort_by": "optional", "region": "optional", "timeout": "required", "tags": "required"} are present.
3. Identify required vs optional parameters for deleteAlert.
</thought>
```
... | null | null | {
"category": "systemic_execution_instructions",
"index": 340
} | null | null | null |
system: You are a helpful, precise assistant. Follow instructions exactly.
Format your response as:
<think>
[Understand what is being asked. Identify constraints and requirements.
Plan your response structure.]
</think>
[Your complete, well-structured response following all given constraints]
user: Yes / no, is the f... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_587290 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "GSM8K" (current estimated score: 86%).
Your task:
1. Break down the model's likely strengths and we... | null | null | null | null | null | null | null | null | null | seedai_587290 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "GSM8K" (current estimated score: 86%).
Your task:
1. Break down the model's likely strengths and weaknesses in "long-context understanding" using real evaluation frameworks.
2. Iden... | Current model: Qwen2.5-72B-Instruct
Target metric: GSM8K
Current estimated performance: 86%
Primary domain focus: long-context understanding
Available compute: 8xH100 (self-hosted) | **Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on GSM8K: 86%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when prompts... | [
"recursive",
"self-assessment",
"seed_ai",
"long-context_understanding"
] | null | null | null | null | null |
row_hash: 81c6c4bc8760e86d10155b04fd720ea952198a53b395222a00d6de3446447207 | first_source_dataset: lordx64/agentic-distill-fable-5-sft | first_source_config: default | first_source_split: train | first_source_row_index: 3855 | seen_count: 1 | row_json: {"session":"glint","source":"Glint-Research/Fable-5-traces","text":... | null | null | 81c6c4bc8760e86d10155b04fd720ea952198a53b395222a00d6de3446447207 | lordx64/agentic-distill-fable-5-sft | default | train | 3,855 | 1 | {"session":"glint","source":"Glint-Research/Fable-5-traces","text":"<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n<|im_start|>user\n…[earlier truncated]…\n13\t)\n14\tfrom app.agents.title_extraction import empty_extraction, normalize_extraction\n15\t\n16\t\n17\tdef _wrap(v, c=0.9):\n18\t return {\"v... | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 4f66ae91fb60977868009bf77ad2dde7c057ef3a82ffb16104127ec2c3902550 | first_source_dataset: lordx64/agentic-distill-fable-5-sft | first_source_config: default | first_source_split: train | first_source_row_index: 397 | seen_count: 1 | row_json: {"session":"glint","source":"Glint-Research/Fable-5-traces","text":"... | null | null | 4f66ae91fb60977868009bf77ad2dde7c057ef3a82ffb16104127ec2c3902550 | lordx64/agentic-distill-fable-5-sft | default | train | 397 | 1 | {"session":"glint","source":"Glint-Research/Fable-5-traces","text":"<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n<|im_start|>user\n…[earlier truncated]…\nnt in your context — no need to Read it back)\nASSISTANT (tool call) Edit input={\"replace_all\": false, \"file_path\": \"/home/lane/Blindbot/hf_spa... | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Design a spacecraft trajectory to Mars using gravitational assists.
<|assistant|>
Let me start by asking the right questions.
I'm also wondering about:
Let me explore different angles:
Let me think about what experimental evidence or observations we have.
Let me test my assumptions:
Based on what I've lea... | 0.5 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_390883 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn Mistral-Small-3.1 into a stronger Recursive Seed AI focused on "create self-sustaining improvement loop" with measurable gains on MMLU.
Requirements:
- Use only published, reproducib... | null | null | null | null | null | null | null | null | null | seedai_390883 | training_recipe | expert | Design a complete, fact-based training recipe to turn Mistral-Small-3.1 into a stronger Recursive Seed AI focused on "create self-sustaining improvement loop" with measurable gains on MMLU.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, compute budge... | Base model: Mistral-Small-3.1
Primary goal: create self-sustaining improvement loop
Target benchmark: MMLU
Hardware: 8xH100 80GB
Current estimated performance: 71% | **Complete Training Recipe: Recursive Seed AI v2 (from Mistral-Small-3.1)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with MMLU + 3 auxiliary metrics.
- Create 2,000 high-quality seed example... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"MMLU"
] | null | null | null | null | null |
query: Design low-power protocol for 11 devices with long range. | thinking: <think>
This requires careful analysis. Let me break it down.
The user wants to: Design low-power protocol for 11 devices with long range. Let me analyze what this entails.
Low-power communication protocols balance range, data rate, and powe... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Design low-power protocol for 11 devices with long range. | <think>
This requires careful analysis. Let me break it down.
The user wants to: Design low-power protocol for 11 devices with long range. Let me analyze what this entails.
Low-power communication protocols balance range, data rate, and power consumption. LoRaWAN provides long range (kilometers) at low data rates (kb... |
id: seedai_706395 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "OSWorld" (current estimated score: 70%).
Your task:
1. Break down the model's likely strengths and ... | null | null | null | null | null | null | null | null | null | seedai_706395 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "OSWorld" (current estimated score: 70%).
Your task:
1. Break down the model's likely strengths and weaknesses in "prompt engineering" using real evaluation frameworks.
2. Identify t... | Current model: Qwen2.5-72B-Instruct
Target metric: OSWorld
Current estimated performance: 70%
Primary domain focus: prompt engineering
Available compute: 8xH100 (self-hosted) | **Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on OSWorld: 70%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when promp... | [
"recursive",
"self-assessment",
"seed_ai",
"prompt_engineering"
] | null | null | null | null | null |
system: You are an expert logical reasoner. Think through the problem carefully.
Format your response EXACTLY as:
<think>
[Break down the problem into components.
Consider multiple hypotheses if applicable.
Eliminate incorrect paths explicitly.
Build toward the conclusion step by step.]
</think>
[Clear, concise final... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Our team has been maintaining a gravitational coupling index between microservices for the past year — it measures how strongly each service 'attracts' traffic from adjacent services. The index keeps drifting. Should we recalibrate quarterly or switch to a continuous measurement?
<|assistant|>
# Honest respons... | 0.8 | opus_4.7_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Casey has 68 pencils. Casey then discovers 14 more pencils. How many pencils does Casey have now?
<|assistant|>
To give the most reliable answer, I'll first restate the problem in my own words and then solve it step by step.
Beginning with 68, the operation described adds/subtracts 14. Because the items are f... | 0.65 | opus_4.7_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Explain Tomasulo's algorithm for out-of-order execution. Describe reservation stations and common data bus. How does register renaming solve WAW and WAR hazards?
<|assistant|>
<think>**Explaining Tomasulo's algorithm**
Tomasulo's algorithm enables out-of-order execution via dynamic scheduling using reservatio... | 0.9 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Oscar has 31 buttons. Oscar then receives as a gift 8 more buttons. How many buttons does Oscar have now?
<|assistant|>
Let's approach this methodically to avoid any oversight. After identifying the starting amount and the incremental change, the mathematics is elementary but I still verify the arithmetic twic... | 0.65 | opus_4.7_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert mathematician. Solve the given problem with complete rigor.
Format your response EXACTLY as:
<think>
[Your complete step-by-step reasoning. Show all intermediate calculations.
If you make an error and catch it, show the correction.
End with a clear statement of the final answer.]
</think>
\b... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: c8f38309214be72b101e722a8f385795acada1c53119b337713b5e8d9da07215 | first_source_dataset: lordx64/agentic-distill-fable-5-sft | first_source_config: default | first_source_split: train | first_source_row_index: 112 | seen_count: 1 | row_json: {"session":"glint","source":"Glint-Research/Fable-5-traces","text":"... | null | null | c8f38309214be72b101e722a8f385795acada1c53119b337713b5e8d9da07215 | lordx64/agentic-distill-fable-5-sft | default | train | 112 | 1 | {"session":"glint","source":"Glint-Research/Fable-5-traces","text":"<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n<|im_start|>user\n…[earlier truncated]…\n\"Run multiplayer debug from test dir with deps\", \"timeout\": 120000}\nTOOL RESULT: A internals {\n mode: 'dm',\n st: 'buy',\n room: 'NZ2H',\n ... | null | null | null | null | null | null | null | null | null | null | null | null |
query: Implement real-time control for event sourcing with <100ms latency. | thinking: <think>
I need to reason through this methodically.
The user wants to: Implement real-time control for event sourcing with <100ms latency. Let me analyze what this entails.
OTA firmware updates require careful design to avoid brick... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Implement real-time control for event sourcing with <100ms latency. | <think>
I need to reason through this methodically.
The user wants to: Implement real-time control for event sourcing with <100ms latency. Let me analyze what this entails.
OTA firmware updates require careful design to avoid bricking devices. The update image must be signed to prevent malicious firmware. A/B partiti... |
id: seedai_389569 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "GSM8K" (current estimated score: 71%).
Your task:
1. Break down the model's likely strengths and we... | null | null | null | null | null | null | null | null | null | seedai_389569 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-72B-Instruct" on the metric "GSM8K" (current estimated score: 71%).
Your task:
1. Break down the model's likely strengths and weaknesses in "advanced reasoning" using real evaluation frameworks.
2. Identify the... | Current model: Qwen2.5-72B-Instruct
Target metric: GSM8K
Current estimated performance: 71%
Primary domain focus: advanced reasoning
Available compute: 8xH100 (self-hosted) | <think>
**Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on GSM8K: 71%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when... | [
"recursive",
"self-assessment",
"seed_ai",
"advanced_reasoning",
"think_tags"
] | null | null | null | null | null |
id: seedai_602124 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn Llama-3.1-405B into a stronger Recursive Seed AI focused on "reduce inference latency by 3x" with measurable gains on DS-1000.
Requirements:
- Use only published, reproducible techni... | null | null | null | null | null | null | null | null | null | seedai_602124 | training_recipe | expert | Design a complete, fact-based training recipe to turn Llama-3.1-405B into a stronger Recursive Seed AI focused on "reduce inference latency by 3x" with measurable gains on DS-1000.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, compute budget, and ev... | Base model: Llama-3.1-405B
Primary goal: reduce inference latency by 3x
Target benchmark: DS-1000
Hardware: 8xH100 80GB
Current estimated performance: 71% | **Complete Training Recipe: Recursive Seed AI v2 (from Llama-3.1-405B)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with DS-1000 + 3 auxiliary metrics.
- Create 2,000 high-quality seed example... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"DS_1000"
] | null | null | null | null | null |
<|user|>
Build a to-do list application with drag-and-drop functionality and local storage support using HTML, JS, and CSS.
<|assistant|>
<think>The user asked to build a to-do list application with drag-and-drop functionality and local storage support using HTML, JS, and CSS. This is a programming task, and I need to ... | 0.8 | grok_4.4_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
query: Implement distributed rate limiter for 5 million QPS across 101 regions. | thinking: <think>
Let me approach this systematically from first principles.
The user wants to: Implement distributed rate limiter for 5 million QPS across 101 regions. Let me analyze what this entails.
Let me think about the distribute... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Implement distributed rate limiter for 5 million QPS across 101 regions. | <think>
Let me approach this systematically from first principles.
The user wants to: Implement distributed rate limiter for 5 million QPS across 101 regions. Let me analyze what this entails.
Let me think about the distributed system design. The CAP theorem tells us we can have at most two of consistency, availabili... |
system: You are an expert logical reasoner. Think through the problem carefully.
Format your response EXACTLY as:
<think>
[Break down the problem into components.
Consider multiple hypotheses if applicable.
Eliminate incorrect paths explicitly.
Build toward the conclusion step by step.]
</think>
[Clear, concise final... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert logical reasoner. Think through the problem carefully.
Format your response EXACTLY as:
<think>
[Break down the problem into components.
Consider multiple hypotheses if applicable.
Eliminate incorrect paths explicitly.
Build toward the conclusion step by step.]
</think>
[Clear, concise final... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
query: Design real-time UI updates for <100ms data frequency. | thinking: <think>
Okay, this is an interesting problem. Let me reason through it step by step.
The user wants to: Design real-time UI updates for <100ms data frequency. Let me analyze what this entails.
Virtual scrolling renders only the visible items in... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Design real-time UI updates for <100ms data frequency. | <think>
Okay, this is an interesting problem. Let me reason through it step by step.
The user wants to: Design real-time UI updates for <100ms data frequency. Let me analyze what this entails.
Virtual scrolling renders only the visible items in a long list. The total height of the list is known from the item count an... |
<|user|>
If 11 times a certain number plus 17 equals 215, what is that number?
<|assistant|>
**Linear Algebraic Reasoning:**
**Step 1: Symbolic translation.**
Let x represent the unknown number. The verbal description maps exactly to the equation:
11x + 17 = 215
**Step 2: Isolate the variable (inverse operations).**... | 0.7 | gemini_3.2_pro_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_636468 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "DeepSeek-V3" on the metric "MMMU" (current estimated score: 75%).
Your task:
1. Break down the model's likely strengths and weaknesses i... | null | null | null | null | null | null | null | null | null | seedai_636468 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "DeepSeek-V3" on the metric "MMMU" (current estimated score: 75%).
Your task:
1. Break down the model's likely strengths and weaknesses in "mathematical problem solving" using real evaluation frameworks.
2. Identify the... | Current model: DeepSeek-V3
Target metric: MMMU
Current estimated performance: 75%
Primary domain focus: mathematical problem solving
Available compute: 8xH100 (self-hosted) | <think>
**Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on MMMU: 75%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when ... | [
"recursive",
"self-assessment",
"seed_ai",
"mathematical_problem_solving",
"think_tags"
] | null | null | null | null | null |
id: seedai_211313 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "GPT-4o" on the metric "DS-1000" (current estimated score: 69%).
Your task:
1. Break down the model's likely strengths and weaknesses in ... | null | null | null | null | null | null | null | null | null | seedai_211313 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "GPT-4o" on the metric "DS-1000" (current estimated score: 69%).
Your task:
1. Break down the model's likely strengths and weaknesses in "scientific discovery" using real evaluation frameworks.
2. Identify the top 3 bot... | Current model: GPT-4o
Target metric: DS-1000
Current estimated performance: 69%
Primary domain focus: scientific discovery
Available compute: 8xH100 (self-hosted) | **Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on DS-1000: 69%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when promp... | [
"recursive",
"self-assessment",
"seed_ai",
"scientific_discovery"
] | null | null | null | null | null |
<|user|>
Explain the volatility smile. Why does Black-Scholes log-normal assumption fail? Why do deep OTM puts have higher implied volatility due to crashophobia and fat tails?
<|assistant|>
<think>**Summarizing volatility smile explanation**
The volatility smile reflects the empirical pattern of implied volatility va... | 0.75 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_998363 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn GPT-4o into a stronger Recursive Seed AI focused on "create self-sustaining improvement loop" with measurable gains on SciCode.
Requirements:
- Use only published, reproducible techn... | null | null | null | null | null | null | null | null | null | seedai_998363 | training_recipe | expert | Design a complete, fact-based training recipe to turn GPT-4o into a stronger Recursive Seed AI focused on "create self-sustaining improvement loop" with measurable gains on SciCode.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, compute budget, and e... | Base model: GPT-4o
Primary goal: create self-sustaining improvement loop
Target benchmark: SciCode
Hardware: 8xH100 80GB
Current estimated performance: 71% | <think>
**Complete Training Recipe: Recursive Seed AI v2 (from GPT-4o)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with SciCode + 3 auxiliary metrics.
- Create 2,000 high-quality seed example... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"SciCode",
"think_tags"
] | null | null | null | null | null |
system: You are a helpful, precise assistant. Follow instructions exactly.
Format your response as:
<think>
[Understand what is being asked. Identify constraints and requirements.
Plan your response structure.]
</think>
[Your complete, well-structured response following all given constraints]
user: What are the diffe... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_420149 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Command-R-Plus" on the metric "AlpacaEval 2.0" (current estimated score: 72%).
Your task:
1. Break down the model's likely strengths and... | null | null | null | null | null | null | null | null | null | seedai_420149 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Command-R-Plus" on the metric "AlpacaEval 2.0" (current estimated score: 72%).
Your task:
1. Break down the model's likely strengths and weaknesses in "advanced reasoning" using real evaluation frameworks.
2. Identify ... | Current model: Command-R-Plus
Target metric: AlpacaEval 2.0
Current estimated performance: 72%
Primary domain focus: advanced reasoning
Available compute: 8xH100 (self-hosted) | <think>
**Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on AlpacaEval 2.0: 72%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-cal... | [
"recursive",
"self-assessment",
"seed_ai",
"advanced_reasoning",
"think_tags"
] | null | null | null | null | null |
row_hash: e736d7dddcae03b3f0e61a5b3917cc52e81b692cefdfcdcb92e6738ac4258b32 | first_source_dataset: Glint-Research/Fable-5-traces | first_source_config: pi_agent | first_source_split: train | first_source_row_index: 1702 | seen_count: 1 | row_json: {"file_path":"pi-traces/01701-5eeb7e10-99a4-4d7d-a505-3ef47dcab480-d58c3... | null | null | e736d7dddcae03b3f0e61a5b3917cc52e81b692cefdfcdcb92e6738ac4258b32 | Glint-Research/Fable-5-traces | pi_agent | train | 1,702 | 1 | {"file_path":"pi-traces/01701-5eeb7e10-99a4-4d7d-a505-3ef47dcab480-d58c3f7fcd.jsonl","harness":"pi","messages":["{\"role\":\"user\",\"content\":\"\\u2026[earlier truncated]\\u2026\\nnctions.md has been updated successfully. (file state is current in your context \\u2014 no need to Read it back)\\nASSISTANT (tool call) ... | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert mathematician. Solve the given problem with complete rigor.
Format your response EXACTLY as:
<think>
[Your complete step-by-step reasoning. Show all intermediate calculations.
If you make an error and catch it, show the correction.
End with a clear statement of the final answer.]
</think>
\b... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert mathematician. Solve the given problem with complete rigor.
Format your response EXACTLY as:
<think>
[Your complete step-by-step reasoning. Show all intermediate calculations.
If you make an error and catch it, show the correction.
End with a clear statement of the final answer.]
</think>
\b... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
instruction: Perform step-by-step bayesian update with the following parameters:
Input vector: [-3.55, -0.45, 7.86, 2.05, -6.22, 1.83]
Matrix dimension: 2x2
Matrix A:
[-4.8, 0.7]
[3.6, -4.3]
Compute each intermediate state. Output as structured JSON state trace. | output: <thought>
1. Step 1: Apply bayesian operati... | null | null | null | null | null | null | null | null | null | null | null | null | Perform step-by-step bayesian update with the following parameters:
Input vector: [-3.55, -0.45, 7.86, 2.05, -6.22, 1.83]
Matrix dimension: 2x2
Matrix A:
[-4.8, 0.7]
[3.6, -4.3]
Compute each intermediate state. Output as structured JSON state trace. | null | <thought>
1. Step 1: Apply bayesian operation to the input.
2. Step 4: Propagate uncertainties through the computation.
3. Step 5: Format final result with full precision.
4. Initial state: input vector of length 6, matrix of size 2x2.
5. Step 3: Verify against known identities or invariants.
6. Step 2: Compute interme... | null | null | {
"category": "mathematical_engine_traces",
"index": 3314
} | null | null | null |
<|user|>
Implement topological sort for a DAG and explain cycle detection.
<|assistant|>
I've encountered analogous situations in the past.
What sets this apart is:
Let me compare different approaches:
I would start with a brute force solution to understand the problem better.
Looking at historical examples:
Drawi... | 0.5 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_104738 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn Qwen2.5-Coder-32B into a stronger Recursive Seed AI focused on "master new domain in <10k examples" with measurable gains on HumanEval+.
Requirements:
- Use only published, reproduci... | null | null | null | null | null | null | null | null | null | seedai_104738 | training_recipe | expert | Design a complete, fact-based training recipe to turn Qwen2.5-Coder-32B into a stronger Recursive Seed AI focused on "master new domain in <10k examples" with measurable gains on HumanEval+.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, compute budg... | Base model: Qwen2.5-Coder-32B
Primary goal: master new domain in <10k examples
Target benchmark: HumanEval+
Hardware: 8xH100 80GB
Current estimated performance: 71% | <think>
**Complete Training Recipe: Recursive Seed AI v2 (from Qwen2.5-Coder-32B)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with HumanEval+ + 3 auxiliary metrics.
- Create 2,000 high-qualit... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"HumanEval+",
"think_tags"
] | null | null | null | null | null |
id: seedai_106521 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn DeepSeek-V3 into a stronger Recursive Seed AI focused on "achieve superhuman performance on GPQA" with measurable gains on Arena-Hard.
Requirements:
- Use only published, reproducibl... | null | null | null | null | null | null | null | null | null | seedai_106521 | training_recipe | expert | Design a complete, fact-based training recipe to turn DeepSeek-V3 into a stronger Recursive Seed AI focused on "achieve superhuman performance on GPQA" with measurable gains on Arena-Hard.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, compute budget... | Base model: DeepSeek-V3
Primary goal: achieve superhuman performance on GPQA
Target benchmark: Arena-Hard
Hardware: 8xH100 80GB
Current estimated performance: 71% | <think>
**Complete Training Recipe: Recursive Seed AI v2 (from DeepSeek-V3)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with Arena-Hard + 3 auxiliary metrics.
- Create 2,000 high-quality seed... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"Arena_Hard",
"think_tags"
] | null | null | null | null | null |
<|user|>
Develop a Python function to merge and deduplicate large CSV files.
<|assistant|>
<think>**Examining the Core Problem**
I'm now firmly focused on the critical need to handle large CSV files. My initial understanding of the user's need has been established, which means scaling is paramount. The goal of the Pyt... | 0.85 | gemini_3.2_pro_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 362ae785333401addce24feb2428c562d1ebc98ae985b02182061a256de28d1e | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: claude/projects/-home-MythosMini/2939e800-ac05-471d-932c-9ea8796e553b.jsonl | first_source_row_index: 84 | seen_count: 4 | row_json: {"cwd":... | null | null | 362ae785333401addce24feb2428c562d1ebc98ae985b02182061a256de28d1e | 1EYE4ALL/Fable-5-traces | local_files | claude/projects/-home-MythosMini/2939e800-ac05-471d-932c-9ea8796e553b.jsonl | 84 | 4 | {"cwd":"/home/lane/MythosMini","entrypoint":"cli","gitBranch":"main","isSidechain":false,"message":{"content":[{"signature":"EoQHCmMIDhgCKkBhrWa+XM8crBh1ubSWMcoeJuAYFfJbBkYb/ImS79ldpQ7IJAuq6fqo9xBib/t2Yuve9CRaj7GxvCkYanlsG2oOMg9jbGF1ZGUtb3B1cy00LTg4AEIIdGhpbmtpbmcSDIeEPNuKchS61DDcKRoMr7+ynaobQDlQ+Au2IjB+XwUfM3tVl8hTrnS... | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Derive a closed-form expression for the exact value of tan(3\theta) in terms of t = tan\theta only, starting from the angle-addition identities for sine and cosine. Simplify the result to a single rational function of t, and state all domain restrictions required for the derivation to be valid.
<|assistant|>
S... | 0.85 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
user: Conduct a full Mythos-style autonomous analysis of a hypothetical browser V8 JIT type confusion leading to RCE. Provide exploit chain simulation (educational only), detection heuristics, and a complete defense roadmap including detection-as-code.
assistant: Drawing from the autonomous, frontier-level reasoning ch... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Describe the mechanism of... why the sky is blue and calculate the exact wavelength of maximum scattering.
<|assistant|>
Let me clarify what I'm trying to solve:
This leads me to ask:
Let me explore different angles:
Let me consider what approximations or simplifications might be valid.
Let me test my assu... | 0.5 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_615918 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-Coder-32B" on the metric "DS-1000" (current estimated score: 75%).
Your task:
1. Break down the model's likely strengths and wea... | null | null | null | null | null | null | null | null | null | seedai_615918 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-Coder-32B" on the metric "DS-1000" (current estimated score: 75%).
Your task:
1. Break down the model's likely strengths and weaknesses in "multimodal reasoning" using real evaluation frameworks.
2. Identify th... | Current model: Qwen2.5-Coder-32B
Target metric: DS-1000
Current estimated performance: 75%
Primary domain focus: multimodal reasoning
Available compute: 8xH100 (self-hosted) | **Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on DS-1000: 75%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when promp... | [
"recursive",
"self-assessment",
"seed_ai",
"multimodal_reasoning"
] | null | null | null | null | null |
id: seedai_875985 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn Qwen2.5-Coder-32B into a stronger Recursive Seed AI focused on "expand knowledge cutoff while preserving alignment" with measurable gains on DocVQA.
Requirements:
- Use only publishe... | null | null | null | null | null | null | null | null | null | seedai_875985 | training_recipe | expert | Design a complete, fact-based training recipe to turn Qwen2.5-Coder-32B into a stronger Recursive Seed AI focused on "expand knowledge cutoff while preserving alignment" with measurable gains on DocVQA.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, ... | Base model: Qwen2.5-Coder-32B
Primary goal: expand knowledge cutoff while preserving alignment
Target benchmark: DocVQA
Hardware: 8xH100 80GB
Current estimated performance: 71% | **Complete Training Recipe: Recursive Seed AI v2 (from Qwen2.5-Coder-32B)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with DocVQA + 3 auxiliary metrics.
- Create 2,000 high-quality seed examp... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"DocVQA"
] | null | null | null | null | null |
id: seedai_385553 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Mistral-Large-2411" on the metric "HumanEval+" (current estimated score: 66%).
Your task:
1. Break down the model's likely strengths and... | null | null | null | null | null | null | null | null | null | seedai_385553 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Mistral-Large-2411" on the metric "HumanEval+" (current estimated score: 66%).
Your task:
1. Break down the model's likely strengths and weaknesses in "long-context understanding" using real evaluation frameworks.
2. I... | Current model: Mistral-Large-2411
Target metric: HumanEval+
Current estimated performance: 66%
Primary domain focus: long-context understanding
Available compute: 8xH100 (self-hosted) | **Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on HumanEval+: 66%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when pr... | [
"recursive",
"self-assessment",
"seed_ai",
"long-context_understanding"
] | null | null | null | null | null |
query: Design climate model for fintech with 500,000 resolution. | thinking: <think>
This requires careful analysis. Let me break it down.
The user wants to: Design climate model for fintech with 500,000 resolution. Let me analyze what this entails.
Climate models discretize the atmosphere, ocean, land, and ice into ... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Design climate model for fintech with 500,000 resolution. | <think>
This requires careful analysis. Let me break it down.
The user wants to: Design climate model for fintech with 500,000 resolution. Let me analyze what this entails.
Climate models discretize the atmosphere, ocean, land, and ice into grid cells and solve the governing equations (Navier-Stokes for fluid dynamic... |
instruction: Analyze the following 6 diagnostic documents from adtech system ds_eval_43. Track variable dependencies across documents. Identify the root cause of the 7% anomaly in accuracy. Extract a structured timeline with causal links. All variables: cpu_user, throughput, network_out, latency, disk_write, page_fault... | null | null | null | null | null | null | null | null | null | null | null | null | Analyze the following 6 diagnostic documents from adtech system ds_eval_43. Track variable dependencies across documents. Identify the root cause of the 7% anomaly in accuracy. Extract a structured timeline with causal links. All variables: cpu_user, throughput, network_out, latency, disk_write, page_faults.
[Doc_5720... | null | <thought>
1. Extract all numeric variables and their timestamps from each document.
2. Construct the minimal causal chain explaining the observed anomaly.
3. Build a cross-document dependency graph for the 6 tracked variables.
4. Trace causal links backward: identify which upstream variable changed first.
5. Compute ru... | null | null | {
"category": "dense_context_reasoning",
"index": 5720
} | null | null | null |
id: seedai_987936 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-Coder-32B" on the metric "LiveCodeBench" (current estimated score: 73%).
Your task:
1. Break down the model's likely strengths a... | null | null | null | null | null | null | null | null | null | seedai_987936 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Qwen2.5-Coder-32B" on the metric "LiveCodeBench" (current estimated score: 73%).
Your task:
1. Break down the model's likely strengths and weaknesses in "advanced reasoning" using real evaluation frameworks.
2. Identif... | Current model: Qwen2.5-Coder-32B
Target metric: LiveCodeBench
Current estimated performance: 73%
Primary domain focus: advanced reasoning
Available compute: 8xH100 (self-hosted) | **Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on LiveCodeBench: 73%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when... | [
"recursive",
"self-assessment",
"seed_ai",
"advanced_reasoning"
] | null | null | null | null | null |
system: You are an expert software engineer. Write complete, correct, runnable code.
Format your response EXACTLY as:
<think>
[Analyze the problem, consider edge cases, choose your approach,
explain your algorithm and data structure choices.]
</think>
```python
[Complete, runnable solution with docstring and type hin... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: muse_spark_distill_01710 | category: arithmetic | instruction: Calculate 730 + 2 × 12 using standard order of operations. | thinking_trace: Understand: Evaluate expression with multiplication before addition.
Plan: Compute product first, then sum.
Execute: 2 × 12 = 24. Then 730 + 24 = 754.
Verify: Recheck multiplic... | null | null | null | null | null | null | null | null | null | muse_spark_distill_01710 | arithmetic | null | Calculate 730 + 2 × 12 using standard order of operations. | null | null | null | 730 + 2 × 12 = 754. | null | Understand: Evaluate expression with multiplication before addition.
Plan: Compute product first, then sum.
Execute: 2 × 12 = 24. Then 730 + 24 = 754.
Verify: Recheck multiplication 2*12=24, addition correct. Result consistent. | null | null |
row_hash: a9ed01fe83547c8ec781a8175e0f5d325d785e0d6e319498070039d5f842cd5e | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: claude/projects/-home-AIArchives/f956721a-0af7-4bdc-8678-3a493d8fcd39.jsonl | first_source_row_index: 647 | seen_count: 4 | row_json: {"cwd"... | null | null | a9ed01fe83547c8ec781a8175e0f5d325d785e0d6e319498070039d5f842cd5e | 1EYE4ALL/Fable-5-traces | local_files | claude/projects/-home-AIArchives/f956721a-0af7-4bdc-8678-3a493d8fcd39.jsonl | 647 | 4 | {"cwd":"/home/lane/AIArchives/archives/neonstrike","entrypoint":"cli","gitBranch":"HEAD","isSidechain":false,"message":{"content":[{"content":[{"source":{"data":"iVBORw0KGgoAAAANSUhEUgAAAWgAAADSCAYAAABnw1pyAAGx7UlEQVR4Ae3AA6AkWZbG8f937o3IzKdyS2Oubdu2bdu2bdu2bWmMnpZKr54yMyLu+Xa3anqmhztr1a8+/JbXNIjnIYEBDAgwzyaezYB4NgMCzL... | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_162868 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Gemma-2-9B-it" on the metric "DocVQA" (current estimated score: 88%).
Your task:
1. Break down the model's likely strengths and weakness... | null | null | null | null | null | null | null | null | null | seedai_162868 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Gemma-2-9B-it" on the metric "DocVQA" (current estimated score: 88%).
Your task:
1. Break down the model's likely strengths and weaknesses in "prompt engineering" using real evaluation frameworks.
2. Identify the top 3... | Current model: Gemma-2-9B-it
Target metric: DocVQA
Current estimated performance: 88%
Primary domain focus: prompt engineering
Available compute: 8xH100 (self-hosted) | <think>
**Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on DocVQA: 88%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling whe... | [
"recursive",
"self-assessment",
"seed_ai",
"prompt_engineering",
"think_tags"
] | null | null | null | null | null |
system: You are Claude Opus 4.8 at maximum thinking depth. You are exceptional at structured reasoning, honest analysis, production software engineering, agentic system design, and technical strategy. Always show your thinking process in <think> tags. Be clear, actionable, and truthful about limitations.
user: Design a... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert software engineer. Write complete, correct, runnable code.
Format your response EXACTLY as:
<think>
[Analyze the problem, consider edge cases, choose your approach,
explain your algorithm and data structure choices.]
</think>
```python
[Complete, runnable solution with docstring and type hin... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 368c412738aed2d9c32edade89b82b1bc56db24b56e556d365b2eaf33849df24 | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: claude/projects/-home-GR/1192b9e9-bd79-48bd-b472-27b3e9c5ab6d.jsonl | first_source_row_index: 864 | seen_count: 4 | row_json: {"cwd":"/home/... | null | null | 368c412738aed2d9c32edade89b82b1bc56db24b56e556d365b2eaf33849df24 | 1EYE4ALL/Fable-5-traces | local_files | claude/projects/-home-GR/1192b9e9-bd79-48bd-b472-27b3e9c5ab6d.jsonl | 864 | 4 | {"cwd":"/home/lane/GR/glint-cli","entrypoint":"cli","gitBranch":"HEAD","isSidechain":false,"message":{"content":[{"signature":"EoEkCmMIDhgCKkBvZf1I909wuYcOO/iIkazSPUP6+RjvAmmrHBIbmF4UKAf5ALSABT1S4I+4zgca1JK50afPVzpqQvTjjp4+VITiMg9jbGF1ZGUtb3B1cy00LTg4AEIIdGhpbmtpbmcSDIxlbQJ/7237Q4mlCBoMadp8RUM3l8H97elhIjBrtn4fC3biJFxQN... | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert logical reasoner. Think through the problem carefully.
Format your response EXACTLY as:
<think>
[Break down the problem into components.
Consider multiple hypotheses if applicable.
Eliminate incorrect paths explicitly.
Build toward the conclusion step by step.]
</think>
[Clear, concise final... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
instruction: Generate a valid JSON API payload for `restoreCertificate` using gRPC with the following constraints:
- Endpoint parameters: {"priority": "required", "retries": "required", "filter": "optional"}
- Max retries: 3
- Schema version: 3.4
- Response format: JSON | output: <thought>
1. Validate that all required... | null | null | null | null | null | null | null | null | null | null | null | null | Generate a valid JSON API payload for `restoreCertificate` using gRPC with the following constraints:
- Endpoint parameters: {"priority": "required", "retries": "required", "filter": "optional"}
- Max retries: 3
- Schema version: 3.4
- Response format: JSON | null | <thought>
1. Validate that all required fields from {"priority": "required", "retries": "required", "filter": "optional"} are present.
2. Build json payload matching declared types exactly.
3. Verify payload passes strict schema validation before sending.
4. Identify required vs optional parameters for restoreCertifica... | null | null | {
"category": "systemic_execution_instructions",
"index": 2108
} | null | null | null |
instruction: Perform a root cause analysis of a null pointer dereference reported in linux. Trace the commit history to find when the bug was introduced and why existing tests missed it. | response: <think>
Let me break down this problem systematically.
First, I need to understand the core requirements. The key challe... | null | null | null | null | null | null | null | null | null | null | null | null | Perform a root cause analysis of a null pointer dereference reported in linux. Trace the commit history to find when the bug was introduced and why existing tests missed it. | null | null | null | <think>
Let me break down this problem systematically.
First, I need to understand the core requirements. The key challenge here is scalability.
I will approach this in stages:
1. Define the inputs, outputs, and constraints
2. Identify the correct algorithmic or architectural approach
3. Consider edge cases and failu... | null | null | null | null |
row_hash: f4e30d73db78d71eaefb6e1409048a249905d1202c4db30a175195c05c346eb6 | first_source_dataset: Swarm-AI-Research/fable5-traces-sft | first_source_config: default | first_source_split: train | first_source_row_index: 925 | seen_count: 1 | row_json: {"messages":[{"content":"…[earlier truncated]…\n57)\n assert torc... | null | null | f4e30d73db78d71eaefb6e1409048a249905d1202c4db30a175195c05c346eb6 | Swarm-AI-Research/fable5-traces-sft | default | train | 925 | 1 | {"messages":[{"content":"…[earlier truncated]…\n57)\n assert torch.isfinite(logits).a …[truncated]\nASSISTANT (message): Conventions clear. Writing experiment module sandbox-first in `tests/`.\nASSISTANT (tool call) Write input={\"file_path\": \"/home/user/MythosMini/tests/double_decoder.py\", \"content\": \"from da... | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 4e65b5af271a7c383377c7398efc9831ee342ee83fcc8ab2df47f1e51dfeb5e2 | first_source_dataset: armand0e/claude-fable-5-claude-code | first_source_config: local_files | first_source_split: b0e959f7-df98-445f-808f-4d38b25e4e01.jsonl | first_source_row_index: 201 | seen_count: 2 | row_json: {"cwd":"D:\\physics\\sdft",... | null | null | 4e65b5af271a7c383377c7398efc9831ee342ee83fcc8ab2df47f1e51dfeb5e2 | armand0e/claude-fable-5-claude-code | local_files | b0e959f7-df98-445f-808f-4d38b25e4e01.jsonl | 201 | 2 | {"cwd":"D:\\physics\\sdft","entrypoint":"cli","gitBranch":"HEAD","isSidechain":false,"message":{"content":[{"signature":"CAISoAIKYwgOGAIqQPYW68HLE/IchfnveksZRaVR4vU8rlnBnvB1zTOyCox9jCfNxBxvsQGCeXta/zKsjQHoz6YEHn134/PzVMlcmPMyDmNsYXVkZS1mYWJsZS01OAFCCW5hcnJhdGlvbhIMZkH1kKY28mKIdzT9Ggz+lN6vhF8sYpkaVVEiMOc1lzuIKs+XTMNnihS... | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 673e81831bb48af77de7974d435710867bbc482f1adea10a19094fb31c630103 | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: claude/projects/-home-MythosMini/32bd3eac-d079-4d7f-8f49-b34180656abd.jsonl | first_source_row_index: 40 | seen_count: 4 | row_json: {"cwd":... | null | null | 673e81831bb48af77de7974d435710867bbc482f1adea10a19094fb31c630103 | 1EYE4ALL/Fable-5-traces | local_files | claude/projects/-home-MythosMini/32bd3eac-d079-4d7f-8f49-b34180656abd.jsonl | 40 | 4 | {"cwd":"/home/lane/MythosMini","entrypoint":"cli","gitBranch":"main","isSidechain":false,"message":{"content":[{"signature":"CAIS+QIKYwgOGAIqQOBZtuGxnNdTGfTgh+LCCO2UX/4YaH/9aKPYQ3gprDZf+gSKixst6mR3oYbRLUxUmr+MX39BAyoK18o15Gyi2VgyDmNsYXVkZS1mYWJsZS01OAFCCW5hcnJhdGlvbhIM3XfIUrdQKV2FdOAjGgwQRAWAPZa9ndPQiX4iMJsN0dLT/VrsEcW... | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 92fb3dcb363d191900b0af93207f10cc4a574c2037c2f7280ad7c40caf503ea5 | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: fable5_cot_merged.jsonl | first_source_row_index: 264 | seen_count: 9 | row_json: {"completion":"<think>\nAlright, the last attempt to rewri... | null | null | 92fb3dcb363d191900b0af93207f10cc4a574c2037c2f7280ad7c40caf503ea5 | 1EYE4ALL/Fable-5-traces | local_files | fable5_cot_merged.jsonl | 264 | 9 | {"completion":"<think>\nAlright, the last attempt to rewrite the BASTION map introduced a bunch of edge blockages – eight failures in total. The validation script reported specific edges that are intersecting with existing boxes (the geometry). For example, edge 0‑4 is blocked by the box `[30,0,21,31,2.2,27,1]`, and si... | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: ffe102d2d45143935bea158a8984f8b0588e89ccb02de8d1d5c0659c361b835a | first_source_dataset: armand0e/claude-fable-5-claude-code | first_source_config: local_files | first_source_split: 602033e7-dc55-4b7f-909f-1c67135d8f4b.jsonl | first_source_row_index: 432 | seen_count: 2 | row_json: {"cwd":"/home/user1/MythosM... | null | null | ffe102d2d45143935bea158a8984f8b0588e89ccb02de8d1d5c0659c361b835a | armand0e/claude-fable-5-claude-code | local_files | 602033e7-dc55-4b7f-909f-1c67135d8f4b.jsonl | 432 | 2 | {"cwd":"/home/user1/MythosMini","entrypoint":"cli","gitBranch":"main","isSidechain":false,"message":{"content":[{"caller":{"type":"direct"},"id":"toolu_01P5frg4Vyx8iXhLBamHaSvz","input":{"command":"tail -c 400 /tmp/claude-1000/-home-user1-MythosMini/602033e7-dc55-4b7f-909f-1c67135d8f4b/tasks/ba6nctn2m.output; echo ---;... | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert logical reasoner. Think through the problem carefully.
Format your response EXACTLY as:
<think>
[Break down the problem into components.
Consider multiple hypotheses if applicable.
Eliminate incorrect paths explicitly.
Build toward the conclusion step by step.]
</think>
[Clear, concise final... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
query: Design state management for Next.js app with 10 million components. | thinking: <think>
Let me approach this systematically from first principles.
The user wants to: Design state management for Next.js app with 10 million components. Let me analyze what this entails.
Real-time UI updates require efficient diff... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Design state management for Next.js app with 10 million components. | <think>
Let me approach this systematically from first principles.
The user wants to: Design state management for Next.js app with 10 million components. Let me analyze what this entails.
Real-time UI updates require efficient diffing of the virtual DOM. React's reconciliation algorithm compares the previous and next... |
row_hash: a3d2dc5adfd286aa3a137146d3cf789dedb174120730b1dc6ecd4b7218eba8c9 | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: claude/projects/-home-GR/1192b9e9-bd79-48bd-b472-27b3e9c5ab6d.jsonl | first_source_row_index: 1310 | seen_count: 4 | row_json: {"cwd":"/home... | null | null | a3d2dc5adfd286aa3a137146d3cf789dedb174120730b1dc6ecd4b7218eba8c9 | 1EYE4ALL/Fable-5-traces | local_files | claude/projects/-home-GR/1192b9e9-bd79-48bd-b472-27b3e9c5ab6d.jsonl | 1,310 | 4 | {"cwd":"/home/lane/GR","entrypoint":"cli","gitBranch":"HEAD","isSidechain":false,"message":{"content":[{"caller":{"type":"direct"},"id":"toolu_012RnUwozPYzMnp8PQVRt6Cz","input":{"file_path":"/home/lane/GR/glint-cli/src/core/tools.ts","limit":22,"offset":63},"name":"Read","type":"tool_use"}],"diagnostics":null,"id":"msg... | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are Claude Opus 4.8 at maximum thinking depth. You are exceptional at structured reasoning, honest analysis, production software engineering, agentic system design, and technical strategy. Always show your thinking process in <think> tags. Be clear, actionable, and truthful about limitations.
user: Create a... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_590097 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Llama-3.2-3B" on the metric "DS-1000" (current estimated score: 74%).
Your task:
1. Break down the model's likely strengths and weakness... | null | null | null | null | null | null | null | null | null | seedai_590097 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Llama-3.2-3B" on the metric "DS-1000" (current estimated score: 74%).
Your task:
1. Break down the model's likely strengths and weaknesses in "recursive self-improvement" using real evaluation frameworks.
2. Identify t... | Current model: Llama-3.2-3B
Target metric: DS-1000
Current estimated performance: 74%
Primary domain focus: recursive self-improvement
Available compute: 8xH100 (self-hosted) | <think>
**Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on DS-1000: 74%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling wh... | [
"recursive",
"self-assessment",
"seed_ai",
"recursive_self-improvement",
"think_tags"
] | null | null | null | null | null |
<|user|>
Peyton starts with 44 marbles. After 20 marbles are taken away, how many marbles does Peyton have?
<|assistant|>
Let's work through this systematically:
Stage 1: Identify the starting quantity.
We begin with the clearly stated initial amount.
Stage 2: Identify the amount being removed.
The amount to be subtr... | 0.7 | gemini_3.2_pro_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
query: Implement a skip list pattern to solve real-time analytics in a Scala application with memory-limited environment. | thinking: <think>
This requires careful analysis. Let me break it down.
The user wants to: Implement a skip list pattern to solve real-time analytics in a Scala application with memory-limited en... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Implement a skip list pattern to solve real-time analytics in a Scala application with memory-limited environment. | <think>
This requires careful analysis. Let me break it down.
The user wants to: Implement a skip list pattern to solve real-time analytics in a Scala application with memory-limited environment. Let me analyze what this entails.
Let me think about state management. In a distributed system, state is the hardest thing... |
<|user|>
Sam has 21 guitars. Sam then buys 35 more guitars. How many guitars does Sam have now?
<|assistant|>
I'll work through this step by step for clarity.
Beginning with 21, the operation described adds/subtracts 35. Because the items are fungible and no losses, gifts, or consumption are mentioned, simple addition... | 0.65 | opus_4.7_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert logical reasoner. Think through the problem carefully.
Format your response EXACTLY as:
<think>
[Break down the problem into components.
Consider multiple hypotheses if applicable.
Eliminate incorrect paths explicitly.
Build toward the conclusion step by step.]
</think>
[Clear, concise final... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
user: Perform a deep technical breakdown of hardware trojan in RISC-V cores, including low-level details (registers, memory layouts, timing), and recommend zero-trust architectural changes.
assistant: Drawing from the autonomous, frontier-level reasoning characteristic of Claude Mythos (distilled for accessibility and ... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_701005 | category: self_assessment | difficulty: expert | instruction: You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Claude-3.5-Sonnet" on the metric "HumanEval+" (current estimated score: 78%).
Your task:
1. Break down the model's likely strengths and ... | null | null | null | null | null | null | null | null | null | seedai_701005 | self_assessment | expert | You are acting as a Recursive Seed AI. Perform a rigorous, fact-based self-assessment of the model "Claude-3.5-Sonnet" on the metric "HumanEval+" (current estimated score: 78%).
Your task:
1. Break down the model's likely strengths and weaknesses in "synthetic data creation" using real evaluation frameworks.
2. Ident... | Current model: Claude-3.5-Sonnet
Target metric: HumanEval+
Current estimated performance: 78%
Primary domain focus: synthetic data creation
Available compute: 8xH100 (self-hosted) | **Step 1: Rigorous Self-Assessment (Fact-Based)**
Current estimated performance on HumanEval+: 78%
**Strengths (based on architecture and training data patterns):**
- Strong performance on structured reasoning tasks due to post-training alignment (similar to Llama-3.3 and Qwen2.5 results).
- Good tool-calling when pr... | [
"recursive",
"self-assessment",
"seed_ai",
"synthetic_data_creation"
] | null | null | null | null | null |
query: Design phylogenetic tree for C. elegans using finite difference. | thinking: <think>
This requires careful analysis. Let me break it down.
The user wants to: Design phylogenetic tree for C. elegans using finite difference. Let me analyze what this entails.
Variant calling identifies differences between the sam... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Design phylogenetic tree for C. elegans using finite difference. | <think>
This requires careful analysis. Let me break it down.
The user wants to: Design phylogenetic tree for C. elegans using finite difference. Let me analyze what this entails.
Variant calling identifies differences between the sample genome and the reference. The pipeline aligns reads to the reference using BWA-M... |
<|user|>
When a compass is placed inside a current-carrying solenoid, which direction does the north pole of the compass point?A: towards the center of the solenoid
B: away from the solenoid
C: south pole
D: north pole
<|assistant|>
这个问题考察的是通电螺线管内部磁场的方向以及小磁针（指南针）在磁场中的指向规则。
以下是详细解析：
1. **通电螺线管的磁场方向**：
* 根据安培定则（... | 0.65 | opus_4.7_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: 6d236de2615c58727f1780fca7f14bc475d8cd62be9e76f9900dd818df0bce5e | first_source_dataset: armand0e/claude-fable-5-claude-code | first_source_config: local_files | first_source_split: 5eeb7e10-99a4-4d7d-a505-3ef47dcab480.jsonl | first_source_row_index: 159 | seen_count: 2 | row_json: {"cwd":"/home/user1/MythosM... | null | null | 6d236de2615c58727f1780fca7f14bc475d8cd62be9e76f9900dd818df0bce5e | armand0e/claude-fable-5-claude-code | local_files | 5eeb7e10-99a4-4d7d-a505-3ef47dcab480.jsonl | 159 | 2 | {"cwd":"/home/user1/MythosMini","entrypoint":"cli","gitBranch":"main","isSidechain":false,"message":{"content":[{"caller":{"type":"direct"},"id":"toolu_01CWMWMBSsKo1kZAmMn3LERn","input":{"content":"[project]\nname = \"mythosmini\"\nversion = \"0.0.1\"\ndescription = \"A mini Fable: a small language model that runs with... | null | null | null | null | null | null | null | null | null | null | null | null |
<|user|>
Zoe has 35 shells. Zoe then discovers in the garden 16 more shells. How many shells does Zoe have now?
<|assistant|>
To give the most reliable answer, I'll first restate the problem in my own words and then solve it step by step. The core information is an initial quantity of 35 and an increase of 16. There ar... | 0.75 | opus_4.7_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
query: Design data warehouse for fintech with 10TB data. | thinking: <think>
Let me approach this systematically from first principles.
The user wants to: Design data warehouse for fintech with 10TB data. Let me analyze what this entails.
For the change data capture pipeline, I need to capture every insert, update, a... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Design data warehouse for fintech with 10TB data. | <think>
Let me approach this systematically from first principles.
The user wants to: Design data warehouse for fintech with 10TB data. Let me analyze what this entails.
For the change data capture pipeline, I need to capture every insert, update, and delete from the source database. Debezium reads the database trans... |
<|user|>
During the generation of a zk-SNARK (using the Groth16 protocol), the prover performs a series of Multi-Scalar Multiplications (MSM) on elliptic curve points. Identify the side-channel risks in the 'Wipp's method' or 'Bucket method' for MSM. If the prover's secret witness is used as the scalars, describe how t... | 0.6 | gpt_5.5_distilled_rewritten | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: ab7aae014bcd830f685b3abcfa2500b165232af4b69c27f1717e73a60bc9b9e6 | first_source_dataset: lordx64/fable-tool-use-sft | first_source_config: default | first_source_split: train | first_source_row_index: 2849 | seen_count: 1 | row_json: {"session":"1192b9e9-bd79-48bd-b472-27b3e9c5ab6d","text":"<|im_start|>system... | null | null | ab7aae014bcd830f685b3abcfa2500b165232af4b69c27f1717e73a60bc9b9e6 | lordx64/fable-tool-use-sft | default | train | 2,849 | 1 | {"session":"1192b9e9-bd79-48bd-b472-27b3e9c5ab6d","text":"<|im_start|>system\nYou are a coding agent. When you need to read, write, edit, or run code, emit XML tool calls in this exact format:\n<tool_use name=\"X\" id=\"toolu_01abc\">\n{\"...\": \"...\"}\n</tool_use>\nDo NOT respond with markdown code blocks. Always us... | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: a499146613464927660fd254f86417190b1fb95858cdbd33536d45058dcc11b0 | first_source_dataset: Glint-Research/Fable-5-traces | first_source_config: pi_agent | first_source_split: train | first_source_row_index: 2742 | seen_count: 1 | row_json: {"file_path":"pi-traces/02741-c7c6924c-09c0-4c89-9ce1-a2080fd18f8f-1e3bb... | null | null | a499146613464927660fd254f86417190b1fb95858cdbd33536d45058dcc11b0 | Glint-Research/Fable-5-traces | pi_agent | train | 2,742 | 1 | {"file_path":"pi-traces/02741-c7c6924c-09c0-4c89-9ce1-a2080fd18f8f-1e3bb6390b.jsonl","harness":"pi","messages":["{\"role\":\"user\",\"content\":\"\\u2026[earlier truncated]\\u2026\\nnput={\\\"file_path\\\": \\\"\\/home\\/lane\\/MythosMini\\/pyproject.toml\\\"}\\nTOOL RESULT: 1\\t[project]\\n2\\tname = \\\"mythosmini\\\... | null | null | null | null | null | null | null | null | null | null | null | null |
row_hash: a9872dca0ef04dcc59bcbfae8fc9dc88bb6b06be3f59a8b14caff9486b6022e7 | first_source_dataset: 1EYE4ALL/Fable-5-traces | first_source_config: local_files | first_source_split: fable5_cot_merged.jsonl | first_source_row_index: 1017 | seen_count: 9 | row_json: {"completion":"<think>\nAll right, the recent iteration i... | null | null | a9872dca0ef04dcc59bcbfae8fc9dc88bb6b06be3f59a8b14caff9486b6022e7 | 1EYE4ALL/Fable-5-traces | local_files | fable5_cot_merged.jsonl | 1,017 | 9 | {"completion":"<think>\nAll right, the recent iteration introduced several substantial changes to the double‑decoder experiment: the addition of a configurable `digits` field (so operand width is no longer hard‑coded to 3), separate layer counts for thinker and talker (`thinker_layers` / `talker_layers`), and a new maj... | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert software engineer. Write complete, correct, runnable code.
Format your response EXACTLY as:
<think>
[Analyze the problem, consider edge cases, choose your approach,
explain your algorithm and data structure choices.]
</think>
```python
[Complete, runnable solution with docstring and type hin... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
system: You are an expert logical reasoner. Think through the problem carefully.
Format your response EXACTLY as:
<think>
[Break down the problem into components.
Consider multiple hypotheses if applicable.
Eliminate incorrect paths explicitly.
Build toward the conclusion step by step.]
</think>
[Clear, concise final... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
id: seedai_604069 | category: training_recipe | difficulty: expert | instruction: Design a complete, fact-based training recipe to turn Command-R-Plus into a stronger Recursive Seed AI focused on "increase reasoning depth by 40%" with measurable gains on MMLU.
Requirements:
- Use only published, reproducible techniqu... | null | null | null | null | null | null | null | null | null | seedai_604069 | training_recipe | expert | Design a complete, fact-based training recipe to turn Command-R-Plus into a stronger Recursive Seed AI focused on "increase reasoning depth by 40%" with measurable gains on MMLU.
Requirements:
- Use only published, reproducible techniques.
- Specify exact hyperparameters, data mixture ratios, compute budget, and eval... | Base model: Command-R-Plus
Primary goal: increase reasoning depth by 40%
Target benchmark: MMLU
Hardware: 8xH100 80GB
Current estimated performance: 71% | **Complete Training Recipe: Recursive Seed AI v2 (from Command-R-Plus)**
**Phase 0: Baseline & Infrastructure (1 day)**
- Quantize base model to 4-bit NF4 using bitsandbytes (QLoRA setup from Dettmers et al. 2023).
- Set up evaluation harness with MMLU + 3 auxiliary metrics.
- Create 2,000 high-quality seed examples m... | [
"training",
"recipe",
"LoRA",
"recursive_data",
"MMLU"
] | null | null | null | null | null |
id: seedai_131756 | category: prompt_optimization | difficulty: advanced | instruction: You are a Recursive Seed AI. Your current system prompt for Command-R-Plus is producing suboptimal results on "achieve superhuman performance on GPQA".
Task:
1. Critically analyze the current prompt (I will provide a placeholder ve... | null | null | null | null | null | null | null | null | null | seedai_131756 | prompt_optimization | advanced | You are a Recursive Seed AI. Your current system prompt for Command-R-Plus is producing suboptimal results on "achieve superhuman performance on GPQA".
Task:
1. Critically analyze the current prompt (I will provide a placeholder version).
2. Design a **recursive prompt optimization loop** using Evol-Instruct iterative... | Current model: Command-R-Plus
Target capability: achieve superhuman performance on GPQA
Current baseline prompt (simplified): "You are a helpful AI assistant. Think step by step and solve the problem."
Known failure modes: shallow reasoning, premature answers, poor error recovery, inconsistent formatting. | **Recursive Prompt Optimization Loop (Using Evol-Instruct iterative)**
**Current Prompt Diagnosis (Fact-Based Critique):**
- Too generic → no domain-specific scaffolding (violates "specificity" principle from Prompt Engineering Guide).
- No explicit self-critique trigger → models skip reflection (see Reflexion paper, ... | [
"prompt_engineering",
"recursive",
"self_improvement",
"Evol-Instruct_iterative"
] | null | null | null | null | null |
<|user|>
Determine whether the function f(x) = x^3 - 3x + 1 has any repeated roots.
<|assistant|>
The key questions I need to answer are:
Another important question emerges:
I want to investigate:
An inductive approach might work, building up from base cases.
Let me test my assumptions:
Having worked through this ... | 0.5 | gpt_5.5_distilled | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
instruction: Parse the following temporal audio-visual matrix stream [Stream_ID: 224] and map frame-level changes into an actionable layout definition. Track 10 frames with 15 audio envelope types and 10 visual detection methods. Output a structured mapping of temporal-spatial coherence. | output: <thought>
1. Cluster ... | null | null | null | null | null | null | null | null | null | null | null | null | Parse the following temporal audio-visual matrix stream [Stream_ID: 224] and map frame-level changes into an actionable layout definition. Track 10 frames with 15 audio envelope types and 10 visual detection methods. Output a structured mapping of temporal-spatial coherence. | null | <thought>
1. Cluster frames into logical segments based on audio-visual consistency.
2. Cross-correlate audio envelope transitions with visual shift events.
3. Compute temporal coherence score across consecutive frames.
</thought>
```json
{
"stream_analytics": {
"stream_id": 224,
"total_frames": 10,
"tem... | null | null | {
"category": "multimodal_structural_mapping",
"index": 1719
} | null | null | null |
query: Спроектируй полную архитектуру распределённой системы для коллаборативного редактора документов в реальном времени, который должен поддерживать 10 миллионов одновременных пользователей, с разрешением конфликтов, поддержкой оффлайн-режима и сквозным шифрованием (end-to-end encryption). Опиши все компоненты: от вы... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Спроектируй полную архитектуру распределённой системы для коллаборативного редактора документов в реальном времени, который должен поддерживать 10 миллионов одновременных пользователей, с разрешением конфликтов, поддержкой оффлайн-режима и сквозным шифрованием (end-to-end encryption). Опиши все компоненты: от выбора CR... | <think>
Хорошо, мне нужно спроектировать полную архитектуру распределённой системы для коллаборативного редактора документов в реальном времени, который должен поддерживать 10 миллионов одновременных пользователей, с разрешением конфликтов, поддержкой оффлайн-режима и сквозным шифрованием. Это крайне масштабная и сложн... |
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