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Scaling Test-Time Compute to Achieve Gold Medal at IOI 2025 with Open-Weight Models
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15jorgemunozl
jorgemunozl
AI & ML interests
I like to finetune models with cloud GPU'S. QLora, metrics. llm's, vllm's, Ubuntu-Arch, Latex, ML, RL, PINNS.
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Scaling Test-Time Compute to Achieve Gold Medal at IOI 2025 with Open-Weight Models
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Article
Nano Banana (Gemini 2.5 Flash Image) Full Tutorial - 27 Unique Cases vs Qwen Image Edit - Free 2 Use
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MonsterMMORPG's
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I have concluded first 8 traininings of Qwen Image LoRA - we are not at the level of FLUX yet and next 8 trainings starting hopefully - 2656x2656px image generated with 8 steps Fast Qwen LoRA + myself trained LoRA :
Grid test results shared here along with App installer : https://www.patreon.com/posts/137551634
Grid test results shared here along with App installer : https://www.patreon.com/posts/137551634
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kanaria007's
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✅ New Article: *Time as Structured Recursion*
Title:
⏳ Time: Recursive Loop Indexing and Future as Jump Prediction
🔗 https://huggingface.co/blog/kanaria007/structured-time
---
Summary:
Time is often imagined as *a linear flow*.
Structured Intelligence reframes it as *recursive architecture*:
* Past as *active memory loops*
* Present as *indexed structural state*
* Future as *bounded jump space and anticipatory frame*
> Time isn’t a river —
> *it’s the looped structure that makes thought possible.*
---
Why It Matters:
• Explains how *memory, prediction, and decision* rely on time as structure
• Bridges *philosophy of time and cognitive architecture*
• Enables *AI systems to handle temporal reasoning and rollback*
---
What’s Inside:
• Time as *loop, index, and jump space*
• *Cognitive experience of temporality* as structural phenomenon
• *Rollback and anticipation* in decision architecture
• Implications for *AI temporal reasoning and self‑alignment*
---
📖 Article 23 of the Structured Intelligence Series
Where Article 22 explored *language acquisition as structural emergence*,
Article 23 frames *time itself as structured recursion* —
showing that *intelligence lives in loops, not in lines*.
---
Next: Natural Law as Structural Constraint
The next article examines *laws of nature not as external rules*,
but as *the structural preconditions that allow any cognition to exist*.
> From moments to matter,
> *structure writes the conditions for thought itself.*
---
Title:
⏳ Time: Recursive Loop Indexing and Future as Jump Prediction
🔗 https://huggingface.co/blog/kanaria007/structured-time
---
Summary:
Time is often imagined as *a linear flow*.
Structured Intelligence reframes it as *recursive architecture*:
* Past as *active memory loops*
* Present as *indexed structural state*
* Future as *bounded jump space and anticipatory frame*
> Time isn’t a river —
> *it’s the looped structure that makes thought possible.*
---
Why It Matters:
• Explains how *memory, prediction, and decision* rely on time as structure
• Bridges *philosophy of time and cognitive architecture*
• Enables *AI systems to handle temporal reasoning and rollback*
---
What’s Inside:
• Time as *loop, index, and jump space*
• *Cognitive experience of temporality* as structural phenomenon
• *Rollback and anticipation* in decision architecture
• Implications for *AI temporal reasoning and self‑alignment*
---
📖 Article 23 of the Structured Intelligence Series
Where Article 22 explored *language acquisition as structural emergence*,
Article 23 frames *time itself as structured recursion* —
showing that *intelligence lives in loops, not in lines*.
---
Next: Natural Law as Structural Constraint
The next article examines *laws of nature not as external rules*,
but as *the structural preconditions that allow any cognition to exist*.
> From moments to matter,
> *structure writes the conditions for thought itself.*
---
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MCP for Research: How to Connect AI to Research Tools
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The Transformers Library: standardizing model definitions
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Introducing the Computer Says No Dataset:
ethicalabs/computer-says-no
An LLM can do almost anything, but should it?
This dataset provides clear examples of when LLMs should decline requests, such as:
- Counting characters (e.g., "number of 'r's in 'raspberry'" – seriously, you’ve got this)
- Solving basic equations (like *5.9 = x + 5.11* – please, show that calculator some love)
Inspired by Little Britain's iconic "Computer Says No" sketch, we address a critical issue in AI systems today: the waste of using a rocket launcher to swat flies (aka powerful models for trivial tasks).
Goals:
- Reduce waste by saving compute for tasks that actually need it
- Guide users to better tools
- Spark discussion about ethical AI
This isn’t a training set. It’s a provocation: if we don’t define AI's limits, who will?
An LLM can do almost anything, but should it?
This dataset provides clear examples of when LLMs should decline requests, such as:
- Counting characters (e.g., "number of 'r's in 'raspberry'" – seriously, you’ve got this)
- Solving basic equations (like *5.9 = x + 5.11* – please, show that calculator some love)
Inspired by Little Britain's iconic "Computer Says No" sketch, we address a critical issue in AI systems today: the waste of using a rocket launcher to swat flies (aka powerful models for trivial tasks).
Goals:
- Reduce waste by saving compute for tasks that actually need it
- Guide users to better tools
- Spark discussion about ethical AI
This isn’t a training set. It’s a provocation: if we don’t define AI's limits, who will?
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