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codelion 
posted an update 8 days ago
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3348
🧠 We just implemented Andrej Karpathy's "third paradigm" for LLM learning!

System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.

🚀 How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.

📊 Results across math benchmarks:
Arena Hard: 29% → 37.6% (+8.6%)
AIME24: 23.33% → 30% (+6.67%)
OptILLMBench: 61% → 65% (+4%)

The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.

✨ Key benefits:
🔄 Cumulative learning over time
📖 Transparent, inspectable strategies
🔌 Works with any OpenAI-compatible API
⚡ Simple integration: just add "spl-" prefix to your model

Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!

This feels like a genuine step toward AI that learns from experience while staying completely interpretable.

🔗 GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
📖 Full article: https://huggingface.co/blog/codelion/system-prompt-learning
🐦 Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486

Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?
codelion 
posted an update 13 days ago
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2330
Introducing AutoThink: Adaptive reasoning for LLMs that improves performance by 43% on reasoning benchmarks!

Instead of using fixed thinking budgets, AutoThink:
- Classifies query complexity (HIGH/LOW) using adaptive classification
- Dynamically allocates thinking tokens based on complexity
- Uses steering vectors derived from Pivotal Token Search to guide reasoning patterns

Results on DeepSeek-R1-Distill-Qwen-1.5B:
- GPQA-Diamond: 31.06% vs 21.72% baseline (+9.34 points)
- MMLU-Pro: 26.38% vs 25.58% baseline (+0.8 points)
- Uses fewer tokens than baseline approaches

Works with any local reasoning model - DeepSeek, Qwen, Llama, custom models. The technique combines our research on Pivotal Token Search (PTS) implementation and adaptive classification frameworks.

Paper: AutoThink: efficient inference for reasoning LLMs
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327

Code and examples:
https://github.com/codelion/optillm/tree/main/optillm/autothink

PTS implementation and technical details:
https://github.com/codelion/pts
https://huggingface.co/blog/codelion/pts

Adaptive classifier framework:
https://github.com/codelion/adaptive-classifier

Would love to hear your thoughts on adaptive resource allocation for LLM reasoning! Have you experimented with similar approaches?
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codelion 
posted an update 20 days ago
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2826
🧬 Hey everyone! Just released **OpenEvolve** - an open-source implementation of Google DeepMind's AlphaEvolve system.

It's an evolutionary coding agent that uses LLMs to discover and optimize algorithms. I successfully replicated DeepMind's results on circle packing (99.97% match!) and evolved a random search into a simulated annealing algorithm.

✨ Key features:
- Evolves entire codebases (not just single functions)
- Works with any OpenAI-compatible API
- LLM ensemble approach for better results
- Multi-objective optimization

👉 Check it out:
GitHub: https://github.com/codelion/openevolve
Blog post: https://huggingface.co/blog/codelion/openevolve

Would love to hear your thoughts or answer any questions about it!
codelion 
posted an update 22 days ago
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2401
Introducing Pivotal Token Search (PTS): A new technique for targeted LLM alignment

Excited to share Pivotal Token Search (PTS), a technique for identifying and optimizing critical decision points in LLM generations!

GitHub repository: https://github.com/codelion/pts

What is PTS?
PTS helps identify specific "pivotal tokens" that dramatically shift the probability of a successful generation. Unlike traditional DPO which treats all tokens equally, PTS focuses optimization on the tokens that actually matter for success.

Inspired by Microsoft's recent Phi-4 paper (which used this technique to achieve SOTA reasoning with only 14B parameters), PTS is especially effective for:
- Mathematical reasoning
- Coding tasks
- Multi-step problem solving
- Any domain where specific decision points strongly impact outcomes

What we're releasing today: codelion/pivotal-token-search-68241145d8b8502122f3ce4f

1. Open-source code:
- Complete implementation of the PTS algorithm
- Data generation pipelines
- Usage examples and documentation

2. Huggingface resources:
- Datasets collection: https://huggingface.co/datasets?other=pts
* Pre-generated preference pairs for various domains
* Ready to use in your DPO training pipelines

- Models collection: https://huggingface.co/models?other=pts
* Pre-trained models fine-tuned with PTS
* Specialized versions for different reasoning tasks

The algorithm is straightforward to implement and can significantly improve your model's reasoning capabilities. Check out the repository for details on getting started!

We welcome feedback, contributions, and collaborations. Let us know if you use PTS in your projects!
codelion 
updated a model 3 months ago
codelion 
published a model 3 months ago
codelion 
updated a model 4 months ago
codelion 
published a model 4 months ago
codelion 
updated a Space 5 months ago
codelion 
updated a model 5 months ago
codelion 
published a model 5 months ago
codelion 
published a Space 5 months ago
codelion 
authored 3 papers 9 months ago
codelion 
posted an update 10 months ago
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2324
We recently worked with OpenAI to fine-tune gpt-4o and built the SOTA model for the patched-codes/static-analysis-eval benchmark. All the code and data patched-codes/synth-vuln-fixes on how we did it is available on their GitHub - https://github.com/openai/build-hours/tree/main/5-4o_fine_tuning.

Here are some tips based on our experience:

→ Establish baseline with "conditioning" / prompting

→ Task-specific datasets are ideal for PEFT; hard to beat gpt-4o on "broad" tasks

→ Add your best system prompt to each example

→ Ensure training data distribution is similar to inference data

→ Shorten instructions with concise prompts; may require more examples.

→ Define clear evaluation metrics (seriously, please eval!)

You can see more details on the benchmark and process here - https://www.patched.codes/blog/the-static-analysis-evaluation-benchmark-measuring-llm-performance-in-fixing-software-vulnerabilities
codelion 
posted an update 11 months ago
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2886
A new paper titled "STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis" shows the benefits of integrating static analysis with LLMs. (https://arxiv.org/abs/2406.10018)

Authors evaluate 4 key questions:

- How does each static analysis integration strategy perform in LLM-based repository-level code completion?
> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.

- How do different combinations of integration strategies affect LLM-based repository-level code completion?
> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.

- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?
> Static analysis and RAG are complementary and boost the overall accuracy.

- What are the online costs of different integration strategies in LLM-based repository-level code completion?
> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.

In my @owasp App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - https://www.youtube.com/watch?v=Cw4-ZnUNVLs)
codelion 
posted an update 11 months ago
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2246
LLM-Assisted Patching of Polyfill Supply Chain Attack

A recent supply chain attack on polyfill.io affected over 100,000 websites (see https://www.patched.codes/blog/patching-the-polyfill-supply-chain-attack). To address this issue, we show how developers can leverage Large Language Models (LLMs) for efficient vulnerability patching:

1. Automated Detection: Using Semgrep rules (see https://semgrep.dev/playground/r/KxUvD7w/asankhaya_personal_org.polyfill-compromise-copy) to identify vulnerable code.

2. LLM-Powered Patching: Utilizing Patchwork (https://github.com/patched-codes/patchwork), an open-source solution that employs LLMs to automatically fix vulnerabilities.

3. Custom Workflows: The "Fixpolyfill" patchflow (https://github.com/patched-codes/patchwork-configs/tree/main/patchflows/Fixpolyfill) , tailored for this specific attack, can be easily run across multiple repositories.

4. Scalable Solutions: Options to scan and patch entire GitHub/GitLab organizations, with automated pull request generation.

5. Rapid Response: LLM-assisted patching enables swift action to minimize damage from supply chain attacks.

This approach demonstrates how LLMs can be effectively used to quickly respond to and remediate widespread security vulnerabilities in code.
codelion 
posted an update 12 months ago
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The new Claude Sonnet 3.5 model from Anthropic AI has been getting good reviews on since last night. It is quite good at coding related tasks. We tried it on the Static Analysis Eval benchmark ( patched-codes/static-analysis-eval) which measures the ability of a LLM to fix vulnerabilities. The model scores 59.21% which is good but not better than other frontier models (like GPT-4, Gemini-1.5 and LLama-3).
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codelion 
posted an update 12 months ago