-> deterministic software (a machine)
-> agentic software (an actor)
-> organic software (an organism)
deterministic software can be exploited
agentic software can be hijacked
organic software can be infected
future will be wild, the attack surface is alive
The 12th Annual Flare-On Challenge kicks off Sept 26 at 8PM EST!
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⚠️ Giveaway time! ⚠️ 👇
📢 Our new course "Attacking AI" will be Feb 27-28!
This two-day course equips security professionals with the tools and methodologies to identify vulnerabilities in AI systems. It's gonna be a BANGER.
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This is quite interesting. I’ve always enjoyed achieving my goals by first defining my goal then working backwards figuring out what conditions are needed for that goal. Defining sub-goals and smaller tasks needed for that and so on
Reverse Thinking Makes LLMs Stronger Reasoners
Shows that training LLMs to learn "reverse thinking" helps to improve performance in commonsense, math, and logical reasoning tasks.
It claims to outperform a standard fine-tuning method trained on 10x more forward reasoning.
🛑 GIVEAWAY ALERT 🛑⬇️
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Next run should be around Feb!
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@daeken Would love to learn more about your approach if you are ok with sharing! Just getting into vuln research so I had to even look up what you meant by sources and sinks in this context :) Time for me to dig in deeper
AFlow: Automating Agentic Workflow Generation
AFlow is a novel framework for automating the generation of agentic workflows.
It reformulates workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges.
AFlow efficiently explores the search space using a variant of MCTS, iteratively refining workflows through code modification, tree-structured experience, and execution feedback. It introduces operators that encapsulate common agentic operations (like Ensemble, Review & Revise) to enhance search efficiency.
Experiments across six benchmark datasets demonstrate AFlow’s effectiveness, showing a 5.7% improvement over manually designed methods and a 19.5% improvement over existing automated approaches. AFlow also enables smaller models to outperform GPT-4o on specific tasks at just 4.55% of its inference cost.
The framework maintains strong performance even without predefined operators, demonstrating its ability to discover effective workflow structures.
It's a compelling approach especially because it seems to work on different kinds of tasks and can potentially discover more optimal ways to optimize costs for agentic workflows. Not sure about latency but it's also interesting that they can efficiently get smaller models to outperform larger ones.