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In this work, we aim to address the problem of extracting information about the underlying HMM using the residual stream, without needing to know the MSP already. To do so, we use the R^2 of the linear regression as a reward signal for evolutionary algorithms, which are deployed to search for the parameters that generated the source HMM. We find that for toy scenarios where the HMM is generated by a small set of latent variables, the $R^2$ reward signal is remarkably smooth and the evolutionary algorithms succeed in approximately recovering the original HMM. 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We explored a traditional difference-in-means approach, using the activations of the models to define the belief states. We also used a smaller dimensional space that encodes the theoretical belief state geometry of a given HMM and show that while both methods allow to steer models\u2019 behaviors the difference-in-means approach is more robust.\"})});export const richText234=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Gon\\xe7alo Paulo, Sinem Erisken, Tassilo Neubauer\"})});export const richText235=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"SimplexBreath\"})});export const richText236=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"MrGonao#1233, orbitsoferis#2992, son_of_hypnos\"})});export const richText237=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"For this hackathon, we handcrafted a network to perfectly predict next token probabilities for the Random-Random-XOR process.  The network takes existing tokens from the process's output, computes several features on these tokens, and then uses these features to calculate next token probabilities.  These probabilities match those from a process simulator (also coded for this hackathon).  The handcrafted network is our core contribution and we describe it in Section 3 of this writeup.   Prior work has demonstrated that a network trained on the Random-Random-XOR process approximates the 36 possible belief states.  Our network does not directly calculate these belief states, demonstrating that networks trained on Hidden Markov Models may not need to comprehend all belief states. Our hope is that this work will aid in the interpretability of neural networks trained on Hidden Markov Models by demonstrating potential shortcuts that neural networks can take.\"})});export const richText238=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Rick Goldstein\"})});export const richText239=/*#__PURE__*/t(r.Fragment,{children:[/*#__PURE__*/e(\"p\",{children:\"For this hackathon, we handcrafted a network to perfectly predict next token probabilities for the Random-Random-XOR process.  The network takes existing tokens from the process's output, computes several features on these tokens, and then uses these features to calculate next token probabilities.  These probabilities match those from a process simulator (also coded for this hackathon).  The handcrafted network is our core contribution and we describe it in Section 3 of this writeup.  \"}),/*#__PURE__*/e(\"p\",{children:\"Prior work has demonstrated that a network trained on the Random-Random-XOR process approximates the 36 possible belief states.  Our network does not directly calculate these belief states, demonstrating that networks trained on Hidden Markov Models may not need to comprehend all belief states. Our hope is that this work will aid in the interpretability of neural networks trained on Hidden Markov Models by demonstrating potential shortcuts that neural networks can take.\"})]});export const richText240=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"rickg4888\"})});export const richText241=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"This work attempts to explore how an approach based on computational mechanics can cope when a more complex hierarchical generative process is involved, i.e, a process that comprises Hidden Markov Models (HMMs) whose transition probabilities change over time.  We find that small transformer models are capable of modeling such changes in an HMM. 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I emphasize the need for robust strategies to mitigate these risks through measures like data security, AI transparency, bias detection, regulatory frameworks, and public awareness efforts.\"}),/*#__PURE__*/e(\"p\",{children:\"In Conclusion, I argue that while AI offers promising potential for more efficient and credible elections in Nigeria and Africa, realizing these benefits requires carefully addressing the associated technological, social, and political challenges in a proactive and rigorous manner.\"})]});export const richText358=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Team 5\"})});export const richText359=/*#__PURE__*/e(r.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"hayhem.\"})});export const richText360=/*#__PURE__*/t(r.Fragment,{children:[/*#__PURE__*/e(\"p\",{children:\"I'm presenting 2 different Approaches to jailbreak closed source LLMs which provide a finetuning API. 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