Links for 2024-10-11
AI:
1. Semantic Training Signals Promote Hierarchical Syntactic Generalization in Transformers https://adityayedetore.github.io/assets/pdf/emnlp_2024_semantic_cues_to_hierarchy.pdf
2. Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. Achieves 5 − 6× gain in sample efficiency, 1.5 − 5× more compute-efficiency, and > 6% gain in accuracy, over ORMs on test-time search. https://arxiv.org/abs/2410.08146
3. LLMs are in-context RL learners, but not great because they can’t explore well. How do we teach LLMs to explore better? Solution: Supervised fine-tuning on full exploration trajectories. https://arxiv.org/abs/2410.06238
4. Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification https://arxiv.org/abs/2410.05318
5. Differential Transformer outperforms Transformer when scaling up model size and training tokens. https://arxiv.org/abs/2410.05258
6. Can we build more capable AI agents by learning from cognitive science? Cognitive Architectures for Language Agents (CoALA) introduces a structured approach to design AI Agents by integrating cognitive architecture principles with modern LLMs. https://arxiv.org/abs/2309.02427
7. LLMs have original, research-worthy ideas https://learnandburn.ai/p/llms-have-original-research-worthy
8. The spontaneous emergence of “a sense of beauty” in untrained deep neural networks. https://psycnet.apa.org/record/2025-32757-001
9. Complexity exposure drives intelligence in LLMs, with optimal performance at the "edge of chaos." https://www.arxiv.org/abs/2410.02536
10. Math transformers learn better when trained from repeated examples. https://arxiv.org/html/2410.07041v1
11. LLMs Can In-context Learn Multiple Tasks in Superposition https://arxiv.org/abs/2410.05603
12. “I think there is a good chance that normalizing flow-based variational inference will displace MCMC as the go-to method for Bayesian posterior inference as soon as everyone gets access to good GPUs.” https://statmodeling.stat.columbia.edu/2024/10/08/defining-statistical-models-in-jax/
13. AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs https://arxiv.org/abs/2410.05295
14. Ancestor simulations: Large Language Models based on historical text could offer informative tools for behavioral science https://www.pnas.org/doi/10.1073/pnas.2407639121
15. Can AI Outpredict Humans? Results From Metaculus's Q3 AI Forecasting Benchmark https://www.lesswrong.com/posts/LHdNtJCm93pxNHJKb/can-ai-outpredict-humans-results-from-metaculus-s-q3-ai
Technology:
1. Expansion microscopy seems to be able to expand proteins to the extent that their structure is viewable by optical microscopy. https://www.nature.com/articles/s41587-024-02431-9
2. Google says its research shows the existence of a "stable computationally complex phase" is reachable with current quantum processors. Even with noise, these quantum computers can perform calculations that are beyond the capabilities of classical supercomputers https://research.google/blog/validating-random-circuit-sampling-as-a-benchmark-for-measuring-quantum-progress/
3. Holographic 3D printing has the potential to revolutionize multiple industries, say Concordia researchers https://www.concordia.ca/news/stories/2024/10/08/holographic-3d-printing-has-the-potential-to-revolutionize-multiple-industries-say-concordia-researchers.html
Miscellaneous:
1. A new study adds evidence that consciousness requires communication between sensory and cognitive regions of the brain’s cortex. https://news.mit.edu/2024/how-sensory-prediction-changes-under-anesthesia-tells-us-how-conscious-cognition-works-1010
2. Values Are Real Like Harry Potter https://www.lesswrong.com/posts/a5hpPfABQnrkfGGxb/values-are-real-like-harry-potter
3. “A simple, and hardly unique economic observation: when you are poor, money is additive. As you get more, it becomes multiplicative. And eventually exponential.” https://aleph.se/andart2/uncategorized/additive-multiplicative-and-exponential-economics/
AI:
1. Semantic Training Signals Promote Hierarchical Syntactic Generalization in Transformers https://adityayedetore.github.io/assets/pdf/emnlp_2024_semantic_cues_to_hierarchy.pdf
2. Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. Achieves 5 − 6× gain in sample efficiency, 1.5 − 5× more compute-efficiency, and > 6% gain in accuracy, over ORMs on test-time search. https://arxiv.org/abs/2410.08146
3. LLMs are in-context RL learners, but not great because they can’t explore well. How do we teach LLMs to explore better? Solution: Supervised fine-tuning on full exploration trajectories. https://arxiv.org/abs/2410.06238
4. Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification https://arxiv.org/abs/2410.05318
5. Differential Transformer outperforms Transformer when scaling up model size and training tokens. https://arxiv.org/abs/2410.05258
6. Can we build more capable AI agents by learning from cognitive science? Cognitive Architectures for Language Agents (CoALA) introduces a structured approach to design AI Agents by integrating cognitive architecture principles with modern LLMs. https://arxiv.org/abs/2309.02427
7. LLMs have original, research-worthy ideas https://learnandburn.ai/p/llms-have-original-research-worthy
8. The spontaneous emergence of “a sense of beauty” in untrained deep neural networks. https://psycnet.apa.org/record/2025-32757-001
9. Complexity exposure drives intelligence in LLMs, with optimal performance at the "edge of chaos." https://www.arxiv.org/abs/2410.02536
10. Math transformers learn better when trained from repeated examples. https://arxiv.org/html/2410.07041v1
11. LLMs Can In-context Learn Multiple Tasks in Superposition https://arxiv.org/abs/2410.05603
12. “I think there is a good chance that normalizing flow-based variational inference will displace MCMC as the go-to method for Bayesian posterior inference as soon as everyone gets access to good GPUs.” https://statmodeling.stat.columbia.edu/2024/10/08/defining-statistical-models-in-jax/
13. AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs https://arxiv.org/abs/2410.05295
14. Ancestor simulations: Large Language Models based on historical text could offer informative tools for behavioral science https://www.pnas.org/doi/10.1073/pnas.2407639121
15. Can AI Outpredict Humans? Results From Metaculus's Q3 AI Forecasting Benchmark https://www.lesswrong.com/posts/LHdNtJCm93pxNHJKb/can-ai-outpredict-humans-results-from-metaculus-s-q3-ai
Technology:
1. Expansion microscopy seems to be able to expand proteins to the extent that their structure is viewable by optical microscopy. https://www.nature.com/articles/s41587-024-02431-9
2. Google says its research shows the existence of a "stable computationally complex phase" is reachable with current quantum processors. Even with noise, these quantum computers can perform calculations that are beyond the capabilities of classical supercomputers https://research.google/blog/validating-random-circuit-sampling-as-a-benchmark-for-measuring-quantum-progress/
3. Holographic 3D printing has the potential to revolutionize multiple industries, say Concordia researchers https://www.concordia.ca/news/stories/2024/10/08/holographic-3d-printing-has-the-potential-to-revolutionize-multiple-industries-say-concordia-researchers.html
Miscellaneous:
1. A new study adds evidence that consciousness requires communication between sensory and cognitive regions of the brain’s cortex. https://news.mit.edu/2024/how-sensory-prediction-changes-under-anesthesia-tells-us-how-conscious-cognition-works-1010
2. Values Are Real Like Harry Potter https://www.lesswrong.com/posts/a5hpPfABQnrkfGGxb/values-are-real-like-harry-potter
3. “A simple, and hardly unique economic observation: when you are poor, money is additive. As you get more, it becomes multiplicative. And eventually exponential.” https://aleph.se/andart2/uncategorized/additive-multiplicative-and-exponential-economics/