Some may find the following both useful and humorous—if you can see the humor in it! Used 3.5. (Remember the good old days when "3.5" referred to ChatGPT?) BTW, this looks much better in markdown.
# Top ML Papers of the Week (January 6 - 12)
## Key Points
* Each implication represents a fundamental shift in AI research directions
* The developments suggest convergence of multiple technical approaches
* These changes have significant implications for both theoretical and applied AI
* The findings point to new research opportunities and challenges
## Research Implications
### 1. Convergence of Knowledge Integration Architectures:
* The emergence of multiple complementary approaches (CAG, RAG, Long Context) represents a significant paradigm shift in how AI systems handle and integrate knowledge
* CAG's precomputation of KV caches offers potential performance benefits while maintaining contextual accuracy
* The comparative advantages of different approaches suggest the potential for hybrid systems that combine multiple techniques
* This convergence indicates a move toward more sophisticated and nuanced knowledge integration strategies
#### Technical Overview
The convergence of knowledge integration architectures represents one of the most significant paradigm shifts in modern AI system design. This convergence is characterized by the simultaneous development and integration of multiple complementary approaches to knowledge handling, each with distinct advantages and operational characteristics.
The emergence of Cache-Augmented Generation (CAG) alongside traditional Retrieval-Augmented Generation (RAG) systems marks a fundamental shift in how we approach knowledge integration in AI systems. CAG's innovation lies in its precomputation of key-value (KV) caches, which provides a novel solution to the context-handling problem that has long challenged language models. This approach offers particular advantages in scenarios where the knowledge base is of manageable size and can be preloaded effectively.
The relationship between these different approaches is particularly interesting when we consider their comparative advantages. Long Context (LC) models have demonstrated superior performance in question-answering benchmarks, while summarization-based retrieval approaches have shown comparable performance to LC models. This suggests a natural convergence point where different approaches might be combined to leverage their respective strengths.
The architectural implications of this convergence are profound. We're seeing the emergence of hybrid systems that can dynamically switch between different knowledge integration strategies based on the specific requirements of the task at hand. For instance, a system might use CAG for frequently accessed, well-structured knowledge, while falling back to RAG for more dynamic or expansive knowledge requirements.
This convergence is also driving innovation in how we think about knowledge representation. The traditional distinction between embedded knowledge (stored in model weights) and retrieved knowledge (accessed through external databases) is becoming increasingly blurred. Modern systems are developing more sophisticated ways of combining these different forms of knowledge, leading to more robust and flexible AI architectures.
The implications for system design are significant. Developers must now consider multiple knowledge integration pathways when designing AI systems, leading to more complex but also more capable architectures. This has sparked new research into optimal ways of combining different approaches, including investigations into hybrid architectures that can leverage the strengths of multiple knowledge integration strategies simultaneously.
The convergence is also pushing the boundaries of what's possible in terms of system performance. By combining different approaches, systems can potentially overcome the limitations of any single approach. For example, the combination of CAG's efficient precomputation with RAG's flexibility in handling dynamic knowledge could lead to systems that are both fast and adaptable.
#### Key Follow-up Questions
1. How might the integration of multiple knowledge architectures affect model training and optimization strategies?
2. What are the computational efficiency implications of implementing hybrid knowledge integration systems?
3. How can we effectively evaluate the performance of systems that combine multiple knowledge integration approaches?
* The dramatic improvements in mathematical reasoning capabilities, particularly through rStar-Math, represent a breakthrough in AI's ability to handle complex mathematical tasks
* The combination of code-augmented CoT data synthesis, SLM-based process reward modeling, and iterative self-evolution demonstrates the power of multi-component approaches
* These advances suggest potential applications in fields requiring sophisticated mathematical reasoning
* The improvements indicate a narrowing gap between human and AI mathematical capabilities
#### Technical Overview
The evolution of mathematical reasoning capabilities in Large Language Models represents one of the most significant and challenging frontiers in AI development. Recent research, particularly through developments like rStar-Math and Meta Chain-of-Thought, has revealed both remarkable progress and concerning limitations in how AI systems approach mathematical problem-solving.
The fundamental challenge lies in the distinction between pattern matching and genuine mathematical reasoning. Recent research from Apple has provided compelling evidence that many current LLMs may be primarily engaging in sophisticated pattern matching rather than true mathematical reasoning. This finding has profound implications for how we understand and develop AI systems capable of mathematical computation.
The development of rStar-Math represents a significant advancement in this area, demonstrating dramatic improvements in mathematical reasoning capabilities through its three-component system: code-augmented CoT data synthesis, SLM-based process reward modeling, and iterative self-evolution. The improvement from 58.8% to 90.0% for Qwen2.5-Math-7B represents one of the most significant leaps forward in mathematical reasoning capabilities.
However, these improvements must be contextualized within the broader understanding of AI's limitations in mathematical reasoning. The GSM-Symbolic benchmark has revealed that even state-of-the-art models show noticeable variance when responding to different instantiations of the same question. This suggests that while models can achieve impressive performance metrics, their underlying understanding of mathematical concepts may be less robust than their results might suggest.
Process Reinforcement through Implicit Rewards represents another significant advancement, particularly in its approach to online reinforcement learning. The achievement of 26.7% pass@1 on AIME 2024 demonstrates that while progress is being made, there remains a substantial gap between AI and human-level mathematical reasoning capabilities.
The field is now at a crucial juncture where we must distinguish between improvements in pattern matching capabilities and genuine advances in mathematical reasoning. This distinction has important implications for how we develop and evaluate AI systems intended for mathematical tasks.
#### Key Follow-up Questions
1. How might we develop more robust evaluation metrics that can distinguish between pattern matching and genuine mathematical reasoning?
2. What role might hybrid approaches, combining symbolic and neural methods, play in advancing mathematical reasoning capabilities?
3. How can we better incorporate formal mathematical principles into the training of AI systems?
* The development of physical AI systems represents a bridge between virtual and real-world applications
* Integration with robotics and real-world systems suggests expanding capabilities for embodied intelligence
* The Cosmos World Foundation Model demonstrates the potential for safe learning in digital environments
* This integration indicates a move toward more practical and applied AI systems
#### Technical Overview
The integration of AI into physical systems represents one of the most significant frontiers in artificial intelligence, marking a crucial transition from purely virtual to embodied intelligence. The Cosmos World Foundation Model and emerging research in physical AI systems demonstrate a fundamental shift in how we approach the merger of digital and physical realms.
Physical AI integration is characterized by several key technological developments. The emergence of digital twins and pre-trained world foundation models allows AI systems to safely learn and interact in simulated environments before deployment in the physical world. This approach significantly reduces the risks associated with hardware damage while allowing for rapid iteration and improvement of AI systems.
The architecture of physical AI systems is particularly noteworthy. These systems typically incorporate multiple layers of sensing, processing, and actuation capabilities. Sensors gather environmental data, which is processed through sophisticated AI algorithms, leading to physical actions through actuators. This creates a continuous feedback loop where the system learns from its interactions with the physical world.
The implications for robotics and autonomous systems are profound. Physical AI systems are showing increasing capability in tasks requiring fine motor control, environmental adaptation, and real-time decision making. The integration of these systems into healthcare, manufacturing, and autonomous vehicles demonstrates their growing practical utility.
A particularly significant development is the emergence of hybrid systems that combine traditional robotics with advanced AI capabilities. These systems can learn from experience, adapt to new situations, and handle complex physical interactions that were previously beyond the capability of conventional robotics.
The challenges in this field are substantial. Issues of safety, reliability, and predictability must be addressed, particularly when AI systems are operating in unstructured physical environments. The need for significant infrastructure changes to support these systems, including enhanced data processing capabilities and improved energy management, presents both technical and logistical challenges.
Looking forward, the field is moving toward increasingly sophisticated integration between AI systems and physical hardware. This includes developments in areas such as tactile sensing, advanced materials that can respond to AI control, and improved methods for physical-digital interaction.
#### Key Follow-up Questions
1. How might advances in quantum computing affect the development of physical AI systems?
2. What role will edge computing play in the future of physical AI integration?
3. How can we better address the safety concerns associated with physical AI systems?
### 4. Question Generation and Model Comprehension:
* Research on LLM question generation reveals important patterns in how AI systems process and inquire about information
* The findings suggest distinct differences between human and AI-generated questions
* These insights have implications for improving AI system comprehension and interaction
* The research points to potential improvements in educational and interactive AI applications
#### Technical Overview
The research on LLM question generation capabilities reveals fundamental insights into how AI systems process and inquire about information. This area represents a crucial intersection between natural language processing, cognitive science, and educational technology.
Recent findings demonstrate distinct patterns in how AI systems approach question generation compared to humans. The research shows that LLMs exhibit a strong preference for asking about specific facts and figures, suggesting a bias toward concrete, verifiable information rather than abstract or conceptual understanding. This tendency manifests in questions that typically require longer, more detailed answers than human-generated questions.
A particularly interesting finding is the distribution pattern of questions across source materials. While human-generated questions tend to concentrate on the beginning of contextual materials, LLM-generated questions show a more balanced distribution across the entire context, with only a slight decrease in focus at both ends. This suggests that LLMs process information more uniformly across the entire input, rather than exhibiting the primacy bias common in human cognition.
The length characteristics of LLM-generated questions are also noteworthy, with different models showing distinct preferences for question length, typically averaging around 20 words. This consistency across models suggests an underlying pattern in how AI systems structure their inquiries, possibly related to the training data and architectural constraints of the models.
The implications for educational applications are significant. The tendency of LLMs to generate questions requiring longer answers suggests they might be particularly useful for creating comprehensive assessment materials. However, this also raises questions about the pedagogical appropriateness of such questions, particularly for different educational levels and learning objectives.
The research also reveals important limitations in current question generation capabilities. While LLMs can generate grammatically correct and contextually relevant questions, they may not always capture the nuanced understanding that characterizes expert human educators' questions. This suggests a need for more sophisticated approaches to question generation that can better mirror human cognitive processes.
#### Key Follow-up Questions
1. How might we better align LLM question generation patterns with human cognitive processes?
2. What role could hybrid systems play in combining the strengths of both human and AI question generation?
3. How can we develop more sophisticated evaluation metrics for assessing the pedagogical value of AI-generated questions?
Thanks, Elvis! Your newsletter is quite helpful to keep in touch with the latest in AI.
What follows in the next two comments is based on the ten papers Elvis chose for this week. I'm noting this just in case it isn't clear.
I'm also sharing links to the Emergent Mind summaries for five of this week's papers:
Transformer^2: Self-adaptive LLMs
https://www.emergentmind.com/research/18d6a640fc53e5b7258dda4a
MiniMax-01: Scaling Foundation Models with Lightning Attention
https://www.emergentmind.com/research/43e061ce569499d2685491f1
Titans: Learning to Memorize at Test Time
https://www.emergentmind.com/assistant/35317107d23ca7cbd602bc77
Enhancing Retrieval-Augmented Generation: A Study of Best Practices
https://www.emergentmind.com/research/4db5b7e30566f56c046175c6
ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning
https://www.emergentmind.com/research/83a083be8c5c1f43ce00234c
Full disclosure: Although I'm a huge fan of Emergent Mind, I have no financial, official, or legal interest in this CS research tool.
Some may find the following both useful and humorous—if you can see the humor in it! Used 3.5. (Remember the good old days when "3.5" referred to ChatGPT?) BTW, this looks much better in markdown.
# Top ML Papers of the Week (January 6 - 12)
## Key Points
* Each implication represents a fundamental shift in AI research directions
* The developments suggest convergence of multiple technical approaches
* These changes have significant implications for both theoretical and applied AI
* The findings point to new research opportunities and challenges
## Research Implications
### 1. Convergence of Knowledge Integration Architectures:
* The emergence of multiple complementary approaches (CAG, RAG, Long Context) represents a significant paradigm shift in how AI systems handle and integrate knowledge
* CAG's precomputation of KV caches offers potential performance benefits while maintaining contextual accuracy
* The comparative advantages of different approaches suggest the potential for hybrid systems that combine multiple techniques
* This convergence indicates a move toward more sophisticated and nuanced knowledge integration strategies
#### Technical Overview
The convergence of knowledge integration architectures represents one of the most significant paradigm shifts in modern AI system design. This convergence is characterized by the simultaneous development and integration of multiple complementary approaches to knowledge handling, each with distinct advantages and operational characteristics.
The emergence of Cache-Augmented Generation (CAG) alongside traditional Retrieval-Augmented Generation (RAG) systems marks a fundamental shift in how we approach knowledge integration in AI systems. CAG's innovation lies in its precomputation of key-value (KV) caches, which provides a novel solution to the context-handling problem that has long challenged language models. This approach offers particular advantages in scenarios where the knowledge base is of manageable size and can be preloaded effectively.
The relationship between these different approaches is particularly interesting when we consider their comparative advantages. Long Context (LC) models have demonstrated superior performance in question-answering benchmarks, while summarization-based retrieval approaches have shown comparable performance to LC models. This suggests a natural convergence point where different approaches might be combined to leverage their respective strengths.
The architectural implications of this convergence are profound. We're seeing the emergence of hybrid systems that can dynamically switch between different knowledge integration strategies based on the specific requirements of the task at hand. For instance, a system might use CAG for frequently accessed, well-structured knowledge, while falling back to RAG for more dynamic or expansive knowledge requirements.
This convergence is also driving innovation in how we think about knowledge representation. The traditional distinction between embedded knowledge (stored in model weights) and retrieved knowledge (accessed through external databases) is becoming increasingly blurred. Modern systems are developing more sophisticated ways of combining these different forms of knowledge, leading to more robust and flexible AI architectures.
The implications for system design are significant. Developers must now consider multiple knowledge integration pathways when designing AI systems, leading to more complex but also more capable architectures. This has sparked new research into optimal ways of combining different approaches, including investigations into hybrid architectures that can leverage the strengths of multiple knowledge integration strategies simultaneously.
The convergence is also pushing the boundaries of what's possible in terms of system performance. By combining different approaches, systems can potentially overcome the limitations of any single approach. For example, the combination of CAG's efficient precomputation with RAG's flexibility in handling dynamic knowledge could lead to systems that are both fast and adaptable.
#### Key Follow-up Questions
1. How might the integration of multiple knowledge architectures affect model training and optimization strategies?
2. What are the computational efficiency implications of implementing hybrid knowledge integration systems?
3. How can we effectively evaluate the performance of systems that combine multiple knowledge integration approaches?
Selected Sources:
(1) https://www.nature.com/articles/s44304-024-00014-x
(2) https://www.sciencedirect.com/science/article/pii/S277250302400032X
(3) https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00361-2
(4) https://marketing.pinecc.com/blog/the-convergence-of-ai/ml/dl-in-network-infrastructure
(5) https://www.esann.org/sites/default/files/proceedings/2024/ES2024-5.pdf
(6) https://pmc.ncbi.nlm.nih.gov/articles/PMC10053494/
(7) https://www.mdpi.com/2075-5309/13/7/1863
(8) https://www.sciencedirect.com/science/article/pii/S2666675821001041
### 2. Mathematical Reasoning Capabilities:
* The dramatic improvements in mathematical reasoning capabilities, particularly through rStar-Math, represent a breakthrough in AI's ability to handle complex mathematical tasks
* The combination of code-augmented CoT data synthesis, SLM-based process reward modeling, and iterative self-evolution demonstrates the power of multi-component approaches
* These advances suggest potential applications in fields requiring sophisticated mathematical reasoning
* The improvements indicate a narrowing gap between human and AI mathematical capabilities
#### Technical Overview
The evolution of mathematical reasoning capabilities in Large Language Models represents one of the most significant and challenging frontiers in AI development. Recent research, particularly through developments like rStar-Math and Meta Chain-of-Thought, has revealed both remarkable progress and concerning limitations in how AI systems approach mathematical problem-solving.
The fundamental challenge lies in the distinction between pattern matching and genuine mathematical reasoning. Recent research from Apple has provided compelling evidence that many current LLMs may be primarily engaging in sophisticated pattern matching rather than true mathematical reasoning. This finding has profound implications for how we understand and develop AI systems capable of mathematical computation.
The development of rStar-Math represents a significant advancement in this area, demonstrating dramatic improvements in mathematical reasoning capabilities through its three-component system: code-augmented CoT data synthesis, SLM-based process reward modeling, and iterative self-evolution. The improvement from 58.8% to 90.0% for Qwen2.5-Math-7B represents one of the most significant leaps forward in mathematical reasoning capabilities.
However, these improvements must be contextualized within the broader understanding of AI's limitations in mathematical reasoning. The GSM-Symbolic benchmark has revealed that even state-of-the-art models show noticeable variance when responding to different instantiations of the same question. This suggests that while models can achieve impressive performance metrics, their underlying understanding of mathematical concepts may be less robust than their results might suggest.
Process Reinforcement through Implicit Rewards represents another significant advancement, particularly in its approach to online reinforcement learning. The achievement of 26.7% pass@1 on AIME 2024 demonstrates that while progress is being made, there remains a substantial gap between AI and human-level mathematical reasoning capabilities.
The field is now at a crucial juncture where we must distinguish between improvements in pattern matching capabilities and genuine advances in mathematical reasoning. This distinction has important implications for how we develop and evaluate AI systems intended for mathematical tasks.
#### Key Follow-up Questions
1. How might we develop more robust evaluation metrics that can distinguish between pattern matching and genuine mathematical reasoning?
2. What role might hybrid approaches, combining symbolic and neural methods, play in advancing mathematical reasoning capabilities?
3. How can we better incorporate formal mathematical principles into the training of AI systems?
Selected Sources:
(1) https://machinelearning.apple.com/research/gsm-symbolic
(2) https://arxiv.org/html/2412.16075v1
(3) https://www.akira.ai/blog/the-evolution-of-mathematical-reasoning-in-llms
(4) https://www.ibm.com/think/news/apple-llm-reasoning
(5) https://epoch.ai/frontiermath/the-benchmark
(6) https://researchfunding.duke.edu/artificial-intelligence-formal-methods-and-mathematical-reasoning-aiming
### 3. Physical AI Integration:
* The development of physical AI systems represents a bridge between virtual and real-world applications
* Integration with robotics and real-world systems suggests expanding capabilities for embodied intelligence
* The Cosmos World Foundation Model demonstrates the potential for safe learning in digital environments
* This integration indicates a move toward more practical and applied AI systems
#### Technical Overview
The integration of AI into physical systems represents one of the most significant frontiers in artificial intelligence, marking a crucial transition from purely virtual to embodied intelligence. The Cosmos World Foundation Model and emerging research in physical AI systems demonstrate a fundamental shift in how we approach the merger of digital and physical realms.
Physical AI integration is characterized by several key technological developments. The emergence of digital twins and pre-trained world foundation models allows AI systems to safely learn and interact in simulated environments before deployment in the physical world. This approach significantly reduces the risks associated with hardware damage while allowing for rapid iteration and improvement of AI systems.
The architecture of physical AI systems is particularly noteworthy. These systems typically incorporate multiple layers of sensing, processing, and actuation capabilities. Sensors gather environmental data, which is processed through sophisticated AI algorithms, leading to physical actions through actuators. This creates a continuous feedback loop where the system learns from its interactions with the physical world.
The implications for robotics and autonomous systems are profound. Physical AI systems are showing increasing capability in tasks requiring fine motor control, environmental adaptation, and real-time decision making. The integration of these systems into healthcare, manufacturing, and autonomous vehicles demonstrates their growing practical utility.
A particularly significant development is the emergence of hybrid systems that combine traditional robotics with advanced AI capabilities. These systems can learn from experience, adapt to new situations, and handle complex physical interactions that were previously beyond the capability of conventional robotics.
The challenges in this field are substantial. Issues of safety, reliability, and predictability must be addressed, particularly when AI systems are operating in unstructured physical environments. The need for significant infrastructure changes to support these systems, including enhanced data processing capabilities and improved energy management, presents both technical and logistical challenges.
Looking forward, the field is moving toward increasingly sophisticated integration between AI systems and physical hardware. This includes developments in areas such as tactile sensing, advanced materials that can respond to AI control, and improved methods for physical-digital interaction.
#### Key Follow-up Questions
1. How might advances in quantum computing affect the development of physical AI systems?
2. What role will edge computing play in the future of physical AI integration?
3. How can we better address the safety concerns associated with physical AI systems?
Selected Sources:
(1) https://www.purdue.edu/research/features/stories/emerging-field-of-physical-ai-takes-shape-in-wide-ranging-discussion/
(2) https://www.xenonstack.com/blog/physical-ai
(3) https://www.rinf.tech/unlocking-the-future-a-comprehensive-guide-to-physical-ai/
(4) https://foundation4pt.org/how-ai-is-set-to-transform-physical-therapy-insights-from-edelle-field-fote/
(5) https://pmc.ncbi.nlm.nih.gov/articles/PMC11668540/
(6) https://humanfactors.jmir.org/2024/1/e55964
(7) https://www.purdue.edu/computes/institute-for-physical-artificial-intelligence/
### 4. Question Generation and Model Comprehension:
* Research on LLM question generation reveals important patterns in how AI systems process and inquire about information
* The findings suggest distinct differences between human and AI-generated questions
* These insights have implications for improving AI system comprehension and interaction
* The research points to potential improvements in educational and interactive AI applications
#### Technical Overview
The research on LLM question generation capabilities reveals fundamental insights into how AI systems process and inquire about information. This area represents a crucial intersection between natural language processing, cognitive science, and educational technology.
Recent findings demonstrate distinct patterns in how AI systems approach question generation compared to humans. The research shows that LLMs exhibit a strong preference for asking about specific facts and figures, suggesting a bias toward concrete, verifiable information rather than abstract or conceptual understanding. This tendency manifests in questions that typically require longer, more detailed answers than human-generated questions.
A particularly interesting finding is the distribution pattern of questions across source materials. While human-generated questions tend to concentrate on the beginning of contextual materials, LLM-generated questions show a more balanced distribution across the entire context, with only a slight decrease in focus at both ends. This suggests that LLMs process information more uniformly across the entire input, rather than exhibiting the primacy bias common in human cognition.
The length characteristics of LLM-generated questions are also noteworthy, with different models showing distinct preferences for question length, typically averaging around 20 words. This consistency across models suggests an underlying pattern in how AI systems structure their inquiries, possibly related to the training data and architectural constraints of the models.
The implications for educational applications are significant. The tendency of LLMs to generate questions requiring longer answers suggests they might be particularly useful for creating comprehensive assessment materials. However, this also raises questions about the pedagogical appropriateness of such questions, particularly for different educational levels and learning objectives.
The research also reveals important limitations in current question generation capabilities. While LLMs can generate grammatically correct and contextually relevant questions, they may not always capture the nuanced understanding that characterizes expert human educators' questions. This suggests a need for more sophisticated approaches to question generation that can better mirror human cognitive processes.
#### Key Follow-up Questions
1. How might we better align LLM question generation patterns with human cognitive processes?
2. What role could hybrid systems play in combining the strengths of both human and AI question generation?
3. How can we develop more sophisticated evaluation metrics for assessing the pedagogical value of AI-generated questions?
Selected Sources:
(1) https://link.springer.com/article/10.1007/s40593-023-00374-x
(2) https://aclanthology.org/2023.bea-1.22.pdf
(3) https://arxiv.org/html/2402.18267v1
(4) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226369/
(5) https://link.springer.com/article/10.1007/s13748-023-00295-9
(6) https://dl.acm.org/doi/10.1145/3468889
## Wrap-Up
### Additional Follow-up Questions
1. How might the integration of these different approaches lead to new AI architectures?
2. What are the computational requirements for implementing these combined approaches?
3. How can we better evaluate the robustness of these systems across different domains?
Selected Sources:
(1) https://research.ibm.com/blog/retrieval-augmented-generation-RAG
(2) https://cloud.google.com/transform/the-prompt-what-are-long-context-windows-and-why-do-they-matter
(3) https://arxiv.org/html/2412.16075v1
(4) https://www.purdue.edu/research/features/stories/emerging-field-of-physical-ai-takes-shape-in-wide-ranging-discussion/
(5) https://arxiv.org/html/2412.15605v1
(6) https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/
(7) https://pmc.ncbi.nlm.nih.gov/articles/PMC11255529/
(8) https://aclanthology.org/2023.bea-1.22.pdf
(9) https://legal.thomsonreuters.com/blog/retrieval-augmented-generation-in-legal-tech/
(10) https://www.imperial.ac.uk/news/208053/skills-development-physical-ai-could-cultivate/
(11) https://link.springer.com/article/10.1007/s13748-023-00295-9
(12) https://arxiv.org/pdf/2410.05229
(13) https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1484848/full
(14) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226369/
(15) https://new.nsf.gov/funding/opportunities/aiming-artificial-intelligence-formal-methods-mathematical
(16) https://arxiv.org/abs/2204.02908
(17) https://arxiv.org/abs/2410.21252
(18) https://openreview.net/forum?id=th63j8qHa6
(19) https://www.sciencedirect.com/science/article/pii/S2095254623000959
(20) https://ieeexplore.ieee.org/document/10542827/
(21) https://community.openai.com/t/foundational-must-read-gpt-llm-papers/197003?page=3
(22) https://openai.com/index/improving-mathematical-reasoning-with-process-supervision/
(23) https://www.mdpi.com/2076-3417/14/22/10616
(24) https://link.springer.com/article/10.1007/s40593-023-00374-x
(25) https://pub.towardsai.net/cache-augmented-generation-cag-vs-retrieval-augmented-generation-rag-c0b3726d187b
(26) https://cloud.google.com/use-cases/retrieval-augmented-generation
(27) https://ai.meta.com/blog/ai-math-theorem-proving/
(28) https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088229
(29) https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
(30) https://aclanthology.org/2022.bea-1.26.pdf
(31) https://www.sciencedirect.com/science/article/pii/S2451958824001246
(32) https://www.sciencedirect.com/science/article/pii/S2666920X24001012
Really interesting stuff!