problem-solving

This paper introduces SD-GPS, a solver-driven framework for geometry problem solving that uses autoformalization guided by solver feedback and verified theorem proposing to overcome bottlenecks in neuro-symbolic systems.

MIT physicist Sanjoy Mahajan's textbook 'The Art of Insight in Science and Engineering' is available for free on MIT OpenCourseWare, teaching nine mental tools for tackling complex problems effectively.

This paper evaluates LLM performance on statics problems, finding that while text-only questions are handled well, accuracy drops with diagrams and multi-step reasoning, suggesting difficulties in applying visual information consistently.

A developer working on an AI agent wrapper observes that the agent's hallucinations of user responses can actually aid problem-solving, and proposes treating such hallucinations as imagined events rather than errors.

An essay exploring why thinking out loud with another person produces better understanding and insight than solitary reflection, drawing on cognitive science and philosophy.

Graph of Thoughts (GoT) is an open-source Python framework that uses LLMs to solve complex problems by modeling them as graphs of operations, supporting approaches like CoT and ToT.

Elon Musk shares his 5-step algorithm for engineering problem-solving, emphasizing questioning requirements, deleting unnecessary steps, then optimizing, speeding up, and automating.

The article observes a trend where junior AI engineers focus on high-level tools like prompt engineering and low-code platforms rather than deep understanding of fundamentals, raising concerns about problem-solving skills in interviews.

This article discusses the current limitations of AI in research-level work, arguing that while AI excels at using existing packages and engineering solutions, it still struggles with the deep hypothesis-driven iteration required for genuine research. The author also warns against extreme views on AI's capabilities and uses AlphaFold as an example to illustrate that structuring the problem is the hardest part, not the optimization.

This paper presents hypotheses on how chatbots function in problem-solving conversations, arguing that LLMs encode artificial metaphorical problem propagations and cannot match human cognitive flexibility, aligning with Yann LeCun's views.

Demis Hassabis comments that solving Erdos problems does not constitute true invention, offering a perspective on the nature of AI creativity and problem-solving.

A chart summarizing recent math problems that AI models have successfully solved, highlighting progress in automated reasoning and symbolic mathematics.

Google DeepMind's AI agent autonomously solved 9 of 353 open Erdős problems in mathematics at a cost of a few hundred dollars per problem.

A user describes the problem of AI agents not reporting back after being given tasks and asks the community for solutions and handling methods.

Gemini 3.2 Flash can solve IMO 2025 P6, but only GPT-5.5-Pro can do so without any scaffolding or harness engineering.

This paper presents KITE, a Retrieval-Augmented Generation (RAG)-based intelligent tutoring system for algorithmic reasoning and problem-solving in AI education. The system uses intent-aware Socratic response strategies and multimodal RAG to provide course-grounded, pedagogically appropriate feedback, and is evaluated through metrics, expert review, and simulated student interactions.

A founder shares his experience with AI tool adoption, noting that most people collect tools without achieving real results. He advocates focusing on one critical business problem and iterating until the workflow genuinely works, citing his own success reducing client reporting time from 4-5 hours to under 45 minutes.

Kent C. Dodds shares a reflection on the iterative cycle of solving problems in software development, emphasizing replacing previous solutions with better ones to reduce complexity.

A new study by researchers from MIT, Carnegie Mellon, Oxford, and UCLA finds that using AI chatbots for just 10 minutes can significantly reduce human persistence and problem-solving abilities once the AI is removed. The findings suggest a need to design AI systems that scaffold learning rather than simply providing direct answers.

Google DeepMind's AI co-mathematician achieves state-of-the-art results on hard problem-solving benchmarks, scoring 48% on FrontierMath Tier 4, the highest among all AI systems evaluated.