and it's not just wasted compute. overthinking actively hurts accuracy. DeepSeek-R1 produces responses 5x longer than Claude 3.7 Sonnet on AIME 2025 with comparable accuracy. QwQ-32B scores 2 percentage points HIGHER with its shortest answers using 31% fewer tokens. 72% of
RESEARCH
-
RFCS Metric Reveals Early Correct Steps
By
–
first, the problem quantified. the researchers created a metric called RFCS (Ratio of First Correct Step) that tracks where in a chain of thought the correct answer first appears. on MATH-500, across every model tested, the right answer shows up well before the end in over half
-
Overthinking in AI: A Sampling Issue
By
–
reasoning models already know when they've solved the problem. we just don't let them stop. new paper from Beihang University and ByteDance shows that the overthinking problem in models like DeepSeek-R1 and Qwen3 isn't a training failure. it's a sampling failure. the fix cuts
-
Research initiatives in AI quantum computing and emerging technologies
By
–
Follow my work and research initiatives: Amazing AI, Data, Quantum Computing & Emerging Technologies https://
drdebashisdutta.com Research & Innovation – Quantum, AI & Advanced Systems https://
researchedge.org -
Research Collaboration in AI and Machine Learning Systems
By
–
I welcome discussions and research collaboration in: • Artificial Intelligence
• Scientific Computing
• Machine Learning Systems
• Advanced Predictive Modeling -
Data-Driven AI Modeling Accelerates Scientific Discovery
By
–
As Artificial Intelligence advances, data-driven modeling will increasingly complement traditional scientific approaches, accelerating discovery across engineering and applied sciences.
-
AI Frameworks Enhance Predictions in High-Dimensional Scientific Data
By
–
One important takeaway: AI-driven frameworks can significantly enhance predictions in high-dimensional scientific datasets where conventional analytical models struggle with nonlinear dynamics.
-
GPT-5.4 outperforms GPT-5.2 on code and knowledge benchmarks
By
–
that’s not true, evals comparing 5.2 vs 5.4 (thinking only) Coding (SWE-Bench Pro) > GPT-5.2: 55.6%
> GPT-5.4: 57.7%
→ ~+2.1 pts improvement in solving real-world repo bug-fix tasks. Knowledge-work benchmark (GDPval) > GPT-5.2: ~71% win/tie vs professionals
> GPT-5.4: 83% -
AI-assisted discovery solves open theoretical physics problem
By
–
Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery Paper:
-
Google/Harvard/CMU neuro-symbolic agent uses Gemini to discover proofs
By
–
Google just solved a theoretical physics problem using Gemini! Google, Harvard, and CMU built a neuro-symbolic system using the Gemini Deep Think model and a tree-search framework to autonomously discover complex mathematical proofs. The agent functions like a digital