Articles for category: AI Research

March 5, 2025

Enhancing Costmap for Off-Road Navigation with Robust IR-RGB Fusion for Improved Day and Night Traversability

[Submitted on 4 Dec 2024 (v1), last revised 2 Mar 2025 (this version, v2)] View a PDF of the paper titled IRisPath: Enhancing Costmap for Off-Road Navigation with Robust IR-RGB Fusion for Improved Day and Night Traversability, by Saksham Sharma and 2 other authors View PDF HTML (experimental) Abstract:Autonomous off-road navigation is required for applications in agriculture, construction, search and rescue and defence. Traditional on-road autonomous methods struggle with dynamic terrains, leading to poor vehicle control in off-road conditions. Recent deep-learning models have used perception sensors along with kinesthetic feedback for navigation on such terrains. However, this approach has out-of-domain

March 5, 2025

ikayaniaamirshahzad@gmail.com

AI Research

Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization

[Submitted on 10 Oct 2024 (v1), last revised 2 Mar 2025 (this version, v2)] View a PDF of the paper titled MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization, by Yougang Lyu and 6 other authors View PDF HTML (experimental) Abstract:As large language models (LLMs) are rapidly advancing and achieving near-human capabilities on specific tasks, aligning them with human values is becoming more urgent. In scenarios where LLMs outperform humans, we face a weak-to-strong alignment problem where we need to effectively align strong student LLMs through weak supervision generated by weak teachers. Existing alignment methods mainly focus on strong-to-weak alignment

March 4, 2025

ikayaniaamirshahzad@gmail.com

AI Research

[2412.17762] The Superposition of Diffusion Models Using the Itô Density Estimator

[Submitted on 23 Dec 2024 (v1), last revised 28 Feb 2025 (this version, v2)] View a PDF of the paper titled The Superposition of Diffusion Models Using the It\^o Density Estimator, by Marta Skreta and Lazar Atanackovic and Avishek Joey Bose and Alexander Tong and Kirill Neklyudov View PDF HTML (experimental) Abstract:The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant computational burden of re-training a larger combined model. In this paper, we cast the problem of combining multiple pre-trained diffusion models at the generation

March 4, 2025

ikayaniaamirshahzad@gmail.com

AI Research

How Google Research is making healthcare more accessible and personalized with AI

Last week, at the Lake Nona Impact Forum for advancing global health, I discussed the potential of AI to meaningfully improve healthcare and advance science. Our recent AI breakthroughs provide unprecedented opportunities to make healthcare more accessible, personalized and effective for everyone, and to significantly accelerate scientific discovery. Here’s an update on our progress, how we’re collaborating with partners to bring AI to global healthcare settings and our recently announced AI co-scientist. AI is making accurate health information more accessible Google is often the first place people turn to when they are looking for answers to health-related questions, so we

March 4, 2025

ikayaniaamirshahzad@gmail.com

AI Research

Optimizing LLM Test-Time Compute Involves Solving a Meta-RL Problem – Machine Learning Blog | ML@CMU

Figure 1: Training models to optimize test-time compute and learn “how to discover” correct responses, as opposed to the traditional learning paradigm of learning “what answer” to output. The major strategy to improve large language models (LLMs) thus far has been to use more and more high-quality data for supervised fine-tuning (SFT) or reinforcement learning (RL). Unfortunately, it seems this form of scaling will soon hit a wall, with the scaling laws for pre-training plateauing, and with reports that high-quality text data for training maybe exhausted by 2028, particularly for more difficult tasks, like solving reasoning problems which seems to

March 4, 2025

ikayaniaamirshahzad@gmail.com

AI Research

Reddit – Dive into anything

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March 4, 2025

ikayaniaamirshahzad@gmail.com

AI Research