Test-Time Training on Graphs with Large Language Models (LLMs)
PulseAugur coverage of Test-Time Training on Graphs with Large Language Models (LLMs) — every cluster mentioning Test-Time Training on Graphs with Large Language Models (LLMs) across labs, papers, and developer communities, ranked by signal.
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Undergrad seeks research collaboration in Test-Time Training for LLMs
An undergraduate student from a tier-2 university in India is seeking research opportunities and compute resources for Test-Time Training (TTT) on Large Language Models (LLMs). The student has a draft paper on self-expl…
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New TTPO method enhances LLM math reasoning without labels
Researchers have developed Test-Time Policy Optimization (TTPO), a novel method for improving large language models' mathematical reasoning capabilities without relying on ground-truth labels. TTPO addresses the fragili…
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New RAD framework enhances long video generation with global memory and local attention
Researchers have introduced a new framework called Recurrent Autoregressive Diffusion (RAD) designed to improve long video generation. RAD integrates temporal recurrent neural network (RNN) layers, specifically LSTM, in…
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New methods tackle catastrophic forgetting in continual learning · 8 sources tracked
Researchers are developing new methods to address catastrophic forgetting in continual learning, a challenge where models lose previously acquired knowledge when learning new tasks. Several papers propose novel techniqu…
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SR-TTT Model Fails to Learn Retrieval, Paper Correction Reveals
A recent arXiv paper corrects previous findings regarding the SR-TTT model, demonstrating that it does not effectively learn retrieval mechanisms. The authors identify evaluation artifacts and a non-causal attention mec…
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Galaxy General unveils WAM-TTT, enabling robots to learn from human videos
Galaxy General has released the world's first Test-Time Training (TTT) framework, WAM-TTT, for embodied AI models. This framework allows robots to learn and adapt in real-time using human demonstration videos, addressin…
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New Asynchronous Perception Machine enables efficient test-time-training
Researchers have introduced the Asynchronous Perception Machine (APM), a novel architecture designed for efficient test-time-training (TTT). APM can process image patches in any order, enabling it to recognize out-of-di…
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New research explores adaptive LLM evaluation and self-improvement techniques · 10 sources tracked
Researchers are developing new methods to evaluate and improve large language models (LLMs). One approach, ATLAS, uses item response theory to significantly reduce the number of items needed for accurate LLM evaluation,…
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New Context Tuning method enhances LLM few-shot adaptation
Researchers have introduced Context Tuning, a novel method designed to improve the few-shot adaptation capabilities of large language models (LLMs) without requiring weight updates. This technique initializes a trainabl…
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New Frame Forgetting Network tackles long video Test Time Training
Researchers have developed a new method called the Frame Forgetting Network (FFN) to improve Test Time Training (TTT) for long videos. Existing TTT methods struggle with the computational demands of processing hours-lon…
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New theory explains and improves test-time training for AI models
Researchers have developed a decision-theoretic framework to understand and improve test-time training (TTT), a method for adapting pretrained models to specific prompts. The new approach treats TTT as implicit Bayesian…
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Linearizing Vision Transformer with Test-Time Training
Researchers have developed a method to adapt pretrained Softmax attention models to linear-complexity architectures using Test-Time Training (TTT). This approach addresses the representational gap between different atte…