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Notes on ai frameworks
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RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks
Researchers have introduced a new benchmark called RealisticTritonBench to improve the evaluation of large language models (LLMs) in generating GPU kernels for AI frameworks using Triton. The new benchmark derives…
MemHarness: Memory Is Reconstructed, Not Replayed
Researchers have developed MemHarness, a framework to improve large language model agents by enabling them to adapt and reconstruct past experiences based on the current context, instead of simply replaying stored…
From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents
Researchers propose a framework called EvoSOP to help large language model (LLM) agents optimize their toolset by synthesizing atomic actions into reusable Standard Operating Procedures (SOPs). This approach aims to…
Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
This research paper presents an uncertainty-aware framework for Retrieval-Augmented Generation (RAG) systems. The system uses a Bayesian Network to estimate the uncertainty of multi-stage reasoning pipelines and…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
The paper presents OmniAct, a framework designed to create persistent embodied agents that can operate autonomously in unstructured environments. It addresses the challenges of integrating cyber (APIs, IoT) and…
ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models
The paper introduces ReasoningLens, an open-source framework designed to help with the visualization and diagnostic auditing of complex reasoning chains in large reasoning models. The tool aims to address the issue of…
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