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Notes on performance improvement
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Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
The authors propose a method to utilize multiple Intel AI PCs with integrated GPUs and NPUs, even when they have insufficient memory for large models like a 70B-parameter LLM. The method involves breaking the model…
Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning
The paper introduces Argus, a persistent, self-evolving runtime system designed for long-horizon reasoning. Argus consists of Manager, Planner, Engineer, and Reviewer that execute bounded missions over durable project…
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
The research focuses on improving the performance of local autonomous computer-use agents (CUAs) under strict hardware constraints. The study investigates inference-time scaling across contextual, temporal, structural…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors present GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating updates across multiple agents in the pipeline. The system uses feedback from an Evaluator to update…
DAIS: Dependency-Aware Intermediate QA Supervision for Complex Reasoning
The paper introduces Dependency-Aware Intermediate QA Supervision (DAIS), a framework that improves the accuracy of answering complex questions by providing intermediate rationales and conditioning local answers on…
Scaling Behavior Foundation Model for Humanoid Robots
Researchers have developed a new method for improving the performance of Behavior Foundation Models (BFMs) in controlling humanoid robots. The method involves coordinating three core components: motion tracking…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers propose a proactive memory agent for long-horizon tasks in which an action agent must make decisions based on state information scattered across an expanding trajectory. The proposed memory agent updates a…
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
The paper 'SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling' presents a new approach to scheduling Language Model (LLM) requests for AI agents, rather than humans. The…
Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
The researchers present an approach called Jailbreak that allows analytical workloads to bypass the traditional database driver and read storage files directly, using Large Language Models (LLMs) to regenerate table…
Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs
Researchers propose AgentSymbiotic, an iterative framework that enhances both large and small Language Model (LLM) performance in web browsing tasks by combining data synthesis with task-performance. Large LLMs…
Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents
Researchers propose Semantic Consistency Policy Optimization (SCPO), a method to optimize reinforcement learning for Language Model (LLM) agents. SCPO aims to reduce semantic credit inconsistencies that occur when…
WinDOM: Self-Family Distillation for Small-Model GUI Grounding
Researchers present WinDOM, a large dataset of bounding box training data for small GUI-grounding agents, which can be used for on-device deployment, accessibility tooling, and low-cost iteration. They also introduce…
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