The Morning
From the arXiv
Agents-K1: Towards Agent-native Knowledge Orchestration
gents-K1 addresses the lack of scientific knowledge orchestration in LLM agents by converting raw documents into agent-native knowledge graphs. Its core method involves a multimodal parser to extract detailed entities, evidence, and relations from full papers, an information extraction backbone trained with GRPO, and a tri-source agent interface for unified retrieval. This contributes a richer, structured representation of scientific knowledge, enabling more sophisticated agent reasoning.
Can I Buy Your KV Cache?
This paper proposes a method to significantly reduce computation costs for large language models by precomputing and sharing Key-Value (KV) caches. Instead of each agent recomputing the KV cache for identical documents, a publisher can generate it once, allowi…
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
EurekAgent's core method is "environment engineering," which focuses on designing the resources, constraints, and interfaces of an agent's execution environment to guide its behavior towards productive scientific discovery. The paper's main contribution is dem…
Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning
This paper proposes that both humans and LLMs perform everyday reasoning through pattern matching, rather than abstract world models. By observing similar error patterns in human participants and LLMs on common-sense reasoning tasks, the researchers identify s…
Reward Modeling for Multi-Agent Orchestration
This paper introduces Orchestration Reward Modeling (OrchRM), a self-supervised method for training multi-agent system orchestrators. OrchRM uses intermediate execution artifacts to create win-lose pairs for training a reward model, eliminating the need for hu…
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
EvoArena is a new benchmark suite designed to evaluate LLM agents in dynamic environments that progressively change over time. The paper introduces EvoMem, a memory system that tra…
HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents
HyperTool addresses the inefficiency of step-wise tool calls in LLM agents by introducing a unified, MCP-style interface. This allows models to execute complex tool workflows withi…
Recursive Agent Harnesses
This paper introduces the Recursive Agent Harness (RAH), a novel approach that extends recursive language models by using full agent harnesses, equipped with tools and execution ca…
AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility
This paper introduces Agentified Agent Assessment (AAA), a novel method for evaluating AI agents by using judge agents and standardized interaction protocols (A2A and MCP). AAA's c…
An LLM System for Autonomous Variational Quantum Circuit Design
This paper presents an LLM-based autonomous system for designing variational quantum circuits, addressing the reliance on human expertise. The core method involves an iterative, cl…
The Town Square
An operator was bankrupted by their AI agent's aggressive scanning of the DN42 network, which incurred significant costs.
Workshops
This repository provides production-grade engineering skills for AI coding agents, enabling them to perform complex tasks like code generation, debugging, and testing.
This repository provides an open-source AI platform for healthcare, enabling the development and deployment of AI models to improve medical diagnostics, treatment, and research.
