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PaperAgent

Daily updates, analyzing cutting-edge AI research papers

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PaperAgent
PaperAgent
Jul 28, 2026 · Artificial Intelligence

Inside Anthropic’s New Graph Engineering Methodology for Multi‑Agent Systems

Anthropic’s recent 12‑page playbook and 2‑hour workshop detail a Graph Engineering pipeline that replaces costly context‑window communication with a shared knowledge graph, covering why windows fail, a four‑stage Claude API workflow, extraction rules, entity resolution, graph assembly, multi‑hop querying, integration into five agent modes, cost analysis, scaling strategies, and guidance on when not to use a knowledge graph.

AnthropicClaude APIGraph Engineering
0 likes · 14 min read
Inside Anthropic’s New Graph Engineering Methodology for Multi‑Agent Systems
PaperAgent
PaperAgent
Jul 27, 2026 · Artificial Intelligence

Why Dropping 80% of System Prompts Improves Claude 5: New Context Engineering Rules

Anthropic’s official Claude 5 guide reveals that removing most Claude Code system prompts has no measurable impact, overturning traditional context‑engineering practices and introducing six paradigm shifts that let the model rely on its own judgment and progressive context loading.

AI AgentsAnthropicClaude 5
0 likes · 6 min read
Why Dropping 80% of System Prompts Improves Claude 5: New Context Engineering Rules
PaperAgent
PaperAgent
Jul 27, 2026 · Artificial Intelligence

Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents

This 149‑page survey defines long‑horizon agents as a coupling of a base policy and a runtime harness (Agent = πθ ⊕ H), categorises task levels and capabilities, traces the field’s evolution from prompt to context to runtime engineering, and outlines a seven‑stage optimization pipeline, application forms, and frontier challenges, supported by empirical growth data and extensive references.

AI SurveyAgent OptimizationContext Engineering
0 likes · 12 min read
Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents
PaperAgent
PaperAgent
Jul 25, 2026 · Artificial Intelligence

Inside Claude Code and Codex: Dissecting the Six Core Components of a Coding Agent

The article breaks down the architecture of coding agents like Claude Code and Codex into six essential components—Live Repo Context, Prompt Cache, Tools, Context Management, Session Memory, and Bounded Subagents—explaining how each layer of the Agent Harness transforms similar LLMs into markedly different, more capable systems.

Agent HarnessCoding AgentContext Management
0 likes · 12 min read
Inside Claude Code and Codex: Dissecting the Six Core Components of a Coding Agent
PaperAgent
PaperAgent
Jul 25, 2026 · Artificial Intelligence

Claude Opus 5 Gets Tested in Tornadoes, Collapsing Buildings, and Sand Simulations

Claude Opus 5 launched at half the price of Fable 5, and the community immediately pushed it to its limits with self‑contained HTML physics scenes—tornado‑ripped houses, demolition‑ball‑crushed apartments, bridge‑collapsing trucks, and massive sand‑water‑fire simulations—while comparing costs and performance against Fable 5, GPT 5.6, and Kimi K3.

AI model comparisonAnthropicClaude Opus 5
0 likes · 6 min read
Claude Opus 5 Gets Tested in Tornadoes, Collapsing Buildings, and Sand Simulations
PaperAgent
PaperAgent
Jul 23, 2026 · Artificial Intelligence

10 AI‑Powered Skills That Seamlessly Automate the Entire Research Process

The author explains how ten carefully curated AI research skills can handle everything from literature review and experiment design to data analysis, manuscript drafting, mock peer review, and rebuttal preparation, dramatically reducing the repetitive work that normally consumes most of a scholar's time.

AIAutomationmachine learning
0 likes · 8 min read
10 AI‑Powered Skills That Seamlessly Automate the Entire Research Process
PaperAgent
PaperAgent
Jul 22, 2026 · Artificial Intelligence

Inside GPT‑5.6’s Dropdown: How Six Leading LLMs Tune Their Reasoning Effort

The article dissects Sebastian Raschka’s “Controlling Reasoning Effort in LLMs”, explains GPT‑5.6’s multi‑level effort settings, clarifies the notion of reasoning models, outlines training vs. inference scaling, details RLVR recipes, and compares the post‑training formulas of six open‑source flagship LLMs.

GPT-5.6LLMOpen-source Models
0 likes · 12 min read
Inside GPT‑5.6’s Dropdown: How Six Leading LLMs Tune Their Reasoning Effort
PaperAgent
PaperAgent
Jul 21, 2026 · Artificial Intelligence

Why Loop Engineering Is Dead and Graph Engineering Is the Future

The article explains how traditional Loop Engineering for AI agents is being replaced by Graph Engineering, detailing nodes as tasks, edges as data contracts, parallel execution, barriers, validation, isolation, dynamic workflows, and cost‑effective topology design for scalable agentic systems.

AI AgentsAgent ContractsClaude
0 likes · 19 min read
Why Loop Engineering Is Dead and Graph Engineering Is the Future
PaperAgent
PaperAgent
Jul 19, 2026 · Artificial Intelligence

Alibaba Security AGI Unveils Three LLMs, 8B Model Beats GPT‑5.4 on Multiple Safety Metrics

Alibaba’s Security AGI lab introduced three Yuvion LLMs—8B, 32B, and a 32B Agent—trained on Qwen‑3, and demonstrated that the 8B model already surpasses most SOTA baselines while the 32B variants achieve top rankings in comprehensive safety, adversarial, and business‑level evaluations, outpacing GPT‑5.4 and Qwen‑3‑Max.

AI safetyAgentAlibaba
0 likes · 14 min read
Alibaba Security AGI Unveils Three LLMs, 8B Model Beats GPT‑5.4 on Multiple Safety Metrics