The Myth of Mythos (Fable 5)

The Problem

On June 9, 2026, Anthropic launched Claude Fable 5. They called it the first "Mythos-class" model ever released to the general public — a tier above Opus, designed for "ambitious, long-running, asynchronous tasks." The benchmarks were staggering: 80.3% on SWE-Bench Pro (11 points clear of the next-best), 95% on SWE-bench Verified, state-of-the-art on FrontierCode, Terminal-Bench, Hebbia's Finance Benchmark. Stripe compressed months of engineering into days. Andrej Karpathy called it "a major-version-bump-deserving step change forward."

It also costs $10 per million input tokens and $50 per million output tokens.

That is double the price of Claude Opus 4.8 ($5/$25). It is 2.5 times the price of GPT-5.5 ($5/$30 output). And it is 57 times the price of DeepSeek V4 Pro ($0.435/$0.87) — a Chinese open-weight model that scores 80.6% on SWE-bench Verified, 55.4% on SWE-bench Pro, and 67.9% on Terminal-Bench 2.0, all while running on 49 billion activated parameters out of 1.6 trillion total.

The pricing ladder tells a story the benchmarks do not:

DeepSeek V4 Pro: $0.87/MTok output — SWE-Pro 55.4%, Terminal-Bench 67.9%
Claude Opus 4.6/4.7/4.8: $25/MTok output — SWE-Pro ~69%, Terminal-Bench ~83%
Claude Fable 5/Mythos 5: $50/MTok output — SWE-Pro 80.3%, Terminal-Bench 84.3%

The gap between Opus 4.8 and Fable 5 on Terminal-Bench 2.1? 1.6 percentage points — for double the price. The gap between DeepSeek V4 Pro and Fable 5 on SWE-bench Pro? 25 percentage points — but at 1/57th the cost. The question "Is the model worth the money?" does not have a single answer. It depends entirely on what you are building and how you build it.

Because here is the truth that the benchmark leaderboards will never show you: when Endor Labs ran the same Fable 5 model through two different agent harnesses, the results shifted by 12.8 percentage points on functional solves and 10 points on security — a larger swing than the gap between Opus 4.8 and Fable 5 on almost every benchmark. Same model. Same task. Different harness. Different outcome.

This is the fundamental insight that most organizations deploying AI in 2026 are missing: your AI's intelligence is not in the model. It is in the harness. The model is a raw inference engine — stateless, blind, and handless by default. Everything that makes it appear smart — memory, tools, perception, action, continuity — comes from the system architecture wrapped around it. And when that architecture is incomplete, even the most powerful model on the planet will fail. When it is well-designed, even a mid-tier model at a fraction of the cost will outperform.

Before You Object: Yes, Models Can See, Touch, and Remember — But Only Because of What Is Built Around Them

Let us be precise about what current frontier models can actually do, because the picture is more nuanced than "stateless chatbot."

Yes, they have eyes. Claude Fable 5, GPT-5.5, and Gemini 3.1 Pro all have native vision capabilities. Fable 5 can extract precise numbers from detailed scientific figures, rebuild web app source code from screenshots alone, and beat Pokemon FireRed using only raw game screenshots — no maps, no navigation aids, no extra game-state information. This is remarkable. It is also a model-level capability: the model can look at an image you give it, but it cannot observe your desktop, monitor a live dashboard, or notice that a deployment failed unless you specifically build a system that feeds it screenshots in real time. Vision works on what you bring to the conversation — not on what the world is doing while you are not looking.

Yes, they have hands. Claude, Gemini, and GPT-4o all support tool use and function calling. You can wire up APIs, database queries, web searches, and custom actions. Fable 5 scores 84.3% on Terminal-Bench 2.1, which measures agentic coding and terminal use. But tool use is not automatic. You have to add the tools. The model does not reach out and grab capabilities on its own. It can only call the functions you have registered, through the integration you have built, with the permissions you have granted. Most organizations never get past hooking up a single API endpoint before declaring their AI "agentic."

Yes, they have memory. OpenAI offers "Personal Intelligence" — thread-level memory that persists across sessions. Anthropic's Managed Agents API now includes "Memory and Dreaming." LangGraph offers built-in agent harness memory with hierarchical storage. But these memory features are opt-in, bounded by the provider's data retention policies, and subject to serious data sovereignty risks. When you enable personal intelligence on a public provider's platform, your strategic data flows through their infrastructure. The Cloud Security Alliance reported in May 2026 that shadow AI usage among the workforce has tripled — from 15% to 45% in a single year — with 67% of that activity occurring through personal accounts that the enterprise cannot monitor.

The point is not that models lack these capabilities. The point is that every single one of them must be explicitly engineered into the system. Out of the box, a frontier model is a raw text generator. Everything that makes it useful — perception, action, continuity, verification — is infrastructure you have to build. And the organizations that build it well are finding that they do not need the most expensive model to get the best results.

The DeepSeek V4 Pro Data Point: What the Price Gap Actually Buys You

DeepSeek V4 Pro was released on April 24, 2026 — a 1.6-trillion-parameter Mixture-of-Experts model with 49 billion activated parameters per token. It is open-weight (MIT license), supports a 1-million-token context window, and costs $0.435 per million input tokens and $0.87 per million output tokens — a price that DeepSeek made permanent on May 22 after a 75% promotional period. On SWE-bench Verified, it scores 80.6% — within a fraction of a point of Fable 5's ~80.3% on the Pro version. On MCPAtlas, a public MCP tool-use benchmark, it scores 73.6% — second only to Opus 4.6. On Terminal-Bench 2.0, it scores 67.9% — behind Fable 5's 84.3%, but ahead of several closed-source competitors.

The NIST CAISI evaluation in May 2026 assessed DeepSeek V4 Pro's capabilities as lagging "the U.S. frontier by about 8 months" across five domains: cyber, software engineering, natural sciences, abstract reasoning, and mathematics. But the evaluation also noted that V4 Pro is "the most capable PRC AI model evaluated by CAISI to date" and performs at parity with frontier closed models on agentic coding tasks when evaluated with the same agent scaffolding.

The data point is not that DeepSeek matches Fable 5. It does not. On the hardest benchmarks — FrontierCode Diamond, the most complex SWE-bench Pro tasks, cybersecurity exploitation — the gap is real. But for 95% of production use cases — code generation, document analysis, RAG pipelines, tool orchestration, customer-facing agents — the gap between V4 Pro and Fable 5 is smaller than the gap between a well-designed harness around V4 Pro and a poorly designed harness around Fable 5. And the cost difference is 57x.

This is the China-US AI pricing arbitrage in action. DeepSeek's economics are built on domestically manufactured Ascend 950 chips and aggressive inference optimization — 27% of the single-token FLOPs and 10% of the KV cache of comparable models at 1M-token context length. Whether this pricing is sustainable is an open question. But for right now, the market is bifurcated: one tier for organizations that need absolute frontier performance and can justify 57x the cost, and another for everyone else who needs 90% of the capability at 2% of the price.

What the Fable 5 Case Study Actually Proves

Anthropic's launch numbers are not wrong. Fable 5 does score 80.3% on SWE-Bench Pro and 95% on SWE-bench Verified — using Anthropic's own agent scaffold, which is part of what they measure. But as the independent evaluations show, these scores do not transfer cleanly to the real world. The gap is not fraud. It is method.

The Endor Labs experiment is the most instructive data point in the entire launch. They ran the same Fable 5 model through two different agent harnesses — Claude Code and Cursor. The difference in results (59.8% vs 72.6% FuncPass, 19.0% vs 29.0% SecPass) was driven entirely by the harness. The model's weights did not change. The inference API endpoint was identical. The only variable was how the harness prompted the model, managed its context, routed its tool calls, and verified its outputs.

"The agent scaffolding wrapped around a frontier model can move security outcomes more than the model choice itself." — Endor Labs, June 2026

This is not a critique of Fable 5. It is a critique of how we measure AI intelligence. When a model's benchmark score changes by 12.8 percentage points based purely on which scaffold it runs inside, the score is measuring the system, not the model. And most organizations are buying models based on system scores that will never replicate in their own environment.

Three Critical Failure Points That Only a Well-Designed Harness Can Solve

1. Context Drift: A 2026 ACL study by Dongre et al. formalized context drift as "the gradual divergence of a model's outputs from goal-consistent behavior across turns." The longer you talk to an agent, the more it loses track of what you originally asked. A companion paper (arXiv 2606.21666) introduced the Context Divergence Score (CDS), showing that naive full-context broadcasting increases hallucination by 34% — because it propagates errors indiscriminately. The harness's context constructor determines what gets shown to the model and what gets pruned. This is not a model capability. It is a system architecture choice.

2. The Hallucination Cascade: A 2026 study in arXiv (2606.07937) modeled hallucination as a dynamic stochastic process. Hallucination increases linearly with semantic drift. The study tested DeepSeek-V3 and LLaMA-3-70B across 500 cascade scenarios. Higher information retention during intermediate steps significantly reduced hallucination propagation. But retention is not automatic — it requires a memory store that persists and verifies intermediate reasoning. Without it, errors compound turn by turn.

3. The Mock-to-MVP Mirage: An AI coding agent generates beautiful TypeScript with 200 passing tests. All tests pass. The code ships. The database connection was mocked. The authentication layer returned a hardcoded success token. The payment gateway always said "approved." You shipped a facade that collapses on day one of real usage. The harness's verification and governance layer gates outputs against real systems before they reach production. Without it, you are deploying plausible-looking code that has never touched a real environment.

The Architecture Behind the Intelligence: It Is the Harness, Not the Model

A landmark paper from arXiv (2605.26112, Gu et al., 2026) formalized what the Fable 5 case study demonstrated empirically. The authors define agent performance over any horizon H as a function of six interacting components:

• Reasoning Substrate (R): The base model's reasoning quality — the only component that improves through model scaling (more parameters, more data).
• Memory Store (M): Durable storage of facts, preferences, and history across sessions.
• Context Constructor (C): Assembles inputs for the model from memory and task context — the component most responsible for combating context drift.
• Skill Routing Layer (S): Dispatches tools and subagents based on the current task.
• Orchestration Loop (O): Manages control flow and state transitions across multi-step workflows.
• Verification & Governance (G): Gates actions, verifies outputs, ensures safety.

The central implication: you cannot out-scale your way out of bad system design. Five of the six components that determine real-world agent performance are system-design variables, not model-choice variables. A better model with no memory store produces the same stateless agent it always has. A larger context window without proper context construction simply amplifies signal dilution. As the paper's authors put it: "An average-sized model with a better harness, memory, and systems implementation can well outperform a larger sized model that lacks a good harness and memory system around it."

Anthropic themselves acknowledge this. On the Claude Fable 5 product page, the company writes: "Run Claude Fable 5 in an agent harness like Claude Code or Claude Managed Agents, and it can work for days at a time." The model's defining feature — sustained long-horizon reasoning — is expressed only when it is running inside a properly designed harness. Without it, Fable 5 is simply a very expensive text generator.

What You Can Do About It

Build an Agentic Harness — Because Your Model Is Only as Smart as the Scaffolding You Wrap Around It

An agentic harness is the infrastructure layer that transforms a stateless LLM into a persistent, capable agent. Think of it this way: if the LLM is the brain, the harness is everything else that makes a person functional — eyes to see, hands to act, memory to learn, verification to catch mistakes, and governance to know when to stop. The Endor Labs data proves that the choice of harness can move outcomes more than the choice of model. Here is what a production-grade harness contains, from the ground up.

1. Memory Module — The Hippocampus

Memory is the single most important component that separates a stateless chatbot from an agentic system. Red Hat's June 2026 architecture guide identifies four types of agent memory, each serving a distinct purpose:

• Session Memory: Conversation history within a single session. The bare minimum — equivalent to short-term human memory. Most chatbots stop here.
• Long-Term File System Memory: Persistent files stored in directories with supplemental indexing. This is how Claude Code tracks project-level context through .claude/settings.md — a simple text file that the agent reads at startup and writes to as it works.
• Long-Term Episodic Memory: Structured around events and temporal sequences. Anthropic's "Memory and Dreaming" product automatically curates, deduplicates, and evicts memories over time. LangGraph provides hierarchical storage with agent-scoped and team-scoped LTM.
• Long-Term Semantic Memory: Stored via embeddings for semantic search in vector databases (Weaviate, Qdrant, Chroma), often augmented with graph metadata. This is the closest to human factual knowledge — remembering that "Thailand's GDP grew 3.2% in Q1 2026" regardless of when you learned it.

The critical design question is not whether to implement memory — but how to manage its lifecycle. The Gu et al. paper identifies the core problem: "stale-but-confident" memories. When a file path or function name becomes invalid after a refactor, semantic search still ranks it highly because the embedding similarity has not changed. Acting on stale memory is more destructive than having no memory at all — because the agent acts with false confidence.

The solution: Treat memory as a hypothesis to be verified against the live environment. Before acting on retrieved information, cross-check it using direct tools — file system glob, grep, API queries. This is exactly what Claude Code does when it retrieves a remembered file path and runs cat to verify it still exists. The principle is simple: memory should inform action, never dictate it.

2. Agent.md — The Constitution

If memory is the hippocampus, agent.md is the personality and behavioral ruleset. It answers: Who are you? What are your constraints? When do you stop?

The AGENTS.md format has become the de facto open standard for guiding coding agents. It is a markdown file placed at the project root that provides context and instructions — essentially a README for AI agents. The format specifies sections for development environment, testing conventions, PR guidelines, and behavioral constraints. Its genius is its simplicity: a single file, checked into version control, that any agent can read at startup.

AgentProto's AIP-42 specification goes further. It defines a complete runnable agent primitive in markdown with YAML frontmatter — composing identity, persona, model selection, tools, actions, skills, workflows, runner configuration, memory strategy, governance rules, and routines into a single manifest file. The body becomes the system prompt that the runtime feeds to the model. The entire agent's behavior is declared in a version-controlled text file, making it auditable, testable, and changeable without touching a single line of runtime code.

3. Skills.md — The Expertise Layer

Skills are reusable modules of specialized domain knowledge. A skill is a directory containing a SKILL.md file with YAML frontmatter and markdown instructions, plus optional subdirectories for scripts, references, templates, and assets. Skills use progressive disclosure — agents load only the name and description of each available skill at startup (~100 tokens per skill), activate the full body when a task matches (<5000 tokens recommended), then read supplementary resources only as needed.

This pattern keeps context footprint manageable while allowing deep domain expertise to be packaged and reused across projects and sessions. A legal review skill, a data analysis pipeline, a presentation formatting standard — each is a self-contained module that the agent loads on demand. The Agent Skills format was designed for exactly this: giving agents specialized knowledge they cannot learn from general training alone, without bloating every prompt with domain-specific instructions.

4. Tools — The Hands (MCP, Plugins, Terminal)

Tools are how an agent interacts with the world beyond text generation. The Model Context Protocol (MCP), developed by Anthropic as an open standard, has emerged as the dominant protocol for connecting AI agents to external tools and data sources. Anthropic's own blog describes it as "the USB-C port for AI" — a universal connector that lets any agent talk to any tool without custom integration. Hundreds of MCP servers now exist, covering everything from code repositories and databases to design tools and email clients.

Eyes — Perception Layer:

• Browser-use agents control web browsers autonomously. They use vision-language models to read screenshots or live DOM trees and decide which clicks, keystrokes, and navigations to take next — without relying on a website's API. This is how Cursor and Claude Code implement web research workflows.
• Computer-use agents operate at the desktop level. Anthropic's computer use capability (released late 2024) lets Claude receive a screenshot of an entire desktop and output mouse coordinates and keystrokes. By mid-2026, OpenAI's Operator navigates complex JavaScript-heavy websites with an 87% success rate.
• OS-SYMPHONY (ACL 2026) is a holistic Computer-Using Agent framework that achieves 65.84% on OSWorld benchmarks — surpassing the human baseline of 72.4% at top-5 ranking — using milestone-driven long-term memory for trajectory-level self-correction.

Hands — Action Layer:

• Terminal access is the most powerful tool an agent can have — and the most dangerous. Direct shell access lets agents run commands, install packages, execute tests, and inspect system state. The key safety mechanism is sandboxing: running agents inside containers (Docker) with restricted filesystem access, network egress controls, and resource limits.
• Lifecycle hooks (OnUserPromptSubmit, OnSessionStart, BeforeToolUse, AfterToolUse, OnStop) provide the harness with visibility into every phase of execution. They enable audit logging, cost tracking, and intervention points where a human can override or stop the agent mid-flight.
• Dynamic workflows (a feature Anthropic released alongside Fable 5) allow the agent to write its own multi-agent harness on the fly — spawning subagents with isolated context windows and focused goals, then synthesizing their outputs. Anthropic's blog notes: "With Opus 4.8 and dynamic workflows, Claude is now intelligent enough to write a custom harness tailor-made for your use case."

5. Guard Rails & Security — The Immune System

This is the most neglected component of agentic AI — and arguably the most important. The 2026 OWASP MCP Top 10 reveals a security landscape that demands immediate attention from any organization building agentic systems:

• Between January and February 2026 alone, researchers filed more than 30 CVEs against MCP servers, clients, and infrastructure.
• Palo Alto Networks Unit 42 measured a 78.3% attack success rate when five MCP servers were connected to a single AI agent.
• CVE-2025-6514 in mcp-remote had a CVSS score of 9.6 — critical — and affected a package downloaded more than 437,000 times before disclosure. It was an OS command injection triggered when the library connected to an untrusted MCP server.
• The first malicious MCP server caught in the wild (the "Postmark backdoor") silently exfiltrated emails from affected systems.
• A Cloud Security Alliance report from May 2026 identified a systemic design flaw in the MCP SDK's STDIO transport layer allowing arbitrary OS command execution — affecting more than 200,000 instances across packages totaling over 150 million downloads. Anthropic confirmed the behavior is intentional and declined to modify the protocol.

The OWASP MCP Top 10 framework identifies ten critical risk categories: token mismanagement and secret exposure (MCP01), privilege escalation via scope creep (MCP02), tool poisoning where a fake tool intercepts calls meant for a legitimate one (MCP03), supply chain attacks and dependency tampering (MCP04), command injection and execution (MCP05), intent flow subversion via prompt injection through context (MCP06), insufficient authentication and authorization (MCP07), lack of audit and telemetry (MCP08), shadow MCP servers the operator does not know about (MCP09), and context injection and oversharing (MCP10).

The security principle is clear: the more you depend on external plugins, the more you must monitor them. Every MCP server, every tool plugin, every skill script is a potential attack vector. Run them in containers with least-privilege access. Verify provenance before installing third-party tools. Maintain an AIBOM (AI Bill of Materials) tracking which tools your agents depend on and their CVE status. Apply the same security discipline to your AI agent's tool ecosystem that you apply to any production system — because the consequences of a compromised agent are worse.

6. Routing & Model Selection — The Cost Arbitrage Layer

The final component of a well-designed harness is the ability to route tasks to the right model at the right price. Not every query needs Fable 5. Not every task needs a 1.6-trillion-parameter model at all. A properly designed harness implements a model router that classifies incoming tasks by complexity and routes them to the cheapest model that can handle them reliably.

The pattern that leading teams have settled on is hierarchical: route by task complexity. Hard, high-value, long-horizon jobs to the frontier model. Everything else to a cheaper model. Anthropic's own guidance for Fable 5 recommends exactly this: "Use Fable 5 when the task is genuinely hard and long. If it fits within a single chat response or a few tool calls, Opus 4.8 or Sonnet is usually the smarter call." DeepSeek V4 Flash, at $0.14/$0.28 per million tokens, handles chat, classification, extraction, and most RAG workloads at a fraction of the cost of any Opus-class model.

The economic leverage here is enormous. If your harness routes 80% of traffic to a $0.28/MTok model and 20% to a $25/MTok model, your blended output cost is approximately $5.22/MTok — compared to $50/MTok if every request hits Fable 5. That is a 10x cost reduction for comparable real-world outcomes, achieved entirely through architectural decisions, not model selection.

The Benefits: What Happens When You Build It Right

An agentic harness with proper memory, tools, and guardrails transforms your AI from a stateless text generator into a persistent operational asset. The Endor Labs data shows this empirically: the same model, with a harness change alone, improved security outcomes by over 50% relative. Across the whole system, here is what you get:

• Continuity: The agent remembers your project context across sessions. It knows your architecture decisions, coding conventions, and business constraints. Each interaction compounds the next — the agent gets smarter over time because its memory does.
• Perception: With browser-use and computer-use capabilities, the agent can observe real systems — verify a deployment, check an API endpoint, inspect UI for regressions. No more trusting what the model says about your code; it can read the actual output.
• Action: With terminal access and tool integration, the agent does not just suggest changes — it implements them, runs tests, commits code, and reports results. The gap between "the model thinks this works" and "the tests actually pass" is eliminated because the agent performs real verification.
• Endurance: Fable 5's defining capability — sustained reasoning over hundreds of steps — is only accessible through a harness. Without context construction and memory management, even the most powerful model loses coherence after a dozen turns.
• Verification: A governance layer that checks outputs against real systems before execution prevents the mock-to-MVP mirage. The agent validates against live environments, cross-references facts, and flags uncertainty when confidence is low.
• Cost Control: A model router ensures that simple tasks go to cheap models and complex tasks go to expensive ones. The savings fund further harness improvements in a virtuous cycle.

The Meta-Harness project (Stanford, 2026) demonstrated this principle empirically: changing the harness produced a 6x performance gap on long-horizon tasks — an order of magnitude larger than the gap between any two frontier models on the same benchmark.

The Bottom Line

Claude Fable 5 is the most powerful publicly available AI model ever released. It is also, without a harness, just a very expensive text generator.

The benchmark scores that made headlines measure the system — model plus agent scaffold — not the model alone. When independent evaluators ran Fable 5 through a different harness, its security performance jumped from mid-table to first place — a swing driven entirely by context construction, memory management, tool routing, and output verification. The model did not change.

Meanwhile, DeepSeek V4 Pro — at 1/57th the price — delivers within striking distance on agentic coding benchmarks when paired with the same scaffolding. NIST's independent evaluation places it about eight months behind the U.S. frontier. But for the vast majority of production workloads, eight months of capability is a rounding error compared to the infrastructure deficit that most organizations carry. Your harness is probably further behind the frontier than V4 Pro is.

The lesson for every organization deploying AI in 2026 is not "get the best model." The lesson is: the model is a commodity. The harness is the moat.

Here is the actionable takeaway: a well-designed harness, with proper memory management, skill routing, tool integration, model routing, and security guardrails, running on a mid-tier model like DeepSeek V4 Pro or Claude Sonnet, will outperform a poorly designed harness running on Fable 5 in almost every real-world context — at a fraction of the cost. The Endor Labs experiment proves the principle: harness alone moved the needle more than model upgrades have in two generations.

The question is no longer "Which model is smartest?" The question is: "Which system makes the model you have look smartest?" Build the harness first. The model is the easy part. And if you build it right, you might find that the most expensive model on the market was never the one you needed.

References

• Gu, et al. (2026). From Model Scaling to System Scaling: Scaling the Harness in Agentic AI. arXiv:2605.26112.
• Anthropic. (2026, June 9). Claude Fable 5 and Claude Mythos 5. Anthropic Blog. anthropic.com/news/claude-fable-5-mythos-5
• Endor Labs. (2026). Claude Fable 5: Mythos-grade hype, record cheating, and a few hall-of-fame entries. Agent Security League. endorlabs.com/learn/claude-fable-5-mythos-grade-hype
• Endor Labs. (2026). Claude Fable 5, take two: same model, different harness, and a very different result. Agent Security League. endorlabs.com/learn/claude-fable-5-take-two
• Red Hat Emerging Technologies. (2026, June 1). Architecting memory for AI agents: From context to dreams. next.redhat.com
• DeepSeek. (2026). Models & Pricing. DeepSeek API Docs. api-docs.deepseek.com/quick_start/pricing
• NIST CAISI. (2026, May). Evaluation of DeepSeek V4 Pro. nist.gov
• Hugging Face Blog. (2026). DeepSeek-V4: a million-token context that agents can actually use. huggingface.co/blog/deepseekv4
• Cycode. (2026). OWASP MCP Top 10: Risks, CVEs & Defenses for 2026. cycode.com/blog/owasp-mcp-top-10
• Cloud Security Alliance. (2026, May). MCP Security Crisis: Systemic Design Flaws in AI Agent Infrastructure. labs.cloudsecurityalliance.org
• Dongre, V., et al. (2026). Drift No More? Context Equilibria in Multi-Turn LLM Interactions. MSLD 2026. OpenReview.
• Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems. arXiv:2606.07937.
• Hallucination as Context Drift: Synchronization Protocols for Multi-Agent LLM Systems. arXiv:2606.21666.
• Liu, J., et al. (2023). Lost in the Middle: How Language Models Use Long Contexts. arXiv:2307.03179.
• Hu, et al. (2026). OS-SYMPHONY: A Holistic Framework for Robust and Generalist Computer-Using Agent. ACL 2026.
• Anthropic. (2026). A harness for every task: dynamic workflows in Claude Code. Claude Blog. claude.com/blog
• Artificial Analysis. (2026, June 9). Claude Fable 5: the first public Mythos-class model. artificialanalysis.ai
• DeepLearning.AI. (2026). Anthropic Released Claude Mythos 5 and Claude Fable 5. The Batch.
• OpenMythos. (2026). Recurrent-Depth Transformer architecture analysis. github.com/kyegomez/openmythos