mlsanityData to 14 Oct 2026

Apodex 1.1: Scaling Agentic Intelligence for Complex Work

arXiv 2608.23283 ↗First posted 24 Aug 2026cs.AI cs.CL cs.LGcited by 2

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this working capability: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. Environment Scaling expands the diversity and verifiability of executable file, search, and code environments, while Agentic Coordination Scaling trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan. A shared execution harness and AgentOS maintain task state and provenance across tools and agents, and training turns environment trajectories and coordination traces into reliable behavior. Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems. The 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form. These results ground agentic intelligence in useful, verifiable work completed over time and advance our goal of building a Heavy-Duty Solver for ambitious, long-running tasks.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 46 of its 59 references that are in the map. First posted on arXiv 24 Aug 2026.

Builds on

46 of 59 references.

Closest ancestors · 10

Same direction, newest first

  1. 2026Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence20 Apr
  2. 2026textttYC-Bench: Benchmarking AI Agents for Long-Term Planning and Consistent Execution1 Apr
  3. 2026APEX-Agents20 Jan
  4. 2025Towards General Agentic Intelligence via Environment Scaling16 Sep
  5. 2025Scaling Agents via Continual Pre-training16 Sep
  6. 2025TaskCraft: Automated Generation of Agentic Tasks11 Jun
  7. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun
  8. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors21 Aug
  9. 2023AgentBench: Evaluating LLMs as Agents7 Aug
  10. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug

Other references · 36

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023Let's Verify Step by Stepcited by 3,789
  5. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  6. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  7. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  8. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  9. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  10. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  11. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  12. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
24 more
  1. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  2. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  3. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  4. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  5. 2023ChatDev: Communicative Agents for Software Developmentcited by 919
  6. 2026GLM-5: from Vibe Coding to Agentic Engineeringcited by 587
  7. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 585
  8. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Searchcited by 422
  9. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learningcited by 386
  10. 2024WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?cited by 363
  11. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
  12. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 324
  13. 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 286
  14. 2025WebSailor: Navigating Super-human Reasoning for Web Agentcited by 229
  15. 2025WebDancer: Towards Autonomous Information Seeking Agencycited by 188
  16. 2025GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Taskscited by 158
  17. 2025Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Librarycited by 101
  18. 2025Agent Learning via Early Experiencecited by 85
  19. 2026FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Taskscited by 47
  20. 2026Rethinking the Trust Region in LLM Reinforcement Learningcited by 37
  21. 2026ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Trainingcited by 32
  22. 2025Part II: ROLL Flash -- Accelerating RLVR and Agentic Training with Asynchronycited by 15
  23. 2026PivotRL: High Accuracy Agentic Post-Training at Low Compute Costcited by 14
  24. 2026Argus: Evidence Assembly for Scalable Deep Research Agentscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026FrontierChallenge: Evaluating Scientific Workflow Completioncited by 1
  2. 2026CompoWorld: Compositional Environment Scaling for General Agentscited by 0

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