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Apodex 1.1: From Release to Hands-On Tests

Apodex released version 1.1 with an open-sourced FrontierAgent framework and 35B mini model, followed by user tests confirming dynamic task replanning.

2026-09-08 ~ 2026-09-09 · 2 episodes · 17 posts

Episode 1 · Apodex Launches 1.1 with Open-Source FrontierAgent Framework and 35B Mini Model (2026-09-08, 14 posts)

Apodex has released version 1.1, open-sourcing the FrontierAgent execution framework alongside a 35B-parameter 1.1 mini model. The entire release rests on one thesis: the meaningful unit for measuring AI capability is end-to-end completion of a real job, producing checkable artifacts like documents and code, not merely answering questions correctly—"a correct answer can still be a failed task." That thesis, paired with a verifiable-deliverable mechanism, makes this a release worth watching for anyone focused on trustworthy agent output.

Confirmed

  • FrontierAgent is open-sourced on GitHub; it launches with a single command on macOS/Linux, with no pre-installation or hard Docker dependency; it features a native command-line TUI with single-agent and Agent Team modes, and currently has around 2.4k stars
  • Apodex 1.1 mini is a 35B-parameter open-weights model on Hugging Face, with FP8, NVFP4, GPTQ-Int4 and other quantized variants, and can be deployed locally alongside FrontierAgent
  • The full model powers the online web workspace, with a free two-week API trial for new users
  • Official figures place 1.1 with Agent Team mode in the current frontier tier of agent systems on benchmarks including APEX-Agents, GDPval, FrontierScience, and Humanity's Last Exam; per reposts, the Apodex 1.1 paper (arXiv:2608.23283) and Agent Team evaluation results have been added to Papers with Code, with the Agent team achieving a 63.3% pass rate on FrontierScience
  • By design, "generating results" and "checking results" are separate steps (Statement Review): key conclusions are double-checked by an independent stage before delivery, flagging weak evidence, citation mismatches, and calculation conflicts while retaining an audit trail
  • Deep Discover mode has a built-in asynchronous Agent Team: the model itself decides task decomposition, the number of sub-agents, and when to aggregate; sub-agents run in parallel and stream results back to a shared task state; new files or changed requirements can be injected mid-run, with the system preserving valid work and re-planning only affected parts—the official demo is a global EV battery supplier risk assessment
  • Official demos handle real professional files directly, e.g. reading raw clinical data tables for cleaning and survival analysis with Kaplan-Meier curves, plus powder XRD indexing and batch protein docking

Why it matters

  • The release shifts from "telling you how" to "working alongside you," making verifiable deliverables—not Q&A accuracy—the capability standard, targeting the trustworthiness of agent output in real professional settings
  • The Statement Review mechanism separating generation from verification was flagged by reviewer aakashgupta as the most underrated capability, and a key safeguard for using agent output in actual work
  • The combination of an open-source framework and open 35B weights lowers the barrier to local reproduction, with the mini model able to run full workflows locally alongside FrontierAgent

Episode 2 · Hands-On: Apodex 1.1 Rearranges Workflows When Requirements Change Mid-Task (2026-09-09, 3 posts)

Users testing Apodex 1.1 found it can automatically reorganize research pipelines and rerun multi-agent task boards when requirements are changed mid-task, demonstrated on a complex open-source model comparison benchmark.