For the past two years, the standard shape of "deep research" products has been an AI that searches a large pile of web pages and assembles a structured report. But real research work usually only begins after the report is written. The raw input may be dozens of papers, lab records, tables, images, and files in specialized formats; researchers must clean data, choose methods, write code, run analyses, and interpret results. A single task contains multiple interdependent stages, and the plan keeps adjusting based on intermediate results. Apodex 1.1, released on August 24, is built for exactly this gap. In the company's own one-liner: it takes reasoning out of the report and puts it into the execution of real tasks.

Two Scaling Paths and One Runtime Foundation

Capability grows along two complementary paths. Environment Scaling expands the executable environments the model can learn and act in — files, search, code, and other environment categories — with training tasks at a scale of tens of millions. Agentic Coordination Scaling expands a task's ability to be decomposed, coordinated, integrated, and reorganized. Both paths run on a shared runtime foundation called AgentOS, which maintains tool calls, file state, and task progress during a single task, manages the creation, scheduling, and lifecycle of Subagents, and provides a unified verification mechanism. Training combines SFT with agentic RL: SFT gives the model the basic shape of behaviors like tool calling, task decomposition, and multi-agent coordination, and agentic RL then refines these on real execution and coordination trajectories, so failure recovery and task delivery become learned behaviors rather than effects produced by external orchestration.

PIVOT-RL: Finding Consequential Decisions in Hundreds of Thousands of Trajectories

Reinforcement learning on long-horizon tasks has a core problem: terminal outcomes provide only coarse supervision. A successful trajectory can still contain inefficient or weakly grounded intermediate decisions, and a failed one can still contain genuinely useful early work. Apodex's answer is PIVOT-RL. Using Hindsight-Guided Trajectory Localization, it runs retrospective analysis over a training corpus of hundreds of thousands of trajectories and questions to identify the consequential decision points — the pivots — where the model starts following an unproductive strategy, relies on insufficient evidence, misuses a tool, or fails to revise a wrong assumption. At each pivot, the working prefix is preserved and a localized continuation task is constructed with a short corrective hint. The hint only provides directional guidance during training, is never a prediction target, and is absent at inference time. For stateful tasks, the corresponding executable environment state is restored. Localized continuations are mixed with full, unhinted tasks during training, so the model learns efficiently where it is genuinely prone to error while retaining the ability to solve a complete task on its own.

Asynchronous Agent Team and Statement Review

In Deep Discover mode, the model dynamically organizes an asynchronous Agent Team based on the task — not a pre-written orchestration script, but the model itself deciding whether a task can be decomposed, how, and across how many Subagents, continually deciding when to consolidate results. Multiple Subagents explore different subtasks or candidate hypotheses in parallel, feeding intermediate results back to the main task continuously instead of waiting for every branch to finish. Mid-task user intervention is treated as part of the task itself: the system must understand the new requirement, judge which completed intermediate results remain valid, update the plan, and continue from the current state rather than starting over. Before delivery, Statement Review keeps generation and review as distinct steps: key claims — whether a piece of data genuinely supports statistical significance, whether a cited paper actually says what the report claims, whether a computed result matches what the code produced — go through an independent check before reaching the user.

Three Use Cases With Numbers

In a legal preference-liability case from a corporate bankruptcy, the system reconstructed the timing and collection history of six payments, arrived at a net exposure of $550K matching the reference answer, and further provided a settlement range of $175K–$350K plus a client-ready legal memo. In a cross-border FX hedging task, it worked out option premiums under different structures, payoffs across five settlement exchange rates, a $7.6M net collar cost, and a 1.1172 breakeven rate — and caught that the CFO's underlying assumption about accounting treatment was itself wrong, re-deriving the impact of all three structures under ASC 815 on P&L, OCI, and reclassification risk. The research case is the most hardcore: starting from the 7M6J protein structure, it chose the Martini 3 coarse-grained method to generate force-field topologies, replicated three copies of the protein, added water and physiological-concentration salt ions, and built a complete GROMACS simulation system with 69,652 simulation sites, completing energy minimization.

The Open-Source Part and AI4AI

On the local ecosystem side, the 35B Apodex 1.1 mini offers a locally deployable option with a disclosed parameter count; the company says it reaches the performance band of selected frontier systems on professional work, finance, and scientific research. The companion FrontierAgent execution harness is open-sourced on GitHub, with a native command-line TUI supporting both ReAct single-agent and Agent Team modes, running out of the box with a single command on macOS and Linux without depending on Docker. The AI4AI experiment shows another use: Apodex 1.1 acts as the Teacher — generating questions, filtering correct trajectories, evaluating, and iterating with no human involvement and no reliance on other strong models — and was used to train Qwen3.5-0.8B on three task types covering 200 questions in clinical trial and drug information retrieval, protein structure database lookup, and protein sequence and function annotation. After 10 rounds of automated iteration, the small model's overall score rose from 51.0% to 56.0%.

So What

The end state this company draws for itself is the Heavy-Duty Solver: a system that takes on increasingly complex, long-horizon work while delivering results that remain verifiable end to end, with Apodex 2.0 to be built from the pretraining stage up. The signal for practitioners is direct: as "answering questions" and "seeing a piece of work through to completion, verifiably" split into two different capabilities, evaluation will shift from single-turn Q&A to long-trajectory execution — Apodex has built two benchmarks of its own, FrontierSearchBench and FrontierResearchBench, pointing exactly in that direction (official release: https://www.apodex.com/blog/apodex-1.1-scaling-agentic-intelligence-for-complex-work).