Apodex 1.1: Scaling Agentic Intelligence for Complex Work

Mon, 24 Aug 2026 00:00:00 +0000

A useful research agent must do more than retrieve information and produce a report. It needs to inspect real files, execute analyses, maintain state, recover from failure, and leave behind work that can be checked and reused. Apodex 1.1 calls this working capability: sustained, verifiable progress toward a real objective.

I contributed to the agent harness and its evaluation and release infrastructure. My work connects long-running execution, multi-agent coordination, and artifact verification through . The results below describe the complete team-built model and system, rather than an isolated contribution from any one component. The author list on this page is abbreviated; the report contains the full credits.

Two complementary ways to scale agent capability

Environment Scaling expands the file, search, and code environments in which agents can act and receive verifiable feedback. Tasks require the model to work with data and executable tools, rather than rely on text-only answers.

Agentic Coordination Scaling trains task decomposition, delegation, integration of asynchronous results, and replanning. A coordinator can assign independent work to sub-agents, absorb their findings, and organize further verification as the task evolves.

Both depend on a shared runtime. AgentOS maintains task state outside the conversation history, coordinates tool and agent execution, and tracks which files are working material and which are final deliverables. The makes ReAct and Agent Team workflows available through a terminal interface and an evaluation harness.

Reported results: ReAct and Agent Team

The August 2026 technical report compares a single-agent ReAct setting with Agent Team for the same Apodex 1.1 model. Selected results are reproduced below for readability.

Evaluation	ReAct	Agent Team	Difference
APEX-Agents	34.4	38.5	+4.1
GDPVal win rate (%)	69.5	78.8	+9.3 pp
FrontierFinance	48.7	54.3	+5.6
FrontierScience-Research	55.0	63.3	+8.3
BioMysteryBench, Human-difficult	23.5	35.3	+11.8
Humanity’s Last Exam	53.2	56.1	+2.9

Differences are in each benchmark’s reported score units; the GDPVal change is in percentage points. Agent Team uses additional coordinated computation, so these comparisons are not matched-compute estimates of the effect of coordination alone. They are a dated snapshot of the report’s evaluation, not claims about a current leaderboard.

Reported results across professional work, finance, scientific research, and general reasoning. Source: Apodex 1.1 technical report, Tables 3–4; reference-system protocols are described in the report.

From runtime behavior to usable research tools

For researchers, the runtime supports keeping a task moving through changing evidence and user feedback: intermediate files survive, the plan can be revised, and results from parallel branches can be integrated. The official explains how this appears in the workbench through live intervention, visible progress, and review of important claims.

The report also introduces a 35B-parameter Mini model as a locally deployable route to these workflows. This is separate from the full-model results above.

Sources: , Sections 3–4 and Tables 3–5; ; .

Watt Tool-Use Post-Training

Sun, 29 Dec 2024 00:00:00 +0000

At Watt, I led the tool-calling roadmap for a Llama 3.3-70B model. We fine-tuned it for parallel function calls and multi-turn instruction following. The model reached No. 1 on Berkeley’s Function-Calling Leaderboard V3 in December 2024.

The surrounding workflow supported more than 200 tools. On a 1,000-task multi-step evaluation, task completion improved from 70% to 90%.

Post-Training | Zhang Handuo's Site

Apodex 1.1: Scaling Agentic Intelligence for Complex Work

Two complementary ways to scale agent capability

Reported results: ReAct and Agent Team

From runtime behavior to usable research tools

Watt Tool-Use Post-Training