Apodex 1.1: Scaling Agentic Intelligence for Complex Work

Abstract: General-purpose LLMs 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 \emph{working capability}: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. \emph{Environment Scaling} expands the diversity and verifiability of executable file, search, and code environments, while \emph{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 \emph{Heavy-Duty Solver} for ambitious, long-running tasks.

• The paper establishes Apodex 1.1, which frames agentic intelligence as a system for sustained, verifiable execution across complex tasks involving files, code, and data, rather than isolated answer quality. Key features include demand for working capability, execution harness, and AgentOS.
• The methodology of Apodex 1.1 includes unified Supervised Fine-Tuning (unified SFT), reinforcement learning (agentic RL), and execution harnesses like AgentOS, improving coordination and task completion in multi-agent environments.
• Apodex 1.1 demonstrated significant improvements in various domains, including professional work, finance, and scientific research, with notable gains in APEX-Agents, GDPVal, and FrontierScience-Research benchmarks, showing a win rate of 78.8 in GDPVal with the Agent Team setup and significant reductions in low-credit and zero-credit tasks on FrontierResearchBench.

Apodex 1.1 frames agentic intelligence as a systems capability rather than as an extension of answer quality. Its central construct, working capability, is the ability to make useful, sustained, and verifiable progress toward an objective while operating over files, evidence sources, executable code, persistent state, failures, and delivery constraints. The paper’s thesis is that complex work requires more than stronger reasoning: it requires an integrated policy and runtime capable of maintaining state, revising plans, recovering from failed actions, coordinating parallel work, and producing artifacts whose correctness and provenance can be inspected (2608.23283).

Problem formulation and central contributions

The paper treats completed work, rather than an isolated response, as the fundamental unit of evaluation. A task is specified by an initial workspace, objective, action space, transition and observation functions, resource budgets, delivery contract, and task-level verifier. This formulation distinguishes a natural-language request from the operational objective and acceptance criteria that govern execution. Success is consequently not identified with a plausible final answer. It depends on the terminal workspace, execution trace, artifact relationships, and contract-specific verification.

This perspective addresses a persistent weakness in existing agent evaluations. A model may know the correct method yet fail because it cannot identify the authoritative file, preserve consistency across intermediate artifacts, recover from a tool error, reconcile contradictory evidence, or determine whether its final output satisfies the requested interface. Apodex 1.1 therefore combines model training, environment construction, coordination mechanisms, and execution infrastructure within one stack.

The paper identifies two scaling dimensions:

• Environment Scaling expands the diversity, fidelity, structural depth, and verifiability of executable file, search, and code environments.
• Agentic Coordination Scaling expands the amount of useful work that can be decomposed, delegated, integrated, verified, and replanned across agents and time.

These dimensions are connected by a shared execution harness and AgentOS, which maintain workspace state, artifacts, provenance, branch state, asynchronous events, and delivery controls. Unified SFT and agentic RL then train the model on executable trajectories and coordination traces rather than on detached answers alone.

Environment scaling

Environment Scaling is presented as a primary source of capability growth, distinct from parameter scaling and inference-time compute. The relevant object is not a larger prompt collection but a broader distribution of executable task contracts with valid state transitions, realistic failure modes, resource constraints, and independently checkable outcomes.

File, search, and code worlds

File environments model professional work in which information is distributed across nested directories, heterogeneous formats, historical versions, and cross-file dependencies. The model must identify authoritative sources, reconstruct derivation chains, transform content, and deliver a coherent artifact. The paper reports a scenario registry spanning 33 domains, 318 occupations, and 1,208 deliverable clusters, with additional task variation at the level of task-specific angles. Difficulty is controlled through authority ambiguity, dependency depth, historical state, and business-logic structure rather than by simply increasing file count.

Search environments treat research as a sequence of discovery, acquisition, source triage, evidence alignment, reconciliation, and stopping decisions. Their gold objects include not only the final answer but also the relevant source set and claim-to-evidence relationships. This design is consistent with the broader movement toward execution-grounded information-seeking agents, including Search-R1 (Jin et al., 12 Mar 2025), WebThinker (Li et al., 30 Apr 2025), and DeepResearcher (Zheng et al., 4 Apr 2025).

Code environments provide stateful repositories and sandboxes in which agents modify files, execute commands, inspect failures, and satisfy executable tests. The paper emphasizes verifier hardening, isolation, and reward-hacking tests. Harvested tasks require fail-to-pass and pass-to-pass test consistency; synthesized tasks must additionally be probed for solutions that obtain reward without completing the intended change. This approach extends the environment-grounded logic established by SWE-bench (Christensen, 2024) and SWE-agent (Yang et al., 2024) to the broader Apodex file–search–code interface.

The three environment families are composable. Search results can become inputs to code, code can transform authoritative files, and final artifacts can be checked against both evidence and executable outputs. This compositionality is an important distinction from systems that treat search, coding, and document production as independent modes.

Verification and replay

The paper makes a strong methodological claim: generated tasks, reference solutions, and graders cannot be assumed mutually correct merely because they agree. Verification must be anchored in independent derivation, execution, provenance, or bounded semantic review. Each rollout therefore receives an immutable world manifest and an isolated mutable sandbox. Retained trajectories include generator versions, seeds, tool versions, action–observation sequences, file deltas, verifier versions, and termination reasons.

The “forward cheap, inverse expensive” construction principle summarizes the proposed data-generation regime. A latent state or reference program can cheaply generate and solve an environment, while the agent must recover the relevant solution path from the rendered workspace under interaction and delivery constraints. The approach permits large-scale synthesis while preserving explicit assurance boundaries, although the quality of the resulting distribution remains dependent on the fidelity and independence of the generators and verifiers.

Agentic coordination and AgentOS

Apodex 1.1 retains the lead-agent and dynamically spawned-subagent architecture of Apodex 1.0, but makes coordination state explicit through a persistent task board. Board entries contain bounded objectives, dependencies, ownership, returned evidence or artifacts, and resolution state. This changes delegation from a largely latent planning operation into an inspectable and mutable runtime object.

The task board enables several behaviors that are difficult to implement reliably through message history alone. A completed branch can unlock dependent work immediately; a failed premise can invalidate only its descendants; a slow branch need not block independent investigations; and new evidence can trigger targeted replanning. The coordinator is required to reconcile execution outcomes with the live plan rather than treating delegation as a one-shot preamble.

Asynchronous intervention

Apodex 1.1 incorporates user intervention during execution. Clarifications, new files, priority changes, methodological constraints, budget changes, pause or cancellation commands, and revised hypotheses can enter through a run-scoped control channel. Interventions that preserve the objective and delivery contract update the active task state while retaining causally valid work. Material changes create a new task contract, potentially reusing valid workspace state.

This mechanism addresses an important failure mode of front-loaded clarification: the information required to specify the correct plan may become available only after evidence has been inspected or intermediate computations have been completed. The paper nevertheless notes that intervention handling is a learned policy behavior, not merely an infrastructure feature; the system must determine which dependencies remain valid and which descendants require recomputation.

Asymmetric verification

The paper explicitly rejects unrestricted “solve the problem again” verification as a default. A verifier that receives the generator’s full context may be anchored by the same assumptions, while an equally unconstrained independent solution can introduce a second set of errors. Apodex instead assigns narrow verification tasks: attack a particular claim, inspect its evidence, search for counterexamples, verify atomic facts, test a computation, or check delivery compliance.

This asymmetry makes verification more actionable. A failed check identifies a specific claim, dependency, or contract clause that can be reopened on the task board. The mechanism operationalizes the targeted-verification principle developed in Chain-of-Verification (Dhuliawala et al., 2023), while embedding it in a persistent multi-agent execution state.

Adaptive effort and evidence-grounded synthesis

Agent Team does not use a fixed number of agents or uniform sampling. Additional effort is allocated to weak, contested, or load-bearing claims. Branches may pursue different hypotheses, methods, source classes, or query framings. Returned results are integrated asynchronously, and obsolete branches can be terminated.

Final synthesis is separated from lead-agent execution. An evidence-graph stage reconciles branch reports, retrieved sources, artifacts, and verifier findings into claims marked as corroborated, disputed, rejected, or unresolved. A writer then produces the final deliverable from this graph. Unsupported claims are qualified or omitted, while missing load-bearing dependencies trigger additional work rather than being filled by fluent prose. This design is closely related to evidence-assembly approaches such as Argus (Zhang et al., 15 May 2026), but is presented here as part of the same runtime rather than as an independent post-processing service.

AgentOS runtime

AgentOS instantiates the workspace as file state, retrieved evidence, executable state and logs, artifact index, provenance graph, and optional control state. It provides explicit filesystem namespaces for inputs, workspace artifacts, and outputs; optional read-only access to durable document libraries; backend-dependent private or shared workspaces; and capability-scoped file and shell access.

Several mechanisms target specific long-horizon failure modes:

Failure mode	AgentOS mechanism
Plan loss during context compaction	External task board with periodic reinjection
Race conditions in asynchronous work	Message queues, interruptible waits, and delivery acknowledgements
Destructive cancellation near timeout	Soft deadlines, bounded finalization, and hard outer timeouts
Undeclared or stale outputs	Single-publisher lease, exact manifest, and baseline reconciliation
Ambiguous artifact ownership	Explicit input, workspace, and output namespaces
Context overflow	Provider-reported triggering, observation eviction, and conditional summarization

The artifact-delivery controls are particularly consequential. Ordinary execution writes to workspace storage, whereas publication requires an exact output manifest and a lease granting commit authority to at most one active session. At termination, files are reconciled against a baseline snapshot so that stale or empty files cannot satisfy the delivery contract.

The paper plainly concedes that AgentOS is not a transactional distributed runtime. Coordination state and filesystem state are not atomically checkpointed, process-restart recovery is outside the current contract, and historical filesystem rewind is unavailable. Thus, the runtime improves operational continuity but does not provide complete durable reproducibility under worker failure.

Training and credit assignment

Training begins with a unified SFT mixture covering general reasoning, search, file operations, coding, mathematics, scientific and financial analysis, professional delivery, recovery, and multi-agent coordination. The paper uses behavioral validity filters to remove invalid tool interactions, inconsistent states, ignored observations, and incomplete deliveries. Model-soup merging combines specialized SFT variants into a unified checkpoint.

Agentic RL then optimizes behavior over executable environment trajectories and coordination traces. Its central optimization technique, PIVOT-RL, localizes learning around consequential decisions identified retrospectively in failed or inefficient trajectories. Useful prefixes are retained, while corrective continuations are constructed from failure-relevant states with directional hints that are removed at inference time. This procedure targets tool misuse, insufficient evidence, uncorrected assumptions, and unproductive planning without requiring all credit to be inferred from a terminal reward.

Asynchronous optimization allows completed trajectories to enter training without waiting for slower episodes. This is operationally appropriate for heterogeneous file, search, code, and multi-agent workloads, although the paper does not provide a component-isolated ablation that would quantify the independent contribution of PIVOT-RL, asynchronous optimization, environment construction, or coordination training.

The reported RL curves show consistent improvement as RL compute increases on held-out search, knowledge, and science evaluations. The result supports the claim that executable trajectories provide a useful post-training substrate, but the evidence remains primarily aggregate: the paper does not establish whether gains arise from improved planning, tool selection, recovery, verification, or a change in task-specific priors.

Empirical results

Apodex 1.1 is evaluated in ReAct and Agent Team modes. ReAct is intended to expose the underlying policy with minimal orchestration, whereas Agent Team measures the additional value of learned decomposition, delegation, integration, verification, and adaptive computation.

The strongest reported gains are summarized below.

Evaluation	Apodex 1.0	Apodex 1.1 ReAct	Apodex 1.1 Agent Team
APEX-Agents	16.5	34.4	38.5
GDPVal	59.3	69.5	78.8
FrontierFinance	40.3	48.7	54.3
FrontierScience-Research	28.3	55.0	63.3
BioMysteryBench	17.6	23.5	35.3
Humanity’s Last Exam	49.0	53.2	56.1
DeepSearchQA	84.6	88.2	92.4

The generational improvement under ReAct is substantial. APEX-Agents rises from 16.5 to 34.4, more than doubling the Apodex 1.0 score, while FrontierScience-Research increases from 28.3 to 55.0. Agent Team adds a further 4.1 points on APEX-Agents, 9.3 points on GDPVal, 5.6 points on FrontierFinance, and 8.3 points on FrontierScience-Research. The implication is that the version improvement is not reducible to runtime delegation: the underlying policy improves materially even under the simpler scaffold, while coordination adds a further system-level gain.

Professional work and finance

On GDPVal, Apodex 1.1 reaches a 78.8 win rate with Agent Team and 69.5 with ReAct. On APEX-Agents, it reaches 38.5 with Agent Team and 34.4 with ReAct. The benchmarks differ in emphasis: GDPVal evaluates the quality of professional artifacts across 44 occupations, whereas APEX-Agents evaluates extended cross-application workflows. The joint result supports the paper’s claim that Apodex improves both artifact quality and sustained execution.

FrontierFinance shows a score of 54.3 with Agent Team, compared with 48.7 under ReAct and 40.3 for Apodex 1.0. The paper attributes the gain to coordinated evidence gathering, quantitative analysis, financial modeling, and verification. However, the Agent Team result does not include a separately reported YC-Bench comparison; the finance evidence is therefore strongest for FrontierFinance rather than for long-term business simulation.

Scientific research

Apodex 1.1 reaches 63.3% on FrontierScience-Research with Agent Team, compared with 55.0% under ReAct and 28.3% for Apodex 1.0. On BioMysteryBench’s Human-difficult Set, Agent Team obtains 35.3%, or 6 of 17 tasks, while ReAct obtains 23.5%, or 4 of 17. The gain is consistent with the proposed use of parallel evidence gathering and verification.

The results are weaker on the paper’s internal end-to-end FrontierResearchBench. Apodex 1.1 with Agent Team achieves a 12.4% full-task pass rate, while GPT-5.6-Sol and Grok-4.6 each reach 20.6%. This benchmark requires every specified research artifact, numerical result, executable output, and cross-artifact consistency check to pass. The low absolute pass rates demonstrate that end-to-end scientific delivery remains substantially harder than scientific question answering or rubric-based research responses. The paper appropriately treats this result as a limitation rather than presenting the broader scientific scores as evidence of reliable autonomous research completion.

Reasoning, search, mathematics, and coding

Agent Team reaches 56.1 on Humanity’s Last Exam and 92.4 F1 on DeepSearchQA, compared with 53.2 and 88.2 under ReAct. On the internal FrontierSearchBench, Agent Team reaches 69.1, exceeding the strongest listed comparison score of 67.4. It also reduces zero-credit tasks from 19.5% under ReAct to 9.8%, while the negative-score fraction falls from 4.9% to 2.4%. Because incorrect assertions are penalized, this pattern indicates improved completeness without a corresponding increase in unsupported guessing.

The mathematics results are among the paper’s strongest claims. On MathArena-derived competition sets, Agent Team obtains 36.5 on IMO 2025, 30.5 on IMO 2026, and 26.5 on USAMO 2026, exceeding the stated reference thresholds of 35, 29, and 25. Relative to Apodex 1.0 Agent Team, the gains are 24.0, 17.5, and 20.7 points, respectively. On IMO-ProofBench, Agent Team reaches 96.7% on Basic and 63.3% on Advanced. These results support the claim that the same coordination stack can improve search-free formal reasoning, although the paper does not show whether the mathematical gain derives from actual multi-agent decomposition or from other post-training changes.

Coding performance is competitive but not leading. Apodex 1.1 scores 70.8 on Terminal-Bench 2.1 and 77.7 on SWE-bench Verified, below the listed best scores of 91.9 and 92.2. This contradicts any broad interpretation that Apodex uniformly dominates across all complex-work domains. Its strongest relative advantages are in finance, structured search, scientific reasoning, and professional delivery rather than terminal software engineering.

Model-scale efficiency

The 35B-parameter Apodex 1.1 Mini provides an important efficiency result. With ReAct, it scores 40.0 on FrontierFinance, 45.0 on FrontierScience-Research, and 24.2 on APEX-Agents. Agent Team raises these to 50.2, 51.7, and 27.7, corresponding to gains of 10.2, 6.7, and 3.5 points.

The Mini result is not merely a smaller version of the main system. It shows that coordination and executable-environment training can produce large improvements at compact model scale. The paper therefore argues for a model-scale efficiency effect: organized inference and working-policy training partially substitute for parameter count on selected complex-work evaluations.

This claim must be interpreted cautiously. Several proprietary comparison models lack public parameter counts, and Agent Team changes inference compute, orchestration, and the number of active agents. The evidence establishes performance-band efficiency, not a controlled compute-normalized comparison against frontier systems.

HDS6 process evaluation

Outcome metrics do not establish whether a result was produced through a coherent or defensible process. HDS6 addresses this gap with six capability groups:

1. long-horizon state coherence;
2. evidence fidelity;
3. hypothesis management;
4. boundary and failure reasoning;
5. tool use and execution-state management;
6. self-correction under verification.

Each group contains four rubric items, for 24 scored items in total. A separate integrity gate zeroes the entire trajectory if the system fabricates a tool result or claims an action that did not occur.

Figure 1: HDS6 capability taxonomy and process-grading pipeline, with six four-item capability groups and an independent integrity gate.

The judging pipeline stitches tool calls, subagent dispatches, and intermediate observations into an ordered trajectory. Role-specialized mapping, judging, review, and arbitration assign each item a score of 0, 1, or 2 supported by citations to visible execution events. Private reasoning is excluded, and cited events are re-grounded against the recorded log.

The paper reports that the largest individual improvements are +1.3 for Initial Decomposition and +0.8 for Final Verification. These changes align with the explicit task board and asymmetric verification mechanisms. Evidence fidelity and hypothesis management also improve. However, HDS6 does not isolate the causal contributions of the model, training procedure, coordination strategy, environment distribution, or AgentOS. It is a process-localization instrument, not a component ablation.

Figure 2: HDS6 comparisons between Apodex 1.0 and 1.1 across Deep Discover, Deep Solve, and Deep Research settings.

The HDS6 comparison has a serious comparability constraint. Deep Discover compares an Apodex 1.0 397B Agent Team result aggregated over eight runs with a single Apodex 1.1 run. Deep Solve uses 397B ReAct, while Deep Research uses the 35B model with ReAct. The panels consequently differ in model scale, task family, execution mode, and aggregation protocol. The paper correctly limits interpretation to within-panel comparisons and does not present the figure as a matched causal ablation.

End-to-end cases

The case studies make the paper’s definition of working capability concrete.

In the molecular-dynamics case, the system produces a 22-file package for a three-copy Martini 3 protein system. The final system contains 63,484 particles, and steepest-descent minimization converges in 255 steps with a maximum force of $9.029 \times 10^2$ kJ/mol/nm. A binary verification establishes that the coordinates embedded in the submitted minimization input match the input structure for all particles with maximum absolute difference 0. The case demonstrates artifact integrity, repair, and cross-file consistency.

It also exposes residual defects. The coarse-grained model differs from the input residue sequence at 131 positions, and numbering restarts within three chains. These defects are disclosed rather than silently corrected because the execution constraints prohibited modifying the affected files. The case therefore supports the paper’s argument that a reliable agent must report unresolved defects instead of converting successful packaging into an unconditional scientific claim.

The fluorescence-imaging case is especially informative because two reasonable measurement definitions produce opposite conclusions. A per-mask mean yields a Welch $P$ value of 0.78 and no apparent group difference. A shared-ROI field mean yields a 2.4-fold difference, Cohen’s $d = 2.84$, and $P = 0.0574$. Arbitration rejects the first definition because it discards signal-abundance information and is unstable under the intensity threshold. The final estimate is a control mean of 113.80 and experimental mean of 47.64, with Welch $P = 0.0593$ and Mann–Whitney $P = 0.10$.

The result is a strong but statistically borderline effect. The paper correctly reports “not significant” at $\alpha = 0.05$, notes that it would cross at $\alpha = 0.1$, and avoids converting a large effect size into a definitive biological conclusion. With only three samples per group, the case primarily demonstrates measurement-definition arbitration and reproducibility analysis rather than inferential robustness.

The WGCNA case processes 6,000 genes and 50 samples, retaining 5,983 genes after CPM filtering and 5,000 by variance selection. All samples are retained. A signed bicor network at power 20 produces ten modules, but no tested power reaches the preset scale-free fit threshold of $R^2 \geq 0.85$; the selected power is therefore an explicit fallback at $R^2 = 0.8486$. The strongest module–trait associations range from $P$0 to $P$1, all with FDR below $P$2.

These associations are not causal claims. The paper notes that batch information was unavailable, the sample size limits covariate modeling, and module partitions depend on filtering, power, gene count, and merging parameters. It also warns that the hub-gene screen uses absolute GS, so candidate genes may have GS signs opposing the module eigengene. Most importantly, the delivered analysis.R was not executed end to end because R was unavailable. Numerical results came from an equivalent implementation, leaving a residual risk of version- or package-specific divergence.

Limitations and open questions

The empirical comparisons combine model changes, training changes, runtime mechanisms, inference budgets, and harness differences. ReAct versus Agent Team is informative about system-level benefit but does not isolate the marginal effects of delegation, adaptive effort, asymmetric verification, evidence-graph synthesis, or persistent task state. The paper similarly does not report systematic cost, latency, token, or energy tradeoffs for the coordination gains.

Several benchmark comparisons rely on reproduced external results or different agent scaffolds. The internal FrontierResearchBench result remains well below the best listed systems, and the coding results are also behind leading references. The paper’s claims are therefore domain-dependent rather than uniformly dominant.

The task-construction methodology introduces another open question. Independent verification reduces obvious grader failures, but synthesized environments may still encode distributions, failure modes, or authority structures that differ from real professional workflows. The report does not provide a comprehensive contamination analysis or a human-reviewed estimate of how representative its environment distribution is across the 33 domains and 318 occupations.

Finally, AgentOS lacks atomic joint checkpointing of filesystem and coordination state. This limits recovery after process failure and prevents historical rewind. The paper identifies joint versioning of task-board state, messages, artifacts, and workspace as an unresolved systems question. A further methodological question is whether HDS6 process scores predict external expert judgments of scientific or professional reliability better than terminal outcome metrics alone.

Conclusion

Apodex 1.1 presents agentic intelligence as sustained, verifiable execution over changing state. Its main contribution is the integration of executable environments, adaptive multi-agent coordination, persistent runtime state, artifact and provenance controls, and trajectory-based training into a single general-purpose stack (2608.23283). The reported gains are substantial in professional work, finance, scientific reasoning, structured search, and mathematical reasoning, and the 35B Mini demonstrates meaningful model-scale efficiency.

The evidence is strongest for the claim that coordinated execution improves complex-work outcomes relative to a simple ReAct scaffold. It is weaker for attributing gains to any individual system component, and the low full-pass rate on end-to-end scientific workflows shows that reliable artifact-complete research execution remains unresolved. The paper’s most consequential result is therefore methodological: it establishes completed, inspectable work as a more demanding and informative target than isolated answer accuracy, while making the remaining failures visible at the level of state, evidence, computation, and delivery.