Dependency-Aware Execution Model

• The Dependency-Aware Execution Model is an architecture that leverages explicit dependency graphs to enforce scheduling constraints, localize failures, and enable adaptive re-execution.
• It employs representations like state-dependency graphs and DAGs to clearly map task preconditions, effects, and dependencies in planning, orchestration, and multi-agent coordination.
• It improves system performance and reliability by reducing recomputation and facilitating parallel execution through structured, dependency-guided scheduling.

A dependency-aware execution model is an execution architecture in which dependency structure is made explicit and then used to constrain what may run, when it may run, how failures are localized, and how partial repair or reuse is performed. In embodied planning, this appears as a bipartite State-Dependency Graph that links action effects and action preconditions and supports dynamic revision after execution failure (Shen et al., 30 Sep 2025). Across adjacent literatures, the same term also denotes DAG-based task scheduling, read-after-write dependency tracking, partial-order synchronization, graded execution/dependency graphs, and label-dependency constraints in concurrent protocols. This suggests a family of models unified less by a single graph formalism than by a common commitment: execution decisions are mediated by explicit dependency representations rather than by purely linear plans or opaque runtime heuristics.

1. Formal dependency representations

The most explicit action-state formulation in the supplied literature is the State-Dependency Graph of SDA-PLANNER. Let $\mathcal V$ be the finite set of mid-level action types. For each $a\in\mathcal V$, the planner extracts an effect set $S_{\mathrm{eff}}[a]=\{(s,v)\}$ and a dependency set $S_{\mathrm{dep}}[a]=\{(s,v)\}$. The resulting graph is a directed bipartite graph

$$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$

with action-nodes $N_a=\{n_a\mid a\in\mathcal V\}$ and state-nodes $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$. An edge $(n_a\to n_s,v)\in E_{a\to s}$ means that action $a$ sets state $s=v$, while $a\in\mathcal V$0 means that action $a\in\mathcal V$1 requires $a\in\mathcal V$2 before execution (Shen et al., 30 Sep 2025).

A second major representation is the ordinary DAG over executable units. In runtime orchestration, each request is modeled as a directed acyclic graph $a\in\mathcal V$3, where $a\in\mathcal V$4 is the set of tasks and $a\in\mathcal V$5 means $a\in\mathcal V$6 cannot start until $a\in\mathcal V$7 completes. This model is enriched by node cost $a\in\mathcal V$8, requiredness $a\in\mathcal V$9, and edge weight $S_{\mathrm{eff}}[a]=\{(s,v)\}$0, together with the precedence constraint

$S_{\mathrm{eff}}[a]=\{(s,v)\}$1

The graph must be acyclic, and predecessors and successors are defined by the usual $S_{\mathrm{eff}}[a]=\{(s,v)\}$2 and $S_{\mathrm{eff}}[a]=\{(s,v)\}$3 sets (Kandiraju, 7 Mar 2026).

The same DAG form is used for multi-robot decomposition in DART-LLM. There, $S_{\mathrm{eff}}[a]=\{(s,v)\}$4 is the set of subtasks generated by a QA LLM, $S_{\mathrm{eff}}[a]=\{(s,v)\}$5 with $S_{\mathrm{eff}}[a]=\{(s,v)\}$6, and $S_{\mathrm{eff}}[a]=\{(s,v)\}$7 means $S_{\mathrm{eff}}[a]=\{(s,v)\}$8 cannot start before $S_{\mathrm{eff}}[a]=\{(s,v)\}$9 completes. The model explicitly ties natural-language decomposition to precedence structure by requiring each subtask to carry its dependency list $S_{\mathrm{dep}}[a]=\{(s,v)\}$0 (Wang et al., 2024).

Other works broaden the notion of dependency beyond precedence. ConFuzzius defines a graph over transactions $S_{\mathrm{dep}}[a]=\{(s,v)\}$1 with

$S_{\mathrm{dep}}[a]=\{(s,v)\}$2

which is a dynamic Read-After-Write dependency extracted from EVM traces (Torres et al., 2020). DGS treats the input itself as partially ordered through a symmetric predicate $S_{\mathrm{dep}}[a]=\{(s,v)\}$3, and requires that any two events processed in parallel be pairwise independent, $S_{\mathrm{dep}}[a]=\{(s,v)\}$4 (Kallas et al., 2021). GRADE goes further still and models a run as a directed, typed temporal multigraph

$S_{\mathrm{dep}}[a]=\{(s,v)\}$5

with one layer for execution order and another for dependency, plus a grading function

$S_{\mathrm{dep}}[a]=\{(s,v)\}$6

so that dependency edges are annotated by how they were recovered (Zhao, 22 Jun 2026).

These formalisms differ in ontology—actions and states, tasks, transactions, events, or run steps—but they share an operational commitment: dependency information is first-class data.

2. Dependency as an execution constraint

Once dependencies are explicit, execution becomes a constrained traversal problem. In the orchestration setting, graph construction is configuration-driven at request time. A build_execution_graph(config) routine creates one node per configured step, assigns timeout and requiredness, and inserts one edge per declared dependency rule, with time complexity $S_{\mathrm{dep}}[a]=\{(s,v)\}$7. Execution then uses a Kahn-style topological scheduler with batching: nodes with zero in-degree enter a ready set, the runtime selects up to maxParallel, invokes them in parallel, and decrements successor in-degrees when a predecessor succeeds—or when the predecessor is optional (Kandiraju, 7 Mar 2026).

This same control logic can be expressed through level decomposition. For the DAG $S_{\mathrm{dep}}[a]=\{(s,v)\}$8, define

$S_{\mathrm{dep}}[a]=\{(s,v)\}$9

All nodes in the same level-set $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$0 can execute in parallel, subject to maxParallel. The critical path

$$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$1

gives a lower bound on latency, with $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$2 and, under infinite parallelism, $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$3 (Kandiraju, 7 Mar 2026).

The multi-robot actuation model in DART-LLM instantiates the same principle with a ready queue and asynchronous ROS2 actions. Initially, Ready ← {T_i | pred(T_i)=∅}. Each ready task is dispatched to its assigned robot, marked running, and upon completion its successors are checked; a successor enters the ready queue only when all predecessors are done. Because ROS2 actions are asynchronous, independent subtasks run in parallel, while dependency edges force serialization where needed (Wang et al., 2024).

Hyperledger Fabric’s dependency-aware execution mechanism applies level-wise scheduling inside a block. Independent transactions, flagged at endorsement, form Level $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$4 with no incoming edges and are executed in parallel using a thread-pool of size up to the number of cores. Dependent transactions are then processed by topological layers; graph build and level assignment both run in $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$5 (Kaul et al., 9 Sep 2025). DGS generalizes the same idea to stream processing, where synchronization is performed only when required by data dependence rather than by a total-order sharding discipline. Its synchronization plans decompose a global partial order into suborders processed in parallel by leaves, with internal nodes synchronizing only those events that depend on multiple substreams (Kallas et al., 2021).

A plausible implication is that “dependency-aware” does not merely mean “graph-based.” It means that eligibility to execute is itself a computed function of predecessor completion, precondition truth, or dependency satisfaction.

3. Local repair, rollback, and adaptive re-execution

A central motivation for explicit dependencies is that they localize recovery. SDA-PLANNER makes this point directly. Execution begins from an initial full candidate plan $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$6 obtained via a single LLM prompt. Actions are executed one by one. On failure at time $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$7, the planner invokes Error-Backtrack-and-Diagnosis to identify the minimal subsequence $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$8 that must be revised, then Adaptive-Action-SubTree-Generation to reconnect from $$\mathcal G=(N_a\cup N_s,\;E_{a\to s}\cup E_{s\to a}),$$9 to $N_a=\{n_a\mid a\in\mathcal V\}$0 under the constraints of $N_a=\{n_a\mid a\in\mathcal V\}$1, reverse-executes from $N_a=\{n_a\mid a\in\mathcal V\}$2 up to $N_a=\{n_a\mid a\in\mathcal V\}$3, inserts the revised subsequence, and resumes execution (Shen et al., 30 Sep 2025).

The diagnostic step is state-centered. If a violated precondition $N_a=\{n_a\mid a\in\mathcal V\}$4 is detected, the planner traces back to the most recent corruption point: $N_a=\{n_a\mid a\in\mathcal V\}$5

$N_a=\{n_a\mid a\in\mathcal V\}$6

The reconstruction window is then chosen so as to include relevant state-preparation actions and subsequent actions that consume the same objects. For local reconstruction, the permissible children of a node $N_a=\{n_a\mid a\in\mathcal V\}$7 are

$N_a=\{n_a\mid a\in\mathcal V\}$8

and breadth-first search over the constrained tree returns the first fully consistent path (Shen et al., 30 Sep 2025).

Dependency-aware rollback in task-based runtimes formalizes an analogous minimal-recomputation objective. Let $N_a=\{n_a\mid a\in\mathcal V\}$9 be the set of $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$0-level tasks that were in-flight on the failed node, $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$1 the checkpoint baseline just before those tasks in the DAG, and $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$2 the minimal superset of tasks that must be re-executed in order to re-produce $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$3. The recovery set is

$$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$4

where $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$5 denotes a path in the task DAG. All tasks in $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$6 are cancelled on every worker and re-enqueued on fresh data replicas, rather than rolling back all tasks after the last globally consistent checkpoint (Dichev et al., 2017).

Urban geospatial editing uses the same dependency-aware recovery logic at the subtask level. For each geometric level $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$7, a validator

$$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$8

interposes after each subtask. If $$N_s=\{n_s\mid s\text{ appears in any }S_{\mathrm{eff}}\text{ or }S_{\mathrm{dep}}\}$$9, the subtask is retried up to a global budget $(n_a\to n_s,v)\in E_{a\to s}$0. The model explicitly states that this “rollback and re-execute” loop prevents error accumulation by never exposing later subtasks to an unvalidated state (Liu et al., 22 Feb 2026).

These systems reject wholesale restart as the default response to error. Their shared claim is narrower and more technical: the dependency structure identifies the smallest semantically necessary repair region.

4. Representative instantiations and reported results

The literature applies dependency-aware execution to embodied planning, multi-robot coordination, orchestration, blockchain, skill retrieval, speculative agents, fuzzing, and diagnostic graph analysis. The following examples summarize representative reported outcomes.

System	Dependency mechanism	Reported result
SDA-PLANNER	State-Dependency Graph; Error Backtrack and Diagnosis; Adaptive Action SubTree Generation	SR $(n_a\to n_s,v)\in E_{a\to s}$1, GC $(n_a\to n_s,v)\in E_{a\to s}$2, No.EC $(n_a\to n_s,v)\in E_{a\to s}$3; $(n_a\to n_s,v)\in E_{a\to s}$4 pp SR and $(n_a\to n_s,v)\in E_{a\to s}$5 pp GC over the best baseline on Seen + Unseen average
DART-LLM	DAG decomposition; dependency-respecting actuation	On L3, GPT-4o/DART-LLM achieves $(n_a\to n_s,v)\in E_{a\to s}$6, $(n_a\to n_s,v)\in E_{a\to s}$7, $(n_a\to n_s,v)\in E_{a\to s}$8, $(n_a\to n_s,v)\in E_{a\to s}$9; $a$0 was $a$1–$a$2 lower than $a$3 on complex L3 tasks
Runtime orchestration	Runtime DAG generation; topological batch scheduling	In Customer 360 retrieval, $a$4 ms, $a$5 ms, speed-up $a$6
Hyperledger Fabric	Endorsement flagging; per-block DAG; parallel commit of independent transactions	Up to $a$7–$a$8 higher throughput at 5 000 transactions; latency $a$9 ms vs. $s=v$0 ms at dependency ratio $s=v$1; rejection rate dropped by over $s=v$2
GoS	Typed skill graph; reverse-aware Personalized PageRank; budgeted hydration	$s=v$3 average reward over vanilla full skill-loading baseline and $s=v$4 input tokens
B-PASTE	Bounded beam of future execution subgraphs; expected critical-path reduction objective	Preliminary internal testing shows up to $s=v$5 end-to-end speedup

The embodied planning results of SDA-PLANNER are reported on the ALFRED benchmark under the unified LOTA-BENCH framework with GPT-4o-mini, temperature=0, and seed=1. The average across Seen + Unseen environments is SR $s=v$6, GC $s=v$7, and No.EC $s=v$8, with relative improvements of $s=v$9 in SR and $a\in\mathcal V$00 in GC against the runner-up Global Planner; the paper states that these gains hold under both simple environment errors and complex action-precondition failures (Shen et al., 30 Sep 2025).

The multi-robot setting shows that explicit dependency modeling can affect both correctness and latency. DART-LLM reports that the improvement $a\in\mathcal V$01 between dependency-aware and naïve linear decomposition on L3 tasks is on the order of $a\in\mathcal V$02–$a\in\mathcal V$03 percentage points, and that ignoring dependency constraints reduces success rate by $a\in\mathcal V$04 while increasing execution time relative to dependency-aware scheduling (Wang et al., 2024).

In data retrieval orchestration, dependency-aware scheduling primarily manifests as parallelism exposure. The “customer360” case uses four level-0 nodes executed in parallel and an optional level-1 node, yielding $a\in\mathcal V$05 relative to a sequential composition of four $a\in\mathcal V$06 ms calls plus $a\in\mathcal V$07 ms overhead (Kandiraju, 7 Mar 2026). In Hyperledger Fabric, the effect is conflict-aware block construction and commit-time concurrency, with up to $a\in\mathcal V$08 higher throughput and sharply lower rejection under contention (Kaul et al., 9 Sep 2025). In skill retrieval and speculative execution for LLM agents, the dependency representation is not an executor DAG over already-issued actions, but a structure for selecting the smallest useful execution-ready bundle or the highest-value speculative branch prefixes under resource limits (Liu et al., 7 Apr 2026, Song, 9 Apr 2026).

5. Complexity, correctness, and analytical guarantees

A substantial part of the literature is concerned with making dependency-awareness analytically tractable. In runtime orchestration, graph construction runs in $a\in\mathcal V$09, each node is processed once, each edge is examined once, and scheduler overhead is $a\in\mathcal V$10 if priority queues are used, else $a\in\mathcal V$11. Memory per request is $a\in\mathcal V$12, and the latency bound is stated as

$a\in\mathcal V$13

with $a\in\mathcal V$14 in the worst-case chain graph and $a\in\mathcal V$15 in an ideal parallel fan-out (Kandiraju, 7 Mar 2026).

DGS provides the strongest correctness theory among the supplied works. Its consistency conditions include: Join–Update commutes, Fork–Join identity, and Independent updates commute. Under these conditions, Theorem 1 shows that any $a\in\mathcal V$16-valid synchronization plan for a consistent $a\in\mathcal V$17 yields exactly the same multiset of outputs as the sequential run. The execution model therefore offers a deterministic equivalence result: respecting the partial-order dependencies guarantees correct semantics (Kallas et al., 2021).

Hyperledger Fabric presents execution-time approximations in layer form. Graph build and Kahn-style topological layering each cost $a\in\mathcal V$18, while total commit time is approximated by

$a\in\mathcal V$19

where $a\in\mathcal V$20 is the maximum depth, $a\in\mathcal V$21 the batch size at layer $a\in\mathcal V$22, $a\in\mathcal V$23 the number of threads allocated, and $a\in\mathcal V$24 the per-transaction processing time (Kaul et al., 9 Sep 2025).

Quantum surface-code compilation supplies a different type of guarantee: dependency extraction induces a DAG $a\in\mathcal V$25, and the optimization problem is then to minimize the number of time-steps $a\in\mathcal V$26 subject to mapping and routing constraints encoded as a SAT formula $a\in\mathcal V$27. The paper proves that fixed-map routing and full map+routing are NP-complete, gives an exact incremental-SAT algorithm, and provides a greedy routing relaxation with an $a\in\mathcal V$28-approximation guarantee, where $a\in\mathcal V$29, $a\in\mathcal V$30, and $a\in\mathcal V$31 (Molavi et al., 2023).

A plausible synthesis is that dependency-aware execution models split into two analytical traditions. One tradition derives explicit runtime bounds and scheduling complexity from DAG structure; the other derives semantic correctness or approximation guarantees from the dependency formulation itself.

6. Misconceptions, limitations, and open questions

A recurrent misconception is that dependency-awareness is synonymous with generic graph learning or with parallel execution alone. The supplied works do not support either reduction. GRADE argues that generic GNNs may misread the dependency layer because off-the-shelf models cannot distinguish an observed edge from an inferred one, and under the full-history assumption the dependency graph can collapse into a deterministic function of run size. In pooled leave-one-corpus-out tests, the feature-based “src-aware” probe maintains AUC $a\in\mathcal V$32 on every held-out corpus, whereas a simple GIN drops to AUC $a\in\mathcal V$33, and relation-aware R-GCN/HGT fall below chance on $a\in\mathcal V$34–$a\in\mathcal V$35 held-out classes (Zhao, 22 Jun 2026). The issue is therefore not merely whether a graph exists, but whether its epistemic status is represented.

A second misconception is that dependency-aware models always require a single monolithic graph. City Editing explicitly states that the paper “does not instantiate a single monolithic graph $a\in\mathcal V$36,” even though its cross-level dependency structure can be formalized over subtasks and its execution remains dependency-aware through strict coarse-to-fine propagation and validation (Liu et al., 22 Feb 2026). Similarly, the context-aware service-protocol literature represents concurrency control through a label-dependency set

$a\in\mathcal V$37

together with operational rules that remove or preserve dependency pairs as labels fire. Because user-specified priorities are error-prone, the model adds verification checks for mutual exclusion conflicts and cross-dependency conflicts, with an $a\in\mathcal V$38 scan to detect cycles that could induce deadlock (Cubo et al., 2010).

A third misconception is methodological: “dependency-aware” does not imply a specific solver family. In surface-code compilation, the summary explicitly notes that Molavi et al. do not use simulated annealing; instead they propose an exact incremental-SAT algorithm and a family of relaxations that decouple mapping from routing and use a greedy routing subroutine (Molavi et al., 2023).

The open questions named in the data are correspondingly concrete. GoS notes that graph quality depends on accurate I/O schemas and documentation, that the graph is currently static, and that retrieval cannot fix downstream execution failures (Liu et al., 7 Apr 2026). B-PASTE treats resource slack as narrow and highly dynamic on Thor-class devices and therefore emphasizes strict priority for authoritative tasks, immediate preemption of speculation under contention, and copy-on-write sandboxes for state safety (Song, 9 Apr 2026). In service protocols, user intervention remains necessary for priority assignment even though verification can detect inconsistencies (Cubo et al., 2010). In several cases, the general direction is clear even where the exact future mechanism is not specified: more useful dependency-aware execution appears to require not only richer dependency extraction, but also finer distinctions between hard constraints, soft preferences, and uncertain or inferred edges.