Versioned Execution Log: Reproducibility & Repair

• Versioned execution logs are structured, persistent records of state transitions and actions that ensure reproducibility and fault isolation in modern systems.
• They utilize tree or DAG-based data structures with checkpoints, lineage fingerprints, and causal dependencies to support scalable replay and robust validation.
• Advanced scheduling heuristics like the persistent-root policy and dynamic programming optimize checkpoint placement and resource constraints for faster repair and audit processes.

A versioned execution log is a structured, persistent record of discrete state transitions, actions, and lineage fingerprints that track the evolution and reproducibility of application workflows or distributed protocols across multiple versions. Such logs are foundational to reproducibility, fault isolation, and efficient repair in modern data-centric, scientific, and agent-based systems. Recent research demonstrates that versioned logs enable time-travel debugging, scalable replay, and robust validation mechanisms by capturing execution lineage, resource access, and causal dependencies, often under strict resource and audit constraints.

1. Formal Definition and Core Data Structures

A versioned execution log encodes program or transaction history as a sequence—or typically, a tree or DAG—of states indexed by a version identifier. For a REPL-style program, let $L_j = [x_{j,1}, \dots, x_{j,\ell_j}]$ represent the ordered cells of the $j$-th version, generating a state chain:

$$ps_{j,0} \xrightarrow{x_{j,1}} ps_{j,1} \xrightarrow{x_{j,2}} \cdots \xrightarrow{x_{j,\ell_j}} ps_{j,\ell_j}$$

Each state $ps_{j,i}$ is accompanied by:

• $\delta_{j,i}$: execution time
• $s_{j,i}$: in-memory checkpoint size
• $g_{j,i}$: lineage fingerprint

Logs merge all version paths into a shared tree $T=(V,E)$, where each node $u$ holds a unique state fingerprint, enabling mergeability detection and state reuse. In ALAS (Geng et al., 5 Nov 2025), each log entry is

$$e = \langle \text{ts},\,\text{nodeId},\,\text{eventType},\,\text{payload},\,\text{version},\,\text{correlationId} \rangle$$

indexed by (version, timestamp) or correlationId for transaction grouping. In distributed systems such as Pilotfish (Kniep et al., 29 Jan 2024), per-object versioned queues serve as the execution log. Each ExecutionWorker maintains:

• $Objects_j$: durable $(oid, ver) \mapsto o$
• $Pending_j$: $(oid, ver) \mapsto [(op, \{TxIdx\})]$
• Version counters and periodic checkpoints.

FlorDB (Garcia et al., 2023) utilizes a unified relational schema: | Table | Key Columns | Description | |-------------|------------------------------------------------------|-----------------------------| | Stmt | stmt_id, version, file, line, code | Catalog of logging sites | | Checkpoint | ckpt_id, version, start_ts, end_ts, path | Model/data checkpoints | | Log | log_id, stmt_id→Stmt, ckpt_id→Checkpoint, ts, args | Each captured log tuple |

2. Lineage Capture and State Mergeability

Execution lineage is ascertained via system call provenance (e.g., SPADE/CRIU in CHEX (Manne et al., 2022)), and cumulative hashes of code segments and syscall orderings:

$$g_{i} = H(g_{i-1}\,||\,h_i\,||\,[\mathrm{sorted}\,E_i])$$

Mergeable states $ps_{j,i}, ps_{k,\ell}$ require $h_{j,i} = h_{k,\ell}$ and $g_{j,i} = g_{k,\ell}$, with identical external file hashes, to collapse paths in the execution log. This canonicalization minimizes replay overhead and maximizes computation sharing.

Pilotfish eschews global logs: conflict detection utilizes versioned per-object queues, ordered by consensus-assigned TxIdx, enforcing orderings that guarantee serializability and linearizability. FlorDB propagates log statements via semantic diff, injecting tracking sites into historical code versions and leveraging code structure mapping for location identification.

3. Replay, Checkpointing, and Resource Constraints

Replay and repair protocols depend critically on the efficient management of the versioned log and its associated checkpoints. CHEX formally poses the multiversion replay scheduling problem:

$$\min_{R}\sum_{\,O_t=CT(u)\,}\delta_u\quad \text{s.t. }\sum_{u\in S_t}s_u\le B$$

where $R$ is a valid replay sequence, $B$ the in-memory cache constraint, and $\delta_u$ cell compute cost. NP-hardness is established via reduction from Bin-Packing.

FlorDB replays historical code with injected log sites by launching minimal tasks from the nearest checkpoint preceding the target instrumentation site. The parallel replay algorithm, leveraging checkpoint partial replay, yields substantial speedups:

$$T_{\text{partial}}(v_i,s) \approx \frac{L_i}{\text{total\_iters}_i} \cdot T_{\text{full}}(v_i)$$

and overall work divided across $p$ parallel workers. Pruning and query optimization restrict replays to relevant (version, site, checkpoint) tuples.

ALAS uses the log to bound repair scope: localized corrections (retry, catch, compensation, loop guards) only affect the minimal neighborhood as dictated by $\mathcal{N}_{\text{aff}}$, tracked under an explicit policy $\Pi$. Upon successful repair, a new log version is committed, branching the execution DAG.

4. Heuristics and Scheduling Algorithms

General replay optimization is computationally intractable. CHEX (Manne et al., 2022) introduces polynomial-time heuristics:

• Persistent-Root Policy (PRP): greedy DFS-based, restricting node eviction until all subtree leaves have been visited; $O(n^2)$ time, $O(n)$ space.
• Parent-Choice Dynamic Programming (PC): memoized DP over ancestor sets, $O(n2^h)$ complexity, leveraging subtree independence.

These policies govern checkpoint placement, minimize compute time, and ensure that resource budgets constrain the scheduling. PRP and PC both outperform simple cache eviction baselines by up to 50–65% when cache size is moderate.

Pilotfish's scalable execution depends on deterministic append-read-update on versioned per-object queues, avoiding locks and bottlenecks. Transactions trigger readiness via head-of-queue checks ensuring consensus order.

5. Validation, Repair, and Auditing Mechanisms

Versioned logs enable robust validation by feeding isolated, bounded log slices to independent validators (e.g., ALAS's validator receives only $\mathcal{L}_{[v-\kappa,v]}$), mitigating context attrition, circular verification, and global recompute. Log entries, indexed by version and correlationId, ensure localized repair can roll forward from specific checkpoints, applying only the necessary corrective edits.

In multi-agent LLM planning (ALAS (Geng et al., 5 Nov 2025)), the protocol guarantees bounded edit radii:

$r = |\mathcal{N}_{\text{aff}}| \le r_{\max}$

with makespan degradation bounded linearly in $r$. Repair policies are grounded and explicitly encoded both in workflow IR and runtime artifacts (Amazon States Language, Argo Workflows). All events are persisted and auditable.

FlorDB fuses log propagation, partial replay, and unified schema to deliver efficient historical querying. Query optimizer analyses and index structures restrict log traversal, and checkpoint parallelization ensures responsiveness.

6. Performance and Experimental Results

Empirical studies confirm the efficiency and scalability of versioned execution log mechanisms:

• CHEX (Manne et al., 2022): PRP and PC heuristics reduce total replay compute by on average 50–65% compared to naive baselines for cache sizes 1–2× single-cell checkpoint. PC achieves near-optimal replay doubling replayable versions when cache is doubled. Auditing overhead is 15–25% of original runtime.
• Pilotfish (Kniep et al., 29 Jan 2024): Linear scaling to 8 workers (1–2% log overhead), 3–10× throughput over single-threaded, low-latency up to contention limits.
• FlorDB (Garcia et al., 2023): 8× parallel speedup, 12× latency reduction, under 2s typical query latencies across 50 code versions; storage overhead is ~1.2× raw logs.
• ALAS (Geng et al., 5 Nov 2025): 83.7% success rate, 60% token reduction, 1.82× speedup, and repair blast radius below $O(\log J)$ per job-shop instance.

7. Integration and Applicability

Versioned execution logs are integrated with multiple workflow and distributed execution engines:

• CHEX and FlorDB leverage system-level checkpoint/restore (CRIU), semantic code mapping, and unified relational storage.
• Pilotfish replaces global WALs and lock managers with efficient in-memory queues, checkpointing for fast crash recovery.
• ALAS encodes logs as artifacts in ASL/Argo, preserving the log schema, version history, and policy-driven repair metadata, with round-trip IR conversion for engine parity.

A plausible implication is that such log architectures will become increasingly central for reproducible science, fault-resistant distributed computing, and production ML debugging. Persisted, queryable, and auditable versioned logs provide grounded support for time-travel analysis, scalable replay, and grounded reliability under resource and audit constraints.