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arxiv: 2605.30052 · v1 · pith:YGMYO5OEnew · submitted 2026-05-28 · 💻 cs.SE · cs.AI· cs.CL

REPOT: Recoverable Program-of-Thought via Checkpoint Repair

Parsa Mazaheri This is my paper

Pith reviewed 2026-06-29 06:20 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.CL
keywords Program-of-Thoughtrecoverable planningLLM agentscheckpoint repairplan verificationpuzzle solvingagent recoverydeterministic replay
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The pith

RePoT recovers from invalid Program-of-Thought plans by replaying to the first error and resuming from the verified prefix with one extra call.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces RePoT to address the fragility of one-shot Program-of-Thought, where a single invalid action in the emitted plan invalidates the entire trajectory. It adds a deterministic replay step that walks the plan through the environment until the first invalid transition, extracts the verified prefix, and issues one targeted LLM call to generate a corrected continuation. This mechanism costs at most one extra call on the roughly 14 percent of cases where the initial plan fails. Across multiple benchmarks and model families the approach produces higher success rates than both plain PoT and a matched-budget retry baseline, with the largest gains on stronger models. A controlled recovery benchmark further isolates that the verified-prefix checkpoint, rather than error messages alone, supplies the effective recovery signal.

Core claim

RePoT uses deterministic verified replay of the generated plan to locate its first invalid transition, then performs one LLM call that resumes from the verified prefix to produce a corrected suffix.

What carries the argument

Deterministic verified replay that extracts a verified prefix for checkpoint repair

If this is right

  • RePoT improves success by 3 to 11 percentage points over standard PoT on PuzzleZoo-775 across four closed-model configurations.
  • It outperforms a matched-budget PoT-retry baseline on Gemini and remains within noise on several other models.
  • The same pattern of gains appears on PlanBench Blocksworld and on three of four open-weights models.
  • Every condition supplying checkpoint information reaches at least 30 percent recovery on GPT-medium and 70 percent on Gemini on the Derail-550 benchmark.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A rule-based dispatcher that chooses between repair and full retry based on verified-prefix length may further improve results on smaller models.
  • The same replay-and-repair pattern could apply to other partially verifiable LLM outputs such as code or mathematical derivations.
  • Recovery margins are expected to widen with model capability because stronger models can better exploit the supplied prefix.

Load-bearing premise

The environment must support deterministic, side-effect-free replay of the generated plan up to the first invalid transition so a verified prefix can be extracted.

What would settle it

An experiment on Derail-550 in which recovery success rates with the verified prefix equal those obtained from error-only feedback would falsify the claim that checkpoint information is the load-bearing recovery signal.

Figures

Figures reproduced from arXiv: 2605.30052 by Parsa Mazaheri.

Figure 1
Figure 1. Figure 1: The REPOT pipeline. (1) Problem provides initial state s0 and goal g. (2) POT call (LLM call #1): the model emits a Python program whose stdout encodes the action plan π. (3) Verified replay: walk π through the environment one step at a time — deterministic, no LLM calls — producing the maximal valid prefix and the failure boundary. If every action is valid, branch to (6) Final via the upper verified, goal… view at source ↗
Figure 2
Figure 2. Figure 2: Open-source replication. REPOT lift tracks model capability: Gemma 4 (top) gains +20 pp over POT; Nemotron-3 Nano 30B FP8 (right) is the predicted capability-floor failure (Eq. 2). 7 Mechanism Analysis 7.1 Checkpoint information is the load-bearing signal DERAIL-550 compares 11 recovery methods on 550 injected errors per model on two reasoning￾thinking-on configurations (Gemini, GPT (med)). The decisive me… view at source ↗
Figure 3
Figure 3. Figure 3: Capability scaling. Each point is one (model, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: DERAIL-550, headline conditions only. The ∼60pp gap between checkpointed and no-checkpoint conditions is the load-bearing finding. The full 11-condition table is in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: ∆(RePoT−PoT) per environment, in per￾centage points, for all four models. Blocksworld is REPOT’s home environment (+5 to +17pp on every model); Hanoi/Checker on strong models are saturated by POT at small N. B Hyperparameters C Data generation and the controller architecture PUZZLEZOO-775 (n = 775) [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Per-environment success rate vs problem complexity for [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: One example per environment at small complexity. Each column is one environment (Tower of Hanoi, [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Paired recovery decomposition on the matched-difficulty subset (both methods’ initial POT failed). Stacks sum to 100% of N. “RePoT only” is mechanism evidence; “PoT-retry only” is fresh-sample evidence. col) to defuse the “POT baseline is weak” re￾view concern. On 100 stratified problems with gpt-5.4-mini-medium: POT final 89/100 vs POT best-in-thought 90/100 (a single problem). REPOT is unaffected: 93/100… view at source ↗
Figure 9
Figure 9. Figure 9: Cost vs accuracy, mean across the four closed [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Decomposition of REPOT’s recovery on PUZZLEZOO-775. Each row is one model; bars sum to 100%. The teal segment is the share of problems where standalone POT fails, REPOT’s first POT call also fails, and the suffix repair call rescues (mechanism). The coral segment is the second-attempt contribution (standalone POT fails but REPOT’s re-rolled POT call succeeds without invoking the repair). On the strongest … view at source ↗
Figure 11
Figure 11. Figure 11: Per-complexity success rate on PLANBENCH BLOCKSWORLD. REPOT’s lift concentrates in the mid-complexity band where POT has both failures to recover from and enough valid prefix to recover into. Two negative-delta cells (gpt-5.4-mini at c= 8, 11) are reported in Appendix J. Stable block (cacheable): {problem.natural_language_prompt} Goal state: {goal_state} Write Python code that prints exactly one line: mov… view at source ↗
Figure 12
Figure 12. Figure 12: Verified-prefix-conditioned repair prompt. [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Adaptive policy routing per open-source model. Bars sum to [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
read the original abstract

One-shot Program-of-Thought (PoT) emits a Python program that prints a primitive-action plan; a single invalid action silently invalidates the trajectory. We introduce RePoT (Recoverable PoT): a deterministic verified replay that walks the plan through the environment to its first invalid transition, then one LLM call that resumes from the verified prefix. RePoT costs at most one extra LLM call on the ~14% of problems where PoT fails. RePoT beats PoT by +3 to +11pp across four closed-model configurations on PuzzleZoo-775 and peaks at 96.9% vs 86.3% on gpt-5.4-mini-medium; against the matched-budget PoT-retry baseline, RePoT wins decisively on Gemini (+3.8pp, 95% CI [+2.2,+5.4]), is within sampling noise on GPT-medium and Claude, and loses on GPT-mini -- a capability-scaling pattern we begin to address with Adaptive RePoT, a rule-based dispatcher that routes between suffix repair and a fresh PoT retry based on verified-prefix length (preliminary). We replicate on PlanBench Blocksworld (+1.1 to +11.4pp) and on four open-weights models (+3.3 to +20.0pp on three of four). On Derail-550, our controlled recovery benchmark, every condition with access to checkpoint information clears >=30% on GPT-medium and >=70% on Gemini, vs <=3.1% for error-only feedback -- showing that checkpoint information, not the specific verified-prefix tail, is the load-bearing recovery signal.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces RePoT, an extension of one-shot Program-of-Thought (PoT) that performs deterministic verified replay of the emitted Python plan to locate the first invalid transition, then issues one additional LLM call to resume from the verified prefix. It reports accuracy gains of +3 to +11pp versus PoT on PuzzleZoo-775 across four closed models (peaking at 96.9% vs 86.3%), decisive wins versus matched-budget PoT-retry on some models, replication on PlanBench (+1.1 to +11.4pp) and open-weight models, and strong results on the new Derail-550 recovery benchmark showing the value of checkpoint information over error-only feedback.

Significance. If the deterministic replay assumption holds, RePoT offers a low-overhead recovery technique that improves PoT success rates with at most one extra LLM call on failing cases. The work supplies confidence intervals, cross-model replications, and a controlled benchmark isolating checkpoint utility; these elements strengthen the empirical case for the approach in environments where side-effect-free replay is feasible.

major comments (2)
  1. [Method description (and Abstract)] The method presupposes that the target environment supports deterministic, side-effect-free replay of the generated plan up to the first invalid transition so that a verified prefix can be extracted. The paper evaluates exclusively on PuzzleZoo-775 and PlanBench (both constructed to satisfy the assumption) and provides no analysis, bounds, or characterization of the broader class of environments where the assumption holds; this is load-bearing for all reported gains versus PoT and error-only baselines.
  2. [Experiments (Derail-550 results)] Derail-550 is presented as a controlled recovery benchmark, yet the manuscript supplies no validation metrics, construction details, or exclusion criteria for the 550 instances; without these, it is difficult to assess whether the >=30% / >=70% recovery rates (versus <=3.1% for error-only) generalize beyond the specific test distribution.
minor comments (2)
  1. [Abstract] The abstract states performance deltas and 95% CIs but does not specify exact baseline implementations, data splits, or statistical testing procedure; adding these details would improve reproducibility.
  2. [Method] Notation for the verified prefix and recovery call could be formalized with a short pseudocode block or equation to clarify the single extra LLM call cost.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below.

read point-by-point responses

  1. Referee: [Method description (and Abstract)] The method presupposes that the target environment supports deterministic, side-effect-free replay of the generated plan up to the first invalid transition so that a verified prefix can be extracted. The paper evaluates exclusively on PuzzleZoo-775 and PlanBench (both constructed to satisfy the assumption) and provides no analysis, bounds, or characterization of the broader class of environments where the assumption holds; this is load-bearing for all reported gains versus PoT and error-only baselines.

    Authors: We agree the deterministic replay assumption is load-bearing. The evaluated benchmarks are standard planning domains constructed to satisfy it. In revision we will add a subsection characterizing the applicable environment class (deterministic transitions, no irreversible side-effects) and explicitly stating the limitation for stochastic or side-effectful settings. We do not provide formal bounds across all environments, as that would constitute a separate theoretical contribution. revision: partial

  2. Referee: [Experiments (Derail-550 results)] Derail-550 is presented as a controlled recovery benchmark, yet the manuscript supplies no validation metrics, construction details, or exclusion criteria for the 550 instances; without these, it is difficult to assess whether the >=30% / >=70% recovery rates (versus <=3.1% for error-only) generalize beyond the specific test distribution.

    Authors: We agree that construction details, exclusion criteria, and validation metrics for Derail-550 are missing. The revised manuscript will include an appendix section supplying these elements, including how instances were generated, filtering rules, and any quality or distribution statistics. revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical comparisons to external baselines

full rationale

The paper reports accuracy improvements from RePoT versus PoT, PoT-retry, and error-only feedback on PuzzleZoo-775, PlanBench, and Derail-550. These are direct experimental measurements against independent methods and benchmarks; no equations, fitted parameters, or self-citations are used to derive the claimed gains. The central assumption (deterministic replay) is stated explicitly as an environmental prerequisite rather than derived from the method itself. All numbers are externally falsifiable and not reduced to quantities fitted from the same data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical prompting technique with no mathematical derivation, free parameters, or postulated entities mentioned.

pith-pipeline@v0.9.1-grok · 5833 in / 1161 out tokens · 29062 ms · 2026-06-29T06:20:25.374486+00:00 · methodology

discussion (0)

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Reference graph

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