{"id":"fa4a535b-bbb6-4560-85b3-b342288236da","arxiv_id":"2608.01652","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"A plan-execute-correct framework with explicit wait primitives and a learned plan-staleness detector enables LLM-based multi-agent coordination that is both faster and more adaptive than repeated-invocation baselines.","lead":"SyncPlan lets a single LLM call produce long action plans for multiple agents, then uses runtime wait checks and a staleness detector to fix plans only when the world changes. On Overcooked and Honor of Kings it beats repeated-invocation LLM coordinators in success rate while cutting wall-clock time by orders of magnitude.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"PSD is structurally blind to plan-invalidating changes on entities its hand-built C_t relevance mask does not mark; no coverage validation is provided.","rationale":"The reader's weakest assumption is exactly the PSD relevance prior, and the manuscript text supports that concern: C_t is rule-based and domain-specific, the PSD logit is gated by C_t, and no experiment validates that the rule covers the actual distribution of plan-invalidating changes. I do not see a more load-bearing problem: the synchronization primitives and deadlock detection are well specified; the HoK results with endogenous dynamics and RL training give independent evidence that the framework can generalize beyond the Overcooked scripted events; and the runtime/efficiency claims are consistent with the architecture. The main residual risk is therefore conditional acceptance pending a coverage check of C_t and code/data release. Since the reader already issued CONDITIONAL and my concern is the same one, the verdict should remain unchanged rather than being strengthened or relaxed. I would not call this a reject: the paper identifies a plausible mechanism, shows large gains, and explicitly scopes C_t as a per-domain design choice; it just does not prove the prior is complete. The proposed oracle test would settle whether the blind spot is real and quantify its impact.","tokens_in":22808,"tokens_out":5486,"duration_ms":67244,"concrete_test":"Instrument the evaluation harness with an oracle: at each frame, determine whether the remaining plan has become invalid (e.g., by rolling out the plan in a lightweight simulator under the observed state transition). For the first oracle-invalidation frame in each HoK and dynamic-Overcooked episode, record the entity whose change caused invalidation and check whether its row in C_t is nonzero and whether the PSD fired. If any episode has an oracle-invalidation frame with a zero C_t row and no PSD trigger, the relevance prior is incomplete; report the fraction of failures/late replans explained by such misses. A cleaner variant: run SyncPlan with C_t replaced by an all-entities mask (or an expanded radius) and compare TAR; an improvement demonstrates the current prior misses valid disturbance classes.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The adaptivity claim rests on the PSD firing whenever the remaining plan is invalid. In Eqs. (4)-(6), the logit is H_C^T H_X; because H_C is built from C_t, any entity j with no nonzero C_t entry has a zero row in H_C and contributes nothing to staleness. C_t is not learned; Appendix A.6/Fig. 10 defines it by hand: HoK marks only self, target, and entities within a fixed radius; Overcooked marks only entities referenced by the current action template. The paper never validates that this relevance prior covers the disturbance types that actually invalidate plans. Overcooked PSD labels are generated from the same three scripted events used at evaluation (A.6, B.4), so the detector may be tuned to those events rather than general invalidation. Concretely, an enemy rotation that breaks an engagement window is invisible until it enters the fixed radius; a pot-content change is invisible if the current step template does not mention the pot. If the first invalidating change is on an unmarked entity, SyncPlan keeps executing a stale plan—exactly the failure mode the adaptivity claim says it avoids. Appendix C.1 cautions against generalizing from training diagnostics, but no similar test is supplied for C_t coverage.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"SyncPlan proposes a plan-execute-correct framework for long-horizon LLM coordination. A centralized LLM coordinator generates per-agent action chains in a single planning call; a runtime executor enforces explicit Wait_agents/Wait_entity synchronization primitives and performs cycle-based deadlock detection; a lightweight Plan Staleness Detector (PSD) decides when the remaining plan is invalid and triggers replanning. The coordinator is optimized via SFT and GRPO with a composite reward. Experiments on Overcooked (three layouts, standard and dynamically perturbed settings) and Honor of Kings (5v5 Commander mode) report higher task achievement rates than LLM baselines at substantially lower wall-clock runtime, with ablations isolating the contributions of the PSD, SFT, and RL.","tokens_in":23160,"tokens_out":5515,"duration_ms":62853,"significance":"The central hypothesis—that a one-shot joint plan plus explicit synchronization plus a lightweight staleness detector can match or exceed the adaptivity of repeated LLM replanning at a fraction of the latency—is well-motivated and practically important. The paper's strengths include a broad evaluation across two environments, a detailed ablation of PSD and periodic replanning, a PSD architecture search, RL training curves, model-scaling analysis, and unusually complete appendices with reward coefficients, thresholds, hyperparameters, and case-study traces. However, the load-bearing component of the adaptivity claim is the PSD, and the current evidence for its general validity is incomplete: the Overcooked PSD is trained on the same scripted event types used at evaluation, and the hand-built C_t relevance mask is not validated for coverage of plan-invalidating changes outside that prior.","major_comments":[{"comment":"The PSD's staleness signal is structurally blind to changes on entities not marked by the hand-built C_t. Since H_C in Eq. (4) is built from C_t, any entity j with no nonzero entry in C_t has a zero row in H_C and contributes nothing to H_C^T H_X in Eq. (6). Appendix A.6/Fig. 10 defines C_t by fixed-radius proximity (HoK) or by the current step template (Overcooked). No experiment checks whether plan-invalidating changes ever occur on unmarked entities. As a concrete counterexample, a pot-content change in Overcooked is invisible if the current step does not mention the pot. Because 'trigger replanning only when needed' is the core adaptivity claim, this coverage gap must be tested—e.g., by injecting perturbations on unmarked entities and measuring PSD recall and downstream plan success.","section":"§4.4 / Appendix A.6 / Eq. (6)"},{"comment":"The Overcooked PSD is trained on labels derived from the same scripted event injection protocol used in evaluation: positive frames are exactly the injected-event frames (A.6), and the dynamic evaluation uses the same three event types (Table 8, B.4). Table 11's high PSD recall is therefore an in-distribution measure. The dynamic-task advantage over w/o PSD may partly reflect the detector having been tuned to these three event types rather than detecting general plan invalidation. The authors should either evaluate on a held-out event type (e.g., an enemy rotation or a resource change not in O1–O3) or explicitly bound the adaptivity claim to the three tested perturbation categories.","section":"Appendix A.6 / B.4"},{"comment":"Table 1 reports only point estimates and permutation-test stars; no error bars, confidence intervals, or trial counts are shown for the Overcooked main comparison. Given that the HoK ablations in Tables 2 and 4 report standard deviations of 3–10 TAR points, several contrasted differences in Table 1 (e.g., Dynamic Ring 88.3% vs 76.5% for w/o SFT) could be within noise. Report per-seed results and confidence intervals, especially for the claimed SOTA comparisons.","section":"Table 1"},{"comment":"The provenance of the SFT corpus is inconsistent. §5.1 states Overcooked SFT data comes from Gemini model distillation, while Appendix A.7 describes constructing SFT QA pairs by replaying 'prompt-only Coordinator' trajectories and treating each chain-update event as a supervision boundary. These are different data sources and imply different claims about independence from the method itself. If the SFT corpus is partly self-generated by the prompt-only SyncPlan, the SFT warm-start is iterative self-imitation rather than expert demonstration, which should be stated and analyzed (e.g., with a distillation-only SFT baseline).","section":"§5.1 vs Appendix A.7"}],"minor_comments":[{"comment":"The notation 'C:S × E →0,1' should read '→{0,1}'; several set/arrow symbols are malformed throughout the problem formulation.","section":"Equation (1)"},{"comment":"Deadlock Rate is listed as a reported metric, but Table 1 has no Deadlock Rate column; deadlock information appears only in training curves (Fig. 5/14). Either add the column or remove the metric from the setup.","section":"§5.1 / Table 1"},{"comment":"The 'less than 0.05% of wall-clock runtime' figure is computed for a specific Overcooked comparison (A-ToM vs Ours Full) and should be contextualized in the main text; as written in the abstract it reads like a universal bound.","section":"Abstract / §5.2"},{"comment":"The semantic penalty equation is incomplete: 'Psem(A) = λ sem' is missing its operand. Table 7 gives the value, but the equation should be fixed.","section":"Appendix A.8"}],"recommendation":"major_revision","confidential_remarks":"The paper is promising and unusually detailed, but the PSD coverage issue and the training/evaluation label overlap are central to the adaptivity claim. I would encourage the editor to request a revision that adds an out-of-distribution perturbation test and clarifies the SFT data provenance. The large number of 2026 references and the very short arXiv-to-submission timeline may also merit a routine provenance check, though I have no specific concerns beyond the technical points above."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe short version: the paper's explicit synchronization primitives and deadlock detection are a genuinely useful contribution to LLM-based coordination, and the reported runtime reductions are large. But the central adaptivity claim is weaker than the abstract suggests—the PSD in Overcooked is trained on the same scripted event protocol it is tested on, and the hand-built relevance matrix C_t leaves a structural blind spot that is never validated.\n\nWhat's actually new: converting coordination intent into executable wait primitives (Wait_agents, Wait_entity) with a lightweight cycle-detecting deadlock check is clean, and that alone moves the field forward. The PSD as a learned replan trigger is a sensible integration of classical plan monitoring with modern LLM planning. The HoK experiments are the most convincing part: the PSD is trained on synthetic perturbations but evaluated on endogenous bot dynamics, so the performance there is not explained by train/eval overlap. The RL reward design with dense progress and outcome penalties is thoughtful, and the ablations (Tables 3–4) show each module earns its place.\n\nThe soft spots. First, Overcooked PSD labels come from the exact injected events used in evaluation (Appendices A.6 and B.4), so the dynamic-task gains are in-distribution for the detector; the HoK results rescue the generalization claim, but only partially. Second, the stress-test concern is real: the chain-entity incidence matrix C_t is built by hand (spatial proximity in HoK, action-template binding in Overcooked), and any plan-invalidating change to an entity outside that mask cannot affect the PSD logit at all. The paper never supplies a coverage analysis showing that its relevance prior spans the actual disturbance types encountered. That is a load-bearing gap for the adaptivity claim, though it does not invalidate the specific experiments if the tested events all involve marked entities. Third, Table 1 reports no error bars, only permutation-test stars; data and code are not yet released. Minor: the SFT corpus is bootstrapped from the authors' own prompt-only coordinator, so SFT gains likely include some self-imitation bias.\n\nBottom line: this paper deserves a serious referee. It introduces a practical framework with honest latency numbers and a non-circular HoK test, but the PSD coverage blind spot and the Overcooked train/eval overlap need to be addressed, and code release would help verification.","headline":"SyncPlan's explicit synchronization primitives are a genuine step forward for LLM coordination, but the learned staleness detector's coverage blind spot and Overcooked train/eval overlap undercut the adaptivity headline.","tokens_in":23689,"tokens_out":4031,"would_cite":true,"duration_ms":43338,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Planning once with enforced waits and on-demand replanning, SyncPlan claims LLM-based multi-agent coordination can be both adaptive and fast, beating repeated-invocation baselines in task success while using under 0.05% of their wall-clock","keywords":["multi-agent coordination","LLM planning","synchronization primitives","plan staleness detection","reinforcement learning","Overcooked","Honor of Kings","long-horizon planning"],"falsifier":"Run SyncPlan with an injected event that invalidates the plan through an entity the relevance rule ignores — for example, a sudden enemy threat appearing just outside the fixed radius in Honor of Kings, or an Overcooked pot that changes state while no remaining step references it — and record whether the PSD triggers a replan and whether task success holds. If the system proceeds with the stale chain, the adaptivity claim is falsified; measuring PSD recall on such out-of-prior events would settle it directly.","tokens_in":22711,"feed_emoji":"🤖","tokens_out":7665,"duration_ms":80099,"temperature":0.7,"pith_summary":"SyncPlan claims that the perceived trade-off between efficiency and adaptivity in LLM-based multi-agent coordination is not fundamental. Instead of re-invoking the LLM during execution, the framework produces the whole per-agent action plan in one call and keeps it alive through three mechanisms: wait primitives that turn coordination dependencies into machine-checkable conditions, a deadlock detector, and a lightweight learned Plan Staleness Detector that triggers replanning only when the remaining plan has actually been invalidated. Because replanning is event-driven rather than periodic, coordination adapts to environmental changes without paying for constant LLM inference: on Overcooked dynamic settings SyncPlan reaches 88.3-100% task achievement versus 60-96% for LLM baselines, with under 0.05% of their runtime, and on Honor of Kings it reaches 86.3% versus 68.75% for the best baseline with a 26x runtime reduction. The paper's central assertion is that when to correct matters more than how often, and that a cheap synchronous execution layer plus selective correction makes one-shot planning viable for real-time environments.","feed_headline":"One-shot plans beat re-planning LLMs at 0.05% of runtime","feed_subtitle":"SyncPlan's wait primitives and staleness detector reach 86-100% task success on Overcooked and Honor of Kings.","key_machinery":"The load-bearing piece is the Plan Staleness Detector (PSD): a small binary classifier fed by three tensors — the chain-entity incidence matrix Ct marking which entities the unexecuted plan steps reference, the normalized entity-state matrix Xt, and the state-difference matrix Δt. Two bilinear pathways embed the chain structure and the state changes separately, and their interaction is mapped through an MLP to a staleness probability Preplan(t); a replan is triggered when it exceeds a threshold. Around it sits the execution layer: per-agent chain pointers advance only when actions complete, Wait_agents and Wait_entity primitives block an agent until a teammate milestone or environment condit","core_discovery":"The central claim is that a single LLM planning call can carry long-horizon multi-agent coordination if two runtime mechanisms are added: explicit synchronization primitives that make the plan's dependencies executable, and a fast Plan Staleness Detector that decides, from the remaining action chains and current entity states, when the plan is no longer valid. When the detector or the wait-graph cycle check flags a problem, the coordinator is re-invoked only at that moment. Optimized by SFT and planning-oriented RL with a composite reward over task progress and execution outcomes, this design reaches 93-100% task achievement on Overcooked dynamic settings (88.3% on the hardest Ring layout) a","pith_inferences":["Editorial inference: the PSD's relevance prior is the untested boundary — a change affecting an entity the rule-based Ct does not mark (e.g., an enemy appearing just outside the fixed radius) would be invisible, and the reported high recall is measured on perturbations that the rule already covers.","Editorial inference: if a cheap detector can decide when a plan is invalid, a natural next step the paper does not take is local chain repair — patching only the invalidated segment of the remaining plan rather than full replanning, which would cut latency further.","Editorial inference: the plan-execute-correct division should transfer to other embodied or robotic settings, but only where an author can hand-write the relevance rule; the paper demonstrates two environments, not a general recipe."],"forward_implications":["If the central claim holds, real-time LLM coordination does not require repeated planning: a single invocation plus cheap staleness monitoring is enough to stay adaptive.","Coordination dependencies expressed as wait predicates become verifiable at runtime, so LLM plans no longer have to be trusted as natural language.","Event-driven replanning dominates fixed-frequency replanning: periodic re-invocation at 4 or 8 frames per second pays an order-of-magnitude runtime penalty for equal or worse task success.","Coordinator training matters as much as the framework: RL with execution-level feedback lifts task success (e.g., 79.0% to 86.3% on Honor of Kings) by aligning plans with the executor's real timing.","Below roughly one billion parameters, coordinators cannot reliably emit the structured action chains, placing a hard floor on model capacity for this style of planning."],"supporting_citations":[{"why":"ProAgent, the repeated-invocation LLM teammate-modeling baseline whose runtime cost motivates one-shot planning; SyncPlan must beat it on both benchmarks.","marker":"[Zhang et al. 2024]"},{"why":"CaPo, a dialogue-based cooperative planner whose multi-round communication is the latency baseline behind the 0.05% runtime comparison.","marker":"[Guo et al. 2025b]"},{"why":"Collab-Overcooked, the public benchmark that supplies the layouts, baselines, and dynamic-event injection hooks used in evaluation.","marker":"[Sun et al. 2025]"},{"why":"Adaptive-ToM, the strongest LLM-based Overcooked baseline in dynamic settings that SyncPlan outperforms.","marker":"[Mu et al. 2026]"},{"why":"DPT-Agent, the Honor of Kings baseline (68.75% TAR) that SyncPlan surpasses at 86.3% TAR with over 26x less runtime.","marker":"[Zhang et al. 2025b]"},{"why":"Qwen2.5, the base model on which the SFT and GRPO coordinator training is run.","marker":"[Hui et al. 2024]"},{"why":"MAPPO, the cooperative MARL policy-gradient baseline whose task-specific training motivates the LLM-coordinator alternative.","marker":"[Yu et al. 2022]"}],"fun_headline_variants":["SyncPlan: one-shot LLM plans, sync waits, staleness-triggered replan","LLM coordinates long-horizon tasks at 0.05% replanner runtime","Plan once, correct when stale: LLM agents hit 93-100% success","Explicit sync and adaptive correction cut multi-agent LLM replanning"],"cache_read_input_tokens":2688,"weakest_assumption_plain":"The Plan Staleness Detector only monitors entities that a hand-written relevance rule marks in Ct; any plan-invalidating change on an unmarked entity passes unseen and the agents keep executing a stale plan — and the paper never shows that its relevance rules cover all disturbance types that actually occur.","fun_headline_variants_meta":{"raw":{"variants":["SyncPlan: one-shot LLM plans, sync waits, staleness-triggered replan","LLM coordinates long-horizon tasks at 0.05% replanner runtime","Plan once, correct when stale: LLM agents hit 93-100% success","Explicit sync and adaptive correction cut multi-agent LLM replanning"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000754,"raw_usage":{"total_tokens":3206,"prompt_tokens":775,"completion_tokens":2431,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":519,"completion_tokens_details":{"reasoning_tokens":2340}},"tokens_in":519,"tokens_out":2431,"duration_ms":19990,"temperature":1.0,"reasoning_tokens":2340,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T23:28:03.266061+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run SyncPlan with an injected event that invalidates the plan through an entity the relevance rule ignores — for example, a sudden enemy threat appearing just outside the fixed radius in Honor of Kings, or an Overcooked pot that changes state while no remaining step references it — and record whether the PSD triggers a replan and whether task success holds. If the system proceeds with the stale chain, the adaptivity claim is falsified; measuring PSD recall on such out-of-prior events would settle it directly.","supporting_citations":[],"review_version":1}