{"id":"cbe23efc-307a-4b11-b53d-403dabf21e05","arxiv_id":"2505.23450","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"An agentic framework using a GPT-4o planner, an OpenVLA executor, and a LoRA-fine-tuned Qwen2.5-VL verifier achieves 79.6% average success on LIBERO by decomposing and verifying subgoals.","lead":"Agentic Robot combines a large-language-model planner, an OpenVLA executor, and a fine-tuned vision-language verifier into a closed loop that decomposes, executes, and checks each step of long-horizon robot tasks. The authors report a 79.6% average success rate on the LIBERO benchmark, claiming gains of 6.1% over SpatialVLA on long-horizon tasks.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Verifier training-data provenance is undisclosed; if the ~500 triplets used to LoRA-fine-tune Qwen2.5-VL come from the evaluated LIBERO suites, the reported long-horizon gains could be benchmark memorization rather than general SAP verification.","rationale":"The reader's conditional verdict identifies the same weakness, and I agree it is the single most load-bearing issue. The manuscript's empirical contribution is a modular coordination protocol; none of its modules are newly trained except the verifier, and the verifier's training data is completely unspecified. Since the paper's own ablation attributes a 26.5-point swing to that fine-tuning, the entire quantitative case for SAP depends on the verifier being a general subgoal-completion classifier rather than a memorizer of the evaluation benchmark. The lack of a disclosed train/test split is therefore not a cosmetic reproducibility gap; it determines whether the central claim is true. I would keep the reader's conditional verdict: the paper should not be accepted as-is, but the issue is resolvable by releasing the verifier data and rerunning with a clean split. I did not find a stronger objection. The numeric inconsistencies in Tables 2 and 3 are real but secondary; they suggest the results need careful checking, not that the framework is incoherent. The absence of code, the closed-source planner, and the lack of comparison to reflective-planning methods are additional reproducibility concerns but do not independently invalidate the causal argument as directly as the verifier provenance question.","tokens_in":14983,"tokens_out":8194,"duration_ms":76381,"concrete_test":"Ask the authors to release the verifier's 500 annotated triplets (or a metadata table). Then retrain the LoRA verifier using only triplets collected from LIBERO-Spatial, LIBERO-Object, and LIBERO-Goal, and evaluate on LIBERO-Long. If the clean-split success rate falls well below 61.8% (especially toward the zero-shot verifier's 35.3%), the reported long-horizon gains are same-task memorization; if it remains within about 2 percentage points, the concern is resolved. In parallel, inspect the triplet metadata for LIBERO-Long task identifiers; any overlap between verifier training tasks and the evaluated LIBERO-Long tasks would directly taint the headline result.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's only newly trained component is the verifier: Qwen2.5-VL-3B-Instruct is fine-tuned with LoRA on 'approximately 500 annotated triplets' (Section 2.4), while the planner (GPT-4o) and executor (OpenVLA) are off-the-shelf. The source of those triplets, the labeling procedure, and any train/test separation relative to the four LIBERO suites are never stated. This is load-bearing because the central causal claim—that SAP's verification loop reduces error accumulation and yields the reported state-of-the-art results—rests entirely on this verifier. The key ablation (Table 3) shows success dropping from 61.8% to 35.3% when the fine-tuned verifier is replaced by a zero-shot VLM, so nearly all of the LIBERO-Long improvement is attributable to the fine-tuned verifier. If its training triplets are sampled from LIBERO-Long demonstrations or rollouts, the verifier can memorize task-specific appearance and success conditions, and the 79.6% average and +6.1% over SpatialVLA on Long would reflect test-domain leakage rather than a general introspection capability. No held-out evaluation is reported: every experiment and ablation uses the same four suites, and the dataset is not released. Internal reporting errors (Table 2's claimed 12.1% average gain is actually 10.4% over all ten tasks; Table 3 gives both 26.5% and +7.8% for the verifier ablation) further undermine confidence in the numbers as reported.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes Agentic Robot, a modular framework for long-horizon robotic manipulation built around a three-component loop: an LRM planner decomposes an instruction into subgoals; an OpenVLA executor performs each subgoal; and a fine-tuned Qwen2.5-VL verifier periodically inspects a sliding window of third-person and wrist images to decide whether to proceed, retry, or recover. The coordination protocol is called Standardized Action Procedure (SAP). On the LIBERO benchmark the authors report 79.6% average success and substantial gains on LIBERO-Long over baseline VLAs, with ablations attributing most of the long-horizon gain to the fine-tuned verifier and to subgoal decomposition.","tokens_in":15312,"tokens_out":5511,"duration_ms":46408,"significance":"If the results are reproducible, the paper makes a useful engineering contribution: it shows that a relatively small fine-tuned VLM verifier, combined with an off-the-shelf VLA, can improve long-horizon reliability substantially, and it provides a clean modular decomposition with interpretable checkpoints. The main table includes three-seed averages with error bars, and the ablation and verification-frequency analyses directly probe the mechanism. The significance is tempered by the fact that the only newly trained component is trained on an undisclosed dataset; if that dataset is drawn from the test suites, the headline numbers would be benchmark-specific rather than evidence for a general verification capability.","major_comments":[{"comment":"The provenance of the approximately 500 annotated triplets used for LoRA fine-tuning of Qwen2.5-VL is not reported. This verifier is the only trained component in the system, and Table 3 shows that replacing the fine-tuned verifier with a zero-shot VLM changes LIBERO-Long success from 61.8% to 35.3%, so essentially all of the long-horizon improvement is attributed to this verifier. The manuscript must state whether the triplets are sampled from the LIBERO tasks or suites used in the evaluation, describe the annotation protocol, and provide a clean train/test split (or a held-out benchmark) before the central claim that SAP's verification loop yields generalizable long-horizon improvement can be evaluated. As written, the reported numbers could reflect test-domain leakage rather than a general introspection capability.","section":"Section 2.4 and Section 3.1"},{"comment":"The claim of a 12.1% average improvement over OpenVLA is not supported by the numbers in Table 2. Averaging the ten task success rates gives OpenVLA 51.4% and Agentic Robot 61.8%, an absolute improvement of 10.4 percentage points; the average of the per-task improvements is also 10.4 points. Please recompute and correct the reported aggregate, and add per-task error bars or confidence intervals for the subgoal-level and overall success rates.","section":"Section 3.3, Table 2"},{"comment":"The text says that replacing the fine-tuned verifier with a zero-shot VLM produces a 26.5% degradation and then states that the fine-tuned model improves the zero-shot baseline by +7.8%; both statements cannot describe the same comparison, since 61.8% versus 35.3% is a 26.5-percentage-point difference. The numbers need to be reconciled. In addition, the ablation table should report error bars and significance tests, because several row differences are small relative to the variation seen in Table 1.","section":"Section 3.5, Table 3"},{"comment":"The abstract reports outperforming OpenVLA by 7.4% on long-horizon tasks, but Table 1 gives 61.6% versus 53.7%, a 7.9-percentage-point difference. Please reconcile all reported performance deltas with the actual tables.","section":"Section 3.2 and Abstract"}],"minor_comments":[{"comment":"The sentence describing LoRA fine-tuning is repeated twice in the same paragraph; the duplication should be removed.","section":"Section 2.4"},{"comment":"The diagnosis module pi_diag is invoked to classify Stuck versus StillTrying, but the implementation or prompt is not specified; please provide details or a reference to Appendix G.","section":"Algorithm 1 and Eq. (10)"},{"comment":"These results are presented without error bars or significance tests; adding standard errors across the three seeds would make the claims about small differences (e.g., the verification-frequency comparisons) much more convincing.","section":"Table 2 and Figure 4"},{"comment":"The phrase 'averaged over three random seeds with 500 evaluation trials' is ambiguous; please clarify whether 500 trials are used per seed or in total across seeds.","section":"Section 3.1"},{"comment":"The task name 'Soup-Sause' is a typo and should read 'Soup-Sauce'.","section":"Appendix D.1"},{"comment":"The heading 'Experiments on Task Division' should presumably read 'Task Decomposition'.","section":"Appendix D heading"}],"recommendation":"major_revision","confidential_remarks":"The verifier training-data provenance is the critical issue; if the authors cannot disclose the source and the train/test split, I would advise the editor to treat the headline results as unverified. The internal numerical inconsistencies in Tables 2 and 3 also need correction before any further consideration."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Fairly straightforward paper to summarize: take an off-the-shelf planner (GPT-4o), an off-the-shelf executor (OpenVLA), add a LoRA-fine-tuned Qwen2.5-VL verifier that checks subgoal completion every 20 frames and triggers a simple retry, and you get 79.6% average success on LIBERO, up from 78.1% for SpatialVLA. The long-horizon gain is more meaningful: 61.6% vs 55.5%. That result is the real news, if it holds. The main table includes three-seed averaging with error bars, which is better than much of the field.\n\nWhat the paper does well: the framework is modular and clearly described, the ablation isolates each component's contribution, and the verification-frequency analysis is a useful practical datapoint. The per-task breakdown in Table 2 and the qualitative examples make the error-recovery mechanism concrete. The authors also flag a real failure case (Moka-Moka), which is more honest than most papers in this space.\n\nThe soft spots are serious, though. The verifier's fine-tuning data provenance is never stated. Around 500 annotated triplets are used to LoRA-tune the verifier, and the key ablation shows that replacing it with a zero-shot VLM drops LIBERO-Long success from 61.8% to 35.3%. If those triplets come from the same LIBERO suites used for evaluation, the reported gains could be largely benchmark memorization rather than a general verification ability. No held-out evaluation is reported. The paper also has internal numerical inconsistencies: Table 2's claimed average improvement over OpenVLA is 12.1%, but the numbers in the table average to about 10.4 points; Table 3 reports both a 26.5% degradation and a +7.8% improvement for the same ablation. Those don't add up.\n\nAlso, the novelty is modest: the plan-execute-verify loop is already in Inner Monologue and Reflective Planning, both cited. The contribution here is the specific integration and the empirical result, not the conceptual loop. That's fine, but the framing as a 'novel coordination protocol' overstates the conceptual delta.\n\nWho is this for? People working on long-horizon manipulation with VLA policies and anyone interested in lightweight verifier-based recovery. It deserves a serious referee: the question of whether a small fine-tuned verifier can reduce error accumulation is worth answering cleanly. But the authors need to disclose the verifier training split, provide held-out results, fix the numerical inconsistencies, and ideally release code before the empirical claims can be trusted.\n\nRecommendation: send it to review, but with the expectation of major revision. The core idea is plausible and the ablation is informative; the undisclosed data provenance is the load-bearing issue that must be resolved.","headline":"A plausible verifier-loop result with a serious data-provenance gap that must be fixed before the headline numbers can be trusted.","tokens_in":15911,"tokens_out":2996,"would_cite":false,"duration_ms":25954,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A three-module loop that verifies each subgoal before moving on makes a vision-language-action policy reach state-of-the-art 79.6% average success on LIBERO, with the largest gains on long-horizon tasks.","keywords":["long-horizon manipulation","vision-language-action models","subgoal verification","error recovery","closed-loop control","LIBERO benchmark","large reasoning models","agentic framework"],"falsifier":"Inspect the released verifier training triplets for overlap with the LIBERO-Long evaluation episodes, or re-run the benchmark with a verifier fine-tuned on triplets from held-out tasks; if the gains over OpenVLA vanish, the SAP loop's benefit is not a general property of the protocol.","tokens_in":14780,"feed_emoji":"🤖","tokens_out":7970,"duration_ms":68708,"temperature":0.7,"pith_summary":"Long-horizon robot manipulation fails today for a predictable reason: errors pile up silently, and neither static planners nor end-to-end visuomotor policies can see their own mistakes. Agentic Robot attacks that problem by adding a verification stage to an existing VLA policy. A large reasoning model decomposes the instruction into 2-5 atomic subgoals, an OpenVLA executor acts on each subgoal, and a fine-tuned vision-language verifier looks at recent camera frames to decide whether the subgoal is done. If it is not done and the arm looks stuck, the system lifts the gripper and retries. On LIBERO this closed loop reaches 79.6% average success, and the paper's core claim is that the coordination protocol—not the executor alone—is what reduces error accumulation and enables recovery. The result matters because it suggests reliable sequential manipulation can be achieved by structurally wrapping existing policies rather than training ever-larger end-to-end models.","feed_headline":"Closed-loop verification lifts robot long-horizon success to 79.6%","feed_subtitle":"Subgoal verification cuts error cascades and enables recovery, beating OpenVLA by 7.4 points on long-horizon tasks.","key_machinery":"The central object is the Standardized Action Procedure (SAP), a coordination protocol that fixes the order and interfaces of perception, planning, execution, and verification in a finite-state machine. The executor runs at 10 Hz while the verifier operates at 0.5 Hz, checking a sliding buffer of $K=2$ image pairs at 20-frame intervals. The load-bearing mechanism is the two-stage temporal verifier: a LoRA-fine-tuned Qwen2.5-VL model that produces a binary answer to 'has the robot completed subgoal $t_i$?' from recent third-person and wrist-camera frames, followed by a separate diagnosis module that classifies a 'No' as either Stuck or StillTrying. This verifier is the only new trained component on top of OpenVLA, and it is what closes the loop, enabling mid-execution correction and recovery. The paper's design encodes the loop as a sequence of agentic steps $S_t = (O_t, t_i, a_t, \\hat{y}_t)$, where $\\hat{y}_t$ is the verification result that gates progression.","core_discovery":"The paper's central claim, stated in its own terms, is that a Standardized Action Procedure (SAP) — a fixed protocol governing how a planner, an executor, and a verifier interact — turns a stateless VLA policy into a self-correcting system. The protocol specifies that a planner (GPT-4o) converts a high-level instruction into semantically coherent subgoals drawn from an atomic skill library; an OpenVLA executor maps each subgoal and current RGB observation to a 7-dimensional action; and a LoRA-fine-tuned Qwen2.5-VL verifier inspects a sliding window of third-person and wrist-camera frames every 20 frames and answers whether the subgoal is complete. A 'No' answer triggers a secondary stuck diagnosis and, when stuck, a recovery action such as lifting the gripper before re-executing the subgoal. The paper reports 79.6% average success across four LIBERO suites, with the largest gain on LIBERO-Long (61.6% vs. 53.7% for OpenVLA), and its ablations attribute the biggest drop (26.5 points) to replacing the fine-tuned verifier with a zero-shot VLM. The conclusion drawn is that SAP-driven coordination between specialized components is what delivers the robustness, not any single module.","pith_inferences":["Since the verifier is the only trained component, the same SAP loop could be dropped onto other VLA backbones (for instance SpatialVLA) and should transfer the reliability gain if the verifier's fine-tuning data are task-agnostic; the paper shows only OpenVLA as executor.","The verifier converts subgoal completion into a visual language question, so the same fine-tuning recipe could be applied to real-world sim-to-real by collecting annotated triplets on the physical robot, where distribution shift is the main risk.","The 20-frame interval being near-optimal suggests an adaptive verification scheduler that checks more often during high-uncertainty phases of a subtask could retain most of the gain at lower compute; the paper lists adaptive verification as future work.","The Moka-Moka failure mode (17% vs. 22% on one subgoal) points to a concrete next step: a planner that tracks prior placements of identical objects, since the current protocol treats each subgoal independently."],"forward_implications":["On LIBERO-Long the framework improves over OpenVLA by an average of 12.1 percentage points, with the largest gains on tasks that were hardest for the baseline: Bowl-Drawer +24, Soup-Sauce +21, Mug-Mug +19.","Verification frequency is a real dial: fetching the verifier every 20 frames keeps peak success while cutting compute by 48% relative to every 10 frames, and every-50-frame checks cost 6 points on LIBERO-Long.","Ablations show each SAP component contributes: removing visual input to the planner costs 4.4 points, removing the recovery routine costs 2.1, removing subgoal decomposition costs 8.1, and replacing the fine-tuned verifier with a zero-shot VLM costs 26.5.","Because the recovery policy is deliberately minimal (lift the gripper and retry), the framework establishes a baseline robustness result that explicitly invites richer recovery strategies such as policy rollback and re-grasping."],"supporting_citations":[{"why":"Supplies the OpenVLA executor that turns each subgoal into 7-DoF actions and defines the main baseline the framework improves on.","marker":"[14]"},{"why":"Supplies the LIBERO benchmark suite used for all success-rate comparisons.","marker":"[17]"},{"why":"Supplies the Qwen2.5-VL-3B model that is fine-tuned into the verifier.","marker":"[1]"},{"why":"Supplies the LoRA method for fine-tuning the verifier on roughly 500 annotated triplets.","marker":"[10]"},{"why":"Supplies SpatialVLA, the previous state-of-the-art baseline beaten by 6.1% on LIBERO-Long.","marker":"[23]"},{"why":"Supplies TraceVLA, a fine-tuned OpenVLA variant used as a comparison baseline.","marker":"[38]"},{"why":"Supplies Diffusion Policy, a diffusion-based visuomotor baseline.","marker":"[5]"},{"why":"Supplies the Atomic Skill Library that constrains planner subgoals to a standardized template.","marker":"[15]"}],"fun_headline_variants":["Self-correcting robot protocol hits 79.6% on long-horizon tasks","Brain-inspired SAP loop lifts robot success to 79.6%","Robotic introspective loop beats OpenVLA by 7.4% on LIBERO","Verifier-driven robot framework reaches 79.6% on long tasks"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The verifier is fine-tuned on about 500 annotated image-triplet examples, and the paper never says where those examples come from; if they were drawn from the same LIBERO tasks used in the evaluation, the reported success-rate gains could be benchmark memorization rather than a general verification ability.","fun_headline_variants_meta":{"raw":{"variants":["Self-correcting robot protocol hits 79.6% on long-horizon tasks","Brain-inspired SAP loop lifts robot success to 79.6%","Robotic introspective loop beats OpenVLA by 7.4% on LIBERO","Verifier-driven robot framework reaches 79.6% on long tasks"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000639,"raw_usage":{"total_tokens":3022,"prompt_tokens":1106,"completion_tokens":1916,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":722,"completion_tokens_details":{"reasoning_tokens":1830}},"tokens_in":722,"tokens_out":1916,"duration_ms":16488,"temperature":1.0,"reasoning_tokens":1830,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T12:44:53.096816+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Inspect the released verifier training triplets for overlap with the LIBERO-Long evaluation episodes, or re-run the benchmark with a verifier fine-tuned on triplets from held-out tasks; if the gains over OpenVLA vanish, the SAP loop's benefit is not a general property of the protocol.","supporting_citations":[{"cited_title":"Libero: Benchmarking knowledge transfer for lifelong robot learning","cited_arxiv_id":null,"evidence_quote":"Supplies the LIBERO benchmark suite used for all success-rate comparisons."}],"review_version":1}