{"id":"1f1fd428-3230-4e89-b40a-4315763b601b","arxiv_id":"2608.08036","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper proposes task-state horizon as a distinct difficulty axis and shows state-management metrics predict embodied agent success better than action length.","lead":"This paper introduces a new measure of how hard a long robot task is: how much world state the agent must remember, discover, and update. The authors built a 588-episode benchmark and show that even top AI agents fail when state tracking is demanding, not just when action sequences are long.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim is not tested: the paper never reports TSH values, and RQ2 correlates rollout-derived state-management metrics with success, not the task-instance TSH tuple, so the claim that TSH beats action length/subgoal count is unsupported.","rationale":"The reader's weakest_assumption focuses on non-uniqueness of the shortest oracle sequence in Definition 2. That is a real concern about whether TSH is well-defined, but I see a more direct, load-bearing gap: even if the oracle sequence were unique, the paper never reports H values and never regresses success on H. RQ2's Figures 6 and 7 use state-management metrics that are derived from rollout behavior and are mechanically tied to success (e.g., AER is the fraction of executable actions in the rollout; RAR is redundant actions in the rollout). Correlating these with SR across scenes or models can be driven by model competence, not by the task-instance property the paper claims to isolate. The phrase 'TSH-controlled' therefore cannot be verified from the paper, and the central claim about TSH outperforming action length and subgoal count is not directly tested. This does not invalidate the benchmark as a useful stress test of state management; it does mean the headline conclusion is conditional on a missing analysis. Since the reader already returned CONDITIONAL, my read does not change the verdict. I mark agreement as partial: the reader identified a definitional weakness, while my concern is that the empirical evaluation never measures the proposed quantity and cannot distinguish task difficulty from agent capability.","tokens_in":27517,"tokens_out":4409,"duration_ms":53730,"concrete_test":"Compute H = (H_maintain, H_explore, H_update) for every baseline and intervention episode using the compiler's oracle graph and release the values. Then, at scene level, fit SR ~ H_maintain + H_explore + H_update + Lo + Ng, with mean SR over the 15 models and 7 conditions as the response, and report partial coefficients or partial Spearman correlations with bootstrap confidence intervals. The central claim requires H components to retain significant predictive association after Lo and Ng are controlled; if the added variance explained is negligible, the claim that TSH is a distinct difficulty axis is falsified. As a robustness check, construct episode pairs matched on Lo and Ng but differing in one H component and compare success rates directly.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing condition for the main claim is that TSH, as defined in Definition 2, is a measurable task-instance property that predicts success beyond action-sequence length Lo and subgoal count Ng. The paper does not establish this. The conclusion states 'TSH-based state-management measures are more closely associated with task success than conventional proxies based on action-sequence length or subgoal count', but RQ2 (Figures 6 and 7) only correlates five rollout-derived metrics (AER, RAR, SEG, ADR, IRR) with success rate. These metrics describe agent behavior, not the tuple H = (H_maintain, H_explore, H_update), and are partly success-conflated: AER and RAR are computed from the same rollouts whose success is the regressand, so a model that executes valid actions and avoids repeats will mechanically have higher SR. Thus the reported correlations can reflect model capability rather than a distinct task-difficulty axis. Moreover, the benchmark is described as 'TSH-controlled' with 'varying TSHs', but no TSH values are reported anywhere: Table 8 gives Lo and Ng per scene but no H components, no distribution of H values, and no matched episodes holding Lo and Ng fixed while varying H. Even setting aside the non-uniqueness issue with shortest oracle sequences in Definition 2, the more immediate gap is that the central variable is never measured. The existing results are consistent with the claim but do not test it.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes task-state horizon (TSH), a tuple (H_maintain, H_explore, H_update) intended to measure the state-management burden of embodied tasks, and introduces RoboGraph, a compiler that turns spatial and temporal state dependencies into executable symbolic scene graphs. The authors release RoboGraphBench, consisting of 588 episodes across 84 scenes and six intervention conditions, and evaluate 15 foundation models in both symbolic and visual closed-loop backends. They report success rates, action-executability and exploration metrics, and correlations between these metrics and task success. The central claim, stated in the conclusion, is that TSH-based state-management measures are more closely associated with task success than action-sequence length or subgoal count.","tokens_in":27697,"tokens_out":3620,"duration_ms":38505,"significance":"If the central claim were established, the paper would provide a useful complement to existing long-horizon benchmarks by separating state-tracking demands from action-planning length. The evaluation is extensive and internally consistent in many respects: 8820 rollouts across 15 models, precise metric formulas in the appendix, a detailed action catalog, and per-scene results. The difficulty analysis also includes useful per-task-family diagnostics. However, the manuscript does not currently test its own central claim: the TSH tuple is never reported for any scene, and the correlational evidence is based on rollout-derived metrics rather than on the task-instance TSH. The paper therefore reads as a promising framework with a substantial verification gap rather than as a completed demonstration.","major_comments":[{"comment":"The central construct is never reported. No table or figure gives H_maintain, H_explore, or H_update for any of the 84 scenes; Table 8 reports only L_o and N_g per scene. The abstract and conclusion describe the benchmark as containing episodes with 'varying TSHs' and 'TSH-controlled tasks', but without the tuple values or a distribution over them, the reader cannot verify that TSH varies independently of L_o and N_g, nor that matched episodes hold L_o and N_g fixed while varying H. The conclusion's claim that TSH-based measures outperform L_o and N_g therefore rests on an unmeasured variable.","section":"Definition 2 and RoboGraphBench"},{"comment":"The correlational evidence in Figures 6 and 7 is computed from the five rollout-derived metrics (AER, RAR, SEG, ADR, IRR), not from the task-instance tuple H defined in Eq. (6). AER and RAR are properties of the policy's behavior in the same rollouts whose success is the regressand, so the reported correlations can be driven by model capability rather than by a distinct task-difficulty axis. Establishing the paper's central claim requires regressing success on the H components themselves, or at least a matched analysis in which H varies while L_o and N_g are held fixed; neither analysis is present.","section":"RQ2, Figures 6 and 7"},{"comment":"Definition 2 defines TSH using 'a shortest oracle action sequence' without addressing non-uniqueness of optimal plans. If different equally short oracle sequences impose different hidden-state dependencies, then H is not a well-defined property of the task instance, and the benchmark would not control a single quantity. The paper provides no sensitivity analysis over optimal tie-breaking, so this load-bearing premise is unvalidated.","section":"Definition 2"},{"comment":"The reported rollout in Table 9 issues 'failed_grab' and 'recover()' as model-selected actions, but the action catalog in Table 5 contains no such entries, and the system-prompt constraints in Figure 12 state that 'recover' is an internal transition, not a second physical action. This inconsistency makes the evaluation protocol ambiguous: it is unclear whether recovery is a free internal reset or a policy-selected action, which directly affects the interpretation of IRR and RQ3.","section":"Appendix, Table 9 and Table 5"}],"minor_comments":[{"comment":"The captions report correlation values denoted by ρ but do not state whether these are Spearman or Pearson coefficients, nor do they give the number of scenes or models used in each correlation; please clarify the statistic and the sample sizes.","section":"Figures 6 and 7"},{"comment":"Several rows in Table 1 contain formatting artifacts, such as missing spaces before numeric values (e.g., the Qwen3-VL-32B row), which make the table harder to read accurately.","section":"Table 1"},{"comment":"The model responses in Table 9 repeatedly refer to a provided 'valid_actions' list, while the harness description states that the policy is not given a list of grounded valid actions; this discrepancy should be reconciled in the protocol description.","section":"Appendix, Table 9"},{"comment":"The SSAL definition assigns a score of 1 to any successful rollout whose non-stop step count N_s is less than the oracle length L_o; the surrounding text says that 'additional non-stop actions reduce the score' but does not discuss sub-oracle-length rollouts, so the intended treatment should be stated explicitly.","section":"Equation (14)"},{"comment":"The main text gives average scene-graph size and subgoal counts without standard deviations, while the appendix reports them; please unify these presentations so the reader does not need to cross-check two sections for the same statistic.","section":"RoboGraphBench, Data analysis"}],"recommendation":"major_revision","confidential_remarks":"The paper advertises a released benchmark but does not provide a code or data release URL or an artifact appendix. For a benchmark paper, release of the compiler, scene definitions, and TSH computation scripts is essential for the central claim to be independently verified. I would ask the authors to address the measurement gap before considering acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper does two things well. It defines task-state horizon as a clean three-component tuple (maintain/explore/update) that captures a real axis of difficulty the field has mostly conflated with action length, and it ships a working compiler, RoboGraph, that turns scene-graph dependencies into executable tasks with intervention conditions. The evaluation is large and carefully built: 588 episodes, 15 models, 8820 rollouts, detailed metrics, a full action catalog, and a step-level example rollout. The internal arithmetic checks out. If the benchmark is released as described, it will be useful for the embodied-agent community regardless of how the formal story shakes out.\n\nThe soft spots are real and they are in the center of the paper. The central claim is that TSH, a task-instance property, predicts success better than action-sequence length or subgoal count. That claim is not tested. The paper never reports a single TSH value. Table 8 gives Lo and Ng per scene but no H_maintain, H_explore, or H_update, and there is no distribution of TSH values or matched episodes that hold Lo and Ng fixed while varying H. RQ2, which is supposed to answer the question, actually correlates five rollout-derived metrics (AER, RAR, SEG, ADR, IRR) with success rate. Those are agent-behavior metrics, not the task-instance tuple. Some of them are partly success-conflated: a model that executes valid actions and avoids repeats will mechanically score higher on AER and RAR. So the reported correlations are consistent with the claim but do not test it; they mostly show that better models have better state-management metrics.\n\nTwo smaller problems. Definition 2 leans on “a shortest oracle action sequence” without addressing non-unique optimal plans; different shortest plans could impose different hidden-state dependencies, which would make TSH not well-defined for the instance. And there is no code or data release linked in the paper, so the benchmark cannot be reproduced or checked.\n\nNone of this is fatal. The TSH idea is worth taking seriously, and the gaps are addressable: report the actual H values for each scene, run the RQ2 analysis with the tuple itself, and release the compiler and environments. A referee should ask for exactly that.\n\nMy recommendation: send it to peer review. The construct and benchmark are substantial enough to deserve referee time, but the revision has to show the central variable, not just describe it.","headline":"The benchmark is big, the TSH construct is well motivated, but the paper never measures the TSH tuple it claims to control, so the central claim is untested.","tokens_in":28311,"tokens_out":2509,"would_cite":false,"duration_ms":25939,"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":"Long-horizon robotic agents fail most when they must maintain, explore, and update task-relevant world state, and this paper shows that this \"task-state horizon\" predicts success better than action length or subgoal count.","keywords":["task-state horizon","long-horizon embodied agents","state management","benchmark compilation","partial observability","closed-loop evaluation","dynamic interventions","state-tracking metrics"],"falsifier":"Enumerate or sample all shortest oracle action sequences for a set of compiled scenes, recompute the TSH tuple for each, and compare: if equally short plans yield different tuples in a nontrivial share of scenes, the TSH-controlled comparison is not controlling a single quantity.","tokens_in":27212,"feed_emoji":"🤖","tokens_out":9101,"duration_ms":87611,"temperature":0.7,"pith_summary":"The paper's central claim is that long-horizon embodied tasks are hard for a reason that action count and subgoal complexity miss: an agent must keep track of task-relevant world state across time, including state it never directly observed and state that changes while it is not looking. The authors formalize this as the task-state horizon (TSH), a tuple measuring state maintenance, state exploration, and state updating, and build a task compiler, RoboGraph, that turns spatial and temporal state dependencies into executable closed-loop task instances with controlled TSH. Using 588 episodes compiled from 84 scenes, with six types of injected interventions, they evaluate 15 agentic models in both symbolic and simulated visual environments. The paper's finding is that TSH-based state-management measures are more closely associated with task success than conventional proxies such as oracle action-sequence length or subgoal count, and that most models degrade sharply as state-tracking demands rise.","feed_headline":"State tracking, not plan length, predicts robot task success","feed_subtitle":"A 588-episode benchmark with injected failures shows where 15 agentic models break down.","key_machinery":"The load-bearing machinery is the TSH tuple plus the compiler that controls it. $H_{\\mathrm{maintain}}$ counts how long a fact must be carried from acquisition to last use, $H_{\\mathrm{explore}}$ counts the minimal view-expanding steps needed to reveal hidden facts, and $H_{\\mathrm{update}}$ counts facts invalidated by exogenous events; RoboGraph encodes these as executable scene graphs with an observation operator $O(G_t,h_t)$ that removes out-of-view facts and an event schedule $\\Lambda=\\{\\langle\\psi_\\lambda,\\delta_\\lambda,\\kappa_\\lambda\\rangle\\}$ that rewrites the graph on semantic triggers. The controlled comparison mechanism is the paired baseline-plus-intervention design: each of 84 scenes yields one baseline and six intervention conditions (action failure, state regression, completed-subgoal rollback, wrong relocation, added occlusion, and added object), so episodes differ in state-tracking demand while sharing the base scene.","core_discovery":"RoboGraph represents a task as a latent scene graph $G_t=(V_t,E_t,X_t)$ with an observation operator that exposes only the currently visible subgraph, so agents act under partial observability. A fact is a state dependency of a decision if the decision relies on the fact while the fact is not recoverable from the current view; dependencies are spatial when the fact is hidden and must be exposed by view-expanding actions, and temporal when an earlier-observed fact still constrains a later decision. The task-state horizon is defined from a shortest oracle action sequence $\\tau^*$ as the tuple $H=(H_{\\mathrm{maintain}},H_{\\mathrm{explore}},H_{\\mathrm{update}})$, respectively the maximum number of steps a temporally dependent fact must be retained before its last use, the maximum minimal number of view-expanding actions needed to expose hidden facts, and the number of facts invalidated by exogenous events along $\\tau^*$. The benchmark then correlates these quantities, and five associated state-management metrics, with success: oracle action length and subgoal count show only moderate correlation with per-scene success, while maintenance metrics on tabletop tasks (AER, $\\rho=0.69$) and indoor tasks (RAR, $\\rho=-0.71$), and within-scene exploration metrics (median SEG/ADR correlations of $0.61$-$0.74$), associate more strongly. On the paper's own account, the most disruptive intervention is added occlusion, and even the best-performing models show residual maintenance, exploration, and updating failures.","pith_inferences":["A natural next step is to construct paired episodes that hold action length and subgoal count fixed while changing only one TSH component; systematic success differences would establish each component as a causal difficulty factor rather than merely a correlate.","The compiler could be extended from symbolic facts to continuous state, such as object poses, articulated joints, or partially known maps, and from deterministic event schedules to stochastic or adversary-triggered ones, making the benchmark a stress test of state tracking under distributional shift.","If TSH is stable across optimal plans, it also gives a curriculum signal: order training or evaluation episodes by $H_{\\mathrm{maintain}}$, $H_{\\mathrm{explore}}$, and $H_{\\mathrm{update}}$ independently of plan length, so models are trained specifically on the state-management skills they lack."],"forward_implications":["TSH-controlled compilation lets benchmark builders vary state-tracking load while holding the underlying scene fixed, making state management an isolable evaluation axis.","State-management metrics diagnose failure modes: high redundancy points to maintenance failures, premature termination to exploration failures, and slow recovery to updating failures.","The comparison results imply that action-sequence length and subgoal count alone understate model difficulty for tasks with hidden or changing state.","Open-weight models in this evaluation achieve near-zero success on demanding episodes, suggesting state tracking is a major bottleneck for current agents, not a marginal one.","Positive correlations between symbolic and visual metrics indicate that the symbolic interface can serve as a cheaper proxy for state-management capability before visual grounding is added."],"supporting_citations":[{"why":"Provides the ALFRED benchmark, a representative long-horizon task set whose difficulty is described through action sequences and subgoal structure, which TSH is introduced to complement.","marker":"Shridhar et al. 2020a"},{"why":"Provides BEHAVIOR-1K, the large predicate-defined activity collection that supplies the subgoal-complexity view of long-horizon difficulty the paper compares against.","marker":"Li et al. 2024"},{"why":"Provides PARTNR, a planning and reasoning benchmark for embodied multi-agent tasks used as a comparison point for existing long-horizon evaluations and for procedural task generation.","marker":"Chang et al. 2025"},{"why":"Supplies the partially observable decision-process formulation used to define observations, state dependencies, and the task-state horizon.","marker":"Kaelbling, Littman, and Cassandra 1998"},{"why":"Provides FindingDory, a memory benchmark for embodied navigation that motivates the maintenance dimension of state management.","marker":"Yadav et al. 2026"},{"why":"Provides SpaMEM, which benchmarks dynamic spatial reasoning through perception-memory integration in embodied environments, a related capability that RoboGraph makes explicit as state updating.","marker":"Liao et al. 2026"},{"why":"Provides WorldLines, which benchmarks and models long-horizon stateful embodied agents, supporting the claim that tracking and revising world state is a distinct bottleneck.","marker":"Zhang et al. 2026"},{"why":"Provides ProcTHOR, the procedural generation approach for interactive homes that scaffolds the automatic scene generation used in RoboGraphBench.","marker":"Deitke et al. 2022"},{"why":"Provides RoboTwin 2.0, the simulator backend used for the visual closed-loop tabletop evaluation.","marker":"Chen et al. 2025"},{"why":"Provides RoboCasa, the simulator backend used for the visual closed-loop indoor kitchen evaluation.","marker":"Nasiriany et al. 2024"}],"fun_headline_variants":["For robots, state tracking beats plan length for success","RoboGraph benchmark: 588 episodes tie success to state horizon","State-tracking horizon, not action count, predicts robot success","Embodied agents fail hard as task-state horizons grow","TSH exposes why long-horizon robots lose track"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The benchmark rests on the assumption that a shortest oracle action sequence is representative of the task: if equally short optimal plans force agents to track different hidden facts, then task-state horizon is not a single well-defined property of a task instance.","fun_headline_variants_meta":{"raw":{"variants":["For robots, state tracking beats plan length for success","RoboGraph benchmark: 588 episodes tie success to state horizon","State-tracking horizon, not action count, predicts robot success","Embodied agents fail hard as task-state horizons grow","TSH exposes why long-horizon robots lose track"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000362,"raw_usage":{"total_tokens":2002,"prompt_tokens":1045,"completion_tokens":957,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":661,"completion_tokens_details":{"reasoning_tokens":875}},"tokens_in":661,"tokens_out":957,"duration_ms":11079,"temperature":1.0,"reasoning_tokens":875,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T00:30:58.942997+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Enumerate or sample all shortest oracle action sequences for a set of compiled scenes, recompute the TSH tuple for each, and compare: if equally short plans yield different tuples in a nontrivial share of scenes, the TSH-controlled comparison is not controlling a single quantity.","supporting_citations":[],"review_version":1}