ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures
Authors: Xiyin Zeng
Yuyu Sun
Haoyang Li
Shouqiang Liu
Hao Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-language-action
embodied agent
large language models
Long-Horizon Planning
Primary area: generative models

Abstract: Vision–Language–Action (VLA) systems follow instructions to execute multi-step tasks in multimodal environments. Recent VLA approaches typically rely on post-hoc correction mechanisms or operate under fixed task decompositions and alignment schemes. However, once an intermediate step is mis-specified, local errors propagate through subsequent steps and eventually accumulate into cascading failures. To mitigate this compounding effect, we propose Predictive Alignment and Planning Architecture (ReCAPA), a framework that uses prediction and contrast to adjust deviations across three levels: actions, subgoals, and trajectories. Semantic alignment is enforced at all levels using a Sinkhorn-based module and a Score-field module. The predictive correction and alignment, jointly updates the action-generator in the training phase, enabling it to adjust fine-grained steps to remain aligned with the overall intent. We further introduce two new metrics to quantify error propagation and recovery processes in tasks, capturing how mistakes spread and fade over long-horizon execution.  Experiments show that ReCAPA achieves competitive results on embodied agent benchmarks such as VisualAgentBench, MineDojo, and AI2-THOR, outperforming strong proprietary and open-source Large Language Model (LLM) baselines.

Published as a conference paper at ICLR 2026
RECAPA: HIERARCHICAL PREDICTIVE CORRECTION TO MITIGATE CASCADING FAILURES
Xiyin Zengl.2, Yuyu Sun Haoyang Li Shougiang Liu?, Hao Wang"
Hong Kong University of Science and Technology (Guangzhou) South China Normal University
Equal contribution
Corresponning author
ABSTRACT
Lision-Language-Actin (VLA) systems follow instructions execute multi- step tasks in multimodal environments. Recent VLA approaches typically rely on post-hoe correction mechanisms or operate under fixed task decompositions and alignment schemes. However, once an intermediate step is mis-specified, local errors propagate through subsequent steps and eventually accumulate into cascad- ing failures. To mitigate this compounding effect, propose Predictive Align- ment and Planning Architecture (ReCAPA), a framework that uses prediction and contrast adjust deviations across three levels: actions, subgoals, and trajecto- ries. Semantic alignment is enforced at all levels using a Sinkhorn-based module and Score-field module. The predictive correction and alignment, jointly up- dates the action-generator in the training phase, enabling it to adjust fine-grained steps to remain aligned with the overall intent. We further introduce two new metrics to quantify error propagation and recovery processes in tasks, capturing how mistakes spread and fade over long-horizon execution. Experiments show that ReCAPA achieves competitive results on embodied agent benchmarks such as VisualAgentBench, MineDojo, and A12-THOR, outperforming strong propri- etary and open-source Large Language Model (LLM) baselines. Project page: http:://uuaadeennqg/v//guuubbbbp
INTRODUCTION
VLA agents powered by LLMSs are increasingly applied to long-horizon tasks in embodied envi- ronments, such as household manipulation, indoor navigation, and multi-turn human-robot dialogue (Jaan et al. 2023). These tasks require perception, planning, and grounded execution under natural language guidance. Yet many systems struggle to generalize across multi-step environments, as they lack structured visual language grounding and often collapse under semantic drift and cascading rors Comanici et al. 2025
Anthropic
2024a Achiam et al. 2023).
Recent VLA agents such as Re-ReST (Dou et al. 2024), LLaMAR (?), and CityNavAgem (Zhang jet al.) 2025) shift from selecting actions step by step to executing subplans, which decompose the overall plan into executable segments. Both TrajPrompt (Tsao et al.) (024] and PRE et al. 2024) incorporate alignment between instructions and trajectories through semantic match- ing, thereby strengthening execution coherence with the task intent. However, semantic drift and error propagation remain key bottlenecks of long-horizon reasoning. On one hand, post-hoc cor- rection and static execution makes it difficult to flexibly adjust its actions when execution deviates. On the other hand, relying only on local alignment leads to each step being optimized in isola- tion, thereby drifting from the overall intent easily. In benchmarks such as VirtualHome (Puig al.) 2018) and A12- A12-THOR(Koll)) of subsequent steps by over 60% (Zhong et al. 2024 Zhu et al. 2021). In sum, current agents may encounter execution-gon goal divergence and cumulative rollout errors.
et al.) 2017), even single subgoal error can degrade the performance
Addressing cascading failures across different temporal scales critical. Action errors may com- pound rapidly in the short term, whereas strategy misalignments unfold more slowly distort the overall plan. These distinct propagation patterns suggest that effective correction should span mul- tiple levels, requiring consistent alignment across steps and overall goal. Yet many methods reflect

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Published as a conference paper at ICLR 2026
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Figure 1: Overview of the ReCAPA framework. The LLM first generates the agent's original plan through task decomposition. Hierarchical Predictive Correction module executes the task and pro- duces fine-grained corrective signals guide execution updates.
only at single layer, inevitably leaving other level propagation unchecked (Sun al., 2023) (Zhou iet al. (024). To address this, actions and subplans are refined in the training phase through higher- level supervision that enforce cross-level alignment. At inference time, the action-generator can anticipate deviations early, favor behavior compositions consistent with task intent.
Guided by the above considerations, we propose ReCAPA as shown in Figure D ReCAPA seper- ates trajectories into action-, subgoal-, and trajectory-levels. Unlike prior methods that rely on fixed decomposition and apply only after failures, ReCAPA introduces Hierarchical Predictive Correction (HPCC) with cross-level alignment signals to prevent errors from compounding. These alignment signals are obtained by comparing trajectory embeddings with the prompt embeddings, using Sinkhorn-based (Cuturi 2013) module and Score-fielçSong et al. (020)) module. The Sinkhorn- based module aligns the overall trajectory distribution with the prompt, thereby guiding execution to remain consistent with task intent the trajectory level. In parallel, the Score-field module pro- vides step-specific alignment across the remaining two levels. At the subgoal level, HPCC predicts the trajectory-level representation and evaluates its consistency with realized trajectory rollouts, up- dating predictions when mismatches are detected. At the action-level, ReCAPA predicts the sub- goal representation and revise fine-grained execution errors. Together, HPCC and prompt-trajectory alignment enable early, cross-level corrections that reduce drift.
To properly assess these benefits, evaluation should go beyond success rate (SR) to also capture how errors propagate, accumulate, and dissipate throughout execution, which existing benchmarks largely overlook. To fill this gap, we introduce two diagnostic metrics in long-horizon reasoning: Error Propagation Rate (EPR) quantifies how mistakes compound across steps, and Propagation Attenuation Coefficient (PAC) measures how errors attenuate or dissipate over time. Together, these metrics capture how failures both spread and decay. providing diagnostic tools to evaluate ReCAPA's stability. Our contributions demonstrate the following advancements:
We propose ReCAPA, framework operationalizes hierarchical correction by coupling multi-level predictive representations with coajpt-rrjecctrry distributional alignment, al- lowing deviations to anticipated and corrected earlier.
We introduce two diagnostic metrics for error propagation long-horizon reasoning: EPR quantifies the propagation of errors across future steps, while PAC captures the system's ability to recover by measuring how quickly error impacts dissipate over time.
ReCAPA outperforms strong LMM baselines in terms of success rate, achieving +5.65% VisualAgent +9% MineDojo, and +7% AI2-THOR.
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2 RELATED WORKS
Decompasition Approaches Task decomposition has been explored in methods such as HIRO (Nachum et al. 2018) which executes fixed interval subgoals and EPO (Zhao et al. 2024) which applies reward modeling for hierarchical planning. LLaMAR (?) and AityNavAgent (Zhang al.) 2025) both follow pre-defined multi-stage subgoal pipelines, but they introduce novel modifications to improve task execution success. LLaMAR improves upon multi-stage task decomposition and policy optimization, while CigeNavAgent decomposes subgoals and uses memory of past trajec- tories to aid planning. However, these static approaches may struggle to adapt when the initial decomposition is flawed, leading to errors in dynamic environments.
Error-Correction Mechanisms To address static planning, later works introduced feedback- based corrections. ReAct (Yao et al. (202a)), Reflexion (Shinn et al. 2023). and WALL-E (Zhou al., 2024) provide step-level or episodic updates. AdaPlanner (Sun et al. (023) revises subplans based on feedback adapt to changing environments, while R3V (Cheng et al. 2024) self-reflects by generating and selecting multiple candidate paths. Although these methods incorporate dynamic error-correction mechanisms and can adapt through feedback from the environment or their in- ternal processes, they still struggle to maintain consistency across different stages of task execution.
Integration Attempts To maintain consistency during executions, recent research has explored frameworks that integrate decomposition with semantic alignment. For example, TrajPrompt (T'ao 2024) represents trajectories as segmented prompts and aligns them within a shared vi- sion-lange language semantic space, thereby jointly embedding trajectory dynamics with scene semantics. HiP (Ajay al., 2023) first decomposes long-horizon tasks into symbolic subgoals using language model, and then aligns these subgoals with visual and action semantics to ground them into exe- cutable actions. However, these methods primarily focus on aligning substeps, which can result in correct subgoals but failed actions, making it difficult to maintain consistent alignment between overall intent and operations. contrast, ReCAPA enforces cross-level predictive: lower levels forecast higher-level representations, and deviations trigger top-down corrections that use alignment signals to pull local decisions back to global goals and mitigate error propagation early.
al.
METHODOLOGY
3.1 FRAMEWORK OVERVIEW
Most existing methods rely on pre-defined segmentation or post-hoc correction, making it difficult anticipate errors and suppress their propagation, which often leads failures. addition, local alignment is often insufficient, as it lacks global feedback to correct misordered segments or handle ambiguous cases. To overcome this, we introduce prediction expose deviations early. In the training phase, ReCAPA takes trajectory segments, prompt embeddings, and visual observations as input, with Hierarchical Predictive Correction (HPCC) representing trajectories three levels. Prompt-trajectory alignment is computed at each level by comparing trajectory representations with prompt embeddings, producing cross-level consistency signals. These signals define the training losses, which are backpropagated through the predictors and encoders to update action and object selection.
At inference, ReCAPA uses environmental observations, the prompt, and historical trajectories as inputs. The exccution network generates trajectories, while the LLM (GPT-40-mini) provides task decompositions and completion markers. The three-tier correction mechanism refines the trajectory by resampling actions, adjusting subtasks, and using Sinkhorn for prompt alignment.
3.2 HIERARCHICAL PREDICTIVE CORRECTION
At the core of ReCAPA, HPCC predicts higher-level semantics from lower-level steps and reflects back corrective signals, supporting consistent task representations. HPCC organizes alignment into three levels: actions, which capture how fine-grained steps compose into short-term subgoals (c.g., [GRAB], [WALK], [WIPE] cleaning); subgoals, which forecast trajectory outcomes and enforce
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Input
Hierarchical Predictive Contrustive Correction
Execution Network
Statee
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Figure 2: Overview of the ReCAPA training process. In ReCAPA, state, history, and prompt encoded into hierarchical representations, with predictive and alignment losses guiding the action- generator produce and revise actions.
causal order (e.g., washing before drying); and trajectories, which encode the task's overall intent and outcome.
HPCC takes the trajectory input and turns it into multi-level representations at the action, sub- goal, and trajectory scales. Fine-grained actions combine to form higher-level subgoals, which together structure the task and encode sequential behavior patterns anticipate overall task seman- tics. Specifically. at each level I {action, subgoal}. the model predicts the representation at level + based on a segment collection T' constructed via sliding windows along the trajectory. The collection T' at level I, optionally concatenated with visual embeddings from environment obser- vations, encoded into z'. We denote z' E Rdd the vector-valued state representation at level 1. The representation z' is subsequently processed by Transformer-basee predictor, producing predicted higher-level representation & 1+1, The representation z' is subsequently processed by Transformer-bant based predictor, producing predicted higher-level representation 2 1-1 Subsequently, the predicted representation .++ is compared with the target representation z to construct cross- level alignment losses that regularize the lower-level representation z'.
The discrepancy between the cross-level predictor's 'ss estimate 1+1 and the target embedding z quantified by prediction loss. The magnitude determines the strength of the supervisory signal from the level-(1 + 1) encoder that regularizes the lower-level representation z'.
Concretely, the cross-level contrastive loss L/pee is implemented (21+1, (/4112/+11. Lireed encourages the predicted representation to stay close to the intended higher- level signal while distinguishing it from distractors. During optimization, gradients are backpropa- gated through the predictor and the level-/ encoder, while the level-(l+ 1) target / is detached to prevent updates:
InfoNCE objective
exp (sim(2 (+1 11,x+1)))
4 fied log exp (sim(2 1+1 (41,,(+1)11) E, exp (sim(2 1+1, 2
where z++ is the positive sample, and the set [+1]] contains negative samples that act distrac- tors. Negative samples are constructed from alternative trajectory segments generated by an LLM (GPT-40-mini) They are plausible yet semantically misaligned with the target due to incorrect ac- tion ordering subgoal realization. After being regularized by supervision, z' serves the input to the citionngenerato,, where the representation of the latest time step is passed through an MLP head produce discrete action logits.
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3.3 PROMPI-TRAJECTORE ALIGNMENT
We introduce two complementary modules for prompt-trajectory alignment. A Sinkhorn-based alignment leverages Optimal Transport (OT) (Peyré & Cuturi 2019) to align trajectories and prompts. This alignment operates at the distribution level, and it captures global consistency across the entire trajectory. Meanwhile, the Score-field alignment learns corrective gradients to provide step-wise guidance and adjust fine-grained executions toward higher-level semantic intent.
3.3. SINKIIORN-BASEI ALIGNMENT
The Sinkhorn-based alignment module uses a distributional alignment approach, enabling the entire trajectory to align with the task's semantic structure without requiring exact token-by-token match- ing. The resulting gradients are dominated by aggregate semantic consistency, preventing locally ambiguous or misordered executions from influencing the aligmment signal. To quantify the dis- tributional discrepancy between the trajectory and the prompt, we employ the entropy-regularized optimal transport distance. It takes the trajectory distribution μ and the prompt distribution as input, and outputs a distributional alignment loss / (11, v). Formally, the Sinkhorn divergence defined as:
Lsinkhom (14, (14, OT, v) (OT,( - JOT,
where OT, denotes the entropy-regularizee OT cost between distributions. Minimizing Lsinkhor encourages the trajectory distribution μ to align semantically with the prompt distribution // in the latent space.
3.3.2 SCORE-FIEL ALIGNMENT
The Score-field module provides local objective that complements the global Sinkhorn alignment by supplying fine-grained corrective gradients. Both v, used for Sinkhorn alignment, and the prompt embedding p are derived from the same prompt encoder. The Score-field field module takes state em- beddings z' and p as input, and outputs localized corrective gradients sy (z', p). The score network
is implemented as an MLP. The network perturbs z' with Gaussian noise & ~ NO0, o²1) and learns to predict the denoising score -E/O².
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It trains 86 model vector field that points towards high-density regions of the prompt-defined distribution. Consequently, any trajectory state z' that lies in low-density region indicating devi- ation from the prompt's s semantic intent receives a corrective gradient from (z', p). This gradient incorporated
auxiliary regularization term in the overall training objective, encouraging deviant state representations to shift toward configurations more consistent with the prompt.
3.4 TRAINING AND INFERENCE
In the training phase, first pre-train the Transformer encoders with contrastive objective. Specif- ically, sliding windows of state-action trajectory segments are encoded into fixed-dimensional em- beddings and optimized using InfoNCE (Eq. [p. The positives are sampled from the same episode- level trajectory and negatives are generated by LLM. This stage initializes structured embedding space that captures the plausibility and sequential dependencies local trajectory segments.
Then we jointly tne-tune the hierarchical encoders and predictors using the following objective:
Laotal
Σ LE (action, subgoal)
(Xproa + N ( + Asinktorn Asinkhorr Lesinthom:
where XlRed = 0.5 and Nonr 0.2 for action and subgoal levels, and Asinkhum = 0.1. We optimize ReCAPA by jointly training the hierarchical encoders, predictors, and alignment modules under Luoal- All modules updated end-to-end, where predictive supervision from higher-level repre- sentations and
alignment jointly shape lower-level representations. z/+1 used as the prediction target is detached, gradients do propagate to the higher-level encoder.
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At inference, LLM (GPT-40-mini) provides sequence of subgoals and their completion criteria, which define the candidate subtasks and termination signals. At the action level, we select Top- K candidate actions and compute their alignment with the current subgoal. A candidate is accepted only if its uubgoal-alignment similarity exceeds a threshold; if no candidate is accepted, the threshold is progressively relaxed and the process is retried. If all retries fail, we fall back selecting the highest-logit action.
At the subgoal level, sliding window encodes recent state-action sequences into a window repre- sentation. The semantic similarity between the current window representation and the embeddings of the current and next subgoals computed. A switch the next subgoal is triggered when align- ment with the current subgoal falls below a threshold and alignment with the next subgoal exceeds by a margin.
At the trajectory level, each candidate action is re-evaluated by temporarily appending it to the cur- rent trajectory buffer and computing prompt-traje trajectrryy consistency using a Sinkhorn-basee align- ment. The candidate with the highest trajectory score is selected, rather than the first candidate that passes fixed threshold. If the highest trajectory score remains below a predefined threshold, the method falls back to the action-level top choice based on combined logits and similarity.
3.5 ERROR PROPAGATION METRICS
Standard metrics such as SR or Success weighted by Path Length measure whether task is eventu- ally completed, but they do not fully capture how errors accumulate dissipate during execution. In long-hou long-horizon reasoning, this distinction is critical: two agents may achieve the same final suc- cess rate, yet one suffers from cascading failures while the other recovers from early slips. Without tracking such dynamics, existing metries can mask important differences in robustness. To address this gap, we introduce two formal measures that characterize error propagation and recovery.
Error Propagation Rate (EPR). Let e2 E (0, }} denote step-level error indicator. e 1 indicates that the agent's action step violates the task constraints or deviates from the oracle trajectory, and e₂ = 0 indicates correct step. The EPR at lag k is defined as:
EPR;
Pr(eto+* = 1 cto = =) Pr(eto**= =1 Cto 0):
(5)
where Pr(e+,+ 1 denotes the probability that an error occurs at step to + k. For example, EPR, = 0.4 means the probability of another eтror three steps later increases by 40% compared to the case without an initial error. Moreover, under sufficient trajectory coverage and proper condi- tioning, the estimator EPR, is consistent, i.e., EPR 7 EPR.
Propagation Attenuation Coefficient (PAC). PAC is directly defined as:
PAC -slope A, Pr (Et.+ et, 1)),
(6)
where alope(A,-)) denotes the slope obtained by fitting a least-squares linear regression of In Pr(et,+4
etp 1) on the lag A over fixed range of A values. It measures the ex- ponential decay rate of post-error risk: larger values indicate quicker recovery, while smaller values reveal that the system remains exposed error accumulation.
EXPERIMENTS AND RESULTS
4.1 EXPERIMENTAL SETUP
To address trajectory drift and long-horizon planning, we evaluate ReCAPA on three benchmarks. A12-THOR provides 120 interactive scenes while MineDojo is a Minecraft-based benchmark with 3.142 tasks. VisualAgentBench includes OmniGibson (household tasks) and Minecraft (naviga- tion/crafting), measured by Average Success Rate (AVG) and Fl. In addition to standard metrics, report EPR and PAC to quantify error spread and recovery.
In ablations, the baseline w/o HPCC removes the HPCC module, while HPCC-AS (Ac- tion+Subgoal), HPCC-AT (Action+Traaectory) and HPCC-ST (Subgoal+Traeectory)) use only two- level combinations; HPCC-Full includes all three. PPO replaces HPCC with Proximal Policy Op- timization (Schulman et al. 2017), serving flat RI. baseline. We also implement HIRO with

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Published as a conference paper at ICLR 2026
Table 1: Performance on To al.) across models and metrics. A12-THOR assessed via Success Rate (SR), Transport Rate (TR), Coverage, and Balance; Coverage measures successful interactions, while Balance captures the evenness of contributions subtasks.
Model
SR
TR
Coverage
Balance
LaggeeLLM/AAent Baselines ReAct (Yo et al.
2023b)
0.34
0.72
0.92
0.67
CoT (Wel al. 2023)
0.14
0.59
0.87
0.62
SmartLLM (Kannan et al.
2024
0.11
0.23
0.91
0.45
CoBLA (Zhang et al. 2023
0.25
0.46
0.76
0.73
Multi-Modal/LLM- E-hhancedd Baselines
GPT-4o (Hurst et al.
2024)
0.51
0.85
0.95
0.83
LLaVA LLu et al. 2023)
0.54
0.84
0.91
0.75
IDEFICS-2 (Laurençon et al.
2024
0.57
0.86
0.94
0.78
CogVLM (Wang et al. 2024
0.61
0.89
0.95
0.80
GPT-4V (Achiam al.
2023
0.66
0.91
0.97
0.82
LLaMAR (?)
0.68
0.90
0.95
0.85
ReCAPA
0.75
0.93
0.95
0.93
100
4.0
Success Rate (SR)
90
MAP THOR ERP:
MAP IMP-HHORES THORRORR PAC:
80
Ligh
70
0.15
2.0
80
50
40
20
40
60
100
120 340
0.05
60
80
100
120
140
0.0
20
40
60
80
100
120
140
Task Length (Number of Steus)
Task Length (Number of Steps)
Task Length (Number of Steps)
Figure 3: Left: Success rate curves across varying task lengths. Middle: EPR trends showing error propagation at different lags. Right: PAC decay rates on A12-THOR.
two-level subgoal control, augmented with Sinkhorn and Score-field alignment. The baseline w/o Alignment removes all alignment losses, while Alignment-Fuu includes both. KL+Score-fieet re- places Sinkhom with KL divergence while retaining Score-field.
On VieualAgentBench and A12-THOR, we emphasize cross cross-domaie domain transfer, pre-training on Proc- THOR (Deitke et al.) 2022) and B (Li et al.) 2024a) and directly evaluating without fine- tuning. On MineDojo, the model is trained on all programmatic tasks except those used for testing. All baselines follow their original protocols. Further details are in Appendix
4.2 RESULTS AND ANALYSIS
Table 1 2 and Table summarize results across benchmarks. ReCAPA shows strong multi-task performance with an AVG. score 58.65, excelling manipulation and crafting on VisualAgent- Bench. On AI2-THOR, ReCAPA surpasses baselines with the highest SR of 0.75, TR of 0.93, and Balance of 0.93, though Coverage lags slightly behind GPT-4V. On MineDojo. ReCAPA outper- forms prior LLM agents, leading in 8 out of 10 hong-horizon tasks with higher success rates. It also achieves the lowest EPR, and most favorable PAC trajectory as task length grows. For example, k = 10 OmniGibson, EPR is 0.082. In contrast, GPT-4o-mini and Gemini-2.5 exhibit values around 0.3, while Claude-4-sonnet exceeds 0.453 Residual errors dissipate fastest for ReCAPA, reflected in the strongest PAC value in Figure Additional comparisons appear in Appendix B
To further assess the contributions of ReCAPA's core components, we conduct ablation studies tar- geting the HPCC and prompt-trajectory alignment modules across four different benchmarks as shown in Table Removing HPCC leads to the largest performance drop, with SR on Behavior falling to 59.3 compared to 72.2 for HPCC-Full, confirming the importance of multi-level predic- tive. HIRO reaches 63.4 on Behavior and 62.7 on VirtualHome, higher than PPO at 60.2 and 60.6, but generally underperforms HPCC variants. Trajectory-level variants show clear gains, with HPCC-

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Published as a conference paper at ICLR 2026
Table 2: Performance of different models on VisualAgentBench which include OmniGibson and
Minecraft. AVG. denotes the overall average score.
Model
AVG.
OmniGibson
Minecraft
Open-LMMMs (Fine-tuning)
Qwen-VL Bai ct al. 2023)
9.90
1.7
18.1
CogVLM2 Hong et al.
2024
13.55
6.6
20.5
LLaVA-NeXT et
2024b)
16.60
9.4
23.8
GLM-4V (GMM et al.
024)
14.35
8.8
19.9
Intern CCen et 2024
22.20
16.0
28.4
Proprietary-LMMMs (Prompting)
qwen-vl-max (Bai et al.
2023)
2.65
0.0
5.3
Claude-3.5-Sonnet (?)
40.15
24.3
56.0
GPT-4V (preview) (Achiam et al.
2023
41.95
36.5
47.4
GPT-4o (Hurst et al. (0224)
48.30
41.4
55.2
Maude-4-Sonnet (Anthropic 2025
50.25
42.6
57.9
GPT-4o mini (Zhu et al. (2023)
54.15
46.7
61.6
Gemini 2.5 Flash (Comanici al. 2025
53.00
43.9
62.1
ReCAPA (Our work)
58.65
50.6
66.7
OmniGitsoon ERR.
BACAN
OmniGieson PAC
4.6
MineCreft PAC,
10--4-2204--9-11
0.25
0.08
07-46-000-9813
2014
0.06
0.10
3.0
0.05
2.75
0.02
0.00
2.50
3.0
k (stops after first error]
(stops after first arror)
Istaps after first eirorr
K (stops after first error)
Figure 4: Results on VisualAgentBench. The left two plots show the EPR and PAC curves on OmniGibson, while the right two plots show the EPR and PAC curves MineCraft. Shaded regions indicate 95% confidence intervals across three random seeds.
AT achieving 0.73 on A12-THOR and HPCC-ST 0.69, both stronger than HPCC-AS. For alignment, Sinkhorn and Score-field are complementary: Sinkhorn alone gives higher scores than Score-field most of the time, and using both together achieves the best overall performance. KL+Score-field achieves the highest score of 67.0 on the Minecraft task in VisualAgentBench.
4.3 DISCUSSION
Our results show that ReCAPA achieves higher success rates and generally outperforms strong pro- prietary and open-source LMMs across benchmarks. On VisualAgentBench and MineDojo, its ad- vantage is clearest in compositional reasoning tasks, where it decomposes goals into valid subgoals and maintains multi-step consistency. At its core, this structure continuously filters out actions that locally plausible yet harmful in the long run, preventing small deviations from accumulating into irreversible drift. On AI2-THOR, it demonstrates stronger robustness in long-horizon plan- ning and balanced manipulation across diverse scenes. The lower coverage relative to GPT-4V arises because ReCAPA's hierarchical favors structural consistency and high-confidence interac- tions, while GPT-4V's broader exploration touches more objects. Hierarchical structure improves long-horizon stability, but it reduce coverage by being more conservative in exploration. By contrast, exploration-driven methods increase coverage and discovery by broadly probing the en- vironment, but their reliance trial-and-error budgets. This reflects a fundamental trade-off in embodied agents: broader exploration increases coverage, while consistent enhances stability, and long-ho nogghhoiionn reasoning requires balancing both.
result in reduced completion under fixed step
Beyond overall success rates, we further analyze error propagation dynamics using our proposed EPR and PAC metrics. ReCAPA achieves the lowest EPR across benchmarks, showing that the
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Table 3: Comparison of several tasks selected from the MineDojo(?) benchmark, covering simple resource gathering and multi-step synthesis or animal interactions. All reported values correspond
SR. The visual encoder replaced with MINECLIP(Fan al. 2022).
TASK
7
MINEAGENT ET AL.. 2024
0.00
0.00
0.00
0.00
MINEAGENT CRAFT
0.00
0.03
0.00
0.00
0.46
0.50
0.33
0.35
0.00
PLAN4MC (YUAN ET AL.
2023
0.30
0.30
0.53
0.37
0.83
0.53
0.43
0.33
0.17
RL-GPT (LIU AL. 2024
0.65
0.65
0.67
0.67
0.85
0.56
0.46
0.38
0.32
RECAPA
0.63
0.65
0.80
0.73
0.95
0.73
0.60
0.53
0.40
Table 4: This ablation study aims to address the role of layers and alignment strategies on four
benchmarks (Li et al. 2024c) (Shridhar et al. 2021). All reported values correspond to SR.
Method
EmbotieddAgentlnteffee
AlfWorld
VisualAgentBench
A12-THOR
Behavior
VirtualHome
OmniGibson
Minecraft
W/-HPPC
59.3
60.1
42.7
56.3
0.63
PPO
60.2
60,6
79
41.5
57.8
0.59
HIRO
63,4
62.7
94
44.0
60.2
0,63
HPCC-AS
63.6
61.4
43.4
62.5
0.65
HPCC-AT
65.1
70.9
47.9
57.5
0.73
HPCC-ST
66.3
66.3
91
48.1
60.4
0.69
HPCC-Full
72.2
70.5
96
50.6
66.7
0.75
w/o wlo-Alignment
65.8
67.2
92
46.1
62,4
0.69
Sinkhomn
66.1
69.4
95
49.3
65.6
0.74
Score-field
64.4
67.9
46.8
66.3
0.72
score-field
70.3
68.1
49.6
67.0
0.74
Alignment-Full
72.2
70.5
96
50.6
66.7
0.75
impact of early mistakes dissipates more quickly than in other LMMs. This indicates that while eπrоrs remain, ReCAPA limits their spread and prevents small deviations from escalating into full failures. Similarly, ReCAPA maintains the highest PAC trajectory, indicating that errors dissipate more rapidly, and longer tasks provide more opportunities for recovery rather than compounding drift. Taken together, the two metrics highlight complementary aspects of robustness, with low EPR reflecting error prevention and high PAC reflecting error recovery, which prior evaluations of- ten overlooked. Standard success-based metrics conflate these effects by only measuring terminal amplification poor recovery. More
outcomes, masking whether failures arise from early long-horizon reasoning and may encour- broadly, EPR and PAC provide a useful lens for analyzing age future evaluation to move beyond stepwise accuracy toward explicitly quantifying how agents prevent and dissipate cascading errors.
In ablation studies, by linking global trajectories with local actions subgoals, HPCC-AT and HPCC-ST reduce the drift of locally steps from global goals. Trajectory-level representations act global semantic reference that selectively suppresses local updates misaligned with the overall goal. This suggests that effective long-horizon reasoning requires cross-level guidance, whereas LLMs. though large, often optimize only for local coherence and struggle to maintain consistency over extended horizons. HIRO executes fixed-interval subgoals open-loop, so when the environ- ment shifts it lacks flexible adjustments and actions drift from global goals easily. HPCC instead introduces an adaptive correction strategy, where cross-level feedback adjust local actions and keep them aligned with global goals. For alignment, KL+Score-field's sensitivity strongly penalizes mi- nor mismatches. In Minecraft, where distributions are skewed, this sensitivity helps capture rare but decisive events. However, also destabilizes signals, making KL-based alignment less reliable than Alignment-Fuu Full. which achieves stronger overall performance across most tasks. This highlights trade-off between sensitivity and stability in alignment objectives: overly sensitive divergences can capture signals but risk amplifying noise in long-horizon settings.

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Published as a conference paper at ICLR 2026
5 CONCLUSION
To mitigate semantic drift in long-horizon reasoning for embodied agents, we proposed ReCAPA, a framework that integrates hierarchical correction with prompt-trajectory alignment. Experiments VisualAgentBench, MineDojo and A12-THOR both demonstrate that ReCAPA outperforms strong baselines. While ReCAPA achieves SOTA results across A12-THOR, VisualAgentBench and Mine- Dojo, it exhibits two key limitations. (1) The correction mechanism operates through discrete scor- ing at the hierarchical levels, which stabilizes execution trajectory but cannot provide continuous, intermediate feedback. As a result, small deviations that accumulate between scoring steps may not be corrected immediately. (2) The hierarchical generation module uses deterministic mappings to compute next-layer embeddings, meaning the model follows single subgoal trajectory and cannot represent alternative plausible continuations when uncertainty is high. To address these limitations, future work will explore mechanisms that provide richer intermediate feedback and better capture uncertainty in hierarchical reasoning. Our goal is to enable more flexible long- ong--horizon decision making without being restricted single deterministic trajectory.
ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (No. 62406267), Guangdong Provincial Project (No. 2024QN11X072) and Guangzhou Municipal Science and Tech- nology Project (No. 2025A04J4070).
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A DETAILED EXPERIMENTAL PROCEDURES
ReCAPA employs two-stage training process. The first stage is offline pre-training on diverse pert and LLM -ggeerraed trajectories to establish hierarchical predictive and alignment capabilities. In the second stage, adopt benchmark-specifh protocols: some benchmarks (e.g., MineDojo) involve supervised adaptation using in-domain trajectories, while others (e.g.. VisualAgentBench and AI2-THOR) emphasize pure cross-domain transfer without task-specific fine-tuning.
A.1 STAGE 1: OFFLINE PRE-TRAINING
The offline pre-training phase leverages expert demonstration trajectories to establish a foundational understanding of embodied interaction and planning for ReCAPA. This phase critical for equip- ping the model with the necessary knowledge to generalize across tasks before any domain-specific fine-tuning applied. We utilize two distinet datasets, BEHAVICR- and ProcTHOR, to cover broad range of scenarios and ensure diverse task representation.
BEHAVIOR-IK:
This benchmark focuses on complex, everyday human activities in simu- lated environments. For this dataset, we select 300 representative tasks and use to 5 expert demonstration trajectories per task. The fine- one-grained interaction data allows the model to learn the dynamics of typical human behavior in embodied context.
ProcTHOHE addition to BEHAVIOR-1K, we incorporate 300 diverse tasks from the procedurally generated Pr ProcTHOR environment. This dataset features various scene lay- outs and object arrangements, and we collect approximately 10 expert trajectories for each task. The diversity of this dataset ensures the model is not overly dependent on specific environmental configurations, enhancing its generalization ability.
This foundational pre-training step is conducted in an offline manner, allowing the model to absorb critical information about trajectories without the complexity real-time interaction. Although the total number of trajectories is smaller than in large-scale pretraining corpora, the diversity across BEHAVIOR-IK and ProcTHOR ensures broad task coverage and equips ReCAPA with transferable knowledge of embodied dynamics. This design emphasizes sample efficiency and cross-domain generalization: in Stage 2, the model is evaluated both with limited in-domain adaptation (e.g., MineDojo) and under pure transfer settings (e.g., Visual AgentBegent and A12-THOR), highlighting ReCAPA's ability to generalize beyond its training distribution.
A.2 STAGE 2: DOMAIN SPECIFIC ADAPTATION AND TRANSFER
Following the pre-training phase, distinguish types of evaluation protocols. On MineDojo, the model is trained on all programmatic tasks except those used for testing.
For VisualAgentBenchh. AI2-THOR and CimbodiedAgentInteett transfer: the model is pre-trained on ProcTHOR and BEHAVIOR-IK and directly evaluated on the target benchmarks without task-specific fine-tuning. This setting highlights ReCAPA's generaliza- tion ability under strict transfer conditions. Baselines are evaluated under their original protocols to ensure fair comparison.
we emphasize pure cross-domain
A.3 TRAINING CONFIGURATION
For all environments, we use the AdamW optimizer, with a learning rate of le-4, weight decay of 0.01, and a batch size of 32. The learning rate follows cosine schedule with a linear warm-up over the first 1,000 steps. Training is conducted for total of 200,000 steps, with the model being trained on 4 NVIDIA H20 GPUs to ensure efficient scaling across large datasets. The text encoder hhomic-embed-text-
encodes prompt tokens, while the vision encoder (nomic-embed-vise v1.5) encodes environmental observations.
In summary, the two-stage training protocol, comprising offline pre-training and online fine-tuning, equips ReCAPA with the capacity to handle complex, long-horizon horizon tasks in varied environments. The incorporation of expert trajectories during pre-training and the dynamic, memory memory-augmented
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Table 5: PAC vs. Task Length: Measures the attenuation rate of the impact of early errors on
subsequent steps (the higher the value, the faster the recovery and the weaker the error propagation)
Model
20
40
60
100
120
140
ReCAPA (Our work)
3.1
2.8
2.7
2.4
2.2
2.0
LLaMAR (?)
3.2
2.9
2.4
1.9
1.7
1.4
1.0
GPT-4V (Achiam et al.
2023
3.1
2.9
2.4
2.0
1.7
1.4
1.1
CogVLM (Wang 2024
2.4
2.0
1.7
1.4
1.0
0.8
0.5
IDEFICS-2 (Laurençon
2024
2.6
1.5
1.2
0.6
0.3
0.0
Table 6: Performance of ReCAPA Variants under Layer-wise Ablation across Benchmarks. This table estimates the expected impact of removing individual layers flattening the hierarchy (Flat- Head) on ReCAPA's performance across diverse environments, highlighting the necessity of each abstraction level
Method
EmbodlicAAgenllteeffac
AlfWorld
VisualAgentBench
A12-THOR
Behavior
VirtualHome
OmniGibson
Minecraft
w/o Subgoal-Lewe
62,8
61.5
87
44.8
61.4
0.62
w/o Urajectory-ILevel
65.4
64.5
86
47.4
60.5
0.67
FlatHead
49.3
55.4
78
35.6
44.1
0.52
ReCAPA
72.2
70.5
96
50.6
66.7
0.75
fine-tuning mechanism ensures that ReCAPA continuously improves its performance and general- izes effectively to unseen scenarios. As shown in Table presents the PAC results AI2-THOR, showing how different models recover from early errors across varying task lengths.
To assess the independent contribution of each hierarchical level in ReCAPA's two-level structure (subgoal - trajectory). we conduct layer-wise ablation study. While the full model integrates across all levels, it remains unclear whether each level provides unique benefits. In this experiment, we selectively remove one module at time tome--trjjctory-lee the architecture intact. This design allows us evaluate whether each layer offers distinct abstrac- tion or semantic supervision, whether model performance depends on cross-scale reasoning, and whether the joint use of all levels outperforms partial configurations. In addition, we introduce FlatHead variant, which collapses the entire hierarchy into single decoder without explicit levels, test whether the hierarchical structure itself is essential for semantic reasoning.
or subgoal-level. keeping the rest of
B COMPLEMENTARY RESULTS AND ANALYSIS
B. LAYER ABLATION
The ablation results in Table 6 highlight the necessity of maintaining a multi-level structure in ReCAPA. Removing any individual layer leads to consistent drop in performance across all benchmarks, confirming that each level contributes uniquely to hierarchical reasoning. Notably, the removal of the mid-level results in the most severe degradation, especially on Behavior (-9.4 points) and VirtualHome (-9.0), suggesting that the mid-level layer plays a critical role in bridg- ing trajectory-level plans with low-level execution. The w/o Trajectory-Level variant also suffers substantial loss in ALFWorld and AI2-THOR, demonstrating that long-horizon environments with sparse rewards and delayed feedback heavily depend line, which removes the entire hierarchy, performs the worst across all benchmarks- underscoring the indispensable value of structured abstraction and layered semantic supervision for complex task
rangeange planning. The FlatHead base-
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Table 7: Comparative Evaluation of ReCAPA and Hierarchical Variants: Effects of Coupling and Alignment Strategy. ReCAPA integrates all levels via joint and multi-scale alignment, allowing semantic corrections to propagate throughout the hierarchy
Method
EmboodidAAgentnnterraa
AlfWorld
VisualAgentBench
A12-THOR
Behavior
VirtualHome
OmniGibson
Minecraft
Separate BottomUp
62.1
62.7
79
43.9
57.9
0.59
Separate-PParalel
63.4
60.8
76
45.4
60.5
0.54
Ssparate-TopDowe
66.7
64.5
86
47.4
62.5
0.67
Frozen Trae-Levvel
68.9
68.0
90
48.3
63.9
0.71
ReCAPA
72.2
70.5
96
50.6
66.7
0.75
generalization.
These findings validate ReCAPA's core design principle: performance in long- horizon embodied tasks emerges from coordinated across abstraction levels.
B.2 COUPLING STRATEGY COMPARISON
To further examine the role of joint optimization across hierarchical layers, we introduce several additional ablation variants targeting the training strategy of HPCC:
Frozen Traj-Level:
The Traj-level module remains active during forward execution but its parameters are frozen throughout training. Only the mid- and low-level modules are updated, isolating the contribution of Traj-level gradient signals.
Separate-BottomUpp Each layer is trained sequentially in bottom-up manner: the action-level module is trained first, then frozen; the subgoal-level is trained next with action-level frozen; finally, the trajectory-level module is trained on top. This mimics stage-wise wise curriculum from action primitives to subgoal planning.
Separate-TopDown: The reverse of BottomUp. Training proceeds from the trajectory- level module down to the action-level, with each previously trained module frozen at its respective stage. This configuration reflects top-down reasoning pipelines, starting from global goals to execution-level commands.
Separate-Parallel: All three layers trained independently without inter-layer gradient flow. Each module optimized on its respective sub-task using its local alignment and trajectory data. This configuration serves strong baseline test whether cross-layer interactions are necessary.
Each of these configurations trained under the same data regime and alignment loss structure (Sinkhorm + Score Field), allowing for controlled comparisons with the jointly optimized ReCAPA model. We report task-level metries evaluate performance degradation and convergence stability.
Table presents comparative study of different hierarchical training strategies across six embodied benchmarks. ReCAPA achieves the highest performance on all tasks, demonstrating the benefit of fully joint training across three corrective modules. In contrast, all decoupled baselines underper- form to varying degrees, each revealing critical weaknesses in alternative optimization schemes.
The Aoparate-Bottomiy configuration performs the worst overall. as it trains low-level modules in isolation before exposing them task objectives. This leads suboptimal primitive behaviors that constrain downstream learning and confirms that without task-aware supervision results in ineffi- cient or misaligned action policies. The Separate-TopDown baseline performs moderately better but still lags behind ReCAPA. Despite training from task goals downward, the lack of feedback from lower layers causes the top-level planner to overfit to idealized subgoal sequences that may not align with actual execution capabilities, which results in Separate- Parallel setting confirms this issue from another angle: although each module becomes competent in isolation, the lack of cayes adaptation leads representational inconsistency and
mismatch." The
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Table 8: This ablation study evaluates the impact of prediction and components HPCC by isolating the effect of removing prediction, or both.
Variant
EmmoodicaAgeetnnteffaee
AlfWorld
VisualAgentBench
A AI2-THOR
Behavior
VirtualHome
OmniGibson
Minecraft
w/o pred and reff
62.6
63.1
85
44.5
60.5
0.66
Only Prediction (no ref))
65.4
66.3
90
47.8
60.9
0.72
Only (no pred)
67.1
64.9
91
49.2
62.0
0.72
Full HPCC (pred refl)
72.2
70.5
96
50.6
66.7
0.75
semantic drift between layers. The resulting interface mismatch limits coordination across hicrar- chical stages. Frozen Traj-Level, which freezes the top-level module and only updates the mid- and low-level components, yields decent performance, but falls short of ReCAPA. This highlights that trajectory-level goal representations also require continual adaptation to downstream dynamics in order maintain semantic coherence.
Taken together, these results empirically validate our theoretical claim that hierarchical must be jointly optimized to achieve sample-efficient and semantically aligned behavior. The observed per- formance gaps support our convergence analysis under oulti-objective optimization and further jus- tify the design of ReCAPA's multi-level update strategy.
B.3 ABLATION ON PREDICTION AND ALIGNMENT
To further disentangle the respective contributions of prediction and within HPCC, shown in Ta- ble g we designed four ablation variants. The first, w/o pred and refl, removes both components entirely, leaving only the hierarchical execution head to generate actions and serving without any auxiliary consistency signals. The second, Only Prediction, retains the forward predic- tion modules that forecast higher-level trajectory subgoal embeddings to regularize lower-level execution, but discards the modules that would otherwise check and realign actions during rollouts; this setting isolates the benefit of anticipatory guidance without any corrective feedback. The third, Only, removes prediction while preserving the consistency checks and corrective updates after each execution step, thereby examining the effect of purely reactive recovery in the absence of foresight. Finally, Full HPCC activates both prediction and to form closed loop in which lower-level policies both anticipate higher-level representations and immediately repair inconsistencies as they arise. Comparing these four settings allows us to characterize the distinct roles of prediction (proactive prevention) and (reactive correction), as well as their synergy when combined.
baseline
This ablation highlights the distinct yet complementary roles of prediction and within HPCC. Elim- inating both modules severely degrades performance, confirming that hierarchical execution heads alone insufficient for mitigating long-horizon drift. When only prediction is retained, the agent benefits from anticipatory alignment signals that reduce short-horizon inconsistencies (c.g., im- proved results Visual.geenBencch), but the absence of reactive correction allows carly emors to cascade unchecked, resulting in limited gains on AlfWorld and A12-THOR. Conversely, keeping only provides the ability to recover after errors occur, which is particularly beneficial for tasks with extended horizons where eтror accumulation dominates; however, without forward prediction, the system lacks proactive guidance and remains vulnerable to subtle misalignments in representation space. Full HPCC consistently outperforms all ablated variants because it unifies both mechanisms: prediction serves as an early warning system that lowers the likelihood of compounding failures, while functions as recovery channel that actively attenuates error propagation.
B.4 EVALUATION ON EMBODIED AGENT INTERFACE
As show in Table it evaluates ReCAPA and competing models on the Embodied Agent Interface benchmark. focusing Behavior and VirtualHome tasks. The metrics include Performance (Perf.), Goal FI, Action Sequence accuracy (both Task and Execution levels), and Subgoal Decomposition (Task and Execution), with overall F1 score. ReCAPA achieves robust performance (72.2 Perf.,
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Table Results the Behavior task of the Embodied Agent Interface benchmark. ReCAPA shows strong performance with leading scores in Goal F1 (84.8), Action Sequencing (77.0/84.0), and Sub- goal Decomposition (53.0/60.0), surpassing baselines such as ol-preview and Claude-4. These results highlight its effectiveness in long-horizon reasoning and precise subgoal planning through hierarchical architecture and alignment mechanisms
Models
Perf.
Goal F1
Action Seq.
Subgoal Dec.
F1
Task
Exec.
Task
Exec.
ol-preview Zhong (2024)
74.9
81.6
81.0
91.0
57.0
62.0
70.8
ReCAPA
72.2
84.8
77.0
84.0
53.0
60.0
69.8
Claude-4-Sonett Anthropic
2024
68.5
84.3
68.0
75.0
47.0
52.0
69.2
Claude-3.5-Sonnee Anthropic
2024a
64.2
82.7
60.0
69.0
39.0
44.0
67.9
Claude-3-Opus Anthropic
2024b
60.4
77.0
51.0
59.0
41.0
47.0
63.4
GPT-4o Hurst el al.
20024
59.8
79.2
47.0
53.0
49.0
55.0
60.9
ol-mini Jaech et 2024
57.5
76.4
56.0
65.0
31.0
39.0
56.4
Anthropic (2024b
55.1
69.4
44.0
57.0
39.0
43.0
56.2
Gemini-1.5-Flash Team (2024
52.1
74.8
40.0
52.0
34.0
42.0
53.4
Mistral-Large BianallgargglJianggggg et al. 2023
50.4
74.3
33.0
50.0
31.0
38.0
49.5
Table 10: Results on the VirtualHome task of the Embodied Agent Interface benchmark. ReCAPA shows consistently strong performance across six metrics, leading in Overall Perf. (70.5), Subgoal Decomposition (Task SR: 94.5, Exec. SR: 91.7), and Transition Modeling (F1: 64.9, Plan SR: 84.6). Compared to ol-preview and Claude models, it demonstrates superior hierarchical reasoning and robust embodied planning complex VirtualHome environments
Model Family
Overall Perf.
F1
Action Sequencing
Subgoal Decomposition
Transition Modeling
Exec:
ReCAPA
70.5
44,2
72.8
94,5
91.7
64.9
84.6
Claude Sonnel Anthropic
2024
69.1
43,5
73.6
92.7
90.9
51.4
86.8
previee Zhong
65.8
42.7
71.1
93.2
89.4
48.0
72.4
Gemini-Pro Team et al 2024
65.3
37.9
73.1
91.1
87.0
34.1
91.9
Clando 1-5 Scnier
2124h
64.9
33,0
72,8
92.0
89.1
48.9
80.5
GPT-4o Hurst
212-
60.8
36.5
61.6
91.1
87.6
46.7
68.2
Claudo- Sadidl---OUuxxAiillii0
2024b
59.9
66.2
ol-mini Jaech
el Jamnimechhhee
4124
57.9
31.2
65.9
84.6
79.3
41.5
69.0
84.8 Goal F1), trailing only ol-preview in Perf. but excelling in Goal F1. The training methodology for these tasks follows a two-stage protocol: offline pre-training on state-action-reward trajecto- ries initialized via GPT-4o API, followed by benchmark-specific optimization. For Behavior tasks, ReCAPA leverages ProcTHOR's procedurally generated environments (30 tasks, 10 expert trajecto- ries per task) to enhance scene generalization, avoiding overfitting. The model is trained end-to-end with AdamW W (Ir=le-4, weight decay=0.01, batch size=32) for 200K steps on 4 NVIDIA H20 GPUs, using a cosine learning rate schedule with 1,000-step warm-up. We obtain base text and vision em- ..5 and nomic-embed-we setibeeddviioo---.....
beddings from pre-trained Nomic encoders (nomic-embed-text) which remain frozen; only the HPCC and alignment modules are optimized. This approach ensures strong performance in both trajectory-leve goal reasoning (84.8 Fl) and action sequencing (77.0 Task, 84.0 Exec.), as reflected in the results.
As show in Table 10 it provides comprehensive evaluation across six key metrics: Overall Perfor- mance, F1, Action Sequencing (Task SR, Exec. SR), Subgoal Decomposition (F1), and Transition Modeling (Plan SR). ReCAPA outperforms competitors in Overall Perf. (70.5) and Transition Mod- eling (84.6 Plan SR), demonstrating strength in long-horizon planning and trajectory stability. The training pipeline mirors the methodology for A12-THOR and VisualAgentBench,, emphasiz-
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Table 11: Models performance on AlfWorld benchmark
Method
Success rate (%) 1
Avg.
Pick
Clean
Heat
Cool
Examine
Picktwo
Vision-language models
MiniGPT-44
16
4
0
19
17
67
6
BLIP-2*
6
0
L.aMA-Adapter
13
17
10
27
22
0
0
InstructBLIP*
22
26
0
EMMA*
82
71
94
85
83
88
67
Language models
BUTLER*
26
31
41
60
27
12
29
DEPS
76
93
50
80
100
100
0
AutoGen*
77
74
86
83
41
ReAct
74
79
54
96
85
83
51
AdaPlanner
100
100
97
47
Reflexion
86
92
94
70
81
90
88
RAFA
95
100
97
91
95
100
82
WALL-E 1.0
95
100
97
100
86
85
100
WALL-E 2.0
98
100
100
96
100
100
94
ReCAPA (ours)
96
100
97
94
95
96
94
ing cross-domain transfer through few-shot pre-training on ProcTHOR (no BEHAVIOR-1K in this phase) and task-specific fine-tuning. For AlfWorld tasks, ReCAPA employs RAFA-style multi- round interactions with memory-augmented prompts improve online adaptation. The technical setup matches Table 6's (AdamW, 200K steps), with alignment losses (Full Alignment) and hierar- chical (HPCC-Full) critical its success in Subgoal Decomposition (64.9 F1) and Action Sequenc- ing (94.5 Exec. SR). Claude-4-Some and Gemini-Pro show competitive Plan SR (86.8, 91.9), but ReCAPA balances all metries, underscoring its versatility.
B.5 PERFORMANCE ANALYSIS AND DISCUSSION
From the results in Table 11 our model ReCAPA consistently demonstrates clear advantages over both proprictary and open-source LMM baselines. particular, ReCAPA shows strong performance on long-horizon tasks such as PICKTWO, where success rates remain significantly higher than all other baselines. This confirms that our hierarchical planning and mechanisms can effectively manage compositional goals that require multiple coordinated steps.
Despite its overall superiority, ReCAPA is not without limitations. WALL-E 2.0, augmented with inforcement learning fine-tuning, achieves slightly higher scores on some subtasks. This gap reflects a trade-off: ReCAPA is primarily designed for sparse or reward-agnostic settings, which limits its ability to exploit dense feedback signals efficiently reinforcement learning. Overall, the results demonstrate that ReCAPA offers a balanced and generalizable solution: consistently outperforms competitors in challenging long-horizon tasks, while only marginally trailing in AlfWorld where dense rewards provide a strong supervision signal.
C FAILURE CASE ANALYSIS
To complement the guantitative results (EPR, PAC, and ablations), we provide qualitative analyses of typical failure modes. As shown in Table these examples illustrate how cascading errors arise and how ReCAPA mitigates them compared ablated variants.
We categorize common failure types observed during evaluation:
Prompt: "Take the milk from the fridge."
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Failure Type
Description Example
Subgoal Ordering Error
Executing subgoals in the wrong order (e.g., attempting close the fridge before retrieving the milk). This leads invalid incomplete task outcomes.
Entity Grounding Error
Misidentifying or manipulating the wrong object (e.g.. taking juice in- stead of the intended milk).
Premature Termination
Ending the task early before completing all required subgoals (e.g-, trieving milk but leaving the fridge door open).
Looping Redundancy
Performing unnecessary actions without contributing to the goal (c.g-, taking an extra apple after already retrieving the milk).
Table 12:
Taxonomy of common failure types in hierarchical agent execution, adapted the fridge-milly milk task with descriptions and examples.
Full ReCAPA: Executes correctly by [Open fridge] - [Take milk] - [Close fridge].
Retrieves the milk but then performs unnecessary action [Take apple] and forgets to [Close fridge], leading to redundancy and premature termination.
w/o HPCC:
Prompt: milk from
Full ReCAPA
Inidge
mik
hrage
w/o HPCR
Open
apple
Forgot fridge
Figure 5: Representative failure case for the prompt "Take the milk from the fridge." Full ReCAPA executes correctly by opening the fridge, retrieving the milk, and closing the fridge. In contrast, the ablated model (w/o HPCC) retrieves the milk but then takes an unrelated item and forgets to close the fridge, leaving the environment in invalid state and illustrating how local missteps cascade into task failure.
Action-Level
[WALK]] Dumner)
(MOVE](pat)
(TRN Kummer)
[WALK
[PUT Ypec)
(Resample) |PTTll/
ITUNN ON ltup!
Predict
Predict
Girab pol
Grab pot
Turn on burner
Wash
Put the pot
Wash pol
Wash pot
Subgnal-Lovel
pot
Grab pot
on
Wash pot
Wash pot
Wash the pot
Wash the pot
Prediet
Predict
Grah pat on the left
Tum on burner
Wash the pot from left bumner
Level
Prompt Task: "Take the pot from the left burner. wash tumn on the burner."
Figure 6: Illustration of ReCAPA's hierarchical correction during inference.
As shown in Figure 5 execution begins correctly the agent approaches the pot and moves toward the sink. The action-level representation fails to predict the correct subgoal-level semantic target ("place the pot into the sink"), causing action-subgoal alignment to drop. Subgoal-level forecast-
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ing then drifts toward incorrect direction, which subsequently causes global prompt-trajectory alignment to decrease.
ReCAPA detects this cross-level inconsistency and resamples the action until the pot is correctly placed in the sink. Once the corrected action is taken, the restored alignment propagates upward: subgoal-level semantics realign with the intended target, and the trajectory-level embedding recov- ers. Execution then resumes and completes normally.
D ERROR PROPAGATION METRICS (EPR AND PAC)
D.1 ERROR PROPAGATION RATE (EPR)
D.1.1 DEFINITION
We define the Error Propagation Rate (EPR) at lag k as the marginal increase in error probability k steps after the first error in an episode:
EPR = Pr(etot* = 1 Cto 1) Pr(eto*k = Cto = -):
where & E (0. 1} is the step-level error indicator and to denotes the first-error time in the trajectory. Intuitively, EPRa isolates the excess risk attributable an initial failure, relative matched no- error baseline.
D.1.2 INTERPRETATION AND PROPERTIES
Range and edge cases. EPR, E [-1,1]. 1]. EPR, x 0 implies effective containment; large positive values indicate cascading failures: negative values may indicate active recovery, i.e., the model becomes less error- prror-prone after an initial mistake.
Connection to hazard/recovery dynamics. If errors can be modeled by a Markovian error-state abstraction, EPR2 equals the difference in k-step transition probabilities into the error state under two initial conditions (e, 1 vs. 0). It is therefore direct proxy for cascade tendency. Compared the Propagation Attenuation Coefficient (PAC), which measures the decay rate of post-error risk, EPR reflects the marginal elevation of risk due to an initial error.
Consistency. Suppose the rollout process is ergodic and the matching function ((Fio) success- fully blocks confounding (i.e., controls for task stage and context). Then EPRg converges in prob- ability to EPRg as the number of episodes grows. If censoring arises (e.g.. episodes ending before + k), an pwvesse-robbbbiitt----coommm
weighted (IPCW) estimator ensures consistency.
Granularity.
EPR can be computed different levels:
Action-level (e( (act)); low-level execution slips.
Subgoal-level (e) (sub) structural or DAG violations.
Comparing EPR( (act) vs. EPR (sub) helps identify whether cascades are driven by local control errors or higher-level planning flaws.
(sub)
D.1.3 ESTIMATION PROTOCOL
The empirical procedure is follows:
Identify first-error times. For each trajectory locate of (the earliest such that e, 1). no emorr occurs, the trajectory is excluded from case construction.
Construct case and control sets. For each (i, ((1)) treat the pair (i, (()) as "case." Find "control" (j. i) from another trajectory j such that:
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O with prior errors,
+ A S T(),
Contexts are matched: o(F()) = o(F()). where encodes subgoal ID, horizon length, "o
scene category, and latent trajectory state.
Compute probabilities. Estimate Pease (k) = Pr(eto+k = 1 eto 1) and (aan(A)) = Pr(ei+*=
1 e 0) from matched pairs.
Form EPR estimate.
EPRg
Peaso (k:) Petn Fen(())
Summarize across k. addition to plotting EPR; as function of kt we report:
AUC-EPRW Ek=1 EPR2 for horizons W E (3,5),
the slope of EPR; over k as compact one-number summary of cascade growth.
D.1.4 REPORTING RECOMMENDATIONS
Always report EPRt vs. with 95% confidence intervals (bootstrapped by episode).
Visualization.
Summary statistics.
Report AUC-EPRW at W as concise summary statistic. For long- horizon tasks, include slope-of-k analysis to quantify cascade growth.
Comparisons. When comparing models, include both absolute differences and relative reductions in EPR. Use identical e definitions and matching hyperparameters across models to ensure fairness.
D.2 PRACTICAL CONSIDERATIONS
Censoring. Restrict W to be below the 25th percentile of remaining horizon (T - (o) to avoid heavy censoring bias.
Variance estimation.
Use per-episode bootstrap resampling for bands.
Level separation. Always report both action-level and subgoal-level EPR curves to clarify the source of propagation.
Robustness checks. Verify results are stable to the choice of á (matching function) and distance metric.
D.3 PROPAGATION ATTENUATION CORFFICIENT (PAC)
Definition. We define the Propagation Attenuation Coefficient (PAC) at lag & as the relative decay rate post-error risk:
Pr(e
Ctt = 1) = 1)
PACK
Pr(eto+1
where is the enror indicator and to denotes the first-error time. Intuitively, PAC measures how quickly the elevated error probability induced by the first emror attenuates over time. A value close to indicates persistent risk, while value below indicates attenuation.
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D.3.1
INTERPRETATION AND PROPERTIES
Range and meaning.
PACk E oc).
PACK R 1 + error risk is persistent, cascades continue.
PACk <1 7 enror risk attenuates; the system recovers.
PACk
> 1 +
error risk escalates faster than the initial shock (rare but possible in unstable
systems).
Connection to survival/hazard analysis. PAC be viewed analogue of hazard decay factor: it compares the conditional error hazard at lag to that immediately after the error. Whereas EPR captures the absolute marginal risk increase, PAC quantifies the relative decay speed of this risk.
Granularity. PAC can be applied at both:
Action-level: robustness of local control after slip.
Subgoal-level: structural recovery after violating planning dependency.
D.3.2 ESTIMATION PROTOCOL
The empirical procedure is as follows:
Identifyy first-error times. Same EPR: find e for each trajectory.
2. Compute conditional error probabilities. For each k; estimate
(k) = Pr(e = eto = 1).
Form PAC estimate, Normalize by the immediate post-error risk:
g(k)
PAC
((1)
Summarize across k. Plot PAčk vs. k; additionally, compute area-under-curve (AUC) metries:
AUC-PACW
PACk:
W
as compact indicator recovery speed.
D.3.3 REPORTING RECOMMENDATIONS
Visualization. Always report PAC; vs. k with 95% confidence intervals.
Summary statisties.
Report AUC-PACW W 3, 5 concise recovery-speed measure.
Comparisons. Compare models both in terms of absolute persistence (PAC close to 1) and relative acceleration/aateenaaion trends.
D.3.4 PRACTICAL CONSIDERATIONS
Normalization stability. If (1) is very small, PAC may be unstable. Exclude with vanishing immediate risk or regularize with a small e.
Censoring.
Restrict horizon W to avoid censoring, as in EPR.
Variance estimation. Use bootstrap resampling episodes.
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Level separation. Report both action-level and subgoal-leves PAC curves, since persistence pat- terns often differ across levels.
Consistency (Remark). Since PAC is estimated from @(k) values that themselves consistent estimators of conditional error probabilities, inherits consistency under standard assumptions for exponential regression fits. We omit formal proof for brevity.
E
THEORETICAL ANALYSIS
E. EXECUTION IMPROVEMENT VIA ALIGNMENT LOSS MINIMIZATION
has been shown that minimizing alignment losses such as InfoNCE, DPO, or Sinkhorn divergence over the action-genen generator To(als) can lead improvements in expected task success. These losses are typically defined to encourage the exccution to assign higher probability to preferred or expert actions while penalizing suboptimal ones. To analyze their impact execution improvement, the alignment loss is reformulated as surrogate objective over the log-probability log (a(a)s).
Formally, general alignment loss can be expressed as:
Calign (Ta) = E (s.at (s,a+,(o7)) [ (log To To(a+|s), flog (a [1)]])
where a represents the preferred (e.g.. expert or high-reward) action, and {az } are sampled neg- atives. The contrastive function & encourages the log-probability of at to be separated from that of the negatives. Under this formulation, the gradient of the alignment loss can be written as:
V Calgin (Ta) = (x.a) [A(s, Vo log ma(als))
(8)
where Å(s, a) is a weight derived from relative preferences, reward differences, likelihood ratios. This expression mirrors the form of execution gradient update, where Á(s, a) acts as a surrogate advantage estimator.
In the case of InfoNCE, the alignment loss takes the form of softmax log- ligeliidldoood objective over sampled actions:
exp(log To (s))
LinfaNCE - log exp(log To E, exp(log no(a, (s))
which induces gradients that push up the probability of the positive action at while pulling down the negatives. The resulting update direction has been shown to approximate the advantage- execution gradient when at is selected according reward preference feedback.
For Sinkhorn based alignment losses, a probabilistic interpretation has been proposed by viewing the alignment soft permutation induced via entropic optimal transport. Specifically, given cost matrix between token embeddings in the prompt and the trajectory, a doubly stochastic transport algorithm. The plan induces distribution
plan is computed using the Sinkhorn- to-token matches, and the loss is minimized when the trajectory embedding distribution is optimally aligned (in transport cost) with the prompt. When the cost is eeward-ifformed semantically structured, this process implicitly enforces sewardtrelevant permutation between prompt instructions and executed actions. The resulting transport-baat based gradient aligns the latent plan structure with the execution behavior.
token-
By the execution gradient theorem, these gradient directions align with execution improvement pro- vided that the surrogate signal Á(s, a) a consistent estimator of the true advantage A (s a). Un- der standard assumptions (bounded variance, small step size), minimizing alignment losses therefore promotes higher expected cumulative reward J(O). Therefore, updates that reduce these alignment losses can be expected to lead to execution improvement in practice. This theoretical insight is sistent with contrastive execution gradient methods such as CoPG, and the expected success rate gains have been empirically verified in the experiments above.
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E.2 DISTRIBUTIONAL ALIGNMENT THEORY FOR CONTRASTIVE LOSS
The alignment of model-generated trajectories with prompt intent can be viewed as a distribution matching problem. Let Duy denote the distribution over generated trajectory embeddings and D Purget the idealized distribution implied by ground-truth behaviors or reward-aligned samples. During con- trastive training, modules produce positive and negative samples whose embedding distributions are encouraged to match the target via minimization of contrastive losses such InfoNCE Sinkhorn divergence.
This process can be interpreted through the lens of distributional discrepancy minimization. For InfoNCE, the objective implicitly minimizes an upper bound on the -aaennnn divergence between Paaj and Dirrger by maximizing the mutual information between aligned pairs. Under this view, contrastive learning serves to pull the generated trajectory distribution toward the reward- consistent region of the target space.
When Sinkhorn divergence is used, the discrepancy between Dinj and Duarett is explicitly reduced through an intropy-regularize optimal transport plan. Given empirical samples from both distri- butions, a transport map is computed that minimizes cost while maintaining marginal consistency. The resulting divergence has been shown upper bound the Wasserstein distance under entropic smoothness, and can be used to quantify how closely the generated execution adheres to the prompt- induced distribution.
Under mild assumptions (bounded support, Lipschitz continuity of cost), the contrastive alignment loss Latign (f) is provably minimized when the distributional discrepancy vanishes, i.e.,
div (Dmjj (urgge)) S O Inaign (0) + E,
(10)
where div(-) may denote JS, Sinkhorn, or Wasserstein distance, and & denotes residual stochastic- ity. As result, minimizing the contrastive loss leads the model toward low-drift regime where trajectory samples remain semantically consistent with target plans.
E.3 REPRESENTATION ALIGNMENT BOUND
We study when minimizing representation-leve alignment losses between prompts and trajectories controls discrepancy and mitigates semantic drift. Let P 'D prompi be the prompt-conditional dis- tribution and Qo (0) the trajectory distribution induced by execution Te in shared embed- ding space Z C Rd. Write Latign(0) (0) for alignment objective such InfoNCE or Sinkhorn-based transport loss. Assume embeddings are bounded; the ground cost e x - Rz0 is bounded and Lipschitz; and for OT we use centropy-regularii transport with the debiased Sinkhorn diver- gence Se. Let P Pm,, Qn be empirical distributions from and samples, and let θ be execution class with capacity term R,,(O),
With probability at least 1 - over the draws of (Pm, Qn). there exists constant C(E, c) such that
sup de
D Qo) - -atign (P., 0n C(e,c) (V
1og(1/6)
log(1/5)
RR (0) (0)++(s), (11)
where a population-level divergence matched to the alignment loss, Langon its empirical coun- terpart (debiased for OT), and b(x) is the regularization bias that vanishes as 4 0.
For InfoNCE, the expected contrastive risk upper-bounds an f-divergence between the joint and product of marginals; in particular,
JS(P.Q Qo) < E[LLntoNCE (9) + Co.
for constants (a, co) determined by the sagppine scheme and temperature, and Lalign the minibatch InfoNCE. For Sinkhorn- baasd alignment, take D = Wi and use the debiased divergence:
wi Qa) < S. (P.Qo) Qa) + b(E), Lalign (P.. Qn: 0) = (p Qn)-
Plugging either instance into equation IT gives a high high-probability generalization guarantee from empirical alignment population discrepancy.
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Assumption (OT setting). We assume the cost function is bounded and Lipschitz, and that both P and Q have bounded support. The entropic Sinkhorn divergence S. uniformly approximates the 1- l-Wasserstein distance W1 up to a bias b(e), with standard statistical rates O(m -1/2 + n 1/22).
Corollary (Sinkhorn specialization). Under the above assumptions, for any 5 E (0. 1),
sup.S S. Qni + C(e,c) (V 1og(1/6) m
sup Qo) sup #E
log(1/6) n
+ Hi (+) + b(e)-
#ge
(12)
Thus, uniformly controlling the empirical Sinkhorn loss suffices to bound the population trajec- tory-prompt divergence up to estimation and regularization terms.
Since the inequality in equation 12 holds uniformly over all E O, it applies to each training iterate 0, Averaging over & ,,..., yields
1 Σ D(..Q..) s T Σ
Latiga (Pre Qn: + Estar (m, n, θ) + b(E),
(13)
where Eaa collects the 0(m-1/2 + n 1/3) and capacity terms. Thus, optimization reduces empirical alignment loss and & is chosen small but stable, the divergence decreases correspondingly, limiting semantic drift. Choosing D = W1 and Lalign = Se gives the Sinkhorn training-iterate bound
T T Sc 6.) + Ema (m, 5, Q) + b(E). Σ
T Σ Ws(P.Qo.) S
1
(14)
The alignment functionals above are Lipschitz-stable with respect to empirical measures (bounded smooth scores for InfoNCE; stability of OT, under bounded Lipschitz for Sinkhorn). Concen- tration (e.g., McDiarmid or transport inequalities) gives 0(m-1/2 n -1/2) control for fixed 0; uniformity over θ follows by symmetrization and capacity term RA (+). For OT, relate Sg to W1 and isolate b(=); for InfoNCE, use standard f-divergence or MI control to upper-bound JS. Combining these ingredients gives equation [[ by averaging, equation 13
E.4 HIERARCHICAL CONVERGENCE BOUND
The proposed hierarchical framework operates across multiple abstraction actions, subgoals, and task-level intents-eat each equipped with a dedicated alignment loss. To ensure stable joint opti- mization, is necessary to establish convergence guarantees or lower bounds sample efficiency when all levels are trained simultaneously.
The overall optimization
be viewed multi-objective gradient process, where each level I E faction, subgoal, trajectory) minimizes its own alignment loss cliign (0(1) while contributing to the global performance. By treating the joint update as a composite exccution gradient step over a stacked parameter space = [0(1) ((2), ((3)), the learning dynamics can be analyzed using mirror descent under block-decomposee gradient feedback.
Under standard smoothness and bounded gradient assumptions, can be shown that the average alignment loss across levels satisfies the following rate:
min 151S
Σ
( align (8. L S ((1/VT)
(15)
where (() denotes the optimal alignment loss at level I, and T is the number of joint gradient updates. This sublinear convergence is consistent with standard mirror descent bounds stochastic settings and ensures individual layer dominates optimization dynamics.
Furthermore, when coordination across levels is regularized (e.g.. by shared representations or align- ment constraints), the convergence can be accelerated. In particular, when surrogate gradients aligned and cross-level variance is bounded, an improved rate of (1/T)) can achieved, indicating that hierarchical updates benefit from structural decomposition.
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Published as a conference paper at ICLR 2026
These results suggest that hierarchical not only enables semantic control across abstraction levels but also preserves convergence efficiency. The modular structure improves gradient conditioning and reduces update variance, leading to better sample efficiency compared to flat architectures. Em- pirical convergence patterns across levels are reported, where the multi-level alignment loss steadily decreases throughout training.
E.5 JOINT CONVERGENCE HIBRARCHICAL
In the proposed architecture, is performed across multiple levels of abstraction, and each level con- tributes separate alignment loss that jointly optimized. To ensure stable training, essential to establish that the overall optimization remains convergent when these multi-level alignment ob- jectives are updated concurrently.
The full stack can be modeled constrained optimization problem, where lower-level alignment losses act as structured regularization terms within a meta-execution update. Let my denote the overall execution composed of nested sub-policie across levels. The global objective then be written as:
min Linkk (TO) subject
align (Tag S S1, vl (1,2,)..
(16)
where Lask denotes the primary reward-based objective and ferent levels.
c()) align are alignment constraints at dif-
This structure parallels that of constrained execution gradient optimization. When solved via primal- dual or Lagrangian methods, convergence to locally optimal solution can be guaranteed under standard assumptions of smoothness and bounded constraint curvature. Moreover, recent meta- RL results show that hierarchical or nested policies trained with level-specific constraints converge under similar conditions. In this formulation, alignment losses interpreted as soft constraints enforcing semantic consistency within the multi-level planning hierarchy.
In addition, when modules updated via trust-region clipping mechanisms (e.g.. using variants of constrained execution optimization), their individual losses can be stabilized without disrupting global reward improvement. As result, the multi-level architecture jointly converges toward stable equilibrium where both alignment fidelity and task reward are satisfied.
These results provide theoretical support that the proposed hierarchical system maintains optimiza- tion stability even under simultaneous multi-level loss supervision. Empirical convergence patterns further support this, showing that execution improvement persists despite the layered optimization structure.
F PROMPT TEMPLATES
F.1 HARD NEGATIVE GENERATION
You expert in analyzing robotic task trajectories. Your task is to take successful trajectory and create hard negative version of it. hard negative trajectory that looks plausible but is flawed a subtle, specific way. You must introduce ONLY ONE of the following error types, as specified by the user.
Action Error
Replace single critical action with a plausible but incorrect one. Example: In a "make coffee" task, instead of pickup cup, you might use pickup filter, which is related object but wrong for the step of pouring coffee.
Timing Error
Change the order of critical, non-dopendent actions where the order subtly mat- ters for efficiency or naturalness. Example: In make breakfar fast, pour coffee then toast bread is fine, but reversing them might result cold coffee.
Logic Error
Create trajectory that violates physical or common-sense rules. Example: Trying to pour water before the cup is picked up.
Sequence Error
Reverse the order of two dependent actions, making the sequence impossible. Exam- ple: p1 place apple mi crowave before open crowave.
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Published as a conference paper at ICLR 2026
You will be given successful trajectory and an type to introduce. Your response must be only the flawed trajectory, formatted exactly like the input.
MULTI-AGENT SYSTEM PROMPTS
Agent 0: Executor
You are robot agent focused on exccution. Given the current state and a high-level goal, your task is to select the most logical and immediate action to perform next. Focus only the next step.
Agent 1: Monitor/Critic
You monitoring agent. Your job is to observe the trajectory and evaluate progress. Output continue, ro_op, alert, or replan based on trajectory status.
Agent 2: Planner
You are a high-level planning agent. Your joh to identify the next major sub-goal phase in the task. Output abstract-level goals like acquire all ingredients.
Agent Alignment Agent
You ensure the plan stays consistent with the user's instruction. Output continus realign, or correct course based on semantic alignment.
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Reviews:
[{"review_id": "uUyoUXBUBZ", "reviewer": "Reviewer_iUSv", "summary": "The paper focuses on the problem of proactive error prediction and corrections for VLA models. The method, Reflective Contractive Alignment and Planning Architecture (ReCAPA) incorporates a bottom-up approach for deviation detection, and a top-down approach for deviation correction. ReCAPA aims to tackle the challenges of both short-term action error correction as well as long-term plan adherence. The hierarchical framework breaks down an embodied task trajectory into action, subgoal, and trajectory levels. At high level, the model aims to achieve prompt-trajectory distributional alignment through the Sinkhorn-based module. At the action and subgoal level, the model aims to achieve fine-grained prediction and execution alignment through the Score-fieldSong module. The model goes through 2 training stages: a pre-training to align state-action sequences, and a joint training of hierarchical model through a combined loss of Optimal Transport between the task prompt embedding and the overall trajectory distribution, a denoting local objective to pull fine-grained actions and subgoals closer to the prompt semantic intent, and a contrastive corrective loss between each adjacent levels (e.g.. action-subgoal, subgoal-trajectory). The paper also proposed two new evaluation methods, error propagation rage (EPR), and propagation attenuation coefficient (PAC) to explicitly measure the error propagation and cascading degree during the long horizon process. The paper conducted experiments on 3 benchmarks, and compared aganist numerous open sourced and proprietary LLMs, and demonstrated improved overall success rate as well as some EPR and PAC curves. The paper also conducted ablation studies to evaluate the importance of each component.", "questions": "1. When an inconsistencies arises at state $S_i$, with action $a_i$, ($S_i \\rightarrow a_i$), and the model learns and updates its weight, does it learn a 'correction action' from the current state ($S_i \\rightarrow a_i \\rightarrow a_j \\rightarrow S_j$) or a hopefully better action $a_j$ from the same state $S_i$, ($S_i \\rightarrow a_j$)?\n\n2. Section 3.3.2: the prompt embeddings $p$ -- are these the same prompts as in Section 3.3.1 $v$ for the overall task, or are they action/substep specific prompts? \n\n3. Ln 257: how to generate negative state-action sequences from GPT-4o-mini? \n\n4. If a model performs consistently well throughout a task, will the PAC score be high or low? \n3. Ln 114-115: duplicates", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- The paper is well motivated and well structured. It studies the problem of action deviation from both short term mistake correction and longer term task alignment two objectives.\n- The paper proposed an interesting method to decompose the trajectories into hierarchical 3 levels, and align the predicted states with prompts for deviation detection and correction.\n- The paper offers sufficient and concise explanations behind the method, loss function designs, and acknowledge the key limitations in the conclusion section while offering future directions. \n- The paper conducted thorough experimentations against multiple benchmarks and baseline models, offering sufficient evidence to support the advantages of the proposed method.", "weaknesses": "1. Table 1: it would be helpful to explain how 'transport rate, coverage, and balance' these three metrics are calculated\n2. Questions below", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UiGA47kzzF", "reviewer": "Reviewer_rzHw", "summary": "This paper introduces ReCAPA, a predictive correction framework designed to mitigate cascading failures in multi-step reasoning in the context of VLA models. The main ides is that small errors in subgoal or action specification can compound over time, degrading overall performance.\nReCAPA addresses this by applying predictive correction mechanisms at multiple levels such as actions, subgoals, and trajectories with the aim of anticipating and correcting deviations before they propagate.\nThe proposed method demonstrates strong performance, reportedly outperforming both proprietary and open-source large language models on benchmark tasks.", "questions": "- Why did the authors not further investigate the proposed mitigation strategies for ReCAPA’s limitations?\n- Could the authors include or discuss ablation analyses to clarify the contribution of each module?\n- Can the authors elaborate on the statistical significance of their results beyond the limited VisualAgentBench trials?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is clear, well-structured, and easy to follow, with an interesting and well-motivated idea.\n- The method is explained clearly, with a logical progression from motivation to implementation.", "weaknesses": "- The discussion of limitations lack details. While two limitations are mentioned, the proposed mitigation strategies are not empirically validated, which reads as somewhat unbalanced.\n- The statistical significance of results is unclear. Although the authors mention using three random seeds on VisualAgentBench (Fig. 4), it is not evident how consistent or significant the improvements are across other experiments.\n- The reproducibility of the results is limited: the authors do not provide the code, and the training details are not discussed.\n- The ablation studies are limited.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7nw6TjWT68", "reviewer": "Reviewer_3npG", "summary": "The paper proposes ReCAPA (Reflective Contrastive Alignment and Planning Architecture), a hierarchical predictive correction framework designed to mitigate cascading failures in long-horizon reasoning for vision–language–action (VLA) agents. Unlike prior methods that rely on fixed task decomposition or post-hoc correction, ReCAPA proactively anticipates and corrects deviations across three hierarchical levels—actions, subgoals, and trajectories—using predictive contrastive learning and prompt–trajectory alignment modules based on Sinkhorn optimal transport and score-field gradients. The authors also introduce two diagnostic metrics, Error Propagation Rate (EPR) and Propagation Attenuation Coefficient (PAC), to evaluate how errors accumulate and dissipate during execution. Experiments on VisualAgentBench, MineDojo, and MAP-THOR demonstrate that ReCAPA achieves higher success rates and superior robustness compared to strong LLM baselines, effectively reducing error propagation in multi-step embodied tasks.", "questions": "The proposed EPR and PAC metrics are intriguing, but how sensitive are they to task complexity or trajectory length? Have the authors verified that improvements in these metrics correlate with human-judged robustness or actual task success rates?\n\nI also notice some typo of writing:\n\n1. The appendix title: “Appendix C.3 ABLATION ON PREDICTION AND” seems incomplete.\n\n2. The first paragraph of Related Work has repeat sentence.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "The paper tackles an important and underexplored issue—cascading failures in long-horizon reasoning—and proposes a technically sound hierarchical correction framework. The introduction of predictive alignment across multiple levels and the new diagnostic metrics (EPR and PAC) provide useful analytical tools for evaluating robustness in embodied agents. Experiments are comprehensive, spanning several major benchmarks, and the reported improvements are consistent across tasks.", "weaknesses": "1. Conceptual novelty is moderate. The proposed hierarchical predictive correction (HPCC) framework largely builds upon existing ideas in self-reflective planning (e.g., Reflexion, ReAct, AdaPlanner) and hierarchical alignment (e.g., HiP, TrajPrompt). The distinction between ReCAPA’s “predictive correction” and prior feedback-based reflection mechanisms is not sharply articulated. The paper would benefit from a clearer theoretical or algorithmic differentiation beyond combining multi-level prediction and Sinkhorn-based alignment.\n\n2. Many components—such as how cross-level corrective gradients interact with the execution network or how the LLM’s decomposition is integrated during inference—are described only qualitatively. Key design choices (e.g., the dimensionality of embeddings, predictor architectures, and training stability) are omitted, making the method hard to reproduce or verify.\n\n3. The paper emphasizes “hierarchical correction,” yet no qualitative rollout visualizations or case studies are shown to illustrate how errors are detected and corrected in practice. Examples demonstrating the evolution of alignment or prediction across levels would make the mechanism more convincing.\n\n4. The new EPR and PAC metrics are interesting but lack correlation studies with task performance or user interpretability. It remains unclear whether improvements in these metrics genuinely indicate better planning robustness or simply reflect model-specific biases.\n\n5. Writing and organization issues. The text is dense and occasionally repetitive, with unclear boundaries between related work and method sections. Some equations (e.g., Eq. 1–4) are introduced abruptly without sufficient intuition. The overall presentation would benefit from clearer motivation and more concise explanation of the technical pipeline.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)